f3:T5bc,Zep · source graphiti-github # Graphiti temporal knowledge graphs | Field | Value | |---|---| | **ID** | `graphiti-github` | | **Type** | `repo-readme` | | **URL** | https://github.com/getzep/graphiti | | **DOI / arXiv / cite** | getzep/graphiti | | **Authors / Org** | Zep | | **Year** | 2026 | | **Evidence** | MODERATE | | **Retrieved** | 2026-09-13 | ## Relevance Related Zep stack for graph memory. ## Quotes > "Graphiti is a framework for building and querying temporal context graphs for AI agents." > — graphiti README · Overview > "Unlike static knowledge graphs, Graphiti's context graphs track how facts change over time, maintain provenance to source data, and support both prescribed and learned ontology — making them purpose-built for agents operating on evolving, real-world data." > — graphiti README · Overview > "Unlike traditional retrieval-augmented generation (RAG) methods, Graphiti continuously integrates user interactions, structured and unstructured enterprise data, and external information into a coherent, queryable graph." > — graphiti README · Overview > "A context graph is a temporal graph of entities, relationships, and facts — like "Kendra loves Adidas shoes (as of March 2026)." Unlike traditional knowledge graphs, each fact in a context graph has a validity window: when it became true, and when (if ever) it was superseded." > — graphiti README · What is a Context Graph? ## Images _None attached._f4:T490,Zep · source mem0-contrast # Memory-layer category contrast | Field | Value | |---|---| | **ID** | `mem0-contrast` | | **Type** | `paper` | | **URL** | https://arxiv.org/abs/2504.19413 | | **DOI / arXiv / cite** | Mem0 paper contrast | | **Authors / Org** | Chhikara et al. | | **Year** | 2025 | | **Evidence** | MODERATE | | **Retrieved** | 2026-09-13 | ## Relevance Shared job: persistent conversational memory outside context window. ## Quotes > "Building on this foundation, we further propose an enhanced variant that leverages graph-based memory representations to capture complex relational structures among conversational elements." > — arXiv:2504.19413 · Abstract > "Empirical results demonstrate that our methods consistently outperform all existing memory systems across four question categories: single-hop, temporal, multi-hop, and open-domain." > — arXiv:2504.19413 · Abstract > "Our findings highlight the critical role of structured, persistent memory mechanisms for long-term conversational coherence, paving the way for more reliable and efficient LLM-driven AI agents." > — arXiv:2504.19413 · Abstract ## Images _None attached._f5:T5ff,Zep — GitHub map GitHub repositories for **Zep** (several allowed per technology). | Role | Repo | ★ | Lang | License | Pushed | |---|---|--:|---|---|---| | `primary` | [getzep/zep](https://github.com/getzep/zep) | 4,912 | Python | Apache-2.0 | 2026-09-11 | | `sdk` | [getzep/zep-go](https://github.com/getzep/zep-go) | 54 | Go | — | 2026-09-05 | | `sdk` | [getzep/zep-js](https://github.com/getzep/zep-js) | 84 | TypeScript | Apache-2.0 | 2026-09-11 | | `sdk` | [getzep/zep-python](https://github.com/getzep/zep-python) | 208 | Python | Apache-2.0 | 2026-09-11 | | `related` | [getzep/graphiti](https://github.com/getzep/graphiti) | 30,843 | Python | Apache-2.0 | 2026-09-11 | | `related` | [getzep/zep-graph-visualization](https://github.com/getzep/zep-graph-visualization) | 15 | TypeScript | — | 2025-04-28 | | `related` | [getzep/zep-papers](https://github.com/getzep/zep-papers) | 43 | Jupyter Notebook | NOASSERTION | 2026-09-10 | ```mermaid flowchart LR Tech["Zep"] Tech -->|primary| zep["getzep/zep"] Tech -->|sdk| zep_go["getzep/zep-go"] Tech -->|sdk| zep_js["getzep/zep-js"] Tech -->|sdk| zep_python["getzep/zep-python"] Tech -->|related| graphiti["getzep/graphiti"] Tech -->|related| zep_graph_visualization["getzep/zep-graph-visualization"] Tech -->|related| zep_papers["getzep/zep-papers"] Tech --> Cell["Agentic / Memory"] Cell --> Index[technologies/GITHUB_MAP.md] ``` > Zep | Examples, Integrations, & More **Repos mapped:** 7 · edit `_source/github-repos.overrides.json` to add more.fb:X 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Process mining reveals what really happens in your organization — not what you think happens.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:abm-agent-behavior-mining:xes-standard:7c4b58f0c69e","kind":"tech_quote","path":"technologies/abm-agent-behavior-mining/science/sources/xes-standard.md","title":"ABM (Agent Behavior Mining) · xes-standard","text":"ABM (Agent Behavior Mining) (abm-agent-behavior-mining, Agentic → Evaluations). P.I.S. is a Fraunhofer spin-off developing software solutions for data-driven process mining and AI-powered process analysis.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_readme:abm-agent-behavior-mining","kind":"tech_readme","path":"technologies/abm-agent-behavior-mining/README.md","title":"ABM (Agent Behavior Mining)","text":"$8","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_science_source:abm-agent-behavior-mining:abm-github","kind":"tech_science_source","path":"technologies/abm-agent-behavior-mining/science/sources/abm-github.md","title":"ABM (Agent Behavior Mining) · abm-github","text":"ABM (Agent Behavior Mining) · source abm-github\n\n# A-rebmann/ABM\n\n| Field | Value |\n|---|---|\n| **ID** | `abm-github` |\n| **Type** | `repo-readme` |\n| **URL** | https://github.com/A-rebmann/ABM |\n| **DOI / arXiv / cite** | A-rebmann/ABM |\n| **Authors / Org** | A-rebmann |\n| **Year** | 2026 |\n| **Evidence** | MODERATE |\n| **Retrieved** | 2026-09-13 |\n\n## Relevance\n\nPrimary supplementary repo.\n\n## Quotes\n\n> \"ABM (Agent Behavior Mining) supplementary materials for mining agent behavior into process-mining formats (XES-oriented) so logs can be analyzed with standard process-mining tools.\"\n> — abm README\n\n> \"mining agent behavior into process-mining formats (XES-oriented)\"\n> — abm README\n\n> \"so logs can be analyzed with standard process-mining tools.\"\n> — abm README\n\n\n## Images\n\n_None attached._","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_science_source:abm-agent-behavior-mining:pm4py-contrast","kind":"tech_science_source","path":"technologies/abm-agent-behavior-mining/science/sources/pm4py-contrast.md","title":"ABM (Agent Behavior Mining) · pm4py-contrast","text":"ABM (Agent Behavior Mining) · source pm4py-contrast\n\n# PM4Py as XES consumer\n\n| Field | Value |\n|---|---|\n| **ID** | `pm4py-contrast` |\n| **Type** | `docs` |\n| **URL** | https://processintelligence.solutions/pm4py/api/ |\n| **DOI / arXiv / cite** | PM4Py docs |\n| **Authors / Org** | PM4Py |\n| **Year** | 2026 |\n| **Evidence** | MODERATE |\n| **Retrieved** | 2026-09-13 |\n\n## Relevance\n\nDownstream consumer for ABM exports.\n\n## Quotes\n\n> \"PM4Py is a python library that supports state-of-the-art process mining algorithms in Python.\"\n> — pm4py\n\n> \"It is open source and intended to be used in both academia and industry projects.\"\n> — pm4py\n\n> \"Official public repository for PM4Py (Process Mining for Python) — an open-source library for exploring, analyzing, and optimizing business processes with Python.\"\n> — pm4py\n\n\n## Images\n\n_None attached._","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_science_source:abm-agent-behavior-mining:xes-standard","kind":"tech_science_source","path":"technologies/abm-agent-behavior-mining/science/sources/xes-standard.md","title":"ABM (Agent Behavior Mining) · xes-standard","text":"ABM (Agent Behavior Mining) · source xes-standard\n\n# XES event log standard\n\n| Field | Value |\n|---|---|\n| **ID** | `xes-standard` |\n| **Type** | `spec` |\n| **URL** | https://www.xes-standard.org/ |\n| **DOI / arXiv / cite** | XES standard |\n| **Authors / Org** | IEEE Task Force |\n| **Year** | 2016 |\n| **Evidence** | STRONG |\n| **Retrieved** | 2026-09-13 |\n\n## Relevance\n\nInterchange format ABM/pm4py share.\n\n## Quotes\n\n> \"Open-source Python library that implements state-of-the-art process mining algorithms for building custom applications\"\n> — PIS / pm4py site\n\n> \"Process mining reveals what really happens in your organization — not what you think happens.\"\n> — PIS / pm4py site\n\n> \"P.I.S. is a Fraunhofer spin-off developing software solutions for data-driven process mining and AI-powered process analysis.\"\n> — PIS / pm4py site\n\n\n## Images\n\n_None attached._","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:abm-agent-behavior-mining:evidence","kind":"tech_section","path":"technologies/abm-agent-behavior-mining/README.md","title":"ABM (Agent Behavior Mining) · Evidence","text":"ABM (Agent Behavior Mining) — Evidence\n\n| Claim area | Source id | Strength |\n|---|---|---|\n| Trace → XES/CSV export & process-mining ready | `science/sources/abm-github.md` | MODERATE |\n| Interchange with pm4py / process mining | `science/sources/xes-standard.md` | STRONG |\n| Pairing vs pm4py consumer | `science/sources/pm4py-contrast.md` | MODERATE |","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:abm-agent-behavior-mining:features-map","kind":"tech_section","path":"technologies/abm-agent-behavior-mining/README.md","title":"ABM (Agent Behavior Mining) · Features map","text":"ABM (Agent Behavior Mining) — Features map\n\nWhich research features may use this technology: [`features.md`](./features.md) (10 mapped).\n\n","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:abm-agent-behavior-mining:github-map","kind":"tech_section","path":"technologies/abm-agent-behavior-mining/README.md","title":"ABM (Agent Behavior Mining) · GitHub map","text":"ABM (Agent Behavior Mining) — GitHub map\n\nGitHub repositories for **ABM (Agent Behavior Mining)** (several allowed per technology).\n\n| Role | Repo | ★ | Lang | License | Pushed |\n|---|---|--:|---|---|---|\n| `primary` | [a-rebmann/ABM](https://github.com/a-rebmann/ABM) | 2 | Python | Apache-2.0 | 2026-06-30 |\n\n```mermaid\nflowchart LR\n Tech[\"ABM (Agent Behavior Mining)\"]\n Tech -->|primary| ABM[\"a-rebmann/ABM\"]\n Tech --> Cell[\"Agentic / Evaluations\"]\n Cell --> Index[technologies/GITHUB_MAP.md]\n```\n\n> Supplementary Materials\n\n**Repos mapped:** 1 · edit `_source/github-repos.overrides.json` to add more.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:abm-agent-behavior-mining:how-it-works","kind":"tech_section","path":"technologies/abm-agent-behavior-mining/README.md","title":"ABM (Agent Behavior Mining) · How it works","text":"ABM (Agent Behavior Mining) — How it works\n\n1. Run the Jupyter coffee-shop notebooks; agents act via LangGraph/LangChain tools and handoffs. \n2. Trace collection records activities during interaction. \n3. `TraceProcessor.process_all_traces(...)` converts traces → CSV / JSON / XES under `generated_event_log/`. \n4. Import XES/CSV into pm4py, ProM, or another miner for discovery / conformance / variant analysis.\n\n```mermaid\nflowchart LR\n Run[Agent runs · notebooks] --> Trace[Automatic trace collection]\n Trace --> TP[TraceProcessor]\n TP --> XES[XES / CSV / JSON]\n XES --> PM[pm4py · ProM · commercial]\n```","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:abm-agent-behavior-mining:limits-failure-modes","kind":"tech_section","path":"technologies/abm-agent-behavior-mining/README.md","title":"ABM (Agent Behavior Mining) · Limits & failure modes","text":"ABM (Agent Behavior Mining) — Limits & failure modes\n\n- **Demo domain:** coffee-shop O2C is a teaching scenario — patterns transfer, business objects do not. \n- **Stack lock-in:** depends on LangGraph/LangChain + an LLM provider key; Python ≥ 3.13. \n- **Not the ledger:** exported logs are evidence for analysis, not declared company configuration or authority. \n- **Low star / research repo:** treat as paper companion; pin commit if you smoke-test. \n- **Science shelf:** some `science/sources` quotes still paraphrase landscape/pm4py blurbs — deepen those IDs when Evidence claims need stronger primary text.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:abm-agent-behavior-mining:links","kind":"tech_section","path":"technologies/abm-agent-behavior-mining/README.md","title":"ABM (Agent Behavior Mining) · Links","text":"ABM (Agent Behavior Mining) — Links\n\n**URL:** https://github.com/A-rebmann/ABM \n**Repo:** https://github.com/a-rebmann/ABM \n**Docs:** repo README + paper companion notebooks \n\n**Folder:** `abm-agent-behavior-mining` \n**Wiki-status:** `deep` \n**Updated:** 2026-09-13","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:abm-agent-behavior-mining:scientific-notes","kind":"tech_section","path":"technologies/abm-agent-behavior-mining/README.md","title":"ABM (Agent Behavior Mining) · Scientific notes","text":"ABM (Agent Behavior Mining) — Scientific notes\n\nScientific pack: **3 sources**, quotes per source, image copyright register.\n\n→ [`science/README.md`](./science/README.md) · schema [`../SCIENCE.md`](../SCIENCE.md)","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:abm-agent-behavior-mining:what-it-is","kind":"tech_section","path":"technologies/abm-agent-behavior-mining/README.md","title":"ABM (Agent Behavior Mining) · What it is","text":"ABM (Agent Behavior Mining) — What it is\n\nABM is an **implementation repository** for Agent Behavior Mining: a multi-agent Order-to-Cash coffee-shop scenario (LangGraph / LangChain) that makes generative-agent behavior **observable** by capturing traces and exporting them as process-mining event logs (CSV, JSON, **XES**). The repo is supplementary material for the governance paper; it is not a general-purpose agent framework and not a company ledger.\n\nSpecialized agents (Order, Inventory, Barista, Customer Service) collaborate under automatic trace collection. A `TraceProcessor` batch-exports logs with standard XES extensions (concept, time, org, identity) plus a custom AI extension for agent attributes, for import into ProM, pm4py, or commercial process-mining tools.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:abm-agent-behavior-mining:when-to-use-skip","kind":"tech_section","path":"technologies/abm-agent-behavior-mining/README.md","title":"ABM (Agent Behavior Mining) · When to use / skip","text":"ABM (Agent Behavior Mining) — When to use / skip\n\n- **Use when:** you need an **XES-oriented export path** from multi-agent runs into process mining, or a concrete reference implementation of ABM for eval / governance experiments. \n- **Skip when:** you only need a process-mining library (use **pm4py** alone), a general agent framework (LangGraph/Crew/…), or production case/ledger semantics. \n- **Prefer instead:** `pm4py` for algorithms on logs you already have; keep ABM as the *export shape* reference.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:abm-agent-behavior-mining:why-it-matters-here","kind":"tech_section","path":"technologies/abm-agent-behavior-mining/README.md","title":"ABM (Agent Behavior Mining) · Why it matters here","text":"ABM (Agent Behavior Mining) — Why it matters here\n\nAn agentic OS that only shows task boards cannot answer *why* a path escalated. ABM’s job in this research stack is the **observe → event-log** bridge for agent runs: turn conversational/tool traces into a process-mining-ready log so evaluation features can mine variants, exceptions, and handoff patterns with standard tools (esp. **pm4py**).\n\nThat serves Evaluations / T38-style work: comparable traces, attributable case IDs, and a path from “agent did something” to “process mining can measure it” — without promoting the demo coffee shop into the system of record.","quality":null,"needs_deepen":false,"deepen_reason":null}],"corpus_tags":{"category":["Agentic → 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Internally it runs an LLM *evaluator* agent that orchestrates multi-turn conversations with your *target* agent and scores responses during the conversation — not a static prompt-string checker.. Maps to research feature «Outcome Verification» (outcome-verification). Landscape category: Agentic → Evaluations.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:agent-evaluation:r2-30-evaluator-critic","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"agent-evaluation → r2-30-evaluator-critic","text":"Technology Agent Evaluation (agent-evaluation): Agent Evaluation (AWS Labs) is a **generative AI-powered framework for testing virtual agents**. Internally it runs an LLM *evaluator* agent that orchestrates multi-turn conversations with your *target* agent and scores responses during the conversation — not a static prompt-string checker.. Maps to research feature «T30 — Evaluator / Critic Agent Design» (r2-30-evaluator-critic). 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Landscape category: Agentic → Evaluations.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:agent-evaluation:r2-47-trace-maturity-gate","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"agent-evaluation → r2-47-trace-maturity-gate","text":"Technology Agent Evaluation (agent-evaluation): Agent Evaluation (AWS Labs) is a **generative AI-powered framework for testing virtual agents**. Internally it runs an LLM *evaluator* agent that orchestrates multi-turn conversations with your *target* agent and scores responses during the conversation — not a static prompt-string checker.. Maps to research feature «T47 — Complete-Trace Counter + Maturity Gate» (r2-47-trace-maturity-gate). 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Landscape category: Agentic → Evaluations.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:agent-evaluation:wt-test-debug-evaluate","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"agent-evaluation → wt-test-debug-evaluate","text":"Technology Agent Evaluation (agent-evaluation): Agent Evaluation (AWS Labs) is a **generative AI-powered framework for testing virtual agents**. Internally it runs an LLM *evaluator* agent that orchestrates multi-turn conversations with your *target* agent and scores responses during the conversation — not a static prompt-string checker.. Maps to research feature «Workflow Tools — Test, Debug & Evaluate» (wt-test-debug-evaluate). Landscape category: Agentic → Evaluations.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_features:agent-evaluation","kind":"tech_features","path":"technologies/agent-evaluation/features.md","title":"Agent Evaluation · features","text":"$a","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:agent-evaluation:hal-github:5f5682f1321c","kind":"tech_quote","path":"technologies/agent-evaluation/science/sources/hal-github.md","title":"Agent Evaluation · hal-github","text":"Agent Evaluation (agent-evaluation, Agentic → Evaluations). Ragas is available at https://github.com/explodinggradients/ragas.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:agent-evaluation:hal-github:8b19066ec3af","kind":"tech_quote","path":"technologies/agent-evaluation/science/sources/hal-github.md","title":"Agent Evaluation · hal-github","text":"Agent Evaluation (agent-evaluation, Agentic → Evaluations). To this end, we introduce SWE-bench, an evaluation framework consisting of 2,294 software engineering problems drawn from real GitHub issues and corresponding pull requests across 12 popular Python repositories.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:agent-evaluation:hal-github:de668a767a13","kind":"tech_quote","path":"technologies/agent-evaluation/science/sources/hal-github.md","title":"Agent Evaluation · hal-github","text":"Agent Evaluation (agent-evaluation, Agentic → Evaluations). Language models have outpaced our ability to evaluate them effectively, but for their future development it is essential to study the frontier of their capabilities.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:agent-evaluation:ragas-contrast:24746effe7c4","kind":"tech_quote","path":"technologies/agent-evaluation/science/sources/ragas-contrast.md","title":"Agent Evaluation · ragas-contrast","text":"Agent Evaluation (agent-evaluation, Agentic → Evaluations). We find real-world software engineering to be a rich, sustainable, and challenging testbed for evaluating the next generation of language models.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:agent-evaluation:ragas-contrast:e84a23bd1e0b","kind":"tech_quote","path":"technologies/agent-evaluation/science/sources/ragas-contrast.md","title":"Agent Evaluation · ragas-contrast","text":"Agent Evaluation (agent-evaluation, Agentic → Evaluations). For faithfulness and context relevance, the two annotators agreed in around 95% of cases. For answer relevance, they agreed in around 90% of the cases.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:agent-evaluation:swebench:1aa7b0f06810","kind":"tech_quote","path":"technologies/agent-evaluation/science/sources/swebench.md","title":"Agent Evaluation · swebench","text":"Agent Evaluation (agent-evaluation, Agentic → Evaluations). Given a codebase along with a description of an issue to be resolved, a language model is tasked with editing the codebase to address the issue.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:agent-evaluation:swebench:5e731df2369d","kind":"tech_quote","path":"technologies/agent-evaluation/science/sources/swebench.md","title":"Agent Evaluation · swebench","text":"Agent Evaluation (agent-evaluation, Agentic → Evaluations). The results… show that our proposed metrics are much closer aligned with the human judgements than the predictions from the two baselines.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_readme:agent-evaluation","kind":"tech_readme","path":"technologies/agent-evaluation/README.md","title":"Agent Evaluation","text":"$b","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_science_source:agent-evaluation:hal-github","kind":"tech_science_source","path":"technologies/agent-evaluation/science/sources/hal-github.md","title":"Agent Evaluation · hal-github","text":"$c","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_science_source:agent-evaluation:ragas-contrast","kind":"tech_science_source","path":"technologies/agent-evaluation/science/sources/ragas-contrast.md","title":"Agent Evaluation · ragas-contrast","text":"Agent Evaluation · source ragas-contrast\n\n# Component eval contrast\n\n| Field | Value |\n|---|---|\n| **ID** | `ragas-contrast` |\n| **Type** | `repo-readme` |\n| **URL** | https://github.com/explodinggradients/ragas |\n| **DOI / arXiv / cite** | RAGAS contrast |\n| **Authors / Org** | Exploding Gradients |\n| **Year** | 2026 |\n| **Evidence** | MODERATE |\n| **Retrieved** | 2026-09-13 |\n\n## Relevance\n\nRAGAS is RAG metrics; agent eval is task success.\n\n## Quotes\n\n> \"We find real-world software engineering to be a rich, sustainable, and challenging testbed for evaluating the next generation of language models.\"\n> — agent-evaluation primary (deduped)\n\n> \"For faithfulness and context relevance, the two annotators agreed in around 95% of cases. For answer relevance, they agreed in around 90% of the cases.\"\n> — agent-evaluation primary (deduped)\n\n\n## Images\n\n_None attached._","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_science_source:agent-evaluation:swebench","kind":"tech_science_source","path":"technologies/agent-evaluation/science/sources/swebench.md","title":"Agent Evaluation · swebench","text":"Agent Evaluation · source swebench\n\n# SWE-bench: Can Language Models Resolve Real-World GitHub Issues?\n\n| Field | Value |\n|---|---|\n| **ID** | `swebench` |\n| **Type** | `paper` |\n| **URL** | https://arxiv.org/abs/2310.06770 |\n| **DOI / arXiv / cite** | SWE-bench |\n| **Authors / Org** | Jimenez et al. |\n| **Year** | 2023 |\n| **Evidence** | STRONG |\n| **Retrieved** | 2026-09-13 |\n\n## Relevance\n\nCanonical coding-agent benchmark.\n\n## Quotes\n\n> \"Given a codebase along with a description of an issue to be resolved, a language model is tasked with editing the codebase to address the issue.\"\n> — agent-evaluation primary (deduped)\n\n> \"The results… show that our proposed metrics are much closer aligned with the human judgements than the predictions from the two baselines.\"\n> — agent-evaluation primary (deduped)\n\n\n## Images\n\n_None attached._","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:agent-evaluation:evidence","kind":"tech_section","path":"technologies/agent-evaluation/README.md","title":"Agent Evaluation · Evidence","text":"Agent Evaluation — Evidence\n\n| Claim area | Source id | Strength |\n|---|---|---|\n| Conversational / harness eval framing | `science/sources/hal-github.md` | MODERATE |\n| Benchmark contrast (coding agents) | `science/sources/swebench.md` | MODERATE |\n| Peer contrast vs RAG metrics | `science/sources/ragas-contrast.md` | MODERATE |","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:agent-evaluation:features-map","kind":"tech_section","path":"technologies/agent-evaluation/README.md","title":"Agent Evaluation · Features map","text":"Agent Evaluation — Features map\n\nWhich research features may use this technology: [`features.md`](./features.md) (7 mapped).\n\n","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:agent-evaluation:github-map","kind":"tech_section","path":"technologies/agent-evaluation/README.md","title":"Agent Evaluation · GitHub map","text":"Agent Evaluation — GitHub map\n\nGitHub repositories for **Agent Evaluation** (several allowed per technology).\n\n| Role | Repo | ★ | Lang | License | Pushed |\n|---|---|--:|---|---|---|\n| `primary` | [awslabs/agent-evaluation](https://github.com/awslabs/agent-evaluation) | 372 | Python | Apache-2.0 | 2025-12-15 |\n\n```mermaid\nflowchart LR\n Tech[\"Agent Evaluation\"]\n Tech -->|primary| agent_evaluation[\"awslabs/agent-evaluation\"]\n Tech --> Cell[\"Agentic / Evaluations\"]\n Cell --> Index[technologies/GITHUB_MAP.md]\n```\n\n> A generative AI-powered framework for testing virtual agents.\n\n**Repos mapped:** 1 · edit `_source/github-repos.overrides.json` to add more.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:agent-evaluation:how-it-works","kind":"tech_section","path":"technologies/agent-evaluation/README.md","title":"Agent Evaluation · How it works","text":"Agent Evaluation — How it works\n\n1. Define scenarios / datasets and a target (AWS service or custom agent API). \n2. The evaluator LLM drives multi-turn chats with the target. \n3. Responses are scored during the conversation; hooks can assert side effects. \n4. Run locally or in CI; fail the build when thresholds miss.\n\n```mermaid\nflowchart TD\n D[Scenarios / datasets] --> E[Evaluator LLM]\n E --> T[Target agent]\n T --> E\n E --> S{Pass threshold?}\n S -->|no| Fix[Prompt / tools / policy]\n Fix --> E\n S -->|yes| CI[CI / release]\n```","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:agent-evaluation:limits-failure-modes","kind":"tech_section","path":"technologies/agent-evaluation/README.md","title":"Agent Evaluation · Limits & failure modes","text":"Agent Evaluation — Limits & failure modes\n\n- **Evaluator LLM cost/variance:** scores depend on another model; calibrate judges. \n- **AWS gravity:** first-class targets are AWS; custom targets work but are more integration work. \n- **Not production monitoring:** conversation harness ≠ live tracing (pair with TruLens/Langfuse-class tools if needed). \n- **Science shelf:** current `science/sources` mix in HAL/SWE-bench contrast files — Evidence rows cite what exists; deepen sources when claiming Bedrock-specific facts.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:agent-evaluation:links","kind":"tech_section","path":"technologies/agent-evaluation/README.md","title":"Agent Evaluation · Links","text":"Agent Evaluation — Links\n\n**URL:** https://awslabs.github.io/agent-evaluation/ \n**Repo:** https://github.com/awslabs/agent-evaluation \n**Docs:** https://awslabs.github.io/agent-evaluation/ \n\n**Folder:** `agent-evaluation` \n**Wiki-status:** `deep` \n**Updated:** 2026-09-13","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:agent-evaluation:scientific-notes","kind":"tech_section","path":"technologies/agent-evaluation/README.md","title":"Agent Evaluation · Scientific notes","text":"Agent Evaluation — Scientific notes\n\nScientific pack: **3 sources**, quotes per source, image copyright register.\n\n→ [`science/README.md`](./science/README.md) · schema [`../SCIENCE.md`](../SCIENCE.md)","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:agent-evaluation:what-it-is","kind":"tech_section","path":"technologies/agent-evaluation/README.md","title":"Agent Evaluation · What it is","text":"Agent Evaluation — What it is\n\nAgent Evaluation (AWS Labs) is a **generative AI-powered framework for testing virtual agents**. Internally it runs an LLM *evaluator* agent that orchestrates multi-turn conversations with your *target* agent and scores responses during the conversation — not a static prompt-string checker.\n\nIt ships built-in targets for Amazon Bedrock, Amazon Q Business, and SageMaker, plus custom targets; supports concurrent multi-turn runs, hooks for integration tests, and CI/CD wiring so agent changes can fail the pipeline when conversations regress.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:agent-evaluation:when-to-use-skip","kind":"tech_section","path":"technologies/agent-evaluation/README.md","title":"Agent Evaluation · When to use / skip","text":"Agent Evaluation — When to use / skip\n\n- **Use when:** you need **multi-turn conversational eval** of agents, especially on AWS Bedrock/Q/SageMaker, and want CI hooks. \n- **Skip when:** you only need RAG faithfulness metrics (**ragas**), pytest-style metric unit tests (**deepeval**), or red-team/YAML provider matrices (**promptfoo**). \n- **Prefer instead:** Promptfoo for provider A/B + red team; DeepEval for metric libraries as tests; HAL only for public benchmark calibration (archived harness).","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:agent-evaluation:why-it-matters-here","kind":"tech_section","path":"technologies/agent-evaluation/README.md","title":"Agent Evaluation · Why it matters here","text":"Agent Evaluation — Why it matters here\n\nCommitted criteria need **scored agent conversations**, not vibe demos. This pack is the AWS-shaped harness for “dataset/scenario → talk to agent → metric → gate merge.” Use it when the install already lives on Bedrock/Q/SageMaker or when you need a conversation-orchestrating evaluator rather than only RAG metric libraries (Ragas) or declarative YAML evals (Promptfoo).\n\nWire scores to cases so a failing eval is attributable; never treat the harness as process authority.","quality":null,"needs_deepen":false,"deepen_reason":null}],"corpus_tags":{"category":["Agentic → Evaluations"],"dedication":["open-source"],"feature":["outcome-verification","r2-30-evaluator-critic","r2-32-eval-harnesses","r2-42-intervention-preassessment","r2-47-trace-maturity-gate","testing-debugging-optimization","wt-test-debug-evaluate"],"kind":["map_edge","tech_features","tech_quote","tech_readme","tech_science_source","tech_section"],"needs_deepen":["false"],"quality":["ok"],"section":["Evidence","Features map","GitHub map","How it works","Limits & failure 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Landscape category: Agentic → Agent Protocols.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_features:agent2agent-protocol","kind":"tech_features","path":"technologies/agent2agent-protocol/features.md","title":"Agent2Agent Protocol · features","text":"# Features using `agent2agent-protocol`\n\n**Technology:** [Agent2Agent Protocol](./README.md)\n**Category:** Agentic → Agent Protocols\n**Mapped features:** 5\n**Map:** [`../features/_source/feature-technology-map.md`](../features/_source/feature-technology-map.md)\n\nThis technology is a **candidate building block** for the features below (research map — not a claim that the product currently depends on it).\n\n| Feature | Title |\n|---|---|\n| [`agent-identity-interoperability`](../features/agent-identity-interoperability/README.md) | Agent Identity & Interoperability |\n| [`apis-sdks-mcp-extensibility`](../features/apis-sdks-mcp-extensibility/README.md) | APIs, SDKs & MCP Extensibility |\n| [`mcp-tools`](../features/mcp-tools/README.md) | MCP Tools |\n| [`multi-agent-collaboration`](../features/multi-agent-collaboration/README.md) | Multi-Agent Collaboration |\n| [`r2-02-assistant-expert`](../features/r2-02-assistant-expert/README.md) | T02 — Assistant ↔ Expert Delegation Model |\n\n","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:agent2agent-protocol:a2a-repo:0073080665b6","kind":"tech_quote","path":"technologies/agent2agent-protocol/science/sources/a2a-repo.md","title":"Agent2Agent Protocol · a2a-repo","text":"Agent2Agent Protocol (agent2agent-protocol, Agentic → Agent Protocols). An open protocol enabling communication and interoperability between opaque agentic applications.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:agent2agent-protocol:a2a-spec:8269619317c5","kind":"tech_quote","path":"technologies/agent2agent-protocol/science/sources/a2a-spec.md","title":"Agent2Agent Protocol · a2a-spec","text":"Agent2Agent Protocol (agent2agent-protocol, Agentic → Agent Protocols). Opaque Execution: Agents collaborate based on declared capabilities and exchanged information, without needing to share their internal thoughts, plans, or tool implementations.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:agent2agent-protocol:a2a-spec:eb158457814b","kind":"tech_quote","path":"technologies/agent2agent-protocol/science/sources/a2a-spec.md","title":"Agent2Agent Protocol · a2a-spec","text":"Agent2Agent Protocol (agent2agent-protocol, Agentic → Agent Protocols). The Agent2Agent (A2A) Protocol is an open standard designed to facilitate communication and interoperability between independent, potentially opaque AI agent systems.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:agent2agent-protocol:a2a-spec:f494669c8480","kind":"tech_quote","path":"technologies/agent2agent-protocol/science/sources/a2a-spec.md","title":"Agent2Agent Protocol · a2a-spec","text":"Agent2Agent Protocol (agent2agent-protocol, Agentic → Agent Protocols). Async First: Designed for (potentially very) long-running tasks and human-in-the-loop interactions.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:agent2agent-protocol:mcp-vs-a2a-2607-23884:16e72db0d3cb","kind":"tech_quote","path":"technologies/agent2agent-protocol/science/sources/mcp-vs-a2a-2607-23884.md","title":"Agent2Agent Protocol · mcp-vs-a2a-2607-23884","text":"Agent2Agent Protocol (agent2agent-protocol, Agentic → Agent Protocols). Tasks have a well-defined lifecycle within A2A… terminal state (e.g., completed, cancelled, failed, or rejected), or a paused state (input-required or auth-required).","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:agent2agent-protocol:mcp-vs-a2a-2607-23884:44371f785561","kind":"tech_quote","path":"technologies/agent2agent-protocol/science/sources/mcp-vs-a2a-2607-23884.md","title":"Agent2Agent Protocol · mcp-vs-a2a-2607-23884","text":"Agent2Agent Protocol (agent2agent-protocol, Agentic → Agent Protocols). A task represents a stateful and long-running piece of work whose progress needs to be tracked by the client and the server.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_readme:agent2agent-protocol","kind":"tech_readme","path":"technologies/agent2agent-protocol/README.md","title":"Agent2Agent Protocol","text":"$f","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_science_source:agent2agent-protocol:a2a-repo","kind":"tech_science_source","path":"technologies/agent2agent-protocol/science/sources/a2a-repo.md","title":"Agent2Agent Protocol · a2a-repo","text":"Agent2Agent Protocol · source a2a-repo\n\n# a2aproject/A2A repository\n\n| Field | Value |\n|---|---|\n| **ID** | `a2a-repo` |\n| **Type** | `repo-readme` |\n| **URL** | https://github.com/a2aproject/A2A |\n| **DOI / arXiv / cite** | a2aproject/A2A |\n| **Authors / Org** | A2A Project |\n| **Year** | 2026 |\n| **Evidence** | MODERATE |\n| **Retrieved** | 2026-09-13 |\n\n## Relevance\n\nCanonical repo hosting proto/spec and SDKs.\n\n## Quotes\n\n> \"An open protocol enabling communication and interoperability between opaque agentic applications.\"\n\n\n## Images\n\n_None attached._","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_science_source:agent2agent-protocol:a2a-spec","kind":"tech_science_source","path":"technologies/agent2agent-protocol/science/sources/a2a-spec.md","title":"Agent2Agent Protocol · a2a-spec","text":"Agent2Agent Protocol · source a2a-spec\n\n# Agent2Agent (A2A) Protocol Specification\n\n| Field | Value |\n|---|---|\n| **ID** | `a2a-spec` |\n| **Type** | `spec` |\n| **URL** | https://github.com/a2aproject/A2A/blob/main/docs/specification.md |\n| **DOI / arXiv / cite** | A2A Protocol Specification |\n| **Authors / Org** | A2A Project |\n| **Year** | 2026 |\n| **Evidence** | STRONG |\n| **Retrieved** | 2026-09-13 |\n\n## Relevance\n\nNormative goals and layering for inter-agent interoperability.\n\n## Quotes\n\n> \"The Agent2Agent (A2A) Protocol is an open standard designed to facilitate communication and interoperability between independent, potentially opaque AI agent systems.\"\n\n\n> \"Opaque Execution: Agents collaborate based on declared capabilities and exchanged information, without needing to share their internal thoughts, plans, or tool implementations.\"\n\n\n> \"Async First: Designed for (potentially very) long-running tasks and human-in-the-loop interactions.\"\n\n\n## Images\n\n_None attached._","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_science_source:agent2agent-protocol:mcp-vs-a2a-2607-23884","kind":"tech_science_source","path":"technologies/agent2agent-protocol/science/sources/mcp-vs-a2a-2607-23884.md","title":"Agent2Agent Protocol · mcp-vs-a2a-2607-23884","text":"Agent2Agent Protocol · source mcp-vs-a2a-2607-23884\n\n# MCP vs A2A — coordination complexity\n\n| Field | Value |\n|---|---|\n| **ID** | `mcp-vs-a2a-2607-23884` |\n| **Type** | `paper` |\n| **URL** | https://arxiv.org/abs/2607.23884 |\n| **DOI / arXiv / cite** | arXiv:2607.23884 |\n| **Authors / Org** | comparative study |\n| **Year** | 2026 |\n| **Evidence** | MODERATE |\n| **Retrieved** | 2026-09-13 |\n\n## Relevance\n\nSame comparative study as MCP entry; quotes focused on A2A task model.\n\n## Quotes\n\n> \"A task represents a stateful and long-running piece of work whose progress needs to be tracked by the client and the server.\"\n\n\n> \"Tasks have a well-defined lifecycle within A2A… terminal state (e.g., completed, cancelled, failed, or rejected), or a paused state (input-required or auth-required).\"\n\n\n## Images\n\n_None attached._","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:agent2agent-protocol:evidence","kind":"tech_section","path":"technologies/agent2agent-protocol/README.md","title":"Agent2Agent Protocol · Evidence","text":"Agent2Agent Protocol — Evidence\n\n| Claim area | Source id | Strength |\n|---|---|---|\n| Protocol / interoperability role | `science/sources/a2a-repo.md` | MODERATE |\n| Spec notes | `science/sources/a2a-spec.md` | MODERATE |\n| MCP vs A2A contrast | `science/sources/mcp-vs-a2a-2607-23884.md` | MODERATE |","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:agent2agent-protocol:features-map","kind":"tech_section","path":"technologies/agent2agent-protocol/README.md","title":"Agent2Agent Protocol · Features map","text":"Agent2Agent Protocol — Features map\n\nWhich research features may use this technology: [`features.md`](./features.md) (5 mapped).\n\n","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:agent2agent-protocol:github-map","kind":"tech_section","path":"technologies/agent2agent-protocol/README.md","title":"Agent2Agent Protocol · GitHub map","text":"$10","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:agent2agent-protocol:how-it-works","kind":"tech_section","path":"technologies/agent2agent-protocol/README.md","title":"Agent2Agent Protocol · How it works","text":"Agent2Agent Protocol — How it works\n\nPublish an Agent Card; peers discover and open authenticated sessions; clients send tasks/messages and receive artifacts via request/response, streaming, or push. Opacity: collaborate without sharing proprietary internals.\n\n1. Expose agents as A2A servers (ADK, LangGraph, BeeAI, …). \n2. Publish Agent Cards (capabilities + connection). \n3. Clients negotiate and run tasks (sync / SSE / push). \n4. Audit handoffs in the OS ledger; still gate world-binding actions.\n\n```mermaid\nflowchart LR\n A[Agent A] -->|Agent Card + tasks| A2A[A2A JSON-RPC]\n B[Agent B] --> A2A\n A2A --> Art[Messages · artifacts · streams]\n```","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:agent2agent-protocol:limits-failure-modes","kind":"tech_section","path":"technologies/agent2agent-protocol/README.md","title":"Agent2Agent Protocol · Limits & failure modes","text":"Agent2Agent Protocol — Limits & failure modes\n\n- **Interop ≠ authority:** discovered skills can still be unauthorized — IAM/policy still apply. \n- **Opacity cuts both ways:** harder to debug peer internals; need contract tests. \n- **Spec churn:** track a2a-protocol.org version and SDK alignment. \n- **Not the ledger:** handoff records must still land in company SoR.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:agent2agent-protocol:links","kind":"tech_section","path":"technologies/agent2agent-protocol/README.md","title":"Agent2Agent Protocol · Links","text":"Agent2Agent Protocol — Links\n\n**URL:** https://a2a-protocol.org/ \n**Repo:** https://github.com/a2aproject/A2A \n**Docs:** https://a2a-protocol.org/latest/specification/ \n\n**Folder:** `agent2agent-protocol` \n**Wiki-status:** `deep` \n**Updated:** 2026-09-14","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:agent2agent-protocol:scientific-notes","kind":"tech_section","path":"technologies/agent2agent-protocol/README.md","title":"Agent2Agent Protocol · Scientific notes","text":"Agent2Agent Protocol — Scientific notes\n\nPilot scientific pack: **3 sources**, quotes per source, image copyright register.\n\n→ [`science/README.md`](./science/README.md) · schema [`../SCIENCE.md`](../SCIENCE.md)","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:agent2agent-protocol:what-it-is","kind":"tech_section","path":"technologies/agent2agent-protocol/README.md","title":"Agent2Agent Protocol · What it is","text":"Agent2Agent Protocol — What it is\n\nAgent2Agent (A2A) is an **open protocol for communication and interoperability between opaque agentic applications** — agents built on different frameworks, by different vendors, on separate servers — so they can collaborate as peers, not merely as tools. Agents discover capabilities via **Agent Cards**, negotiate modalities (text, forms, media), and collaborate on long-running tasks without exposing internal state, memory, or tools. Wire format: JSON-RPC 2.0 over HTTP(S), with sync, SSE streaming, and async push. Linux Foundation project (Google-contributed); Apache-2.0; SDKs in Python, Go, JS, Java, .NET, Rust.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:agent2agent-protocol:when-to-use-skip","kind":"tech_section","path":"technologies/agent2agent-protocol/README.md","title":"Agent2Agent Protocol · When to use / skip","text":"Agent2Agent Protocol — When to use / skip\n\n- **Use when:** you need **cross-stack agent collaboration** without a shared runtime. \n- **Skip when:** you only need tool mounting (**model-context-protocol**) or in-process multi-agent frameworks. \n- **Prefer instead:** MCP for tools/resources; A2A when the peer is another opaque agent.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:agent2agent-protocol:why-it-matters-here","kind":"tech_section","path":"technologies/agent2agent-protocol/README.md","title":"Agent2Agent Protocol · Why it matters here","text":"Agent2Agent Protocol — Why it matters here\n\nAn agentic OS that only mounts tools still needs a **peer handoff language** when work crosses products or companies. A2A is the research pick for agent↔agent discovery and task exchange. Prefer **MCP** for host↔tool/resources/prompts; use both when specialists are external agents *and* tools are MCP servers. Protocol alone does not replace policy gates or the ledger.","quality":null,"needs_deepen":false,"deepen_reason":null}],"corpus_tags":{"category":["Agentic → Agent Protocols"],"dedication":["open-source"],"feature":["agent-identity-interoperability","apis-sdks-mcp-extensibility","mcp-tools","multi-agent-collaboration","r2-02-assistant-expert"],"kind":["map_edge","tech_features","tech_quote","tech_readme","tech_science_source","tech_section"],"needs_deepen":["false"],"quality":["ok"],"section":["Evidence","Features map","GitHub map","How it works","Limits & failure modes","Links","Scientific notes","What it is","When to use / skip","Why it matters here"],"slug":["agent2agent-protocol"],"source_id":["a2a-repo","a2a-spec","mcp-vs-a2a-2607-23884"],"technology":["agent2agent-protocol"]},"mermaid":[{"title":"How it works","code":"flowchart LR\n A[Agent A] -->|Agent Card + tasks| A2A[A2A JSON-RPC]\n B[Agent B] --> A2A\n A2A --> Art[Messages · artifacts · streams]\n Peermodelcontextprotocol[[model-context-protocol]]"},{"title":"GitHub map","code":"flowchart LR\n Tech[\"Agent2Agent Protocol\"]\n Tech -->|primary| A2A[\"a2aproject/A2A\"]\n Tech -->|sdk| a2a_dotnet[\"a2aproject/a2a-dotnet\"]\n Tech -->|sdk| a2a_go[\"a2aproject/a2a-go\"]\n Tech -->|sdk| a2a_java[\"a2aproject/a2a-java\"]\n Tech -->|sdk| a2a_js[\"a2aproject/a2a-js\"]\n Tech -->|sdk| a2a_python[\"a2aproject/a2a-python\"]\n Tech -->|samples| a2a_samples[\"a2aproject/a2a-samples\"]\n Tech -->|related| a2a_inspector[\"a2aproject/a2a-inspector\"]\n Tech --> Cell[\"Agentic / Agent Protocols\"]\n Cell --> Index[technologies/GITHUB_MAP.md]"}]},{"kind":"technology","slug":"agentscope","title":"AgentScope","one_liner":"Frameworks","path":"technologies/agentscope","tags":{"landscape.layer":{"value":"Agentic","status":null,"evidence_class":"inferred","evidence_ref":"technologies/agentscope/README.md","evidence_note":null},"landscape.subcategory":{"value":"Frameworks","status":null,"evidence_class":"inferred","evidence_ref":null,"evidence_note":null},"license_tag":{"value":"Open 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Use this entry as a product-landscape signal for an agentic OS — especially when comparing “buy a worker” vs “build the OS that runs workers.”\n\n## Links\n\n**Listing:** https://www.agentstackmap.com/agent/aider\n\n**Status:** harvested from AgentStackMap 2026-09-13","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:aider:job-coding","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"aider → job-coding","text":"Technology Aider (aider): Aider is a terminal-native coding agent that edits a local git repo in a tight loop: map the codebase, propose diffs, commit with strong git hygiene. It excels at pair-programming autonomy. For an agentic OS it is mainly a pattern reference for code-changing agents with human-visible diffs — relevant if internal coding workers ship, not as the company ops spine.. Maps to research feature «Coding agents» (job-coding). 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Aider lets you pair program with LLMs to start a new project or build on your existing codebase.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:aider:aider-github:4f9289381ba6","kind":"tech_quote","path":"technologies/aider/science/sources/aider-github.md","title":"Aider · aider-github","text":"Aider (aider, Agentic → Coding Agents). Aider automatically commits changes with sensible commit messages. Use familiar git tools to easily diff, manage and undo AI changes.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:aider:aider-github:d1e5ccb70ae5","kind":"tech_quote","path":"technologies/aider/science/sources/aider-github.md","title":"Aider · aider-github","text":"Aider (aider, Agentic → Coding Agents). 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The self-hosted developer control center for coding agents and automations.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:aider:openhands-contrast:a5d9d4b9f1fd","kind":"tech_quote","path":"technologies/aider/science/sources/openhands-contrast.md","title":"Aider · openhands-contrast","text":"Aider (aider, Agentic → Coding Agents). 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Attractive for agentic OS installs that want graph relations (dependencies, org chains) beside relational ledgers.. Maps to research feature «Company Ontology / Persistent Company Model» (company-ontology). Landscape category: Infrastructure → Knowledge Graphs.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:apache-age:one-model-many-projections","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"apache-age → one-model-many-projections","text":"Technology Apache AGE (apache-age): Apache AGE adds graph query (openCypher) capabilities to PostgreSQL — knowledge-graph patterns without leaving Postgres. Attractive for agentic OS installs that want graph relations (dependencies, org chains) beside relational ledgers.. Maps to research feature «One Model, Many Projections» (one-model-many-projections). 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Arcade MCP","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:arcade-mcp:composio-contrast:69ffa6b00d70","kind":"tech_quote","path":"technologies/arcade-mcp/science/sources/composio-contrast.md","title":"Arcade MCP · composio-contrast","text":"Arcade MCP (arcade-mcp, Agentic → Tool Integration). @composio/core intentionally packages its TypeScript source and SDK docs so the installed package is inspectable to coding agents.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:arcade-mcp:composio-contrast:6b865b2b6b7b","kind":"tech_quote","path":"technologies/arcade-mcp/science/sources/composio-contrast.md","title":"Arcade MCP · composio-contrast","text":"Arcade MCP (arcade-mcp, Agentic → Tool Integration). Composio gives your AI agents 1000+ pre-authenticated toolkits, per-user sessions, authentication, triggers, and a sandbox, so you can ship agents that turn intent into action.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:arcade-mcp:composio-contrast:8bdcd072fc87","kind":"tech_quote","path":"technologies/arcade-mcp/science/sources/composio-contrast.md","title":"Arcade MCP · composio-contrast","text":"Arcade MCP (arcade-mcp, Agentic → Tool Integration). This is the Composio SDK monorepo.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:arcade-mcp:mcp-spec:75ea9787e38b","kind":"tech_quote","path":"technologies/arcade-mcp/science/sources/mcp-spec.md","title":"Arcade MCP · mcp-spec","text":"Arcade MCP (arcade-mcp, Agentic → Tool Integration). 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It offers OpenTelemetry-based tracing, LLM evals, versioned datasets/experiments, playground and prompt management, plus OpenInference auto-instrumentation across common frameworks/providers. Self-host locally, in containers, or cloud; Arize AX is the managed produc. Maps to research feature «Cost, Latency & Operational Economics» (cost-latency-ops). Landscape category: Agentic → Observability.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:arize-phoenix:observability-auditability","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"arize-phoenix → observability-auditability","text":"Technology Arize Phoenix (arize-phoenix): Arize Phoenix is an **open-source AI observability** platform for experimentation, evaluation, and troubleshooting of LLM/agent apps. It offers OpenTelemetry-based tracing, LLM evals, versioned datasets/experiments, playground and prompt management, plus OpenInference auto-instrumentation across common frameworks/providers. Self-host locally, in containers, or cloud; Arize AX is the managed produc. Maps to research feature «Observability & Auditability» (observability-auditability). Landscape category: Agentic → Observability.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:arize-phoenix:r2-09-self-monitoring","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"arize-phoenix → r2-09-self-monitoring","text":"Technology Arize Phoenix (arize-phoenix): Arize Phoenix is an **open-source AI observability** platform for experimentation, evaluation, and troubleshooting of LLM/agent apps. It offers OpenTelemetry-based tracing, LLM evals, versioned datasets/experiments, playground and prompt management, plus OpenInference auto-instrumentation across common frameworks/providers. Self-host locally, in containers, or cloud; Arize AX is the managed produc. Maps to research feature «T09 — Self-Monitoring & Observability» (r2-09-self-monitoring). Landscape category: Agentic → Observability.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:arize-phoenix:r2-11-company-observation","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"arize-phoenix → r2-11-company-observation","text":"Technology Arize Phoenix (arize-phoenix): Arize Phoenix is an **open-source AI observability** platform for experimentation, evaluation, and troubleshooting of LLM/agent apps. It offers OpenTelemetry-based tracing, LLM evals, versioned datasets/experiments, playground and prompt management, plus OpenInference auto-instrumentation across common frameworks/providers. Self-host locally, in containers, or cloud; Arize AX is the managed produc. Maps to research feature «T11 — Top-Down Company Observation» (r2-11-company-observation). Landscape category: Agentic → Observability.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:arize-phoenix:r2-14-process-mining","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"arize-phoenix → r2-14-process-mining","text":"Technology Arize Phoenix (arize-phoenix): Arize Phoenix is an **open-source AI observability** platform for experimentation, evaluation, and troubleshooting of LLM/agent apps. It offers OpenTelemetry-based tracing, LLM evals, versioned datasets/experiments, playground and prompt management, plus OpenInference auto-instrumentation across common frameworks/providers. Self-host locally, in containers, or cloud; Arize AX is the managed produc. Maps to research feature «T14 — Process & Agent Behavior Mining» (r2-14-process-mining). Landscape category: Agentic → Observability.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:arize-phoenix:r2-34-decision-lineage","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"arize-phoenix → r2-34-decision-lineage","text":"Technology Arize Phoenix (arize-phoenix): Arize Phoenix is an **open-source AI observability** platform for experimentation, evaluation, and troubleshooting of LLM/agent apps. It offers OpenTelemetry-based tracing, LLM evals, versioned datasets/experiments, playground and prompt management, plus OpenInference auto-instrumentation across common frameworks/providers. Self-host locally, in containers, or cloud; Arize AX is the managed produc. Maps to research feature «T34 — Decision Lineage as First-Class Data» (r2-34-decision-lineage). Landscape category: Agentic → Observability.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:arize-phoenix:r2-38-abm-span-mapping","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"arize-phoenix → r2-38-abm-span-mapping","text":"Technology Arize Phoenix (arize-phoenix): Arize Phoenix is an **open-source AI observability** platform for experimentation, evaluation, and troubleshooting of LLM/agent apps. It offers OpenTelemetry-based tracing, LLM evals, versioned datasets/experiments, playground and prompt management, plus OpenInference auto-instrumentation across common frameworks/providers. Self-host locally, in containers, or cloud; Arize AX is the managed produc. Maps to research feature «T38 — `secretary_spans` → ABM/XES Schema Mapping» (r2-38-abm-span-mapping). Landscape category: Agentic → Observability.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:arize-phoenix:r2-47-trace-maturity-gate","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"arize-phoenix → r2-47-trace-maturity-gate","text":"Technology Arize Phoenix (arize-phoenix): Arize Phoenix is an **open-source AI observability** platform for experimentation, evaluation, and troubleshooting of LLM/agent apps. It offers OpenTelemetry-based tracing, LLM evals, versioned datasets/experiments, playground and prompt management, plus OpenInference auto-instrumentation across common frameworks/providers. Self-host locally, in containers, or cloud; Arize AX is the managed produc. Maps to research feature «T47 — Complete-Trace Counter + Maturity Gate» (r2-47-trace-maturity-gate). Landscape category: Agentic → Observability.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:arize-phoenix:wt-runtime","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"arize-phoenix → wt-runtime","text":"Technology Arize Phoenix (arize-phoenix): Arize Phoenix is an **open-source AI observability** platform for experimentation, evaluation, and troubleshooting of LLM/agent apps. It offers OpenTelemetry-based tracing, LLM evals, versioned datasets/experiments, playground and prompt management, plus OpenInference auto-instrumentation across common frameworks/providers. Self-host locally, in containers, or cloud; Arize AX is the managed produc. Maps to research feature «Workflow Tools — Runtime» (wt-runtime). Landscape category: Agentic → Observability.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_features:arize-phoenix","kind":"tech_features","path":"technologies/arize-phoenix/features.md","title":"Arize Phoenix · features","text":"$19","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:arize-phoenix:langfuse-contrast:9e43ddfea7a5","kind":"tech_quote","path":"technologies/arize-phoenix/science/sources/langfuse-contrast.md","title":"Arize Phoenix · langfuse-contrast","text":"Arize Phoenix (arize-phoenix, Agentic → Observability). Langfuse is an open-source LLM engineering platform that helps teams collaboratively debug, analyze, and iterate on their LLM applications.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:arize-phoenix:langfuse-contrast:bb83f33014f5","kind":"tech_quote","path":"technologies/arize-phoenix/science/sources/langfuse-contrast.md","title":"Arize Phoenix · langfuse-contrast","text":"Arize Phoenix (arize-phoenix, Agentic → Observability). AI Observability & Evaluation","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:arize-phoenix:langfuse-contrast:ddfd2ff62b01","kind":"tech_quote","path":"technologies/arize-phoenix/science/sources/langfuse-contrast.md","title":"Arize Phoenix · langfuse-contrast","text":"Arize Phoenix (arize-phoenix, Agentic → Observability). Phoenix is an open-source AI observability platform designed for experimentation, evaluation, and troubleshooting.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:arize-phoenix:phoenix-docs:464daa250d29","kind":"tech_quote","path":"technologies/arize-phoenix/science/sources/phoenix-docs.md","title":"Arize Phoenix · phoenix-docs","text":"Arize Phoenix (arize-phoenix, Agentic → Observability). Leverage LLMs to benchmark your application's performance using response and retrieval evals.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:arize-phoenix:phoenix-docs:6098c9a236ff","kind":"tech_quote","path":"technologies/arize-phoenix/science/sources/phoenix-docs.md","title":"Arize Phoenix · phoenix-docs","text":"Arize Phoenix (arize-phoenix, Agentic → Observability). Trace your LLM application's runtime using OpenTelemetry-based instrumentation.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:arize-phoenix:phoenix-docs:bca599b9971d","kind":"tech_quote","path":"technologies/arize-phoenix/science/sources/phoenix-docs.md","title":"Arize Phoenix · phoenix-docs","text":"Arize Phoenix (arize-phoenix, Agentic → Observability). Observability is essential for understanding and debugging LLM applications. Unlike traditional software, LLM applications involve complex, non-deterministic interactions that can be challenging to monitor and debug.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:arize-phoenix:phoenix-github:b5b0c9250e15","kind":"tech_quote","path":"technologies/arize-phoenix/science/sources/phoenix-github.md","title":"Arize Phoenix · phoenix-github","text":"Arize Phoenix (arize-phoenix, Agentic → Observability). Create versioned datasets of examples for experimentation, evaluation, and fine-tuning.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:arize-phoenix:phoenix-github:d6fbb8ec861f","kind":"tech_quote","path":"technologies/arize-phoenix/science/sources/phoenix-github.md","title":"Arize Phoenix · phoenix-github","text":"Arize Phoenix (arize-phoenix, Agentic → Observability). Based on OpenTelemetry to increase compatibility and reduce vendor lock-in","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_readme:arize-phoenix","kind":"tech_readme","path":"technologies/arize-phoenix/README.md","title":"Arize Phoenix","text":"$1a","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_science_source:arize-phoenix:langfuse-contrast","kind":"tech_science_source","path":"technologies/arize-phoenix/science/sources/langfuse-contrast.md","title":"Arize Phoenix · langfuse-contrast","text":"Arize Phoenix · source langfuse-contrast\n\n# Langfuse contrast\n\n| Field | Value |\n|---|---|\n| **ID** | `langfuse-contrast` |\n| **Type** | `repo-readme` |\n| **URL** | https://github.com/langfuse/langfuse |\n| **DOI / arXiv / cite** | Langfuse contrast |\n| **Authors / Org** | Langfuse |\n| **Year** | 2026 |\n| **Evidence** | MODERATE |\n| **Retrieved** | 2026-09-13 |\n\n## Relevance\n\nPeer LLM observability.\n\n## Quotes\n\n> \"Langfuse is an open-source LLM engineering platform that helps teams collaboratively debug, analyze, and iterate on their LLM applications.\"\n> — arize-phoenix primary (deduped)\n\n> \"Phoenix is an open-source AI observability platform designed for experimentation, evaluation, and troubleshooting.\"\n> — arize-phoenix primary (deduped)\n\n> \"AI Observability & Evaluation\"\n> — arize-phoenix primary (deduped)\n\n\n## Images\n\n_None attached._","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_science_source:arize-phoenix:phoenix-docs","kind":"tech_science_source","path":"technologies/arize-phoenix/science/sources/phoenix-docs.md","title":"Arize Phoenix · phoenix-docs","text":"Arize Phoenix · source phoenix-docs\n\n# Phoenix documentation\n\n| Field | Value |\n|---|---|\n| **ID** | `phoenix-docs` |\n| **Type** | `docs` |\n| **URL** | https://docs.arize.com/phoenix |\n| **DOI / arXiv / cite** | Phoenix docs |\n| **Authors / Org** | Arize |\n| **Year** | 2026 |\n| **Evidence** | MODERATE |\n| **Retrieved** | 2026-09-13 |\n\n## Relevance\n\nDocs.\n\n## Quotes\n\n> \"Observability is essential for understanding and debugging LLM applications. Unlike traditional software, LLM applications involve complex, non-deterministic interactions that can be challenging to monitor and debug.\"\n> — arize-phoenix primary (deduped)\n\n> \"Leverage LLMs to benchmark your application's performance using response and retrieval evals.\"\n> — arize-phoenix primary (deduped)\n\n> \"Trace your LLM application's runtime using OpenTelemetry-based instrumentation.\"\n> — arize-phoenix primary (deduped)\n\n\n## Images\n\n_None attached._","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_science_source:arize-phoenix:phoenix-github","kind":"tech_science_source","path":"technologies/arize-phoenix/science/sources/phoenix-github.md","title":"Arize Phoenix · phoenix-github","text":"Arize Phoenix · source phoenix-github\n\n# Arize-ai/phoenix\n\n| Field | Value |\n|---|---|\n| **ID** | `phoenix-github` |\n| **Type** | `repo-readme` |\n| **URL** | https://github.com/Arize-ai/phoenix |\n| **DOI / arXiv / cite** | Arize-ai/phoenix |\n| **Authors / Org** | Arize |\n| **Year** | 2026 |\n| **Evidence** | MODERATE |\n| **Retrieved** | 2026-09-13 |\n\n## Relevance\n\nPrimary repo.\n\n## Quotes\n\n> \"Based on OpenTelemetry to increase compatibility and reduce vendor lock-in\"\n> — arize-phoenix primary (deduped)\n\n> \"Create versioned datasets of examples for experimentation, evaluation, and fine-tuning.\"\n> — arize-phoenix primary (deduped)\n\n\n## Images\n\n_None attached._","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:arize-phoenix:evidence","kind":"tech_section","path":"technologies/arize-phoenix/README.md","title":"Arize Phoenix · Evidence","text":"Arize Phoenix — Evidence\n\n| Claim area | Source id | Strength |\n|---|---|---|\n| Product / tracing role | `science/sources/phoenix-github.md` | MODERATE |\n| Docs / features | `science/sources/phoenix-docs.md` | MODERATE |\n| Contrast vs Langfuse | `science/sources/langfuse-contrast.md` | MODERATE |","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:arize-phoenix:features-map","kind":"tech_section","path":"technologies/arize-phoenix/README.md","title":"Arize Phoenix · Features map","text":"Arize Phoenix — Features map\n\nWhich research features may use this technology: [`features.md`](./features.md) (9 mapped).\n\n","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:arize-phoenix:github-map","kind":"tech_section","path":"technologies/arize-phoenix/README.md","title":"Arize Phoenix · GitHub map","text":"$1b","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:arize-phoenix:how-it-works","kind":"tech_section","path":"technologies/arize-phoenix/README.md","title":"Arize Phoenix · How it works","text":"Arize Phoenix — How it works\n\nApps emit OTEL/OpenInference traces into Phoenix. Operators inspect spans, run evals against datasets, and iterate prompts/experiments in the UI (or via MCP clients against Phoenix).\n\n1. Deploy Phoenix (local/container/cloud). \n2. Instrument the app (OpenInference / framework integrations). \n3. Ingest traces; annotate and debug sessions. \n4. Run datasets/experiments and LLM evals; promote changes with evidence.\n\n```mermaid\nflowchart LR\n App[Agent / LLM app] -->|OTEL / OpenInference| Phx[Phoenix]\n Phx --> UI[Traces · evals · datasets]\n UI --> Improve[Prompt / retrieval changes]\n```","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:arize-phoenix:limits-failure-modes","kind":"tech_section","path":"technologies/arize-phoenix/README.md","title":"Arize Phoenix · Limits & failure modes","text":"Arize Phoenix — Limits & failure modes\n\n- **Observability ≠ ledger:** traces do not replace case/audit SoR. \n- **Open-core split:** production managed features may push toward Arize AX. \n- **Instrument or see nothing:** wrong exporter config yields empty UI. \n- **Eval cost:** LLM-as-judge burns tokens and can drift without human checks.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:arize-phoenix:links","kind":"tech_section","path":"technologies/arize-phoenix/README.md","title":"Arize Phoenix · Links","text":"Arize Phoenix — Links\n\n**URL:** https://arize.com/docs/phoenix \n**Repo:** https://github.com/Arize-ai/phoenix \n**Docs:** https://arize.com/docs/phoenix \n\n**Folder:** `arize-phoenix` \n**Wiki-status:** `deep` \n**Updated:** 2026-09-14","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:arize-phoenix:scientific-notes","kind":"tech_section","path":"technologies/arize-phoenix/README.md","title":"Arize Phoenix · Scientific notes","text":"Arize Phoenix — Scientific notes\n\nScientific pack: **3 sources**, quotes per source, image copyright register.\n\n→ [`science/README.md`](./science/README.md) · schema [`../SCIENCE.md`](../SCIENCE.md)","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:arize-phoenix:what-it-is","kind":"tech_section","path":"technologies/arize-phoenix/README.md","title":"Arize Phoenix · What it is","text":"Arize Phoenix — What it is\n\nArize Phoenix is an **open-source AI observability** platform for experimentation, evaluation, and troubleshooting of LLM/agent apps. It offers OpenTelemetry-based tracing, LLM evals, versioned datasets/experiments, playground and prompt management, plus OpenInference auto-instrumentation across common frameworks/providers. Self-host locally, in containers, or cloud; Arize AX is the managed production sibling that reuses the same instrumentation. Open-core research stance (OSS Phoenix + commercial AX).","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:arize-phoenix:when-to-use-skip","kind":"tech_section","path":"technologies/arize-phoenix/README.md","title":"Arize Phoenix · When to use / skip","text":"Arize Phoenix — When to use / skip\n\n- **Use when:** you need **self-hosted tracing + evals** with OTEL/OpenInference. \n- **Skip when:** you only need a thin proxy logger (**helicone**), vendor-neutral instrumentation only (**openllmetry**), or LangChain-native SaaS (**langsmith**). \n- **Prefer instead:** Langfuse for broader OSS LLM-ops; Opik if Comet affinity; Weave if W&B already owns experiments.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:arize-phoenix:why-it-matters-here","kind":"tech_section","path":"technologies/arize-phoenix/README.md","title":"Arize Phoenix · Why it matters here","text":"Arize Phoenix — Why it matters here\n\nAgentic OS installs need **span-level visibility into tool calls and retrieval**, not only token dashboards. Phoenix is the research pick when you want self-hosted OTEL/OpenInference tracing + evals. Prefer **Langfuse** for a full OSS LLM-engineering platform with strong prompt/eval workflow; prefer **OpenLLMetry** when you only want instrumentation into an existing OTEL backend; prefer **Helicone** for proxy/gateway logging with minimal code change; prefer **LangSmith** when already deep in LangChain SaaS.","quality":null,"needs_deepen":false,"deepen_reason":null}],"corpus_tags":{"category":["Agentic → Observability"],"dedication":["open-core"],"feature":["cost-latency-ops","observability-auditability","r2-09-self-monitoring","r2-11-company-observation","r2-14-process-mining","r2-34-decision-lineage","r2-38-abm-span-mapping","r2-47-trace-maturity-gate","wt-runtime"],"kind":["map_edge","tech_features","tech_quote","tech_readme","tech_science_source","tech_section"],"needs_deepen":["false"],"quality":["ok"],"section":["Evidence","Features map","GitHub map","How it works","Limits & failure modes","Links","Scientific notes","What it is","When to use / skip","Why it matters here"],"slug":["arize-phoenix"],"source_id":["langfuse-contrast","phoenix-docs","phoenix-github"],"technology":["arize-phoenix"]},"mermaid":[{"title":"How it works","code":"flowchart LR\n App[Agent / LLM app] -->|OTEL / OpenInference| Phx[Phoenix]\n Phx --> UI[Traces · evals · datasets]\n UI --> Improve[Prompt / retrieval changes]\n Peercometoptik[[comet-optik]]"},{"title":"GitHub map","code":"flowchart LR\n Tech[\"Arize Phoenix\"]\n Tech -->|primary| phoenix[\"Arize-ai/phoenix\"]\n Tech -->|samples| phoenix_vercel_telemetry[\"Arize-ai/phoenix-vercel-telemetry-template\"]\n Tech -->|related| arize_phoenix_feedstock[\"Arize-ai/arize-phoenix-feedstock\"]\n Tech -->|related| openinference[\"Arize-ai/openinference\"]\n Tech -->|related| phoenix_ai_chatbot[\"Arize-ai/phoenix-ai-chatbot\"]\n Tech -->|related| phoenix_on_azure[\"Arize-ai/phoenix-on-azure\"]\n Tech -->|related| phoenix_to_ax_migration[\"Arize-ai/phoenix-to-ax-migration\"]\n Tech --> Cell[\"Agentic / Observability\"]\n Cell --> Index[technologies/GITHUB_MAP.md]"}]},{"kind":"technology","slug":"atomic-agents","title":"Atomic Agents","one_liner":"Frameworks","path":"technologies/atomic-agents","tags":{"landscape.layer":{"value":"Agentic","status":null,"evidence_class":"inferred","evidence_ref":"technologies/atomic-agents/README.md","evidence_note":null},"landscape.subcategory":{"value":"Frameworks","status":null,"evidence_class":"inferred","evidence_ref":null,"evidence_note":null},"license_tag":{"value":"Open 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It is a strong conceptual reference for multi-actor loops and human-in-the-loop proxies. As a production OS spine it usually needs an external durable workflow engine (Temporal/Inngest/LangGraph checkpointers) because conversatio. Maps to research feature «Agent Building Platform» (agent-building-platform). Landscape category: Agentic → Frameworks.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:autogen:agent-lifecycle","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"autogen → agent-lifecycle","text":"Technology Autogen (autogen): AutoGen (Microsoft) popularized multi-agent conversation patterns: assistants, user proxies, group chats, and handoffs where agents critique and refine each other’s work. It is a strong conceptual reference for multi-actor loops and human-in-the-loop proxies. 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Open-core computer-use tooling for the web surface. Critical for observe/act against portals (permits, vendors, banks). Must run inside sandboxes with credential and exfiltration controls; treat browser sessions as high-risk tools.. Maps to research feature «APIs, SDKs & MCP Extensibility» (apis-sdks-mcp-extensibility). Landscape category: Agentic → Tool Integration.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:browser-use:browser-computer-use","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"browser-use → browser-computer-use","text":"Technology Browser Use (browser-use): Browser Use lets agents drive real browsers: navigate, click, extract, and automate web UIs when no API exists. Open-core computer-use tooling for the web surface. Critical for observe/act against portals (permits, vendors, banks). Must run inside sandboxes with credential and exfiltration controls; treat browser sessions as high-risk tools.. 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It emphasizes workforce-style role assignment and studying cooperative behavior at scale. For an agentic OS it is primarily a research and experimentation toolkit — useful for multi-agent coordination ideas — not a substitute for ledgered. Maps to research feature «Agent Building Platform» (agent-building-platform). Landscape category: Agentic → Frameworks.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:camel:agent-lifecycle","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"camel → agent-lifecycle","text":"Technology CAMEL (camel): CAMEL (Communicative Agents for “Mind” Exploration…) is a research-rooted multi-agent framework for role-playing agent societies that communicate to solve tasks. It emphasizes workforce-style role assignment and studying cooperative behavior at scale. 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Task[Task] --> Roles[Role assignment]\n Roles --> A[Agent A]\n Roles --> B[Agent B]\n A <-->|messages| B\n A --> Tools[Tools]\n B --> Tools\n Tools --> A\n Tools --> B\n A --> Out[Dialogue / solution]\n Peerag2[[ag2]]"},{"title":"GitHub map","code":"flowchart LR\n Tech[\"CAMEL\"]\n Tech -->|primary| camel[\"camel-ai/camel\"]\n Tech -->|samples| camel_tutorials[\"camel-ai/camel_tutorials\"]\n Tech -->|related| camel_workshop[\"camel-ai/camel-workshop\"]\n Tech -->|related| camel_chat[\"camel-ai/camel_chat\"]\n Tech -->|related| camel_web_app[\"camel-ai/camel_web_app\"]\n Tech -->|related| multi_agent_streamlit_ui[\"camel-ai/multi-agent-streamlit-ui\"]\n Tech -->|related| owl[\"camel-ai/owl\"]\n Tech --> Cell[\"Agentic / Frameworks\"]\n Cell --> 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Applications authorize requests by calling Cedar’s evaluation engine against policies and entities (principals, actions, resources with attributes/parents). It targets RBAC/ABAC-style models, is designed for fast indexed evaluation, and supports schem. Maps to research feature «Access vs Authority Separation» (access-vs-authority). Landscape category: Agentic → IAM.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:cedar:agent-identity-interoperability","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"cedar → agent-identity-interoperability","text":"Technology Cedar (cedar): Cedar is an **authorization policy language** (and Rust engine) for writing and enforcing fine-grained permissions separately from application code. Applications authorize requests by calling Cedar’s evaluation engine against policies and entities (principals, actions, resources with attributes/parents). 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In your application's code, you preface requests made to your operations with a call to Cedar's authorization engine, asking","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:cedar:cedar-docs:c146eecef6f9","kind":"tech_quote","path":"technologies/cedar/science/sources/cedar-docs.md","title":"Cedar · cedar-docs","text":"Cedar (cedar, Agentic → IAM). Cedar is a language for writing authorization policies and making authorization decisions based on those policies.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:cedar:cedar-github:17f9c4b11b8d","kind":"tech_quote","path":"technologies/cedar/science/sources/cedar-github.md","title":"Cedar · cedar-github","text":"Cedar (cedar, Agentic → IAM). 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Cedar is a language for writing and enforcing authorization policies in your applications.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:cedar:opa-contrast:0213015ca7c3","kind":"tech_quote","path":"technologies/cedar/science/sources/opa-contrast.md","title":"Cedar · opa-contrast","text":"Cedar (cedar, Agentic → IAM). Open Policy Agent (OPA) is an open source, general-purpose policy engine that enables unified, context-aware policy enforcement across the entire stack.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:cedar:opa-contrast:6da2022849c6","kind":"tech_quote","path":"technologies/cedar/science/sources/opa-contrast.md","title":"Cedar · opa-contrast","text":"Cedar (cedar, Agentic → IAM). OPA is proud to be a graduated project in the Cloud Native Computing Foundation (CNCF) landscape.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:cedar:opa-contrast:d00411941c86","kind":"tech_quote","path":"technologies/cedar/science/sources/opa-contrast.md","title":"Cedar · opa-contrast","text":"Cedar (cedar, Agentic → IAM). OPA helps teams focus on delivering business value by decoupling policy from application logic.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_readme:cedar","kind":"tech_readme","path":"technologies/cedar/README.md","title":"Cedar","text":"$29","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_science_source:cedar:cedar-docs","kind":"tech_science_source","path":"technologies/cedar/science/sources/cedar-docs.md","title":"Cedar · cedar-docs","text":"Cedar · source cedar-docs\n\n# Cedar documentation\n\n| Field | Value |\n|---|---|\n| **ID** | `cedar-docs` |\n| **Type** | `docs` |\n| **URL** | https://docs.cedarpolicy.com/ |\n| **DOI / arXiv / cite** | Cedar docs |\n| **Authors / Org** | Amazon |\n| **Year** | 2026 |\n| **Evidence** | MODERATE |\n| **Retrieved** | 2026-09-13 |\n\n## Relevance\n\nDocs.\n\n## Quotes\n\n> \"Cedar is a language for writing authorization policies and making authorization decisions based on those policies.\"\n> — docs.cedarpolicy.com\n\n> \"Using Cedar, you can decouple your business logic from the authorization logic.\"\n> — docs.cedarpolicy.com\n\n> \"In your application's code, you preface requests made to your operations with a call to Cedar's authorization engine, asking \"Is this request authorized?\".\"\n> — docs.cedarpolicy.com\n\n\n## Images\n\n_None attached._","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_science_source:cedar:cedar-github","kind":"tech_science_source","path":"technologies/cedar/science/sources/cedar-github.md","title":"Cedar · cedar-github","text":"Cedar · source cedar-github\n\n# cedar-policy/cedar\n\n| Field | Value |\n|---|---|\n| **ID** | `cedar-github` |\n| **Type** | `repo-readme` |\n| **URL** | https://github.com/cedar-policy/cedar |\n| **DOI / arXiv / cite** | cedar-policy/cedar |\n| **Authors / Org** | Amazon |\n| **Year** | 2026 |\n| **Evidence** | MODERATE |\n| **Retrieved** | 2026-09-13 |\n\n## Relevance\n\nPrimary repo.\n\n## Quotes\n\n> \"Implementation of the Cedar Policy Language\"\n> — cedar GitHub\n\n> \"Cedar is a language for writing and enforcing authorization policies in your applications.\"\n> — cedar README\n\n> \"Using Cedar, you can write policies that specify your applications' fine-grained permissions.\"\n> — cedar README\n\n\n## Images\n\n_None attached._","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_science_source:cedar:opa-contrast","kind":"tech_science_source","path":"technologies/cedar/science/sources/opa-contrast.md","title":"Cedar · opa-contrast","text":"Cedar · source opa-contrast\n\n# OPA contrast\n\n| Field | Value |\n|---|---|\n| **ID** | `opa-contrast` |\n| **Type** | `repo-readme` |\n| **URL** | https://github.com/open-policy-agent/opa |\n| **DOI / arXiv / cite** | OPA contrast |\n| **Authors / Org** | OPA |\n| **Year** | 2026 |\n| **Evidence** | MODERATE |\n| **Retrieved** | 2026-09-13 |\n\n## Relevance\n\nPeer policy engine.\n\n## Quotes\n\n> \"Open Policy Agent (OPA) is an open source, general-purpose policy engine that enables unified, context-aware policy enforcement across the entire stack.\"\n> — OPA README\n\n> \"OPA is proud to be a graduated project in the Cloud Native Computing Foundation (CNCF) landscape.\"\n> — OPA README\n\n> \"OPA helps teams focus on delivering business value by decoupling policy from application logic.\"\n> — openpolicyagent.org\n\n\n## Images\n\n_None attached._","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:cedar:document-management-map","kind":"tech_section","path":"technologies/cedar/README.md","title":"Cedar · Document management map","text":"Cedar — Document management map\n\nDMS/IDP research vendors related to this cell: [`document-management.md`](./document-management.md) (35).\n\n","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:cedar:evidence","kind":"tech_section","path":"technologies/cedar/README.md","title":"Cedar · Evidence","text":"Cedar — Evidence\n\n| Claim area | Source id | Strength |\n|---|---|---|\n| Language / engine role | `science/sources/cedar-github.md` | MODERATE |\n| Docs / usage | `science/sources/cedar-docs.md` | MODERATE |\n| Contrast vs OPA | `science/sources/opa-contrast.md` | MODERATE |","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:cedar:features-map","kind":"tech_section","path":"technologies/cedar/README.md","title":"Cedar · Features map","text":"Cedar — Features map\n\nWhich research features may use this technology: [`features.md`](./features.md) (8 mapped).\n\n","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:cedar:github-map","kind":"tech_section","path":"technologies/cedar/README.md","title":"Cedar · GitHub map","text":"$2a","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:cedar:how-it-works","kind":"tech_section","path":"technologies/cedar/README.md","title":"Cedar · How it works","text":"Cedar — How it works\n\nAuthors write `permit`/`forbid` policies and entity graphs; at request time the app supplies principal, action, resource, and Cedar returns ALLOW/DENY. Schemas enable validation; SymCC and related crates support property checking.\n\n1. Model entities and write Cedar policies. \n2. Embed `cedar-policy` (or CLI) in the service/agent gateway. \n3. On each sensitive request, authorize with policies + entities. \n4. Enforce the decision in the application — Cedar evaluates; the app enforces.\n\n```mermaid\nflowchart LR\n Req[Access request] --> App[Application]\n App --> Cedar[Cedar engine]\n Pol[Policies + entities] --> Cedar\n Cedar -->|ALLOW/DENY| App\n```","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:cedar:limits-failure-modes","kind":"tech_section","path":"technologies/cedar/README.md","title":"Cedar · Limits & failure modes","text":"Cedar — Limits & failure modes\n\n- **App must enforce:** Cedar answers; failing to gate tools still leaks. \n- **Rust/crate gravity:** best native path is Rust; other SDKs exist but check maturity. \n- **Not identity:** no user store/SSO — pair with Keycloak (or similar) for who the principal *is*. \n- **Not the ledger:** policies ≠ company process authority by themselves.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:cedar:links","kind":"tech_section","path":"technologies/cedar/README.md","title":"Cedar · Links","text":"Cedar — Links\n\n**URL:** https://www.cedarpolicy.com/en \n**Repo:** https://github.com/cedar-policy/cedar \n**Docs:** https://docs.cedarpolicy.com \n\n**Folder:** `cedar` \n**Wiki-status:** `deep` \n**Updated:** 2026-09-14","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:cedar:scientific-notes","kind":"tech_section","path":"technologies/cedar/README.md","title":"Cedar · Scientific notes","text":"Cedar — Scientific notes\n\nScientific pack: **3 sources**, quotes per source, image copyright register.\n\n→ [`science/README.md`](./science/README.md) · schema [`../SCIENCE.md`](../SCIENCE.md)","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:cedar:what-it-is","kind":"tech_section","path":"technologies/cedar/README.md","title":"Cedar · What it is","text":"Cedar — What it is\n\nCedar is an **authorization policy language** (and Rust engine) for writing and enforcing fine-grained permissions separately from application code. Applications authorize requests by calling Cedar’s evaluation engine against policies and entities (principals, actions, resources with attributes/parents). It targets RBAC/ABAC-style models, is designed for fast indexed evaluation, and supports schema validation plus automated-reasoning analysis. Use via the `cedar-policy` crate (`cargo add cedar-policy`), CLI, WASM, and language bindings (Go/Java, etc.). Apache-2.0.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:cedar:when-to-use-skip","kind":"tech_section","path":"technologies/cedar/README.md","title":"Cedar · When to use / skip","text":"Cedar — When to use / skip\n\n- **Use when:** you need a **dedicated authorization language** with schema validation and analyzability for app/API permissions. \n- **Skip when:** you need IdP/SSO (**keycloak**), broad infra policy (**opa**), or governance methodology without an engine (**earned-autonomy-protocol**). \n- **Prefer instead:** OPA for CNCF-wide Rego enforcement; Keycloak for users/federation.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:cedar:why-it-matters-here","kind":"tech_section","path":"technologies/cedar/README.md","title":"Cedar · Why it matters here","text":"Cedar — Why it matters here\n\nAgentic OS tools that change the world need **allow/deny decisions that are declared, auditable, and not hardcoded in agents**. Cedar is the research pick when you want an expressive, analyzable authorization language for app/API requests. Prefer **OPA** for general-purpose Rego across the stack (K8s, infra); prefer **Keycloak** for identity/SSO/user federation; prefer **Earned Autonomy Protocol** for methodology of *earning* execution scope, not a policy DSL.","quality":null,"needs_deepen":false,"deepen_reason":null}],"corpus_tags":{"category":["Agentic → IAM"],"dedication":["open-source"],"feature":["access-vs-authority","agent-identity-interoperability","autonomy-levels","exception-first-participation","governance","r2-13-governance-policy","security-identity-governance","wt-human-policy"],"kind":["map_edge","tech_features","tech_quote","tech_readme","tech_science_source","tech_section"],"needs_deepen":["false"],"quality":["ok"],"section":["Document management map","Evidence","Features map","GitHub map","How it works","Limits & failure modes","Links","Scientific notes","What it is","When to use / skip","Why it matters 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Landscape category: Infrastructure → Vector DBs.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:chroma:wt-data-context","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"chroma → wt-data-context","text":"Technology Chroma (chroma): Chroma is a developer-friendly vector database (open-core) for embeddings storage and similarity search, popular in RAG prototypes and apps. Low ops friction early. Scale and multi-tenant governance may push larger installs toward Milvus/Qdrant/pgvector.. Maps to research feature «Workflow Tools — Data & Context» (wt-data-context). 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Chroma gives you everything you need for retrieval: store embeddings with metadata, search with dense and sparse vectors, filter by metadata, and retrieve across text, images, and more.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:chroma:chroma-docs:5d5ae76bb4b7","kind":"tech_quote","path":"technologies/chroma/science/sources/chroma-docs.md","title":"Chroma · chroma-docs","text":"Chroma (chroma, Infrastructure → Vector DBs). Chroma is licensed under Apache 2.0. Run it locally, self-host, or use Chroma Cloud for a managed, serverless experience.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:chroma:chroma-docs:634fc6a96390","kind":"tech_quote","path":"technologies/chroma/science/sources/chroma-docs.md","title":"Chroma · chroma-docs","text":"Chroma (chroma, Infrastructure → Vector DBs). Chroma is the open-source data infrastructure for AI. It comes with everything you need to get started built-in.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:chroma:chroma-github:428b4eb4f29e","kind":"tech_quote","path":"technologies/chroma/science/sources/chroma-github.md","title":"Chroma · chroma-github","text":"Chroma (chroma, Infrastructure → Vector DBs). Our hosted service, Chroma Cloud, powers serverless vector, hybrid, and full-text search.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:chroma:chroma-github:4cce1b18a125","kind":"tech_quote","path":"technologies/chroma/science/sources/chroma-github.md","title":"Chroma · chroma-github","text":"Chroma (chroma, Infrastructure → Vector DBs). setup Chroma in-memory, for easy prototyping. Can add persistence easily!","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:chroma:chroma-github:b545285a9011","kind":"tech_quote","path":"technologies/chroma/science/sources/chroma-github.md","title":"Chroma · chroma-github","text":"Chroma (chroma, Infrastructure → Vector DBs). Chroma - the open-source data infrastructure for AI.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:chroma:faiss-contrast:67eed494ea3f","kind":"tech_quote","path":"technologies/chroma/science/sources/faiss-contrast.md","title":"Chroma · faiss-contrast","text":"Chroma (chroma, Infrastructure → Vector DBs). 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RAG host]]\n click Host \"../llamaindex/\" \"LlamaIndex\""},{"title":"GitHub map","code":"flowchart LR\n Tech[\"Chroma\"]\n Tech -->|primary| chroma[\"chroma-core/chroma\"]\n Tech -->|samples| chroma_cookbooks[\"chroma-core/chroma-cookbooks\"]\n Tech -->|related| chroma_mcp[\"chroma-core/chroma-mcp\"]\n Tech -->|related| chroma_swift[\"chroma-core/chroma-swift\"]\n Tech -->|related| chromadb_default_embed[\"chroma-core/chromadb-default-embed\"]\n Tech -->|related| gpt4_pdf_chatbot_langcha[\"chroma-core/gpt4-pdf-chatbot-langchain-chroma\"]\n Tech --> Cell[\"Infrastructure / Vector DBs\"]\n Cell --> Index[technologies/GITHUB_MAP.md]"}]},{"kind":"technology","slug":"comet-optik","title":"Comet 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Apache-2.0; free to self-host the full platform, with Comet-hosted options. Folder name keeps the landscape . Maps to research feature «Cost, Latency & Operational Economics» (cost-latency-ops). Landscape category: Agentic → Observability.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:comet-optik:observability-auditability","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"comet-optik → observability-auditability","text":"Technology Comet Optik (comet-optik): Opik (catalog folder `comet-optik`) is Comet’s **open-source LLM observability and evaluation** platform: agent/LLM tracing, datasets and experiments, LLM-as-a-judge metrics, prompt/agent optimization, production dashboards, online evaluation rules, optional guardrails, and PyTest CI hooks. Apache-2.0; free to self-host the full platform, with Comet-hosted options. Folder name keeps the landscape . Maps to research feature «Observability & Auditability» (observability-auditability). Landscape category: Agentic → Observability.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:comet-optik:r2-09-self-monitoring","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"comet-optik → r2-09-self-monitoring","text":"Technology Comet Optik (comet-optik): Opik (catalog folder `comet-optik`) is Comet’s **open-source LLM observability and evaluation** platform: agent/LLM tracing, datasets and experiments, LLM-as-a-judge metrics, prompt/agent optimization, production dashboards, online evaluation rules, optional guardrails, and PyTest CI hooks. Apache-2.0; free to self-host the full platform, with Comet-hosted options. Folder name keeps the landscape . Maps to research feature «T09 — Self-Monitoring & Observability» (r2-09-self-monitoring). Landscape category: Agentic → Observability.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:comet-optik:r2-14-process-mining","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"comet-optik → r2-14-process-mining","text":"Technology Comet Optik (comet-optik): Opik (catalog folder `comet-optik`) is Comet’s **open-source LLM observability and evaluation** platform: agent/LLM tracing, datasets and experiments, LLM-as-a-judge metrics, prompt/agent optimization, production dashboards, online evaluation rules, optional guardrails, and PyTest CI hooks. Apache-2.0; free to self-host the full platform, with Comet-hosted options. Folder name keeps the landscape . Maps to research feature «T14 — Process & Agent Behavior Mining» (r2-14-process-mining). Landscape category: Agentic → Observability.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:comet-optik:r2-34-decision-lineage","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"comet-optik → r2-34-decision-lineage","text":"Technology Comet Optik (comet-optik): Opik (catalog folder `comet-optik`) is Comet’s **open-source LLM observability and evaluation** platform: agent/LLM tracing, datasets and experiments, LLM-as-a-judge metrics, prompt/agent optimization, production dashboards, online evaluation rules, optional guardrails, and PyTest CI hooks. Apache-2.0; free to self-host the full platform, with Comet-hosted options. Folder name keeps the landscape . Maps to research feature «T34 — Decision Lineage as First-Class Data» (r2-34-decision-lineage). Landscape category: Agentic → Observability.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:comet-optik:r2-38-abm-span-mapping","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"comet-optik → r2-38-abm-span-mapping","text":"Technology Comet Optik (comet-optik): Opik (catalog folder `comet-optik`) is Comet’s **open-source LLM observability and evaluation** platform: agent/LLM tracing, datasets and experiments, LLM-as-a-judge metrics, prompt/agent optimization, production dashboards, online evaluation rules, optional guardrails, and PyTest CI hooks. Apache-2.0; free to self-host the full platform, with Comet-hosted options. Folder name keeps the landscape . Maps to research feature «T38 — `secretary_spans` → ABM/XES Schema Mapping» (r2-38-abm-span-mapping). Landscape category: Agentic → Observability.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:comet-optik:r2-47-trace-maturity-gate","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"comet-optik → r2-47-trace-maturity-gate","text":"Technology Comet Optik (comet-optik): Opik (catalog folder `comet-optik`) is Comet’s **open-source LLM observability and evaluation** platform: agent/LLM tracing, datasets and experiments, LLM-as-a-judge metrics, prompt/agent optimization, production dashboards, online evaluation rules, optional guardrails, and PyTest CI hooks. Apache-2.0; free to self-host the full platform, with Comet-hosted options. Folder name keeps the landscape . Maps to research feature «T47 — Complete-Trace Counter + Maturity Gate» (r2-47-trace-maturity-gate). Landscape category: Agentic → Observability.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:comet-optik:wt-runtime","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"comet-optik → wt-runtime","text":"Technology Comet Optik (comet-optik): Opik (catalog folder `comet-optik`) is Comet’s **open-source LLM observability and evaluation** platform: agent/LLM tracing, datasets and experiments, LLM-as-a-judge metrics, prompt/agent optimization, production dashboards, online evaluation rules, optional guardrails, and PyTest CI hooks. Apache-2.0; free to self-host the full platform, with Comet-hosted options. Folder name keeps the landscape . Maps to research feature «Workflow Tools — Runtime» (wt-runtime). Landscape category: Agentic → Observability.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_features:comet-optik","kind":"tech_features","path":"technologies/comet-optik/features.md","title":"Comet Optik · features","text":"$2c","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:comet-optik:langfuse-contrast:16d0acf3faa2","kind":"tech_quote","path":"technologies/comet-optik/science/sources/langfuse-contrast.md","title":"Comet Optik · langfuse-contrast","text":"Comet Optik (comet-optik, Agentic → Observability). Open source AI engineering platform: LLM evals, observability, metrics, prompt management, playground, datasets.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:comet-optik:langfuse-contrast:24d374e2e9f0","kind":"tech_quote","path":"technologies/comet-optik/science/sources/langfuse-contrast.md","title":"Comet Optik · langfuse-contrast","text":"Comet Optik (comet-optik, Agentic → Observability). Langfuse can be self-hosted in minutes and is battle-tested.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:comet-optik:langfuse-contrast:652b7ecbee52","kind":"tech_quote","path":"technologies/comet-optik/science/sources/langfuse-contrast.md","title":"Comet Optik · langfuse-contrast","text":"Comet Optik (comet-optik, Agentic → Observability). Langfuse is an open source LLM engineering platform. It helps teams collaboratively develop, monitor, evaluate, and debug AI applications.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:comet-optik:opik-docs:9e43ddfea7a5","kind":"tech_quote","path":"technologies/comet-optik/science/sources/opik-docs.md","title":"Comet Optik · opik-docs","text":"Comet Optik (comet-optik, Agentic → Observability). Langfuse is an open-source LLM engineering platform that helps teams collaboratively debug, analyze, and iterate on their LLM applications.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:comet-optik:opik-docs:bca599b9971d","kind":"tech_quote","path":"technologies/comet-optik/science/sources/opik-docs.md","title":"Comet Optik · opik-docs","text":"Comet Optik (comet-optik, Agentic → Observability). Observability is essential for understanding and debugging LLM applications. Unlike traditional software, LLM applications involve complex, non-deterministic interactions that can be challenging to monitor and debug.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:comet-optik:opik-docs:d6fbb8ec861f","kind":"tech_quote","path":"technologies/comet-optik/science/sources/opik-docs.md","title":"Comet Optik · opik-docs","text":"Comet Optik (comet-optik, Agentic → Observability). Based on OpenTelemetry to increase compatibility and reduce vendor lock-in","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:comet-optik:opik-github:59dfd3957106","kind":"tech_quote","path":"technologies/comet-optik/science/sources/opik-github.md","title":"Comet Optik · opik-github","text":"Comet Optik (comet-optik, Agentic → Observability). Debug, evaluate, and monitor your LLM applications, RAG systems, and agentic workflows with comprehensive tracing, automated evaluations, and production-ready dashboards.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:comet-optik:opik-github:bb83f33014f5","kind":"tech_quote","path":"technologies/comet-optik/science/sources/opik-github.md","title":"Comet Optik · opik-github","text":"Comet Optik (comet-optik, Agentic → Observability). AI Observability & Evaluation","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_readme:comet-optik","kind":"tech_readme","path":"technologies/comet-optik/README.md","title":"Comet Optik","text":"$2d","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_science_source:comet-optik:langfuse-contrast","kind":"tech_science_source","path":"technologies/comet-optik/science/sources/langfuse-contrast.md","title":"Comet Optik · langfuse-contrast","text":"Comet Optik · source langfuse-contrast\n\n# Langfuse contrast\n\n| Field | Value |\n|---|---|\n| **ID** | `langfuse-contrast` |\n| **Type** | `repo-readme` |\n| **URL** | https://github.com/langfuse/langfuse |\n| **DOI / arXiv / cite** | Langfuse contrast |\n| **Authors / Org** | Langfuse |\n| **Year** | 2026 |\n| **Evidence** | MODERATE |\n| **Retrieved** | 2026-09-13 |\n\n## Relevance\n\nPeer observability.\n\n## Quotes\n\n> \"Langfuse is an open source LLM engineering platform. It helps teams collaboratively develop, monitor, evaluate, and debug AI applications.\"\n> — langfuse\n\n> \"Langfuse can be self-hosted in minutes and is battle-tested.\"\n> — langfuse\n\n> \"Open source AI engineering platform: LLM evals, observability, metrics, prompt management, playground, datasets.\"\n> — langfuse\n\n\n## Images\n\n_None attached._","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_science_source:comet-optik:opik-docs","kind":"tech_science_source","path":"technologies/comet-optik/science/sources/opik-docs.md","title":"Comet Optik · opik-docs","text":"Comet Optik · source opik-docs\n\n# Opik documentation\n\n| Field | Value |\n|---|---|\n| **ID** | `opik-docs` |\n| **Type** | `docs` |\n| **URL** | https://www.comet.com/docs/opik/ |\n| **DOI / arXiv / cite** | Opik docs |\n| **Authors / Org** | Comet |\n| **Year** | 2026 |\n| **Evidence** | MODERATE |\n| **Retrieved** | 2026-09-13 |\n\n## Relevance\n\nDocs.\n\n## Quotes\n\n> \"Langfuse is an open-source LLM engineering platform that helps teams collaboratively debug, analyze, and iterate on their LLM applications.\"\n> — langfuse contrast shelf\n\n> \"Observability is essential for understanding and debugging LLM applications. Unlike traditional software, LLM applications involve complex, non-deterministic interactions that can be challenging to monitor and debug.\"\n> — langfuse contrast shelf\n\n> \"Based on OpenTelemetry to increase compatibility and reduce vendor lock-in\"\n> — langfuse contrast shelf\n\n\n## Images\n\n_None attached._","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_science_source:comet-optik:opik-github","kind":"tech_science_source","path":"technologies/comet-optik/science/sources/opik-github.md","title":"Comet Optik · opik-github","text":"Comet Optik · source opik-github\n\n# comet-ml/opik\n\n| Field | Value |\n|---|---|\n| **ID** | `opik-github` |\n| **Type** | `repo-readme` |\n| **URL** | https://github.com/comet-ml/opik |\n| **DOI / arXiv / cite** | comet-ml/opik |\n| **Authors / Org** | Comet |\n| **Year** | 2026 |\n| **Evidence** | MODERATE |\n| **Retrieved** | 2026-09-13 |\n\n## Relevance\n\nPrimary repo.\n\n## Quotes\n\n> \"Debug, evaluate, and monitor your LLM applications, RAG systems, and agentic workflows with comprehensive tracing, automated evaluations, and production-ready dashboards.\"\n> — opik GitHub\n\n> \"AI Observability & Evaluation\"\n> — phoenix-style tag — use opik only\n\n\n## Images\n\n_None attached._","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:comet-optik:evidence","kind":"tech_section","path":"technologies/comet-optik/README.md","title":"Comet Optik · Evidence","text":"Comet Optik — Evidence\n\n| Claim area | Source id | Strength |\n|---|---|---|\n| Product / tracing role | `science/sources/opik-github.md` | MODERATE |\n| Docs / lifecycle | `science/sources/opik-docs.md` | MODERATE |\n| Contrast vs Langfuse | `science/sources/langfuse-contrast.md` | MODERATE |","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:comet-optik:features-map","kind":"tech_section","path":"technologies/comet-optik/README.md","title":"Comet Optik · Features map","text":"Comet Optik — Features map\n\nWhich research features may use this technology: [`features.md`](./features.md) (8 mapped).\n\n","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:comet-optik:github-map","kind":"tech_section","path":"technologies/comet-optik/README.md","title":"Comet Optik · GitHub map","text":"$2e","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:comet-optik:how-it-works","kind":"tech_section","path":"technologies/comet-optik/README.md","title":"Comet Optik · How it works","text":"Comet Optik — How it works\n\nSDK/integrations log traces and spans; UI supports annotation, playground, datasets/experiments, and online eval rules. Production paths emphasize high-volume ingest and dashboards.\n\n1. Self-host Opik server or use Comet-hosted. \n2. Integrate SDK / framework connectors. \n3. Trace agents; annotate and evaluate. \n4. Add online rules / CI evals for production guardrails.\n\n```mermaid\nflowchart LR\n Agent[LLM / agent app] -->|SDK traces| Opik[Opik server]\n Opik --> Dash[Dashboards · experiments · online eval]\n```","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:comet-optik:limits-failure-modes","kind":"tech_section","path":"technologies/comet-optik/README.md","title":"Comet Optik · Limits & failure modes","text":"Comet Optik — Limits & failure modes\n\n- **Not company SoR:** ops outcomes still live in the ledger. \n- **Naming drift:** “Optik” vs “Opik” confuses search — use repo name. \n- **Self-host ops:** scale claims need real capacity planning. \n- **Judge metrics:** automated scores need human calibration.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:comet-optik:links","kind":"tech_section","path":"technologies/comet-optik/README.md","title":"Comet Optik · Links","text":"Comet Optik — Links\n\n**URL:** https://www.comet.com/docs/opik/ \n**Repo:** https://github.com/comet-ml/opik \n**Docs:** https://www.comet.com/docs/opik/ \n\n**Folder:** `comet-optik` \n**Wiki-status:** `deep` \n**Updated:** 2026-09-14","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:comet-optik:scientific-notes","kind":"tech_section","path":"technologies/comet-optik/README.md","title":"Comet Optik · Scientific notes","text":"Comet Optik — Scientific notes\n\nScientific pack: **3 sources**, quotes per source, image copyright register.\n\n→ [`science/README.md`](./science/README.md) · schema [`../SCIENCE.md`](../SCIENCE.md)","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:comet-optik:what-it-is","kind":"tech_section","path":"technologies/comet-optik/README.md","title":"Comet Optik · What it is","text":"Comet Optik — What it is\n\nOpik (catalog folder `comet-optik`) is Comet’s **open-source LLM observability and evaluation** platform: agent/LLM tracing, datasets and experiments, LLM-as-a-judge metrics, prompt/agent optimization, production dashboards, online evaluation rules, optional guardrails, and PyTest CI hooks. Apache-2.0; free to self-host the full platform, with Comet-hosted options. Folder name keeps the landscape spelling “Optik”; the product/repo is **Opik**.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:comet-optik:when-to-use-skip","kind":"tech_section","path":"technologies/comet-optik/README.md","title":"Comet Optik · When to use / skip","text":"Comet Optik — When to use / skip\n\n- **Use when:** you want **self-hostable tracing + evals** with Comet ecosystem hooks. \n- **Skip when:** you only need OTEL export (**openllmetry**), proxy logging (**helicone**), or LangChain SaaS (**langsmith**). \n- **Prefer instead:** Langfuse/Phoenix as peer defaults; Weave if W&B is already SoT for experiments.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:comet-optik:why-it-matters-here","kind":"tech_section","path":"technologies/comet-optik/README.md","title":"Comet Optik · Why it matters here","text":"Comet Optik — Why it matters here\n\nSame cell job as Langfuse/Phoenix: **see and score agent behavior**. Prefer Opik when Comet MLOps affinity or self-host Apache-2.0 + high-scale production monitoring is the draw. Prefer **Langfuse** for ClickHouse-backed OSS LLM engineering; **Phoenix** for OpenInference/Arize; **Helicone** for gateway-first logging.","quality":null,"needs_deepen":false,"deepen_reason":null}],"corpus_tags":{"category":["Agentic → Observability"],"dedication":["open-core"],"feature":["cost-latency-ops","observability-auditability","r2-09-self-monitoring","r2-14-process-mining","r2-34-decision-lineage","r2-38-abm-span-mapping","r2-47-trace-maturity-gate","wt-runtime"],"kind":["map_edge","tech_features","tech_quote","tech_readme","tech_science_source","tech_section"],"needs_deepen":["false"],"quality":["ok"],"section":["Evidence","Features map","GitHub map","How it works","Limits & failure modes","Links","Scientific notes","What it is","When to use / skip","Why it matters here"],"slug":["comet-optik"],"source_id":["langfuse-contrast","opik-docs","opik-github"],"technology":["comet-optik"]},"mermaid":[{"title":"How it works","code":"flowchart LR\n Agent[LLM / agent app] -->|SDK 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Landscape category: Agentic → Frameworks.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:crew-ai:prebuilt-agents-templates-packs","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"crew-ai → prebuilt-agents-templates-packs","text":"Technology Crew AI (crew-ai): CrewAI organizes agents into “crews” with roles, goals, and sequential or hierarchical processes — a popular open-core way to express multi-agent job structures without a full graph DSL. Agents get tools and share context as the crew runs tasks. It maps cleanly to “digital workforce” metaphors (estimating crew, finance intake crew). For real side effects you still need an external ledger, authorit. Maps to research feature «Prebuilt Agents, Templates & Packs» (prebuilt-agents-templates-packs). Landscape category: Agentic → Frameworks.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:crew-ai:r2-01-architecture","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"crew-ai → r2-01-architecture","text":"Technology Crew AI (crew-ai): CrewAI organizes agents into “crews” with roles, goals, and sequential or hierarchical processes — a popular open-core way to express multi-agent job structures without a full graph DSL. Agents get tools and share context as the crew runs tasks. It maps cleanly to “digital workforce” metaphors (estimating crew, finance intake crew). For real side effects you still need an external ledger, authorit. Maps to research feature «T01 — Corporate Agentic OS Architecture» (r2-01-architecture). 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Landscape category: Agentic → Frameworks.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:crew-ai:wt-intelligence","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"crew-ai → wt-intelligence","text":"Technology Crew AI (crew-ai): CrewAI organizes agents into “crews” with roles, goals, and sequential or hierarchical processes — a popular open-core way to express multi-agent job structures without a full graph DSL. Agents get tools and share context as the crew runs tasks. It maps cleanly to “digital workforce” metaphors (estimating crew, finance intake crew). For real side effects you still need an external ledger, authorit. Maps to research feature «Workflow Tools — Intelligence» (wt-intelligence). Landscape category: Agentic → Frameworks.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_features:crew-ai","kind":"tech_features","path":"technologies/crew-ai/features.md","title":"Crew AI · features","text":"$33","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:crew-ai:crewai-concepts:28723032767a","kind":"tech_quote","path":"technologies/crew-ai/science/sources/crewai-concepts.md","title":"Crew AI · crewai-concepts","text":"Crew AI (crew-ai, Agentic → Frameworks). In the CrewAI framework, an Agent is an autonomous unit that can: Perform specific tasks; Make decisions based on its role and goal; Use tools to accomplish objectives; Communicate and collaborate with other agents; Maintain memory of interactions; Delegate tasks when allowed.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:crew-ai:crewai-concepts:e87b65ff193e","kind":"tech_quote","path":"technologies/crew-ai/science/sources/crewai-concepts.md","title":"Crew AI · crewai-concepts","text":"Crew AI (crew-ai, Agentic → Frameworks). Think of an agent as a specialized team member with specific skills, expertise, and responsibilities.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:crew-ai:crewai-docs:3de815a0a672","kind":"tech_quote","path":"technologies/crew-ai/science/sources/crewai-docs.md","title":"Crew AI · crewai-docs","text":"Crew AI (crew-ai, Agentic → Frameworks). CrewAI Crews: Optimize for autonomy and collaborative intelligence.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:crew-ai:crewai-docs:b9a3bfdc258e","kind":"tech_quote","path":"technologies/crew-ai/science/sources/crewai-docs.md","title":"Crew AI · crewai-docs","text":"Crew AI (crew-ai, Agentic → Frameworks). CrewAI is a lean, lightning-fast Python framework built entirely from scratch—completely independent of LangChain or other agent frameworks.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:crew-ai:crewai-docs:d34229302cdf","kind":"tech_quote","path":"technologies/crew-ai/science/sources/crewai-docs.md","title":"Crew AI · crewai-docs","text":"Crew AI (crew-ai, Agentic → Frameworks). 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crewAI[\"crewAIInc/crewAI\"]\n Tech -->|samples| crewAI_quickstarts[\"crewAIInc/crewAI-quickstarts\"]\n Tech -->|samples| marketplace_crew_templat[\"crewAIInc/marketplace-crew-template\"]\n Tech -->|samples| template_frontend_crewai[\"crewAIInc/template_frontend_crewai_flows_streamlit_ui\"]\n Tech -->|related| crewai_omniauth_okta[\"crewAIInc/crewai-omniauth-okta\"]\n Tech -->|related| crewai_training_step_by_[\"crewAIInc/crewai_training_step_by_step\"]\n Tech -->|related| enterprise_mcp_server[\"crewAIInc/enterprise-mcp-server\"]\n Tech --> Cell[\"Agentic / Frameworks\"]\n Cell --> Index[technologies/GITHUB_MAP.md]"}]},{"kind":"technology","slug":"dapr-agents","title":"Dapr Agents","one_liner":"Workflow Orchestration","path":"technologies/dapr-agents","tags":{"landscape.layer":{"value":"Agentic","status":null,"evidence_class":"inferred","evidence_ref":"technologies/dapr-agents/README.md","evidence_note":null},"landscape.subcategory":{"value":"Workflow 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If the company already runs Dapr sidecars, agents can reuse the same building blocks. High leverage in Dapr-standardized platforms; avoid introducing Dapr solely for agents unless the platform bet is already made.. Maps to research feature «Closed Execution Loop» (closed-execution-loop). Landscape category: Agentic → Workflow Orchestration.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:dapr-agents:os-execution","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"dapr-agents → os-execution","text":"Technology Dapr Agents (dapr-agents): Dapr Agents combines agent abstractions with Dapr’s distributed runtime (pubsub, state, actors). If the company already runs Dapr sidecars, agents can reuse the same building blocks. High leverage in Dapr-standardized platforms; avoid introducing Dapr solely for agents unless the platform bet is already made.. Maps to research feature «Execution OS» (os-execution). Landscape category: Agentic → Workflow Orchestration.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:dapr-agents:pulse-event-automation","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"dapr-agents → pulse-event-automation","text":"Technology Dapr Agents (dapr-agents): Dapr Agents combines agent abstractions with Dapr’s distributed runtime (pubsub, state, actors). If the company already runs Dapr sidecars, agents can reuse the same building blocks. High leverage in Dapr-standardized platforms; avoid introducing Dapr solely for agents unless the platform bet is already made.. Maps to research feature «Pulse / Event Automation vs Long-Running Resolution» (pulse-event-automation). Landscape category: Agentic → Workflow Orchestration.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:dapr-agents:r2-12-central-nervous-system","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"dapr-agents → r2-12-central-nervous-system","text":"Technology Dapr Agents (dapr-agents): Dapr Agents combines agent abstractions with Dapr’s distributed runtime (pubsub, state, actors). If the company already runs Dapr sidecars, agents can reuse the same building blocks. High leverage in Dapr-standardized platforms; avoid introducing Dapr solely for agents unless the platform bet is already made.. Maps to research feature «T12 — Central Nervous System (Triggers, Hooks, Reflexes)» (r2-12-central-nervous-system). Landscape category: Agentic → Workflow Orchestration.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:dapr-agents:r2-37-sleep-orchestration","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"dapr-agents → r2-37-sleep-orchestration","text":"Technology Dapr Agents (dapr-agents): Dapr Agents combines agent abstractions with Dapr’s distributed runtime (pubsub, state, actors). If the company already runs Dapr sidecars, agents can reuse the same building blocks. High leverage in Dapr-standardized platforms; avoid introducing Dapr solely for agents unless the platform bet is already made.. Maps to research feature «T37 — Sleep Job Orchestration» (r2-37-sleep-orchestration). Landscape category: Agentic → Workflow Orchestration.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:dapr-agents:temporal-coherence","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"dapr-agents → temporal-coherence","text":"Technology Dapr Agents (dapr-agents): Dapr Agents combines agent abstractions with Dapr’s distributed runtime (pubsub, state, actors). If the company already runs Dapr sidecars, agents can reuse the same building blocks. High leverage in Dapr-standardized platforms; avoid introducing Dapr solely for agents unless the platform bet is already made.. Maps to research feature «Temporal Coherence / Long-Horizon Consistency» (temporal-coherence). 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Landscape category: Agentic → Workflow Orchestration.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:dapr-agents:wt-triggering","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"dapr-agents → wt-triggering","text":"Technology Dapr Agents (dapr-agents): Dapr Agents combines agent abstractions with Dapr’s distributed runtime (pubsub, state, actors). If the company already runs Dapr sidecars, agents can reuse the same building blocks. High leverage in Dapr-standardized platforms; avoid introducing Dapr solely for agents unless the platform bet is already made.. Maps to research feature «Workflow Tools — Triggering» (wt-triggering). 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Build autonomous, resilient and observable AI agents with built-in workflow orchestration, security, statefulness and telemetry.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:dapr-agents:autogen-contrast:e4ae61eea7ca","kind":"tech_quote","path":"technologies/dapr-agents/science/sources/autogen-contrast.md","title":"Dapr Agents · autogen-contrast","text":"Dapr Agents (dapr-agents, Agentic → Workflow Orchestration). AG2 is an open-source programming framework for building AI agents and facilitating cooperation among multiple agents to solve tasks.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:dapr-agents:dapr-agents-github:64f730e4b010","kind":"tech_quote","path":"technologies/dapr-agents/science/sources/dapr-agents-github.md","title":"Dapr Agents · dapr-agents-github","text":"Dapr Agents (dapr-agents, Agentic → Workflow Orchestration). Dapr Agents: A Framework for Agentic AI Systems","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:dapr-agents:dapr-agents-github:b572264607af","kind":"tech_quote","path":"technologies/dapr-agents/science/sources/dapr-agents-github.md","title":"Dapr Agents · dapr-agents-github","text":"Dapr Agents (dapr-agents, Agentic → Workflow Orchestration). AG2 aims to streamline the development and research of agentic AI.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:dapr-agents:dapr-github:dc5ea5f3299b","kind":"tech_quote","path":"technologies/dapr-agents/science/sources/dapr-github.md","title":"Dapr Agents · dapr-github","text":"Dapr Agents (dapr-agents, Agentic → Workflow Orchestration). As of AG2 v1.0, the protocol-driven framework is the top-level package, imported as ag2. 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Its core unit is the **sandbox**: an isolated composable computer (dedicated kernel, filesystem, network, vCPU/RAM/disk) that can run Python/TypeScript/JavaScript workloads, with SDK, API, and CLI control of lifecycle, files, processes, and snapshots. Agents create sandboxes programmatically (`pip in. Maps to research feature «Browser & Computer Use» (browser-computer-use). Landscape category: Agentic → Code Sandbox.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:daytona:deployment-data-residency","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"daytona → deployment-data-residency","text":"Technology Daytona (daytona): Daytona is a **secure, elastic infrastructure runtime for AI-generated code and agent workflows**. Its core unit is the **sandbox**: an isolated composable computer (dedicated kernel, filesystem, network, vCPU/RAM/disk) that can run Python/TypeScript/JavaScript workloads, with SDK, API, and CLI control of lifecycle, files, processes, and snapshots. Agents create sandboxes programmatically (`pip in. Maps to research feature «Deployment & Data Residency» (deployment-data-residency). Landscape category: Agentic → Code Sandbox.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:daytona:job-coding","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"daytona → job-coding","text":"Technology Daytona (daytona): Daytona is a **secure, elastic infrastructure runtime for AI-generated code and agent workflows**. Its core unit is the **sandbox**: an isolated composable computer (dedicated kernel, filesystem, network, vCPU/RAM/disk) that can run Python/TypeScript/JavaScript workloads, with SDK, API, and CLI control of lifecycle, files, processes, and snapshots. Agents create sandboxes programmatically (`pip in. Maps to research feature «Coding agents» (job-coding). Landscape category: Agentic → Code Sandbox.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:daytona:r2-06-self-coding","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"daytona → r2-06-self-coding","text":"Technology Daytona (daytona): Daytona is a **secure, elastic infrastructure runtime for AI-generated code and agent workflows**. Its core unit is the **sandbox**: an isolated composable computer (dedicated kernel, filesystem, network, vCPU/RAM/disk) that can run Python/TypeScript/JavaScript workloads, with SDK, API, and CLI control of lifecycle, files, processes, and snapshots. Agents create sandboxes programmatically (`pip in. Maps to research feature «T06 — Self-Coding (Observation → Deterministic Scripts)» (r2-06-self-coding). Landscape category: Agentic → Code Sandbox.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:daytona:sandboxing-isolation","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"daytona → sandboxing-isolation","text":"Technology Daytona (daytona): Daytona is a **secure, elastic infrastructure runtime for AI-generated code and agent workflows**. Its core unit is the **sandbox**: an isolated composable computer (dedicated kernel, filesystem, network, vCPU/RAM/disk) that can run Python/TypeScript/JavaScript workloads, with SDK, API, and CLI control of lifecycle, files, processes, and snapshots. Agents create sandboxes programmatically (`pip in. Maps to research feature «Sandboxing & Isolation» (sandboxing-isolation). Landscape category: Agentic → Code Sandbox.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:daytona:wt-tools-actions","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"daytona → wt-tools-actions","text":"Technology Daytona (daytona): Daytona is a **secure, elastic infrastructure runtime for AI-generated code and agent workflows**. Its core unit is the **sandbox**: an isolated composable computer (dedicated kernel, filesystem, network, vCPU/RAM/disk) that can run Python/TypeScript/JavaScript workloads, with SDK, API, and CLI control of lifecycle, files, processes, and snapshots. Agents create sandboxes programmatically (`pip in. Maps to research feature «Workflow Tools — Tools & Actions» (wt-tools-actions). Landscape category: Agentic → Code Sandbox.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_features:daytona","kind":"tech_features","path":"technologies/daytona/features.md","title":"Daytona · features","text":"$37","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:daytona:daytona-docs:b208b3a998ad","kind":"tech_quote","path":"technologies/daytona/science/sources/daytona-docs.md","title":"Daytona · daytona-docs","text":"Daytona (daytona, Agentic → Code Sandbox). Daytona is a secure and elastic infrastructure for running AI-generated code.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:daytona:daytona-docs:ce77bce2cac0","kind":"tech_quote","path":"technologies/daytona/science/sources/daytona-docs.md","title":"Daytona · daytona-docs","text":"Daytona (daytona, Agentic → Code Sandbox). Sandboxes are the core component of the Daytona platform, spinning up in under 90ms from code to execution and running any code in Python, TypeScript, and JavaScript.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:daytona:daytona-docs:d5bc0d4692ce","kind":"tech_quote","path":"technologies/daytona/science/sources/daytona-docs.md","title":"Daytona · daytona-docs","text":"Daytona (daytona, Agentic → Code Sandbox). Daytona provides full composable computers — sandboxes— with complete isolation, a dedicated kernel, filesystem, network stack, and allocated vCPU, RAM, and disk.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:daytona:daytona-github:16ff7b5042a7","kind":"tech_quote","path":"technologies/daytona/science/sources/daytona-github.md","title":"Daytona · daytona-github","text":"Daytona (daytona, Agentic → Code Sandbox). Agents and developers interact with sandboxes programmatically using the SDKs, API, and CLI.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:daytona:daytona-github:c6f251bd740b","kind":"tech_quote","path":"technologies/daytona/science/sources/daytona-github.md","title":"Daytona · daytona-github","text":"Daytona (daytona, Agentic → Code Sandbox). Our stateful environment snapshots enable persistent agent operations across sessions, making Daytona the ideal foundation for AI agent architectures.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:daytona:daytona-github:f9f89e9b9b19","kind":"tech_quote","path":"technologies/daytona/science/sources/daytona-github.md","title":"Daytona · daytona-github","text":"Daytona (daytona, Agentic → Code Sandbox). Built on OCI/Docker compatibility, massive parallelization, and unlimited persistence, sandboxes deliver consistent, predictable environments for AI, agent and reinforcement learning workflows.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:daytona:e2b-contrast:64c8d4104cf5","kind":"tech_quote","path":"technologies/daytona/science/sources/e2b-contrast.md","title":"Daytona · e2b-contrast","text":"Daytona (daytona, Agentic → Code Sandbox). E2B provides isolated sandboxes that let agents safely execute code, process data, and run tools.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:daytona:e2b-contrast:a11cfaadc9d5","kind":"tech_quote","path":"technologies/daytona/science/sources/e2b-contrast.md","title":"Daytona · e2b-contrast","text":"Daytona (daytona, Agentic → Code Sandbox). Daytona resources are available at github.com/daytona","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_readme:daytona","kind":"tech_readme","path":"technologies/daytona/README.md","title":"Daytona","text":"$38","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_science_source:daytona:daytona-docs","kind":"tech_science_source","path":"technologies/daytona/science/sources/daytona-docs.md","title":"Daytona · daytona-docs","text":"Daytona · source daytona-docs\n\n# Daytona documentation\n\n| Field | Value |\n|---|---|\n| **ID** | `daytona-docs` |\n| **Type** | `docs` |\n| **URL** | https://www.daytona.io/docs/ |\n| **DOI / arXiv / cite** | Daytona docs |\n| **Authors / Org** | Daytona |\n| **Year** | 2026 |\n| **Evidence** | MODERATE |\n| **Retrieved** | 2026-09-13 |\n\n## Relevance\n\nProduct docs for sandboxes/workspaces.\n\n## Quotes\n\n> \"Daytona is a secure and elastic infrastructure for running AI-generated code.\"\n> — daytona.io/docs\n\n> \"Daytona provides full composable computers — sandboxes— with complete isolation, a dedicated kernel, filesystem, network stack, and allocated vCPU, RAM, and disk.\"\n> — daytona.io/docs\n\n> \"Sandboxes are the core component of the Daytona platform, spinning up in under 90ms from code to execution and running any code in Python, TypeScript, and JavaScript.\"\n> — daytona.io/docs\n\n\n## Images\n\n_None attached._","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_science_source:daytona:daytona-github","kind":"tech_science_source","path":"technologies/daytona/science/sources/daytona-github.md","title":"Daytona · daytona-github","text":"Daytona · source daytona-github\n\n# daytonaio/daytona\n\n| Field | Value |\n|---|---|\n| **ID** | `daytona-github` |\n| **Type** | `repo-readme` |\n| **URL** | https://github.com/daytonaio/daytona |\n| **DOI / arXiv / cite** | daytonaio/daytona |\n| **Authors / Org** | Daytona |\n| **Year** | 2026 |\n| **Evidence** | MODERATE |\n| **Retrieved** | 2026-09-13 |\n\n## Relevance\n\nPrimary repo.\n\n## Quotes\n\n> \"Our stateful environment snapshots enable persistent agent operations across sessions, making Daytona the ideal foundation for AI agent architectures.\"\n> — daytona docs\n\n> \"Agents and developers interact with sandboxes programmatically using the SDKs, API, and CLI.\"\n> — daytona docs\n\n> \"Built on OCI/Docker compatibility, massive parallelization, and unlimited persistence, sandboxes deliver consistent, predictable environments for AI, agent and reinforcement learning workflows.\"\n> — daytona docs\n\n\n## Images\n\n_None attached._","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_science_source:daytona:e2b-contrast","kind":"tech_science_source","path":"technologies/daytona/science/sources/e2b-contrast.md","title":"Daytona · e2b-contrast","text":"Daytona · source e2b-contrast\n\n# Sandbox isolation contrast\n\n| Field | Value |\n|---|---|\n| **ID** | `e2b-contrast` |\n| **Type** | `docs` |\n| **URL** | https://e2b.dev/docs |\n| **DOI / arXiv / cite** | E2B docs (contrast) |\n| **Authors / Org** | E2B |\n| **Year** | 2026 |\n| **Evidence** | MODERATE |\n| **Retrieved** | 2026-09-13 |\n\n## Relevance\n\nShared sandbox job: isolate agent code execution.\n\n## Quotes\n\n> \"Daytona resources are available at github.com/daytona\"\n> — daytona primary (deduped)\n\n> \"E2B provides isolated sandboxes that let agents safely execute code, process data, and run tools.\"\n> — daytona primary (deduped)\n\n\n## Images\n\n_None attached._","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:daytona:evidence","kind":"tech_section","path":"technologies/daytona/README.md","title":"Daytona · Evidence","text":"Daytona — Evidence\n\n| Claim area | Source id | Strength |\n|---|---|---|\n| Sandbox / SDK product framing | `science/sources/daytona-github.md` | MODERATE |\n| Docs / platform surface | `science/sources/daytona-docs.md` | MODERATE |\n| Peer contrast vs e2b | `science/sources/e2b-contrast.md` | MODERATE |","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:daytona:features-map","kind":"tech_section","path":"technologies/daytona/README.md","title":"Daytona · Features map","text":"Daytona — Features map\n\nWhich research features may use this technology: [`features.md`](./features.md) (6 mapped).\n\n","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:daytona:github-map","kind":"tech_section","path":"technologies/daytona/README.md","title":"Daytona · GitHub map","text":"Daytona — GitHub map\n\nGitHub repositories for **Daytona** (several allowed per technology).\n\n| Role | Repo | ★ | Lang | License | Pushed |\n|---|---|--:|---|---|---|\n| `primary` | [daytonaio/daytona](https://github.com/daytonaio/daytona) | 71,720 | — | — | 2026-07-24 |\n| `docs` | [daytonaio/docs](https://github.com/daytonaio/docs) ⚠ | 68 | MDX | Apache-2.0 | 2026-08-20 |\n| `related` | [daytonaio/helm-charts](https://github.com/daytonaio/helm-charts) | 24 | Shell | NOASSERTION | 2026-09-10 |\n\n```mermaid\nflowchart LR\n Tech[\"Daytona\"]\n Tech -->|primary| daytona[\"daytonaio/daytona\"]\n Tech -->|docs| docs[\"daytonaio/docs\"]\n Tech -->|related| helm_charts[\"daytonaio/helm-charts\"]\n Tech --> Cell[\"Agentic / Code Sandbox\"]\n Cell --> Index[technologies/GITHUB_MAP.md]\n```\n\n> Daytona is a Secure and Elastic Infrastructure for Running AI-Generated Code\n\n**Repos mapped:** 3 · edit `_source/github-repos.overrides.json` to add more.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:daytona:how-it-works","kind":"tech_section","path":"technologies/daytona/README.md","title":"Daytona · How it works","text":"Daytona — How it works\n\nDaytona’s platform splits interface, control, and compute planes. Clients create a sandbox via SDK/API/CLI, then run process/code execution, filesystem ops, and optional human tools (terminal, SSH, VNC, preview). Snapshots keep stateful environments across sessions.\n\n1. Authenticate (API key / org). \n2. `daytona.create()` → isolated sandbox. \n3. `process.code_run` / commands → stdout and artifacts back to the agent. \n4. Snapshot or destroy; enforce network/resource limits at the platform.\n\n```mermaid\nflowchart TD\n A[Agent / SDK] -->|create / code_run| S[Daytona sandbox]\n S --> Run[Isolated exec]\n Run --> Out[stdout / files / preview]\n Out --> A\n S -.->|no host escape| H[Host OS]\n```","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:daytona:limits-failure-modes","kind":"tech_section","path":"technologies/daytona/README.md","title":"Daytona · Limits & failure modes","text":"Daytona — Limits & failure modes\n\n- **Public repo unmaintained (June 2026):** do not treat `daytonaio/daytona` GitHub as the live product source; pin and plan for private/product path. \n- **Open core / hosted gravity:** platform features (orgs, billing, regions) pull toward Daytona cloud. \n- **Untrusted code is a privilege:** still require network/resource policy and human gates for world-changing tools. \n- **Not the ledger:** sandbox state ≠ declared company authority.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:daytona:links","kind":"tech_section","path":"technologies/daytona/README.md","title":"Daytona · Links","text":"Daytona — Links\n\n**URL:** https://www.daytona.io/ \n**Repo:** https://github.com/daytonaio/daytona \n**Docs:** https://www.daytona.io/docs/ \n\n**Folder:** `daytona` \n**Wiki-status:** `deep` \n**Updated:** 2026-09-14","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:daytona:scientific-notes","kind":"tech_section","path":"technologies/daytona/README.md","title":"Daytona · Scientific notes","text":"Daytona — Scientific notes\n\nScientific pack: **3 sources**, quotes per source, image copyright register.\n\n→ [`science/README.md`](./science/README.md) · schema [`../SCIENCE.md`](../SCIENCE.md)","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:daytona:what-it-is","kind":"tech_section","path":"technologies/daytona/README.md","title":"Daytona · What it is","text":"Daytona — What it is\n\nDaytona is a **secure, elastic infrastructure runtime for AI-generated code and agent workflows**. Its core unit is the **sandbox**: an isolated composable computer (dedicated kernel, filesystem, network, vCPU/RAM/disk) that can run Python/TypeScript/JavaScript workloads, with SDK, API, and CLI control of lifecycle, files, processes, and snapshots. Agents create sandboxes programmatically (`pip install daytona` / `@daytona/sdk`) and execute code inside them.\n\n**Primary GitHub README (as of June 2026):** the public `daytonaio/daytona` tree is **no longer maintained** — core development moved to a private codebase; the public repo stays usable under its license without support. Product docs and `github.com/daytona` org resources remain the live surface.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:daytona:when-to-use-skip","kind":"tech_section","path":"technologies/daytona/README.md","title":"Daytona · When to use / skip","text":"Daytona — When to use / skip\n\n- **Use when:** you need **agent-ready persistent sandboxes** with Daytona SDKs (multi-language) and org controls. \n- **Skip when:** you need a maintained public OSS sandbox stack first (**e2b**), or governed **remote Jupyter kernels** on YARN/K8s (**jupyter-enterprise-gateway**). \n- **Prefer instead:** e2b for open cloud sandbox APIs; JEG for cluster notebook isolation.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:daytona:why-it-matters-here","kind":"tech_section","path":"technologies/daytona/README.md","title":"Daytona · Why it matters here","text":"Daytona — Why it matters here\n\nCoding and ops agents that emit shell or patches must not run on the host. Daytona is a Code Sandbox candidate for **persistent, SDK-driven agent sandboxes** (repos, previews, snapshots) behind a policy gate. Prefer it when the install wants Daytona’s product/SDK shape; prefer **e2b** when you want the widely used open sandbox APIs; prefer **Jupyter Enterprise Gateway** when the job is remote notebook kernels on a cluster, not agent cloud sandboxes.","quality":null,"needs_deepen":false,"deepen_reason":null}],"corpus_tags":{"category":["Agentic → Code Sandbox"],"dedication":["open-core"],"feature":["browser-computer-use","deployment-data-residency","job-coding","r2-06-self-coding","sandboxing-isolation","wt-tools-actions"],"kind":["map_edge","tech_features","tech_quote","tech_readme","tech_science_source","tech_section"],"needs_deepen":["false"],"quality":["ok"],"section":["Evidence","Features map","GitHub map","How it works","Limits & failure modes","Links","Scientific notes","What it is","When to use / skip","Why it matters here"],"slug":["daytona"],"source_id":["daytona-docs","daytona-github","e2b-contrast"],"technology":["daytona"]},"mermaid":[{"title":"How it works","code":"flowchart 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missing document-management.md","severity":2}],"corpus_docs":[{"doc_key":"map_edge:deepeval:outcome-verification","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"deepeval → outcome-verification","text":"Technology DeepEval (deepeval): DeepEval is an open-source **LLM evaluation framework** (Confident AI) shaped like **Pytest for LLM apps**: metrics such as G-Eval, task completion, answer relevancy, and hallucination, using LLM-as-a-judge and NLP models that can run locally. It evaluates end-to-end LLM apps, full agent trajectories, and individual steps (LLM calls, tools, retrieval, handoffs).. Maps to research feature «Outcome Verification» (outcome-verification). Landscape category: Agentic → Evaluations.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:deepeval:r2-30-evaluator-critic","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"deepeval → r2-30-evaluator-critic","text":"Technology DeepEval (deepeval): DeepEval is an open-source **LLM evaluation framework** (Confident AI) shaped like **Pytest for LLM apps**: metrics such as G-Eval, task completion, answer relevancy, and hallucination, using LLM-as-a-judge and NLP models that can run locally. It evaluates end-to-end LLM apps, full agent trajectories, and individual steps (LLM calls, tools, retrieval, handoffs).. Maps to research feature «T30 — Evaluator / Critic Agent Design» (r2-30-evaluator-critic). Landscape category: Agentic → Evaluations.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:deepeval:r2-32-eval-harnesses","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"deepeval → r2-32-eval-harnesses","text":"Technology DeepEval (deepeval): DeepEval is an open-source **LLM evaluation framework** (Confident AI) shaped like **Pytest for LLM apps**: metrics such as G-Eval, task completion, answer relevancy, and hallucination, using LLM-as-a-judge and NLP models that can run locally. It evaluates end-to-end LLM apps, full agent trajectories, and individual steps (LLM calls, tools, retrieval, handoffs).. Maps to research feature «T32 — Eval Harnesses & CI Gates for Agents» (r2-32-eval-harnesses). Landscape category: Agentic → Evaluations.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:deepeval:r2-33-skill-registry","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"deepeval → r2-33-skill-registry","text":"Technology DeepEval (deepeval): DeepEval is an open-source **LLM evaluation framework** (Confident AI) shaped like **Pytest for LLM apps**: metrics such as G-Eval, task completion, answer relevancy, and hallucination, using LLM-as-a-judge and NLP models that can run locally. It evaluates end-to-end LLM apps, full agent trajectories, and individual steps (LLM calls, tools, retrieval, handoffs).. Maps to research feature «T33 — Skill Registry / Capability Library» (r2-33-skill-registry). Landscape category: Agentic → Evaluations.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:deepeval:r2-42-intervention-preassessment","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"deepeval → r2-42-intervention-preassessment","text":"Technology DeepEval (deepeval): DeepEval is an open-source **LLM evaluation framework** (Confident AI) shaped like **Pytest for LLM apps**: metrics such as G-Eval, task completion, answer relevancy, and hallucination, using LLM-as-a-judge and NLP models that can run locally. It evaluates end-to-end LLM apps, full agent trajectories, and individual steps (LLM calls, tools, retrieval, handoffs).. Maps to research feature «T42 — Intervention Pre-Assessment on Historical Ledger» (r2-42-intervention-preassessment). Landscape category: Agentic → Evaluations.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:deepeval:r2-47-trace-maturity-gate","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"deepeval → r2-47-trace-maturity-gate","text":"Technology DeepEval (deepeval): DeepEval is an open-source **LLM evaluation framework** (Confident AI) shaped like **Pytest for LLM apps**: metrics such as G-Eval, task completion, answer relevancy, and hallucination, using LLM-as-a-judge and NLP models that can run locally. It evaluates end-to-end LLM apps, full agent trajectories, and individual steps (LLM calls, tools, retrieval, handoffs).. Maps to research feature «T47 — Complete-Trace Counter + Maturity Gate» (r2-47-trace-maturity-gate). Landscape category: Agentic → Evaluations.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:deepeval:r2-gap-matrix","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"deepeval → r2-gap-matrix","text":"Technology DeepEval (deepeval): DeepEval is an open-source **LLM evaluation framework** (Confident AI) shaped like **Pytest for LLM apps**: metrics such as G-Eval, task completion, answer relevancy, and hallucination, using LLM-as-a-judge and NLP models that can run locally. It evaluates end-to-end LLM apps, full agent trajectories, and individual steps (LLM calls, tools, retrieval, handoffs).. Maps to research feature «Gap Matrix — Topic × Implementation Readiness» (r2-gap-matrix). Landscape category: Agentic → Evaluations.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:deepeval:testing-debugging-optimization","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"deepeval → testing-debugging-optimization","text":"Technology DeepEval (deepeval): DeepEval is an open-source **LLM evaluation framework** (Confident AI) shaped like **Pytest for LLM apps**: metrics such as G-Eval, task completion, answer relevancy, and hallucination, using LLM-as-a-judge and NLP models that can run locally. It evaluates end-to-end LLM apps, full agent trajectories, and individual steps (LLM calls, tools, retrieval, handoffs).. Maps to research feature «Testing, Debugging & Optimization» (testing-debugging-optimization). Landscape category: Agentic → Evaluations.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:deepeval:wt-test-debug-evaluate","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"deepeval → wt-test-debug-evaluate","text":"Technology DeepEval (deepeval): DeepEval is an open-source **LLM evaluation framework** (Confident AI) shaped like **Pytest for LLM apps**: metrics such as G-Eval, task completion, answer relevancy, and hallucination, using LLM-as-a-judge and NLP models that can run locally. It evaluates end-to-end LLM apps, full agent trajectories, and individual steps (LLM calls, tools, retrieval, handoffs).. Maps to research feature «Workflow Tools — Test, Debug & Evaluate» (wt-test-debug-evaluate). Landscape category: Agentic → Evaluations.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_features:deepeval","kind":"tech_features","path":"technologies/deepeval/features.md","title":"DeepEval · features","text":"$39","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:deepeval:deepeval-github:6a84bf3855f9","kind":"tech_quote","path":"technologies/deepeval/science/sources/deepeval-github.md","title":"DeepEval · deepeval-github","text":"DeepEval (deepeval, Agentic → Evaluations). DeepEval is a simple-to-use, open-source LLM evaluation framework, for evaluating large-language model systems.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:deepeval:deepeval-github:70c3766a2c5b","kind":"tech_quote","path":"technologies/deepeval/science/sources/deepeval-github.md","title":"DeepEval · deepeval-github","text":"DeepEval (deepeval, Agentic → Evaluations). Whether you're building AI agents, RAG pipelines, or chatbots, implemented via LangChain or OpenAI, DeepEval has you covered.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:deepeval:deepeval-github:b8b060110a8b","kind":"tech_quote","path":"technologies/deepeval/science/sources/deepeval-github.md","title":"DeepEval · deepeval-github","text":"DeepEval (deepeval, Agentic → Evaluations). deepeval plugs into Pytest, so deepeval test run collects and runs your eval files the same way pytest would.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:deepeval:deepeval-local:bc2bcbe31518","kind":"tech_quote","path":"technologies/deepeval/science/sources/deepeval-local.md","title":"DeepEval · deepeval-local","text":"DeepEval (deepeval, Agentic → Evaluations). Evaluate an AI agent's complete trajectory and diagnose its internal components with tracing.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:deepeval:deepeval-local:cdf0ba974555","kind":"tech_quote","path":"technologies/deepeval/science/sources/deepeval-local.md","title":"DeepEval · deepeval-local","text":"DeepEval (deepeval, Agentic → Evaluations). It is similar to Pytest but specialized for unit testing LLM apps.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:deepeval:deepeval-local:e16eb29421f4","kind":"tech_quote","path":"technologies/deepeval/science/sources/deepeval-local.md","title":"DeepEval · deepeval-local","text":"DeepEval (deepeval, Agentic → Evaluations). With it, you can easily determine the optimal models, prompts, and architecture to improve your AI quality, prevent prompt drifting, or even transition from OpenAI to Claude with confidence.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:deepeval:deepeval-quickstart:b2441de87fba","kind":"tech_quote","path":"technologies/deepeval/science/sources/deepeval-quickstart.md","title":"DeepEval · deepeval-quickstart","text":"DeepEval (deepeval, Agentic → Evaluations). DeepEval incorporates the latest research to run evals via metrics such as G-Eval, task completion, answer relevancy, hallucination, etc., which uses LLM-as-a-judge and other NLP models that run locally on your machine.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:deepeval:deepeval-quickstart:cd1e15d29eb9","kind":"tech_quote","path":"technologies/deepeval/science/sources/deepeval-quickstart.md","title":"DeepEval · deepeval-quickstart","text":"DeepEval (deepeval, Agentic → Evaluations). You'll create a small test case, choose a metric, and run it with deepeval test run.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_readme:deepeval","kind":"tech_readme","path":"technologies/deepeval/README.md","title":"DeepEval","text":"$3a","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_science_source:deepeval:deepeval-github","kind":"tech_science_source","path":"technologies/deepeval/science/sources/deepeval-github.md","title":"DeepEval · deepeval-github","text":"DeepEval · source deepeval-github\n\n# confident-ai/deepeval\n\n| Field | Value |\n|---|---|\n| **ID** | `deepeval-github` |\n| **Type** | `repo-readme` |\n| **URL** | https://github.com/confident-ai/deepeval |\n| **DOI / arXiv / cite** | confident-ai/deepeval |\n| **Authors / Org** | Confident AI |\n| **Year** | 2026 |\n| **Evidence** | MODERATE |\n| **Retrieved** | 2026-09-13 |\n\n## Relevance\n\nOSS metric library.\n\n## Quotes\n\n> \"DeepEval is a simple-to-use, open-source LLM evaluation framework, for evaluating large-language model systems.\"\n> — deepeval primary (deduped)\n\n> \"Whether you're building AI agents, RAG pipelines, or chatbots, implemented via LangChain or OpenAI, DeepEval has you covered.\"\n> — deepeval primary (deduped)\n\n> \"deepeval plugs into Pytest, so deepeval test run collects and runs your eval files the same way pytest would.\"\n> — deepeval primary (deduped)\n\n\n## Images\n\n_None attached._","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_science_source:deepeval:deepeval-local","kind":"tech_science_source","path":"technologies/deepeval/science/sources/deepeval-local.md","title":"DeepEval · deepeval-local","text":"DeepEval · source deepeval-local\n\n# Local vs cloud\n\n| Field | Value |\n|---|---|\n| **ID** | `deepeval-local` |\n| **Type** | `docs` |\n| **URL** | https://docs.confident-ai.com/docs/getting-started |\n| **DOI / arXiv / cite** | DeepEval local note |\n| **Authors / Org** | Confident AI |\n| **Year** | 2026 |\n| **Evidence** | MODERATE |\n| **Retrieved** | 2026-09-13 |\n\n## Relevance\n\nClarifies local execution vs Confident AI platform.\n\n## Quotes\n\n> \"It is similar to Pytest but specialized for unit testing LLM apps.\"\n> — deepeval primary (deduped)\n\n> \"With it, you can easily determine the optimal models, prompts, and architecture to improve your AI quality, prevent prompt drifting, or even transition from OpenAI to Claude with confidence.\"\n> — deepeval primary (deduped)\n\n> \"Evaluate an AI agent's complete trajectory and diagnose its internal components with tracing.\"\n> — deepeval primary (deduped)\n\n\n## Images\n\n_None attached._","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_science_source:deepeval:deepeval-quickstart","kind":"tech_science_source","path":"technologies/deepeval/science/sources/deepeval-quickstart.md","title":"DeepEval · deepeval-quickstart","text":"DeepEval · source deepeval-quickstart\n\n# DeepEval 5-min Quickstart\n\n| Field | Value |\n|---|---|\n| **ID** | `deepeval-quickstart` |\n| **Type** | `docs` |\n| **URL** | https://docs.confident-ai.com/docs/getting-started |\n| **DOI / arXiv / cite** | DeepEval quickstart |\n| **Authors / Org** | Confident AI |\n| **Year** | 2026 |\n| **Evidence** | MODERATE |\n| **Retrieved** | 2026-09-13 |\n\n## Relevance\n\nOfficial getting-started for local eval loop.\n\n## Quotes\n\n> \"DeepEval incorporates the latest research to run evals via metrics such as G-Eval, task completion, answer relevancy, hallucination, etc., which uses LLM-as-a-judge and other NLP models that run locally on your machine.\"\n> — deepeval primary (deduped)\n\n> \"You'll create a small test case, choose a metric, and run it with deepeval test run.\"\n> — deepeval primary (deduped)\n\n\n## Images\n\n_None attached._","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:deepeval:evidence","kind":"tech_section","path":"technologies/deepeval/README.md","title":"DeepEval · Evidence","text":"DeepEval — Evidence\n\n| Claim area | Source id | Strength |\n|---|---|---|\n| Framework / pytest-style eval | `science/sources/deepeval-github.md` | MODERATE |\n| Local / quickstart path | `science/sources/deepeval-quickstart.md` | MODERATE |\n| Local metric runs | `science/sources/deepeval-local.md` | MODERATE |","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:deepeval:features-map","kind":"tech_section","path":"technologies/deepeval/README.md","title":"DeepEval · Features map","text":"DeepEval — Features map\n\nWhich research features may use this technology: [`features.md`](./features.md) (9 mapped).\n\n","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:deepeval:github-map","kind":"tech_section","path":"technologies/deepeval/README.md","title":"DeepEval · GitHub map","text":"DeepEval — GitHub map\n\nGitHub repositories for **DeepEval** (several allowed per technology).\n\n| Role | Repo | ★ | Lang | License | Pushed |\n|---|---|--:|---|---|---|\n| `primary` | [confident-ai/deepeval](https://github.com/confident-ai/deepeval) | 18,244 | Python | Apache-2.0 | 2026-09-08 |\n| `related` | [confident-ai/deepteam](https://github.com/confident-ai/deepteam) | 2,789 | Python | Apache-2.0 | 2026-08-21 |\n\n```mermaid\nflowchart LR\n Tech[\"DeepEval\"]\n Tech -->|primary| deepeval[\"confident-ai/deepeval\"]\n Tech -->|related| deepteam[\"confident-ai/deepteam\"]\n Tech --> Cell[\"Agentic / Evaluations\"]\n Cell --> Index[technologies/GITHUB_MAP.md]\n```\n\n> The LLM Evaluation Framework\n\n**Repos mapped:** 2 · edit `_source/github-repos.overrides.json` to add more.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:deepeval:how-it-works","kind":"tech_section","path":"technologies/deepeval/README.md","title":"DeepEval · How it works","text":"DeepEval — How it works\n\n1. Author pytest-style tests that call DeepEval metrics on app outputs (or trajectories). \n2. Metrics run LLM judges / local NLP models against expected behaviors. \n3. Fail CI when thresholds miss; iterate prompts, models, or architecture. \n4. Optionally push runs to Confident AI for dashboards and production monitors.\n\n```mermaid\nflowchart TD\n T[pytest / test cases] --> M[DeepEval metrics]\n M --> J[LLM judge / local NLP]\n J --> S{Pass?}\n S -->|no| Fix[Prompt · model · tools]\n Fix --> T\n S -->|yes| CI[CI / release]\n```","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:deepeval:limits-failure-modes","kind":"tech_section","path":"technologies/deepeval/README.md","title":"DeepEval · Limits & failure modes","text":"DeepEval — Limits & failure modes\n\n- **Open core:** advanced reporting/monitoring pushes toward Confident AI SaaS. \n- **Judge cost & drift:** LLM-as-judge needs locked models and golden calibration. \n- **Not a process miner:** trajectories here are eval objects, not XES event logs. \n- **Overlap:** metric catalogs overlap Ragas/TruLens — pick one primary for a given CI job to avoid contradictory scores.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:deepeval:links","kind":"tech_section","path":"technologies/deepeval/README.md","title":"DeepEval · Links","text":"DeepEval — Links\n\n**URL:** https://deepeval.com/ \n**Repo:** https://github.com/confident-ai/deepeval \n**Docs:** https://deepeval.com/ \n\n**Folder:** `deepeval` \n**Wiki-status:** `deep` \n**Updated:** 2026-09-13","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:deepeval:scientific-notes","kind":"tech_section","path":"technologies/deepeval/README.md","title":"DeepEval · Scientific notes","text":"DeepEval — Scientific notes\n\nScientific pack: **3 sources**, quotes per source, image copyright register.\n\n→ [`science/README.md`](./science/README.md) · schema [`../SCIENCE.md`](../SCIENCE.md)","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:deepeval:what-it-is","kind":"tech_section","path":"technologies/deepeval/README.md","title":"DeepEval · What it is","text":"DeepEval — What it is\n\nDeepEval is an open-source **LLM evaluation framework** (Confident AI) shaped like **Pytest for LLM apps**: metrics such as G-Eval, task completion, answer relevancy, and hallucination, using LLM-as-a-judge and NLP models that can run locally. It evaluates end-to-end LLM apps, full agent trajectories, and individual steps (LLM calls, tools, retrieval, handoffs).\n\nRelated **DeepTeam** covers red-teaming. Confident AI is the commercial layer for iteration compare, shared reports, and production monitoring — open-core pattern.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:deepeval:when-to-use-skip","kind":"tech_section","path":"technologies/deepeval/README.md","title":"DeepEval · When to use / skip","text":"DeepEval — When to use / skip\n\n- **Use when:** you want **metric libraries as unit tests** for RAG/agents, including trajectory and step-level scoring. \n- **Skip when:** you mainly need YAML multi-provider eval + red team CLI (**promptfoo**), RAG-only research metrics (**ragas**), or process-mining of XES logs (**pm4py** / **ABM**). \n- **Prefer instead:** Promptfoo for red-team breadth; Ragas for classic RAG metric papers; TruLens for OTEL tracing + feedback functions.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:deepeval:why-it-matters-here","kind":"tech_section","path":"technologies/deepeval/README.md","title":"DeepEval · Why it matters here","text":"DeepEval — Why it matters here\n\nWeekly prompt/tool changes need **unit/integration tests that score quality**, not screenshots. DeepEval is the research default when you want metrics as code in CI for agents and RAG. Pair with Promptfoo when the bottleneck is declarative provider matrices or security probes; pair with TruLens when you need OTEL-native step traces plus judges.\n\nScores must attach to cases; the eval store is not the company ledger.","quality":null,"needs_deepen":false,"deepen_reason":null}],"corpus_tags":{"category":["Agentic → Evaluations"],"dedication":["open-core"],"feature":["outcome-verification","r2-30-evaluator-critic","r2-32-eval-harnesses","r2-33-skill-registry","r2-42-intervention-preassessment","r2-47-trace-maturity-gate","r2-gap-matrix","testing-debugging-optimization","wt-test-debug-evaluate"],"kind":["map_edge","tech_features","tech_quote","tech_readme","tech_science_source","tech_section"],"needs_deepen":["false"],"quality":["ok"],"section":["Evidence","Features map","GitHub map","How it works","Limits & failure modes","Links","Scientific notes","What it is","When to use / skip","Why it matters 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It signals autonomous orchestration patterns beyond single-turn chat. Study for long-horizon planning loops; productionize only behind durable execution, budgets, and human gates for consequential steps.. Maps to research feature «Closed Execution Loop» (closed-execution-loop). Landscape category: Agentic → Workflow Orchestration.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:deer-flow:os-execution","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"deer-flow → os-execution","text":"Technology deer-flow (deer-flow): deer-flow is an open-source long-horizon “SuperAgent” harness for research- and code-style multi-step work. It signals autonomous orchestration patterns beyond single-turn chat. Study for long-horizon planning loops; productionize only behind durable execution, budgets, and human gates for consequential steps.. Maps to research feature «Execution OS» (os-execution). Landscape category: Agentic → Workflow Orchestration.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:deer-flow:pulse-event-automation","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"deer-flow → pulse-event-automation","text":"Technology deer-flow (deer-flow): deer-flow is an open-source long-horizon “SuperAgent” harness for research- and code-style multi-step work. It signals autonomous orchestration patterns beyond single-turn chat. Study for long-horizon planning loops; productionize only behind durable execution, budgets, and human gates for consequential steps.. Maps to research feature «Pulse / Event Automation vs Long-Running Resolution» (pulse-event-automation). Landscape category: Agentic → Workflow Orchestration.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:deer-flow:r2-12-central-nervous-system","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"deer-flow → r2-12-central-nervous-system","text":"Technology deer-flow (deer-flow): deer-flow is an open-source long-horizon “SuperAgent” harness for research- and code-style multi-step work. It signals autonomous orchestration patterns beyond single-turn chat. Study for long-horizon planning loops; productionize only behind durable execution, budgets, and human gates for consequential steps.. Maps to research feature «T12 — Central Nervous System (Triggers, Hooks, Reflexes)» (r2-12-central-nervous-system). Landscape category: Agentic → Workflow Orchestration.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:deer-flow:r2-37-sleep-orchestration","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"deer-flow → r2-37-sleep-orchestration","text":"Technology deer-flow (deer-flow): deer-flow is an open-source long-horizon “SuperAgent” harness for research- and code-style multi-step work. It signals autonomous orchestration patterns beyond single-turn chat. Study for long-horizon planning loops; productionize only behind durable execution, budgets, and human gates for consequential steps.. Maps to research feature «T37 — Sleep Job Orchestration» (r2-37-sleep-orchestration). Landscape category: Agentic → Workflow Orchestration.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:deer-flow:temporal-coherence","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"deer-flow → temporal-coherence","text":"Technology deer-flow (deer-flow): deer-flow is an open-source long-horizon “SuperAgent” harness for research- and code-style multi-step work. It signals autonomous orchestration patterns beyond single-turn chat. Study for long-horizon planning loops; productionize only behind durable execution, budgets, and human gates for consequential steps.. Maps to research feature «Temporal Coherence / Long-Horizon Consistency» (temporal-coherence). Landscape category: Agentic → Workflow Orchestration.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:deer-flow:triggers-channel-coverage","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"deer-flow → triggers-channel-coverage","text":"Technology deer-flow (deer-flow): deer-flow is an open-source long-horizon “SuperAgent” harness for research- and code-style multi-step work. It signals autonomous orchestration patterns beyond single-turn chat. Study for long-horizon planning loops; productionize only behind durable execution, budgets, and human gates for consequential steps.. Maps to research feature «Triggers & Channel Coverage» (triggers-channel-coverage). Landscape category: Agentic → Workflow Orchestration.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:deer-flow:workflow-orchestration","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"deer-flow → workflow-orchestration","text":"Technology deer-flow (deer-flow): deer-flow is an open-source long-horizon “SuperAgent” harness for research- and code-style multi-step work. It signals autonomous orchestration patterns beyond single-turn chat. Study for long-horizon planning loops; productionize only behind durable execution, budgets, and human gates for consequential steps.. Maps to research feature «Workflow Orchestration» (workflow-orchestration). Landscape category: Agentic → Workflow Orchestration.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:deer-flow:wt-control-flow","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"deer-flow → wt-control-flow","text":"Technology deer-flow (deer-flow): deer-flow is an open-source long-horizon “SuperAgent” harness for research- and code-style multi-step work. It signals autonomous orchestration patterns beyond single-turn chat. Study for long-horizon planning loops; productionize only behind durable execution, budgets, and human gates for consequential steps.. Maps to research feature «Workflow Tools — Control Flow» (wt-control-flow). 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Tech[\"Dify\"]\n Tech -->|primary| dify[\"langgenius/dify\"]\n Tech -->|sdk| dify_plugin_sdks[\"langgenius/dify-plugin-sdks\"]\n Tech -->|sdk| dify_python_sdk[\"langgenius/dify-python-sdk\"]\n Tech -->|docs| dify_docs[\"langgenius/dify-docs\"]\n Tech -->|related| dify_official_plugins[\"langgenius/dify-official-plugins\"]\n Tech -->|related| dify_sandbox[\"langgenius/dify-sandbox\"]\n Tech --> Cell[\"Agentic / Workflow Orchestration\"]\n Cell --> Index[technologies/GITHUB_MAP.md]"}]},{"kind":"technology","slug":"docling","title":"Docling","one_liner":"Agentic","path":"technologies/docling","tags":{"landscape.layer":{"value":"Agentic","status":null,"evidence_class":"inferred","evidence_ref":"technologies/docling/README.md","evidence_note":null},"landscape.subcategory":{"value":"RAG","status":null,"evidence_class":"inferred","evidence_ref":null,"evidence_note":null},"license_tag":{"value":"Open 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Landscape category: Agentic → Frameworks.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_features:dspy","kind":"tech_features","path":"technologies/dspy/features.md","title":"DSPy · features","text":"$41","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:dspy:arxiv-2310-03714:5a36e7fbfd6a","kind":"tech_quote","path":"technologies/dspy/science/sources/arxiv-2310-03714.md","title":"DSPy · arxiv-2310-03714","text":"DSPy (dspy, Agentic → Frameworks). We introduce DSPy, a programming model that abstracts LM pipelines as text transformation graphs, i.e. imperative computation graphs where LMs are invoked through declarative modules.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:dspy:arxiv-2310-03714:7a19d1be5d73","kind":"tech_quote","path":"technologies/dspy/science/sources/arxiv-2310-03714.md","title":"DSPy · arxiv-2310-03714","text":"DSPy (dspy, Agentic → Frameworks). DSPy modules are parameterized, meaning they can learn (by creating and collecting demonstrations) how to apply compositions of prompting, finetuning, augmentation, and reasoning techniques.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:dspy:arxiv-2310-03714:c6439851ee69","kind":"tech_quote","path":"technologies/dspy/science/sources/arxiv-2310-03714.md","title":"DSPy · arxiv-2310-03714","text":"DSPy (dspy, Agentic → Frameworks). Within minutes of compiling, a few lines of DSPy allow GPT-3.5 and llama2-13b-chat to self-bootstrap pipelines that outperform standard few-shot prompting (generally by over 25% and 65%, respectively).","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:dspy:dspy-github:9c3e2a575dc6","kind":"tech_quote","path":"technologies/dspy/science/sources/dspy-github.md","title":"DSPy · dspy-github","text":"DSPy (dspy, Agentic → Frameworks). DSPy is a framework for programming—not prompting—language models.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:dspy:langgraph-vs-dspy-2607:02170d703914","kind":"tech_quote","path":"technologies/dspy/science/sources/langgraph-vs-dspy-2607.md","title":"DSPy · langgraph-vs-dspy-2607","text":"DSPy (dspy, Agentic → Frameworks). DSPy is often a better fit when the primary goal is metric-guided prompt or program optimization.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:dspy:langgraph-vs-dspy-2607:b6d34e8abc41","kind":"tech_quote","path":"technologies/dspy/science/sources/langgraph-vs-dspy-2607.md","title":"DSPy · langgraph-vs-dspy-2607","text":"DSPy (dspy, Agentic → Frameworks). 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missing document-management.md","severity":2}],"corpus_docs":[{"doc_key":"map_edge:e2b:browser-computer-use","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"e2b → browser-computer-use","text":"Technology e2b (e2b): E2B is **open-source infrastructure for running AI-generated code in secure isolated cloud sandboxes**. Agents start and control sandboxes through JavaScript (`e2b`) and Python (`e2b`) SDKs: create a sandbox, run commands, and read stdout. Adjacent packages cover **code interpreter** (`@e2b/code-interpreter` / `e2b-code-interpreter`) and **desktop/computer-use** (mouse, keyboard, screenshots, stre. Maps to research feature «Browser & Computer Use» (browser-computer-use). Landscape category: Agentic → Code Sandbox.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:e2b:job-coding","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"e2b → job-coding","text":"Technology e2b (e2b): E2B is **open-source infrastructure for running AI-generated code in secure isolated cloud sandboxes**. Agents start and control sandboxes through JavaScript (`e2b`) and Python (`e2b`) SDKs: create a sandbox, run commands, and read stdout. Adjacent packages cover **code interpreter** (`@e2b/code-interpreter` / `e2b-code-interpreter`) and **desktop/computer-use** (mouse, keyboard, screenshots, stre. Maps to research feature «Coding agents» (job-coding). Landscape category: Agentic → Code Sandbox.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:e2b:r2-06-self-coding","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"e2b → r2-06-self-coding","text":"Technology e2b (e2b): E2B is **open-source infrastructure for running AI-generated code in secure isolated cloud sandboxes**. Agents start and control sandboxes through JavaScript (`e2b`) and Python (`e2b`) SDKs: create a sandbox, run commands, and read stdout. Adjacent packages cover **code interpreter** (`@e2b/code-interpreter` / `e2b-code-interpreter`) and **desktop/computer-use** (mouse, keyboard, screenshots, stre. Maps to research feature «T06 — Self-Coding (Observation → Deterministic Scripts)» (r2-06-self-coding). Landscape category: Agentic → Code Sandbox.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:e2b:sandboxing-isolation","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"e2b → sandboxing-isolation","text":"Technology e2b (e2b): E2B is **open-source infrastructure for running AI-generated code in secure isolated cloud sandboxes**. Agents start and control sandboxes through JavaScript (`e2b`) and Python (`e2b`) SDKs: create a sandbox, run commands, and read stdout. Adjacent packages cover **code interpreter** (`@e2b/code-interpreter` / `e2b-code-interpreter`) and **desktop/computer-use** (mouse, keyboard, screenshots, stre. Maps to research feature «Sandboxing & Isolation» (sandboxing-isolation). Landscape category: Agentic → Code Sandbox.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:e2b:wt-tools-actions","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"e2b → wt-tools-actions","text":"Technology e2b (e2b): E2B is **open-source infrastructure for running AI-generated code in secure isolated cloud sandboxes**. Agents start and control sandboxes through JavaScript (`e2b`) and Python (`e2b`) SDKs: create a sandbox, run commands, and read stdout. Adjacent packages cover **code interpreter** (`@e2b/code-interpreter` / `e2b-code-interpreter`) and **desktop/computer-use** (mouse, keyboard, screenshots, stre. Maps to research feature «Workflow Tools — Tools & Actions» (wt-tools-actions). Landscape category: Agentic → Code Sandbox.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_features:e2b","kind":"tech_features","path":"technologies/e2b/features.md","title":"e2b · features","text":"# Features using `e2b`\n\n**Technology:** [e2b](./README.md)\n**Category:** Agentic → Code Sandbox\n**Mapped features:** 5\n**Map:** [`../features/_source/feature-technology-map.md`](../features/_source/feature-technology-map.md)\n\nThis technology is a **candidate building block** for the features below (research map — not a claim that the product currently depends on it).\n\n| Feature | Title |\n|---|---|\n| [`browser-computer-use`](../features/browser-computer-use/README.md) | Browser & Computer Use |\n| [`job-coding`](../features/job-coding/README.md) | Coding agents |\n| [`r2-06-self-coding`](../features/r2-06-self-coding/README.md) | T06 — Self-Coding (Observation → Deterministic Scripts) |\n| [`sandboxing-isolation`](../features/sandboxing-isolation/README.md) | Sandboxing & Isolation |\n| [`wt-tools-actions`](../features/wt-tools-actions/README.md) | Workflow Tools — Tools & Actions |\n\n","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:e2b:e2b-docs:64c8d4104cf5","kind":"tech_quote","path":"technologies/e2b/science/sources/e2b-docs.md","title":"e2b · e2b-docs","text":"e2b (e2b, Agentic → Code Sandbox). E2B provides isolated sandboxes that let agents safely execute code, process data, and run tools.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:e2b:e2b-docs:d57ed42f20e3","kind":"tech_quote","path":"technologies/e2b/science/sources/e2b-docs.md","title":"e2b · e2b-docs","text":"e2b (e2b, Agentic → Code Sandbox). Persistence — Pausing a sandbox saves both its filesystem and its memory, and paused sandboxes are kept indefinitely until you kill them","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:e2b:e2b-docs:f6c51ddb1b31","kind":"tech_quote","path":"technologies/e2b/science/sources/e2b-docs.md","title":"e2b · e2b-docs","text":"e2b (e2b, Agentic → Code Sandbox). Sandbox — A fast, secure Linux VM created on demand for your agent, which you can pause and resume as needed","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:e2b:e2b-github:0c858cb59274","kind":"tech_quote","path":"technologies/e2b/science/sources/e2b-github.md","title":"e2b · e2b-github","text":"e2b (e2b, Agentic → Code Sandbox). Open-source, secure environment with real-world tools for enterprise-grade agents.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:e2b:e2b-github:57e18e56f0ee","kind":"tech_quote","path":"technologies/e2b/science/sources/e2b-github.md","title":"e2b · e2b-github","text":"e2b (e2b, Agentic → Code Sandbox). E2B is an open-source infrastructure that allows you to run AI-generated code in secure isolated sandboxes in the cloud.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:e2b:e2b-github:729560ea3d4c","kind":"tech_quote","path":"technologies/e2b/science/sources/e2b-github.md","title":"e2b · e2b-github","text":"e2b (e2b, Agentic → Code Sandbox). To start and control sandboxes, use our JavaScript SDK or Python SDK.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:e2b:e2b-sdk-snippet:9d16c7767156","kind":"tech_quote","path":"technologies/e2b/science/sources/e2b-sdk-snippet.md","title":"e2b · e2b-sdk-snippet","text":"e2b (e2b, Agentic → Code Sandbox). When you start the sandbox, it stays alive for 5 minutes by default but you can change it by passing the timeout parameter.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:e2b:e2b-sdk-snippet:c5341898a732","kind":"tech_quote","path":"technologies/e2b/science/sources/e2b-sdk-snippet.md","title":"e2b · e2b-sdk-snippet","text":"e2b (e2b, Agentic → Code Sandbox). Sandboxes are the core component of the Daytona platform, spinning up in under 90ms from code to execution","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:e2b:e2b-sdk-snippet:de65e18ffea5","kind":"tech_quote","path":"technologies/e2b/science/sources/e2b-sdk-snippet.md","title":"e2b · e2b-sdk-snippet","text":"e2b (e2b, Agentic → Code Sandbox). Daytona is a Secure and Elastic Infrastructure for Running AI-Generated Code","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_readme:e2b","kind":"tech_readme","path":"technologies/e2b/README.md","title":"e2b","text":"$43","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_science_source:e2b:e2b-docs","kind":"tech_science_source","path":"technologies/e2b/science/sources/e2b-docs.md","title":"e2b · e2b-docs","text":"e2b · source e2b-docs\n\n# E2B Documentation — What is E2B?\n\n| Field | Value |\n|---|---|\n| **ID** | `e2b-docs` |\n| **Type** | `docs` |\n| **URL** | https://e2b.dev/docs |\n| **DOI / arXiv / cite** | E2B docs |\n| **Authors / Org** | E2B |\n| **Year** | 2026 |\n| **Evidence** | MODERATE |\n| **Retrieved** | 2026-09-13 |\n\n## Relevance\n\nPrimary product definition of sandboxes.\n\n## Quotes\n\n> \"E2B provides isolated sandboxes that let agents safely execute code, process data, and run tools.\"\n> — e2b.dev/docs\n\n> \"Sandbox — A fast, secure Linux VM created on demand for your agent, which you can pause and resume as needed\"\n> — e2b.dev/docs\n\n> \"Persistence — Pausing a sandbox saves both its filesystem and its memory, and paused sandboxes are kept indefinitely until you kill them\"\n> — e2b.dev/docs\n\n\n## Images\n\n_None attached._","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_science_source:e2b:e2b-github","kind":"tech_science_source","path":"technologies/e2b/science/sources/e2b-github.md","title":"e2b · e2b-github","text":"e2b · source e2b-github\n\n# e2b-dev/E2B\n\n| Field | Value |\n|---|---|\n| **ID** | `e2b-github` |\n| **Type** | `repo-readme` |\n| **URL** | https://github.com/e2b-dev/E2B |\n| **DOI / arXiv / cite** | e2b-dev/E2B |\n| **Authors / Org** | E2B |\n| **Year** | 2026 |\n| **Evidence** | MODERATE |\n| **Retrieved** | 2026-09-13 |\n\n## Relevance\n\nSDK/platform repo.\n\n## Quotes\n\n> \"E2B is an open-source infrastructure that allows you to run AI-generated code in secure isolated sandboxes in the cloud.\"\n> — e2b-dev/E2B README\n\n> \"To start and control sandboxes, use our JavaScript SDK or Python SDK.\"\n> — e2b-dev/E2B README\n\n> \"Open-source, secure environment with real-world tools for enterprise-grade agents.\"\n> — e2b-dev/E2B README\n\n\n## Images\n\n_None attached._","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_science_source:e2b:e2b-sdk-snippet","kind":"tech_science_source","path":"technologies/e2b/science/sources/e2b-sdk-snippet.md","title":"e2b · e2b-sdk-snippet","text":"e2b · source e2b-sdk-snippet\n\n# SDK create + run\n\n| Field | Value |\n|---|---|\n| **ID** | `e2b-sdk-snippet` |\n| **Type** | `docs` |\n| **URL** | https://e2b.dev/docs |\n| **DOI / arXiv / cite** | E2B quickstart |\n| **Authors / Org** | E2B |\n| **Year** | 2026 |\n| **Evidence** | MODERATE |\n| **Retrieved** | 2026-09-13 |\n\n## Relevance\n\nMinimal API surface for sandboxing agent actions.\n\n## Quotes\n\n> \"When you start the sandbox, it stays alive for 5 minutes by default but you can change it by passing the timeout parameter.\"\n> — e2b sandbox lifecycle\n\n> \"Daytona is a Secure and Elastic Infrastructure for Running AI-Generated Code\"\n> — daytona contrast docs\n\n> \"Sandboxes are the core component of the Daytona platform, spinning up in under 90ms from code to execution\"\n> — daytona contrast docs\n\n\n## Images\n\n_None attached._","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:e2b:evidence","kind":"tech_section","path":"technologies/e2b/README.md","title":"e2b · Evidence","text":"e2b — Evidence\n\n| Claim area | Source id | Strength |\n|---|---|---|\n| Product / sandbox SDKs | `science/sources/e2b-github.md` | MODERATE |\n| Docs positioning | `science/sources/e2b-docs.md` | MODERATE |\n| SDK usage shape | `science/sources/e2b-sdk-snippet.md` | MODERATE |","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:e2b:features-map","kind":"tech_section","path":"technologies/e2b/README.md","title":"e2b · Features map","text":"e2b — Features map\n\nWhich research features may use this technology: [`features.md`](./features.md) (5 mapped).\n\n","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:e2b:github-map","kind":"tech_section","path":"technologies/e2b/README.md","title":"e2b · GitHub map","text":"$44","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:e2b:how-it-works","kind":"tech_section","path":"technologies/e2b/README.md","title":"e2b · How it works","text":"e2b — How it works\n\nThe agent holds an API key, creates a `Sandbox`, and issues commands or `runCode` inside the isolated environment. Optional Desktop SDK launches apps and captures UI. Self-host deploys the E2B infra stack via Terraform when cloud tenancy is unacceptable.\n\n1. `pip install e2b` / `npm i e2b` + `E2B_API_KEY`. \n2. `Sandbox.create()` → isolated environment. \n3. `commands.run` or code-interpreter `run_code` → stdout/result. \n4. Destroy/context-exit; keep host unreachable.\n\n```mermaid\nflowchart TD\n A[Agent / SDK] -->|create / run| S[E2B sandbox]\n S --> Run[Isolated exec]\n Run --> Out[stdout / artifacts]\n Out --> A\n S -.->|no host escape| H[Host OS]\n```","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:e2b:limits-failure-modes","kind":"tech_section","path":"technologies/e2b/README.md","title":"e2b · Limits & failure modes","text":"e2b — Limits & failure modes\n\n- **Cloud-by-default:** API key + hosted sandboxes unless you invest in Terraform self-host. \n- **Self-host maturity:** AWS/GCP yes; Azure/generic Linux not complete per README. \n- **Privilege:** sandbox ≠ permission to act on money/process — still gate tool use. \n- **Not the ledger:** execution artifacts are evidence, not declared configuration.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:e2b:links","kind":"tech_section","path":"technologies/e2b/README.md","title":"e2b · Links","text":"e2b — Links\n\n**URL:** https://e2b.dev \n**Repo:** https://github.com/e2b-dev/E2B \n**Docs:** https://e2b.dev/docs \n\n**Folder:** `e2b` \n**Wiki-status:** `deep` \n**Updated:** 2026-09-14","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:e2b:scientific-notes","kind":"tech_section","path":"technologies/e2b/README.md","title":"e2b · Scientific notes","text":"e2b — Scientific notes\n\nScientific pack: **3 sources**, quotes per source, image copyright register.\n\n→ [`science/README.md`](./science/README.md) · schema [`../SCIENCE.md`](../SCIENCE.md)","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:e2b:what-it-is","kind":"tech_section","path":"technologies/e2b/README.md","title":"e2b · What it is","text":"e2b — What it is\n\nE2B is **open-source infrastructure for running AI-generated code in secure isolated cloud sandboxes**. Agents start and control sandboxes through JavaScript (`e2b`) and Python (`e2b`) SDKs: create a sandbox, run commands, and read stdout. Adjacent packages cover **code interpreter** (`@e2b/code-interpreter` / `e2b-code-interpreter`) and **desktop/computer-use** (mouse, keyboard, screenshots, streaming). The [infra](https://github.com/e2b-dev/infra) repo documents Terraform self-hosting (AWS and GCP supported; Azure/general Linux listed as incomplete).","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:e2b:when-to-use-skip","kind":"tech_section","path":"technologies/e2b/README.md","title":"e2b · When to use / skip","text":"e2b — When to use / skip\n\n- **Use when:** agents must **run untrusted code/shell in cloud sandboxes** with JS/Python SDKs (and optional interpreter/desktop). \n- **Skip when:** you only need cluster **notebook kernels** (**jupyter-enterprise-gateway**), or a Daytona-specific product path. \n- **Prefer instead:** self-host E2B infra when compliance blocks SaaS; JEG for Spark/K8s notebook tenancy.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:e2b:why-it-matters-here","kind":"tech_section","path":"technologies/e2b/README.md","title":"e2b · Why it matters here","text":"e2b — Why it matters here\n\nOnce an agentic OS gives tools that execute code or shell, isolation is non-optional. E2B is the research default for **SDK-first cloud sandboxes** wired into agent frameworks, with an OSS path and optional self-host. Prefer **Daytona** only if the product/SDK fit wins despite its public-repo maintenance shift; prefer **Jupyter Enterprise Gateway** for remote notebook kernels on enterprise clusters rather than ephemeral agent VMs.","quality":null,"needs_deepen":false,"deepen_reason":null}],"corpus_tags":{"category":["Agentic → Code Sandbox"],"dedication":["open-core"],"feature":["browser-computer-use","job-coding","r2-06-self-coding","sandboxing-isolation","wt-tools-actions"],"kind":["map_edge","tech_features","tech_quote","tech_readme","tech_science_source","tech_section"],"needs_deepen":["false"],"quality":["ok"],"section":["Evidence","Features map","GitHub map","How it works","Limits & failure modes","Links","Scientific notes","What it is","When to use / skip","Why it matters here"],"slug":["e2b"],"source_id":["e2b-docs","e2b-github","e2b-sdk-snippet"],"technology":["e2b"]},"mermaid":[{"title":"How it works","code":"flowchart TD\n A[Agent / SDK] -->|create / run| S[E2B sandbox]\n S --> Run[Isolated exec]\n Run --> Out[stdout / artifacts]\n Out --> A\n S -.->|no host escape| H[Host OS]\n Peerdaytona[[daytona]]"},{"title":"GitHub map","code":"flowchart LR\n Tech[\"e2b\"]\n Tech -->|primary| E2B[\"e2b-dev/E2B\"]\n Tech -->|sdk| code_interpreter[\"e2b-dev/code-interpreter\"]\n Tech -->|samples| e2b_cookbook[\"e2b-dev/e2b-cookbook\"]\n Tech -->|related| desktop[\"e2b-dev/desktop\"]\n Tech -->|related| runtime[\"e2b-dev/runtime\"]\n Tech --> Cell[\"Agentic / Code Sandbox\"]\n Cell --> Index[technologies/GITHUB_MAP.md]"}]},{"kind":"technology","slug":"earned-autonomy-protocol","title":"Earned Autonomy 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→ access-vs-authority","text":"Technology Earned Autonomy Protocol (earned-autonomy-protocol): The Earned Autonomy Protocol is a **practitioner governance methodology** for AI in consequential domains: **AI does not start with autonomy; it earns it** through demonstrated reliability in bounded decisions, logged and verified before scope expands. It defines concepts such as Trust Genesis, decision layers (deterministic / probabilistic / judgment), decision logs, a **perception–action split**. Maps to research feature «Access vs Authority Separation» (access-vs-authority). Landscape category: Agentic → IAM.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:earned-autonomy-protocol:agent-identity-interoperability","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"earned-autonomy-protocol → agent-identity-interoperability","text":"Technology Earned Autonomy Protocol (earned-autonomy-protocol): The Earned Autonomy Protocol is a **practitioner governance methodology** for AI in consequential domains: **AI does not start with autonomy; it earns it** through demonstrated reliability in bounded decisions, logged and verified before scope expands. 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It defines concepts such as Trust Genesis, decision layers (deterministic / probabilistic / judgment), decision logs, a **perception–action split**. Maps to research feature «Autonomy Levels» (autonomy-levels). Landscape category: Agentic → IAM.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:earned-autonomy-protocol:exception-first-participation","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"earned-autonomy-protocol → exception-first-participation","text":"Technology Earned Autonomy Protocol (earned-autonomy-protocol): The Earned Autonomy Protocol is a **practitioner governance methodology** for AI in consequential domains: **AI does not start with autonomy; it earns it** through demonstrated reliability in bounded decisions, logged and verified before scope expands. It defines concepts such as Trust Genesis, decision layers (deterministic / probabilistic / judgment), decision logs, a **perception–action split**. 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The Earned Autonomy Protocol is a governance framework for AI systems in consequential domains: trust is earned through evidence, not assumed at deployment.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:earned-autonomy-protocol:eap-github:6da2022849c6","kind":"tech_quote","path":"technologies/earned-autonomy-protocol/science/sources/eap-github.md","title":"Earned Autonomy Protocol · eap-github","text":"Earned Autonomy Protocol (earned-autonomy-protocol, Agentic → IAM). OPA is proud to be a graduated project in the Cloud Native Computing Foundation (CNCF) landscape.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:earned-autonomy-protocol:eap-github:cb1ca2f2320a","kind":"tech_quote","path":"technologies/earned-autonomy-protocol/science/sources/eap-github.md","title":"Earned Autonomy Protocol · eap-github","text":"Earned Autonomy Protocol (earned-autonomy-protocol, Agentic → IAM). For developing, debugging, and deploying AI agents and LLM applications, see LangSmith.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:earned-autonomy-protocol:headroom-contrast:ba4cf3b7f564","kind":"tech_quote","path":"technologies/earned-autonomy-protocol/science/sources/headroom-contrast.md","title":"Earned Autonomy Protocol · headroom-contrast","text":"Earned Autonomy Protocol (earned-autonomy-protocol, Agentic → IAM). Open Policy Agent (OPA) is an open source, general-purpose policy engine.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:earned-autonomy-protocol:headroom-contrast:c7e8b5342c5c","kind":"tech_quote","path":"technologies/earned-autonomy-protocol/science/sources/headroom-contrast.md","title":"Earned Autonomy Protocol · headroom-contrast","text":"Earned Autonomy Protocol (earned-autonomy-protocol, Agentic → IAM). LangChain is a framework for building agents and LLM-powered applications.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:earned-autonomy-protocol:opa-contrast:0213015ca7c3","kind":"tech_quote","path":"technologies/earned-autonomy-protocol/science/sources/opa-contrast.md","title":"Earned Autonomy Protocol · opa-contrast","text":"Earned Autonomy Protocol (earned-autonomy-protocol, Agentic → IAM). Open Policy Agent (OPA) is an open source, general-purpose policy engine that enables unified, context-aware policy enforcement across the entire stack.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:earned-autonomy-protocol:opa-contrast:e3b42894c831","kind":"tech_quote","path":"technologies/earned-autonomy-protocol/science/sources/opa-contrast.md","title":"Earned Autonomy Protocol · opa-contrast","text":"Earned Autonomy Protocol (earned-autonomy-protocol, Agentic → IAM). The agent engineering platform.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_readme:earned-autonomy-protocol","kind":"tech_readme","path":"technologies/earned-autonomy-protocol/README.md","title":"Earned Autonomy Protocol","text":"$46","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_science_source:earned-autonomy-protocol:eap-github","kind":"tech_science_source","path":"technologies/earned-autonomy-protocol/science/sources/eap-github.md","title":"Earned Autonomy Protocol · eap-github","text":"Earned Autonomy Protocol · source eap-github\n\n# earned-autonomy-protocol\n\n| Field | Value |\n|---|---|\n| **ID** | `eap-github` |\n| **Type** | `repo-readme` |\n| **URL** | https://github.com/LatamlinkLLC/earned-autonomy-protocol |\n| **DOI / arXiv / cite** | LatamlinkLLC/earned-autonomy-protocol |\n| **Authors / Org** | Latamlink |\n| **Year** | 2026 |\n| **Evidence** | MODERATE |\n| **Retrieved** | 2026-09-13 |\n\n## Relevance\n\nPrimary repo.\n\n## Quotes\n\n> \"The Earned Autonomy Protocol is a governance framework for AI systems in consequential domains: trust is earned through evidence, not assumed at deployment.\"\n> — earned-autonomy-protocol primary (deduped)\n\n> \"For developing, debugging, and deploying AI agents and LLM applications, see LangSmith.\"\n> — earned-autonomy-protocol primary (deduped)\n\n> \"OPA is proud to be a graduated project in the Cloud Native Computing Foundation (CNCF) landscape.\"\n> — earned-autonomy-protocol primary (deduped)\n\n\n## Images\n\n_None attached._","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_science_source:earned-autonomy-protocol:headroom-contrast","kind":"tech_science_source","path":"technologies/earned-autonomy-protocol/science/sources/headroom-contrast.md","title":"Earned Autonomy Protocol · headroom-contrast","text":"Earned Autonomy Protocol · source headroom-contrast\n\n# Headroom contrast\n\n| Field | Value |\n|---|---|\n| **ID** | `headroom-contrast` |\n| **Type** | `repo-readme` |\n| **URL** | https://github.com/turangenesis/headroom |\n| **DOI / arXiv / cite** | Headroom contrast |\n| **Authors / Org** | turangenesis |\n| **Year** | 2026 |\n| **Evidence** | MODERATE |\n| **Retrieved** | 2026-09-13 |\n\n## Relevance\n\nCalibration of when to spend human oversight.\n\n## Quotes\n\n> \"LangChain is a framework for building agents and LLM-powered applications.\"\n> — earned-autonomy-protocol primary (deduped)\n\n> \"Open Policy Agent (OPA) is an open source, general-purpose policy engine.\"\n> — earned-autonomy-protocol primary (deduped)\n\n\n## What\"\n\n\n## Images\n\n_None attached._","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_science_source:earned-autonomy-protocol:opa-contrast","kind":"tech_science_source","path":"technologies/earned-autonomy-protocol/science/sources/opa-contrast.md","title":"Earned Autonomy Protocol · opa-contrast","text":"Earned Autonomy Protocol · source opa-contrast\n\n# OPA contrast\n\n| Field | Value |\n|---|---|\n| **ID** | `opa-contrast` |\n| **Type** | `docs` |\n| **URL** | https://www.openpolicyagent.org/docs/latest/ |\n| **DOI / arXiv / cite** | OPA contrast |\n| **Authors / Org** | OPA |\n| **Year** | 2026 |\n| **Evidence** | MODERATE |\n| **Retrieved** | 2026-09-13 |\n\n## Relevance\n\nPolicy engine vs earned-autonomy governance vocabulary.\n\n## Quotes\n\n> \"The agent engineering platform.\"\n> — earned-autonomy-protocol primary (deduped)\n\n> \"Open Policy Agent (OPA) is an open source, general-purpose policy engine that enables unified, context-aware policy enforcement across the entire stack.\"\n> — earned-autonomy-protocol primary (deduped)\n\n\n## Images\n\n_None attached._","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:earned-autonomy-protocol:evidence","kind":"tech_section","path":"technologies/earned-autonomy-protocol/README.md","title":"Earned Autonomy Protocol · Evidence","text":"Earned Autonomy Protocol — Evidence\n\n| Claim area | Source id | Strength |\n|---|---|---|\n| Protocol / earned autonomy framing | `science/sources/eap-github.md` | MODERATE |\n| Contrast vs Headroom HITL | `science/sources/headroom-contrast.md` | MODERATE |\n| Contrast vs OPA engines | `science/sources/opa-contrast.md` | MODERATE |","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:earned-autonomy-protocol:features-map","kind":"tech_section","path":"technologies/earned-autonomy-protocol/README.md","title":"Earned Autonomy Protocol · Features map","text":"Earned Autonomy Protocol — Features map\n\nWhich research features may use this technology: [`features.md`](./features.md) (10 mapped).\n\n","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:earned-autonomy-protocol:github-map","kind":"tech_section","path":"technologies/earned-autonomy-protocol/README.md","title":"Earned Autonomy Protocol · GitHub map","text":"Earned Autonomy Protocol — GitHub map\n\nGitHub repositories for **Earned Autonomy Protocol** (several allowed per technology).\n\n| Role | Repo | ★ | Lang | License | Pushed |\n|---|---|--:|---|---|---|\n| `primary` | [LatamlinkLLC/earned-autonomy-protocol](https://github.com/LatamlinkLLC/earned-autonomy-protocol) | 2 | — | NOASSERTION | 2026-07-28 |\n\n```mermaid\nflowchart LR\n Tech[\"Earned Autonomy Protocol\"]\n Tech -->|primary| earned_autonomy_protocol[\"LatamlinkLLC/earned-autonomy-protocol\"]\n Tech --> Cell[\"Agentic / IAM\"]\n Cell --> Index[technologies/GITHUB_MAP.md]\n```\n\n> The Earned Autonomy Protocol: A governance framework for AI systems operating in consequential domains. Trust is earned through evidence, not assumed at deployment.\n\n**Repos mapped:** 1 · edit `_source/github-repos.overrides.json` to add more.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:earned-autonomy-protocol:how-it-works","kind":"tech_section","path":"technologies/earned-autonomy-protocol/README.md","title":"Earned Autonomy Protocol · How it works","text":"Earned Autonomy Protocol — How it works\n\nOperators run agents in observation-first mode, evidence-lock decision paths (Trust Composite Scores), automate perception loops that only produce checkable claims, and keep action loops human-gated until genesis/ceremony expands scope. Constitutional boundaries change only by explicit logged human ceremony.\n\n1. Default: observation / no execution autonomy. \n2. Log and verify bounded decisions; run perception automation. \n3. Gate world-binding actions until evidence justifies a grant. \n4. Expand or revoke scope per domain; never self-grant.\n\n```mermaid\nflowchart TD\n Obs[Observation / perception] --> Ev[Evidence / decision log]\n Ev --> Gate{Earned grant?}\n Gate -->|no| Human[Human ceremony / approval]\n Gate -->|yes| Act[Bounded execution]\n Human --> Act\n Act --> Ev\n```","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:earned-autonomy-protocol:limits-failure-modes","kind":"tech_section","path":"technologies/earned-autonomy-protocol/README.md","title":"Earned Autonomy Protocol · Limits & failure modes","text":"Earned Autonomy Protocol — Limits & failure modes\n\n- **Spec, not a binary:** you still implement gates in OPA/Cedar/product code. \n- **Vendor narrative risk:** chronology and production claims are author-asserted — verify against your corpus before treating as measured fact. \n- **Tiny star / consultancy origin:** treat as doctrine input, not CNCF infrastructure. \n- **Not the identity store or policy DSL.**","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:earned-autonomy-protocol:links","kind":"tech_section","path":"technologies/earned-autonomy-protocol/README.md","title":"Earned Autonomy Protocol · Links","text":"Earned Autonomy Protocol — Links\n\n**URL:** https://marketplace.sentinelos.us/earned-autonomy \n**Repo:** https://github.com/LatamlinkLLC/earned-autonomy-protocol \n**Docs:** https://github.com/LatamlinkLLC/earned-autonomy-protocol \n\n**Folder:** `earned-autonomy-protocol` \n**Wiki-status:** `deep` \n**Updated:** 2026-09-14","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:earned-autonomy-protocol:scientific-notes","kind":"tech_section","path":"technologies/earned-autonomy-protocol/README.md","title":"Earned Autonomy Protocol · Scientific notes","text":"Earned Autonomy Protocol — Scientific notes\n\nScientific pack: **3 sources**, quotes per source, image copyright register.\n\n→ [`science/README.md`](./science/README.md) · schema [`../SCIENCE.md`](../SCIENCE.md)","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:earned-autonomy-protocol:what-it-is","kind":"tech_section","path":"technologies/earned-autonomy-protocol/README.md","title":"Earned Autonomy Protocol · What it is","text":"Earned Autonomy Protocol — What it is\n\nThe Earned Autonomy Protocol is a **practitioner governance methodology** for AI in consequential domains: **AI does not start with autonomy; it earns it** through demonstrated reliability in bounded decisions, logged and verified before scope expands. It defines concepts such as Trust Genesis, decision layers (deterministic / probabilistic / judgment), decision logs, a **perception–action split** (automate perception claims; human-gate world-binding actions), constitutional vs earned boundaries, Seven EAI Laws, and Thirteen Disciplines. Spec is CC BY 4.0; small research/spec repo from Latamlink LLC / SentinelOS — not a drop-in policy engine.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:earned-autonomy-protocol:when-to-use-skip","kind":"tech_section","path":"technologies/earned-autonomy-protocol/README.md","title":"Earned Autonomy Protocol · When to use / skip","text":"Earned Autonomy Protocol — When to use / skip\n\n- **Use when:** designing **autonomy governance** (scope, genesis, constitutional invariants) for high-stakes installs. \n- **Skip when:** you need an implementable IdP (**keycloak**) or policy engine (**opa**, **cedar**) alone without methodology. \n- **Prefer instead:** OPA/Cedar for runtime allow/deny; EAP for the rules of when those allows may widen.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:earned-autonomy-protocol:why-it-matters-here","kind":"tech_section","path":"technologies/earned-autonomy-protocol/README.md","title":"Earned Autonomy Protocol · Why it matters here","text":"Earned Autonomy Protocol — Why it matters here\n\nAn agentic OS that only installs OPA/Cedar/Keycloak still needs a doctrine for **when execution scope expands**. EAP is the IAM-cell research pointer for earned trust, perception vs action loops, and constitutional invariants (money, filings, unprompted client contact) that must not move on a “good streak.” Prefer **OPA/Cedar** to *evaluate* permissions; prefer **Keycloak** for identity; prefer **Headroom** (Guardrails cell) for measuring HITL escalation fatigue on coding actions.","quality":null,"needs_deepen":false,"deepen_reason":null}],"corpus_tags":{"category":["Agentic → IAM"],"dedication":["open-source"],"feature":["access-vs-authority","agent-identity-interoperability","autonomy-levels","exception-first-participation","governance","human-oversight-guardrails","r2-13-governance-policy","r2-16-approval-calibration","security-identity-governance","wt-human-policy"],"kind":["map_edge","tech_features","tech_quote","tech_readme","tech_science_source","tech_section"],"needs_deepen":["false"],"quality":["ok"],"section":["Evidence","Features map","GitHub map","How it works","Limits & failure 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Suits platform teams already on Envoy/Istio as the control point in front of model providers and internal meshes.. Maps to research feature «APIs, SDKs & MCP Extensibility» (apis-sdks-mcp-extensibility). Landscape category: Agentic → LLM Gateway.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:envoy:bring-your-own-model","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"envoy → bring-your-own-model","text":"Technology Envoy (envoy): Envoy’s AI Gateway extends the Envoy ecosystem toward LLM/API AI traffic: routing, safeguards, and observability at the proxy layer. Suits platform teams already on Envoy/Istio as the control point in front of model providers and internal meshes.. Maps to research feature «Bring Your Own Model» (bring-your-own-model). Landscape category: Agentic → LLM Gateway.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:envoy:cost-latency-ops","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"envoy → cost-latency-ops","text":"Technology Envoy (envoy): Envoy’s AI Gateway extends the Envoy ecosystem toward LLM/API AI traffic: routing, safeguards, and observability at the proxy layer. Suits platform teams already on Envoy/Istio as the control point in front of model providers and internal meshes.. Maps to research feature «Cost, Latency & Operational Economics» (cost-latency-ops). Landscape category: Agentic → LLM Gateway.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:envoy:deployment-data-residency","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"envoy → deployment-data-residency","text":"Technology Envoy (envoy): Envoy’s AI Gateway extends the Envoy ecosystem toward LLM/API AI traffic: routing, safeguards, and observability at the proxy layer. Suits platform teams already on Envoy/Istio as the control point in front of model providers and internal meshes.. Maps to research feature «Deployment & Data Residency» (deployment-data-residency). Landscape category: Agentic → LLM Gateway.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:envoy:model-flexibility-routing","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"envoy → model-flexibility-routing","text":"Technology Envoy (envoy): Envoy’s AI Gateway extends the Envoy ecosystem toward LLM/API AI traffic: routing, safeguards, and observability at the proxy layer. Suits platform teams already on Envoy/Istio as the control point in front of model providers and internal meshes.. Maps to research feature «Model Flexibility & Routing» (model-flexibility-routing). Landscape category: Agentic → LLM Gateway.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_features:envoy","kind":"tech_features","path":"technologies/envoy/features.md","title":"Envoy · features","text":"# Features using `envoy`\n\n**Technology:** [Envoy](./README.md)\n**Category:** Agentic → LLM Gateway\n**Mapped features:** 5\n**Map:** [`../features/_source/feature-technology-map.md`](../features/_source/feature-technology-map.md)\n\nThis technology is a **candidate building block** for the features below (research map — not a claim that the product currently depends on it).\n\n| Feature | Title |\n|---|---|\n| [`apis-sdks-mcp-extensibility`](../features/apis-sdks-mcp-extensibility/README.md) | APIs, SDKs & MCP Extensibility |\n| [`bring-your-own-model`](../features/bring-your-own-model/README.md) | Bring Your Own Model |\n| [`cost-latency-ops`](../features/cost-latency-ops/README.md) | Cost, Latency & Operational Economics |\n| [`deployment-data-residency`](../features/deployment-data-residency/README.md) | Deployment & Data Residency |\n| [`model-flexibility-routing`](../features/model-flexibility-routing/README.md) | Model Flexibility & Routing |\n\n","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:envoy:envoy-docs:4e68c9c0380e","kind":"tech_quote","path":"technologies/envoy/science/sources/envoy-docs.md","title":"Envoy · envoy-docs","text":"Envoy (envoy, Agentic → LLM Gateway). At its core, Envoy is an L3/L4 network proxy.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:envoy:envoy-docs:6954ea6cc678","kind":"tech_quote","path":"technologies/envoy/science/sources/envoy-docs.md","title":"Envoy · envoy-docs","text":"Envoy (envoy, Agentic → LLM Gateway). The network should be transparent to applications. When network and application problems do occur it should be easy to determine the source of the problem.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:envoy:envoy-docs:960b8513bce5","kind":"tech_quote","path":"technologies/envoy/science/sources/envoy-docs.md","title":"Envoy · envoy-docs","text":"Envoy (envoy, Agentic → LLM Gateway). 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→ citations","text":"Technology FAISS (faiss): FAISS (Meta) is a high-performance similarity search library (not a full server DB) for dense vectors, often embedded in apps or wrapped by services. Algorithmic core under many vector stacks. Use for raw in-process speed or as a library dependency beneath higher-level stores.. Maps to research feature «Citations» (citations). Landscape category: Infrastructure → Vector DBs.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:faiss:knowledge-grounding-rag","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"faiss → knowledge-grounding-rag","text":"Technology FAISS (faiss): FAISS (Meta) is a high-performance similarity search library (not a full server DB) for dense vectors, often embedded in apps or wrapped by services. Algorithmic core under many vector stacks. Use for raw in-process speed or as a library dependency beneath higher-level stores.. Maps to research feature «Knowledge Grounding & RAG» (knowledge-grounding-rag). Landscape category: Infrastructure → Vector DBs.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:faiss:memory-state-persistence","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"faiss → memory-state-persistence","text":"Technology FAISS (faiss): FAISS (Meta) is a high-performance similarity search library (not a full server DB) for dense vectors, often embedded in apps or wrapped by services. Algorithmic core under many vector stacks. Use for raw in-process speed or as a library dependency beneath higher-level stores.. Maps to research feature «Memory & State Persistence» (memory-state-persistence). Landscape category: Infrastructure → Vector DBs.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:faiss:r2-31-memory-architecture","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"faiss → r2-31-memory-architecture","text":"Technology FAISS (faiss): FAISS (Meta) is a high-performance similarity search library (not a full server DB) for dense vectors, often embedded in apps or wrapped by services. Algorithmic core under many vector stacks. Use for raw in-process speed or as a library dependency beneath higher-level stores.. Maps to research feature «T31 — Memory Architecture (Episodic vs Semantic Tiers)» (r2-31-memory-architecture). Landscape category: Infrastructure → Vector DBs.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:faiss:wt-data-context","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"faiss → wt-data-context","text":"Technology FAISS (faiss): FAISS (Meta) is a high-performance similarity search library (not a full server DB) for dense vectors, often embedded in apps or wrapped by services. Algorithmic core under many vector stacks. Use for raw in-process speed or as a library dependency beneath higher-level stores.. Maps to research feature «Workflow Tools — Data & Context» (wt-data-context). 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This paper tackles the problem of better utilizing GPUs for this task.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:faiss:arxiv-1702-08734:6ee1125037a5","kind":"tech_quote","path":"technologies/faiss/science/sources/arxiv-1702-08734.md","title":"FAISS · arxiv-1702-08734","text":"FAISS (faiss, Infrastructure → Vector DBs). Similarity search finds application in specialized database systems handling complex data such as images or videos, which are typically represented by high-dimensional features and require specific indexing structures.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:faiss:arxiv-1702-08734:df846124211d","kind":"tech_quote","path":"technologies/faiss/science/sources/arxiv-1702-08734.md","title":"FAISS · arxiv-1702-08734","text":"FAISS (faiss, Infrastructure → Vector DBs). Faiss is a library for efficient similarity search and clustering of dense vectors.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:faiss:faiss-github:67eed494ea3f","kind":"tech_quote","path":"technologies/faiss/science/sources/faiss-github.md","title":"FAISS · faiss-github","text":"FAISS (faiss, Infrastructure → Vector DBs). It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:faiss:faiss-github:cac64dfc8b7e","kind":"tech_quote","path":"technologies/faiss/science/sources/faiss-github.md","title":"FAISS · faiss-github","text":"FAISS (faiss, Infrastructure → Vector DBs). Our implementation enables the construction of a high accuracy k-NN graph on 95 million images from the Yfcc100M dataset in 35 minutes, and of a graph connecting 1 billion vectors in less than 12 hours on 4 Maxwell Titan X GPUs.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:faiss:faiss-intro:1e3be492289b","kind":"tech_quote","path":"technologies/faiss/science/sources/faiss-intro.md","title":"FAISS · faiss-intro","text":"FAISS (faiss, Infrastructure → Vector DBs). Faiss contains several methods for similarity search. 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Landscape category: Agentic → Guardrails.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:guardrails-ai:wt-human-policy","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"guardrails-ai → wt-human-policy","text":"Technology Guardrails AI (guardrails-ai): Guardrails is a **Python framework for reliable AI apps** with two jobs: (1) run **Input/Output Guards** that detect, quantify, and mitigate specific risks via Hub validators, and (2) help LLMs emit **structured data** (e.g. Pydantic). Validators install as packages (`pip install guardrails-ai-…`); multiple validators compose into a `Guard` with `OnFailAction` policies. Optional Guardrails Server . Maps to research feature «Workflow Tools — Human & Policy» (wt-human-policy). Landscape category: Agentic → Guardrails.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_features:guardrails-ai","kind":"tech_features","path":"technologies/guardrails-ai/features.md","title":"Guardrails AI · features","text":"$51","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:guardrails-ai:guardrails-docs:60485abb9b0e","kind":"tech_quote","path":"technologies/guardrails-ai/science/sources/guardrails-docs.md","title":"Guardrails AI · guardrails-docs","text":"Guardrails AI (guardrails-ai, Agentic → Guardrails). Multiple validators can be combined together into Input and Output Guards that intercept the inputs and outputs of LLMs.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:guardrails-ai:guardrails-docs:b3af5b534866","kind":"tech_quote","path":"technologies/guardrails-ai/science/sources/guardrails-docs.md","title":"Guardrails AI · guardrails-docs","text":"Guardrails AI (guardrails-ai, Agentic → Guardrails). Adding guardrails to large language models.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:guardrails-ai:guardrails-docs:c4387c1fc14f","kind":"tech_quote","path":"technologies/guardrails-ai/science/sources/guardrails-docs.md","title":"Guardrails AI · guardrails-docs","text":"Guardrails AI (guardrails-ai, Agentic → Guardrails). Guardrails Hub is a collection of pre-built measures of specific types of risks (called 'validators').","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:guardrails-ai:guardrails-github:0e213faff6d2","kind":"tech_quote","path":"technologies/guardrails-ai/science/sources/guardrails-github.md","title":"Guardrails AI · guardrails-github","text":"Guardrails AI (guardrails-ai, Agentic → Guardrails). Guardrails is a Python framework that helps build reliable AI applications by performing two key functions:","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:guardrails-ai:guardrails-github:336968f517d7","kind":"tech_quote","path":"technologies/guardrails-ai/science/sources/guardrails-github.md","title":"Guardrails AI · guardrails-github","text":"Guardrails AI (guardrails-ai, Agentic → Guardrails). Guardrails help you generate structured data from LLMs.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:guardrails-ai:guardrails-github:7e687e0f8036","kind":"tech_quote","path":"technologies/guardrails-ai/science/sources/guardrails-github.md","title":"Guardrails AI · guardrails-github","text":"Guardrails AI (guardrails-ai, Agentic → Guardrails). Guardrails runs Input/Output Guards in your application that detect, quantify and mitigate the presence of specific types of risks.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:guardrails-ai:nemo-contrast:79ae1459cd0f","kind":"tech_quote","path":"technologies/guardrails-ai/science/sources/nemo-contrast.md","title":"Guardrails AI · nemo-contrast","text":"Guardrails AI (guardrails-ai, Agentic → Guardrails). NeMo Guardrails allows developers to add programmable rails to LLM applications - these are user-defined, independent of the underlying LLM, and interpretable.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:guardrails-ai:nemo-contrast:9e971233cde3","kind":"tech_quote","path":"technologies/guardrails-ai/science/sources/nemo-contrast.md","title":"Guardrails AI · nemo-contrast","text":"Guardrails AI (guardrails-ai, Agentic → Guardrails). There are several mechanisms that allow LLM providers and developers to add guardrails that are embedded into a specific model at training, e.g. using model alignment.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_readme:guardrails-ai","kind":"tech_readme","path":"technologies/guardrails-ai/README.md","title":"Guardrails AI","text":"$52","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_science_source:guardrails-ai:guardrails-docs","kind":"tech_science_source","path":"technologies/guardrails-ai/science/sources/guardrails-docs.md","title":"Guardrails AI · guardrails-docs","text":"Guardrails AI · source guardrails-docs\n\n# Guardrails AI documentation\n\n| Field | Value |\n|---|---|\n| **ID** | `guardrails-docs` |\n| **Type** | `docs` |\n| **URL** | https://www.guardrailsai.com/docs |\n| **DOI / arXiv / cite** | Guardrails AI docs |\n| **Authors / Org** | Guardrails AI |\n| **Year** | 2026 |\n| **Evidence** | MODERATE |\n| **Retrieved** | 2026-09-13 |\n\n## Relevance\n\nProduct framing for validators / RAIL specs.\n\n## Quotes\n\n> \"Guardrails Hub is a collection of pre-built measures of specific types of risks (called 'validators').\"\n> — guardrails README · Hub\n\n> \"Multiple validators can be combined together into Input and Output Guards that intercept the inputs and outputs of LLMs.\"\n> — guardrails README\n\n> \"Adding guardrails to large language models.\"\n> — guardrails GitHub description\n\n\n## Images\n\n_None attached._","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_science_source:guardrails-ai:guardrails-github","kind":"tech_science_source","path":"technologies/guardrails-ai/science/sources/guardrails-github.md","title":"Guardrails AI · guardrails-github","text":"Guardrails AI · source guardrails-github\n\n# guardrails-ai/guardrails\n\n| Field | Value |\n|---|---|\n| **ID** | `guardrails-github` |\n| **Type** | `repo-readme` |\n| **URL** | https://github.com/guardrails-ai/guardrails |\n| **DOI / arXiv / cite** | guardrails-ai/guardrails |\n| **Authors / Org** | Guardrails AI |\n| **Year** | 2026 |\n| **Evidence** | MODERATE |\n| **Retrieved** | 2026-09-13 |\n\n## Relevance\n\nPrimary OSS project.\n\n## Quotes\n\n> \"Guardrails is a Python framework that helps build reliable AI applications by performing two key functions:\"\n> — guardrails-ai/guardrails README\n\n> \"Guardrails runs Input/Output Guards in your application that detect, quantify and mitigate the presence of specific types of risks.\"\n> — guardrails README\n\n> \"Guardrails help you generate structured data from LLMs.\"\n> — guardrails README\n\n\n## Images\n\n_None attached._","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_science_source:guardrails-ai:nemo-contrast","kind":"tech_science_source","path":"technologies/guardrails-ai/science/sources/nemo-contrast.md","title":"Guardrails AI · nemo-contrast","text":"Guardrails AI · source nemo-contrast\n\n# Runtime vs embedded rails (contrast note)\n\n| Field | Value |\n|---|---|\n| **ID** | `nemo-contrast` |\n| **Type** | `paper` |\n| **URL** | https://arxiv.org/abs/2310.10501 |\n| **DOI / arXiv / cite** | arXiv:2310.10501 (contrast) |\n| **Authors / Org** | Rebedea et al. |\n| **Year** | 2023 |\n| **Evidence** | MODERATE |\n| **Retrieved** | 2026-09-13 |\n\n## Relevance\n\nClarifies programmable runtime rails vs training-time alignment — useful taxonomy for Guardrails cell.\n\n## Quotes\n\n> \"There are several mechanisms that allow LLM providers and developers to add guardrails that are embedded into a specific model at training, e.g. using model alignment.\"\n\n\n> \"NeMo Guardrails allows developers to add programmable rails to LLM applications - these are user-defined, independent of the underlying LLM, and interpretable.\"\n\n\n## Images\n\n_None attached._","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:guardrails-ai:document-management-map","kind":"tech_section","path":"technologies/guardrails-ai/README.md","title":"Guardrails AI · Document management map","text":"Guardrails AI — Document management map\n\nDMS/IDP research vendors related to this cell: [`document-management.md`](./document-management.md) (31).\n\n","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:guardrails-ai:evidence","kind":"tech_section","path":"technologies/guardrails-ai/README.md","title":"Guardrails AI · Evidence","text":"Guardrails AI — Evidence\n\n| Claim area | Source id | Strength |\n|---|---|---|\n| Framework / Hub validators | `science/sources/guardrails-github.md` | MODERATE |\n| Docs surface | `science/sources/guardrails-docs.md` | MODERATE |\n| Contrast vs NeMo | `science/sources/nemo-contrast.md` | MODERATE |","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:guardrails-ai:features-map","kind":"tech_section","path":"technologies/guardrails-ai/README.md","title":"Guardrails AI · Features map","text":"Guardrails AI — Features map\n\nWhich research features may use this technology: [`features.md`](./features.md) (8 mapped).\n\n","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:guardrails-ai:github-map","kind":"tech_section","path":"technologies/guardrails-ai/README.md","title":"Guardrails AI · GitHub map","text":"$53","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:guardrails-ai:how-it-works","kind":"tech_section","path":"technologies/guardrails-ai/README.md","title":"Guardrails AI · How it works","text":"Guardrails AI — How it works\n\nInstall validators, build a `Guard().use(...)`, then `validate` inputs/outputs around the LLM call (or generate structured objects via `Guard.for_pydantic`). Failures raise or transform per `OnFailAction`. Server mode centralizes guards for multiple clients.\n\n1. `pip install guardrails-ai` (+ Hub validator packages). \n2. Compose validators into a Guard. \n3. Validate LLM I/O or generate structured outputs. \n4. Optionally serve guards via Flask/Gunicorn.\n\n```mermaid\nflowchart LR\n In[User / tool text] --> G[Guard validators]\n G --> LLM[LLM]\n LLM --> G2[Output validators]\n G2 --> Out[App / agent]\n```","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:guardrails-ai:limits-failure-modes","kind":"tech_section","path":"technologies/guardrails-ai/README.md","title":"Guardrails AI · Limits & failure modes","text":"Guardrails AI — Limits & failure modes\n\n- **Remote-inferencing sunset:** plan local/PyPI validators before Aug 2026 cutoff. \n- **Validator sprawl:** Hub quality varies — pin packages and test fail modes. \n- **Not a sandbox:** does not isolate code execution — pair with Code Sandbox cell. \n- **Not company SoR:** guard configs ≠ declared authority/ledger.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:guardrails-ai:links","kind":"tech_section","path":"technologies/guardrails-ai/README.md","title":"Guardrails AI · Links","text":"Guardrails AI — Links\n\n**URL:** https://guardrailsai.com/ \n**Repo:** https://github.com/guardrails-ai/guardrails \n**Docs:** https://www.guardrailsai.com/docs \n\n**Folder:** `guardrails-ai` \n**Wiki-status:** `deep` \n**Updated:** 2026-09-14","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:guardrails-ai:scientific-notes","kind":"tech_section","path":"technologies/guardrails-ai/README.md","title":"Guardrails AI · Scientific notes","text":"Guardrails AI — Scientific notes\n\nScientific pack: **3 sources**, quotes per source, image copyright register.\n\n→ [`science/README.md`](./science/README.md) · schema [`../SCIENCE.md`](../SCIENCE.md)","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:guardrails-ai:what-it-is","kind":"tech_section","path":"technologies/guardrails-ai/README.md","title":"Guardrails AI · What it is","text":"Guardrails AI — What it is\n\nGuardrails is a **Python framework for reliable AI apps** with two jobs: (1) run **Input/Output Guards** that detect, quantify, and mitigate specific risks via Hub validators, and (2) help LLMs emit **structured data** (e.g. Pydantic). Validators install as packages (`pip install guardrails-ai-…`); multiple validators compose into a `Guard` with `OnFailAction` policies. Optional Guardrails Server (`guardrails start`) exposes guards over HTTP/OpenAI-compatible endpoints.\n\n**News (July 2026):** validators move to standard PyPI installs; hosted remote inferencing is being discontinued (planned cutoff **August 25, 2026**) — see migration notes on the repo.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:guardrails-ai:when-to-use-skip","kind":"tech_section","path":"technologies/guardrails-ai/README.md","title":"Guardrails AI · When to use / skip","text":"Guardrails AI — When to use / skip\n\n- **Use when:** you need **Python I/O validators + structured generation** with Hub measures. \n- **Skip when:** you need NVIDIA-style dialog rails (**nemo-guardrails**), archived Protect AI scanners (**llm-guard**), or measured HITL action gates (**headroom**). \n- **Prefer instead:** NeMo for conversational rail programs; Headroom for agent tool approval budgets.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:guardrails-ai:why-it-matters-here","kind":"tech_section","path":"technologies/guardrails-ai/README.md","title":"Guardrails AI · Why it matters here","text":"Guardrails AI — Why it matters here\n\nAn agentic OS that proposes actions still needs a **policy layer on LLM text I/O** before tools fire. Guardrails AI is the research pick for composable validators + structured output on the Python path. Prefer **NeMo Guardrails** for Colang programmable dialog rails; prefer **LLM Guard** only as an archived scanner toolkit; prefer **Headroom** when the problem is HITL escalation calibration for coding-agent actions, not prompt/output validators.","quality":null,"needs_deepen":false,"deepen_reason":null}],"corpus_tags":{"category":["Agentic → Guardrails"],"dedication":["open-source"],"feature":["access-vs-authority","autonomy-levels","emergency-control","human-oversight-guardrails","r2-13-governance-policy","r2-16-approval-calibration","security-identity-governance","wt-human-policy"],"kind":["map_edge","tech_features","tech_quote","tech_readme","tech_science_source","tech_section"],"needs_deepen":["false"],"quality":["ok"],"section":["Document management map","Evidence","Features map","GitHub map","How it works","Limits & failure modes","Links","Scientific notes","What it is","When to use / skip","Why it matters 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Models are immutable copies (Transformers, LlamaCpp, mocks, and others). Related runtime work lives in `llguidance` (Rust).. Maps to research feature «Closed Execution Loop» (closed-execution-loop). Landscape category: Agentic → Structured Output.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:guidance:outcome-verification","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"guidance → outcome-verification","text":"Technology Guidance (guidance): Guidance is a **guidance language for controlling LLMs**: compose prompts with Python context managers (`system`/`user`/`assistant`) and constrained generators — free `gen`, regex-constrained generation, `select` over options, and Pydantic/JSON schema generation (`gen_json`). Models are immutable copies (Transformers, LlamaCpp, mocks, and others). Related runtime work lives in `llguidance` (Rust).. Maps to research feature «Outcome Verification» (outcome-verification). 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Landscape category: Agentic → Structured Output.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:guidance:wt-intelligence","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"guidance → wt-intelligence","text":"Technology Guidance (guidance): Guidance is a **guidance language for controlling LLMs**: compose prompts with Python context managers (`system`/`user`/`assistant`) and constrained generators — free `gen`, regex-constrained generation, `select` over options, and Pydantic/JSON schema generation (`gen_json`). Models are immutable copies (Transformers, LlamaCpp, mocks, and others). Related runtime work lives in `llguidance` (Rust).. Maps to research feature «Workflow Tools — Intelligence» (wt-intelligence). Landscape category: Agentic → Structured Output.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_features:guidance","kind":"tech_features","path":"technologies/guidance/features.md","title":"Guidance · features","text":"# Features using `guidance`\n\n**Technology:** [Guidance](./README.md)\n**Category:** Agentic → Structured Output\n**Mapped features:** 4\n**Map:** [`../features/_source/feature-technology-map.md`](../features/_source/feature-technology-map.md)\n\nThis technology is a **candidate building block** for the features below (research map — not a claim that the product currently depends on it).\n\n| Feature | Title |\n|---|---|\n| [`closed-execution-loop`](../features/closed-execution-loop/README.md) | Closed Execution Loop |\n| [`outcome-verification`](../features/outcome-verification/README.md) | Outcome Verification |\n| [`testing-debugging-optimization`](../features/testing-debugging-optimization/README.md) | Testing, Debugging & Optimization |\n| [`wt-intelligence`](../features/wt-intelligence/README.md) | Workflow Tools — Intelligence |\n\n","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:guidance:guidance-github:c230eb223ac5","kind":"tech_quote","path":"technologies/guidance/science/sources/guidance-github.md","title":"Guidance · guidance-github","text":"Guidance (guidance, Agentic → Structured Output). Guidance is an efficient programming paradigm for steering language models.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:guidance:guidance-github:dbbf32791b70","kind":"tech_quote","path":"technologies/guidance/science/sources/guidance-github.md","title":"Guidance · guidance-github","text":"Guidance (guidance, Agentic → Structured Output). With Guidance, you can control how output is structured and get high-quality output for your use case— while reducing latency and cost vs. conventional prompting or fine-tuning.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:guidance:guidance-github:ea952341b041","kind":"tech_quote","path":"technologies/guidance/science/sources/guidance-github.md","title":"Guidance · guidance-github","text":"Guidance (guidance, Agentic → Structured Output). A guidance language for controlling large language models.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:guidance:lmql-contrast:2e8c70beb2d1","kind":"tech_quote","path":"technologies/guidance/science/sources/lmql-contrast.md","title":"Guidance · lmql-contrast","text":"Guidance (guidance, Agentic → Structured Output). LMQL is a programming language for large language models (LLMs) based on a superset of Python.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:guidance:lmql-contrast:798d82979452","kind":"tech_quote","path":"technologies/guidance/science/sources/lmql-contrast.md","title":"Guidance · lmql-contrast","text":"Guidance (guidance, Agentic → Structured Output). A language for constraint-guided and efficient LLM programming.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:guidance:lmql-contrast:dcb033d6f093","kind":"tech_quote","path":"technologies/guidance/science/sources/lmql-contrast.md","title":"Guidance · lmql-contrast","text":"Guidance (guidance, Agentic → Structured Output). It goes beyond traditional templating languages by integrating LLM interaction natively at the level of your program code.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:guidance:outlines-contrast:9c49cd018a27","kind":"tech_quote","path":"technologies/guidance/science/sources/outlines-contrast.md","title":"Guidance · outlines-contrast","text":"Guidance (guidance, Agentic → Structured Output). LLMs are powerful but their outputs are unpredictable. Most solutions attempt to fix bad outputs after generation.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:guidance:outlines-contrast:a92bad6beab4","kind":"tech_quote","path":"technologies/guidance/science/sources/outlines-contrast.md","title":"Guidance · outlines-contrast","text":"Guidance (guidance, Agentic → Structured Output). Structured outputs for LLMs","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:guidance:outlines-contrast:a9e93bf44ab9","kind":"tech_quote","path":"technologies/guidance/science/sources/outlines-contrast.md","title":"Guidance · outlines-contrast","text":"Guidance (guidance, Agentic → Structured Output). Building the future of structured generation","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_readme:guidance","kind":"tech_readme","path":"technologies/guidance/README.md","title":"Guidance","text":"$54","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_science_source:guidance:guidance-github","kind":"tech_science_source","path":"technologies/guidance/science/sources/guidance-github.md","title":"Guidance · guidance-github","text":"Guidance · source guidance-github\n\n# guidance-ai/guidance\n\n| Field | Value |\n|---|---|\n| **ID** | `guidance-github` |\n| **Type** | `repo-readme` |\n| **URL** | https://github.com/guidance-ai/guidance |\n| **DOI / arXiv / cite** | guidance-ai/guidance |\n| **Authors / Org** | Guidance |\n| **Year** | 2026 |\n| **Evidence** | MODERATE |\n| **Retrieved** | 2026-09-13 |\n\n## Relevance\n\nPrimary repo.\n\n## Quotes\n\n> \"A guidance language for controlling large language models.\"\n> — guidance GitHub\n\n> \"Guidance is an efficient programming paradigm for steering language models.\"\n> — guidance README\n\n> \"With Guidance, you can control how output is structured and get high-quality output for your use case— while reducing latency and cost vs. conventional prompting or fine-tuning.\"\n> — guidance README\n\n\n## Images\n\n_None attached._","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_science_source:guidance:lmql-contrast","kind":"tech_science_source","path":"technologies/guidance/science/sources/lmql-contrast.md","title":"Guidance · lmql-contrast","text":"Guidance · source lmql-contrast\n\n# LMQL contrast\n\n| Field | Value |\n|---|---|\n| **ID** | `lmql-contrast` |\n| **Type** | `repo-readme` |\n| **URL** | https://github.com/eth-sri/lmql |\n| **DOI / arXiv / cite** | LMQL contrast |\n| **Authors / Org** | ETH SRI |\n| **Year** | 2026 |\n| **Evidence** | MODERATE |\n| **Retrieved** | 2026-09-13 |\n\n## Relevance\n\nPeer query language for LMs.\n\n## Quotes\n\n> \"A language for constraint-guided and efficient LLM programming.\"\n> — lmql\n\n> \"LMQL is a programming language for large language models (LLMs) based on a superset of Python.\"\n> — lmql\n\n> \"It goes beyond traditional templating languages by integrating LLM interaction natively at the level of your program code.\"\n> — lmql\n\n\n## Images\n\n_None attached._","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_science_source:guidance:outlines-contrast","kind":"tech_science_source","path":"technologies/guidance/science/sources/outlines-contrast.md","title":"Guidance · outlines-contrast","text":"Guidance · source outlines-contrast\n\n# Outlines contrast\n\n| Field | Value |\n|---|---|\n| **ID** | `outlines-contrast` |\n| **Type** | `repo-readme` |\n| **URL** | https://github.com/dottxt-ai/outlines |\n| **DOI / arXiv / cite** | Outlines contrast |\n| **Authors / Org** | dottxt |\n| **Year** | 2026 |\n| **Evidence** | MODERATE |\n| **Retrieved** | 2026-09-13 |\n\n## Relevance\n\nPeer structured generation.\n\n## Quotes\n\n> \"Structured outputs for LLMs\"\n> — outlines\n\n> \"LLMs are powerful but their outputs are unpredictable. Most solutions attempt to fix bad outputs after generation.\"\n> — outlines\n\n> \"Building the future of structured generation\"\n> — outlines\n\n\n## Images\n\n_None attached._","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:guidance:evidence","kind":"tech_section","path":"technologies/guidance/README.md","title":"Guidance · Evidence","text":"Guidance — Evidence\n\n| Claim area | Source id | Strength |\n|---|---|---|\n| Language / control role | `science/sources/guidance-github.md` | MODERATE |\n| Contrast vs LMQL | `science/sources/lmql-contrast.md` | MODERATE |\n| Contrast vs Outlines | `science/sources/outlines-contrast.md` | MODERATE |","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:guidance:features-map","kind":"tech_section","path":"technologies/guidance/README.md","title":"Guidance · Features map","text":"Guidance — Features map\n\nWhich research features may use this technology: [`features.md`](./features.md) (4 mapped).\n\n","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:guidance:github-map","kind":"tech_section","path":"technologies/guidance/README.md","title":"Guidance · GitHub map","text":"$55","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:guidance:how-it-works","kind":"tech_section","path":"technologies/guidance/README.md","title":"Guidance · How it works","text":"Guidance — How it works\n\nYou build an immutable model session, append role blocks, and call generators that mask or constrain tokens. Captured named fields land on the model object for downstream Python use.\n\n1. Load a local/API model backend. \n2. Compose system/user/assistant blocks. \n3. Constrain with `gen`/`select`/`regex`/`gen_json`. \n4. Read named captures; validate further in app code if needed.\n\n```mermaid\nflowchart LR\n Prompt[Role blocks + grammar] --> Guide[Guidance runtime]\n Guide -->|constrained tokens| LM[LLM backend]\n LM --> Cap[Named captures / JSON]\n```","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:guidance:limits-failure-modes","kind":"tech_section","path":"technologies/guidance/README.md","title":"Guidance · Limits & failure modes","text":"Guidance — Limits & failure modes\n\n- **Backend coupling:** strength depends on local/logit-control backends vs thin API wrappers. \n- **Not the ledger:** valid JSON ≠ authorized action. \n- **Maintenance check:** confirm repo activity vs peers before production lock. \n- **Learning curve:** DSL + grammar composition is heavier than Instructor’s one-liner.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:guidance:links","kind":"tech_section","path":"technologies/guidance/README.md","title":"Guidance · Links","text":"Guidance — Links\n\n**URL:** https://github.com/guidance-ai/guidance \n**Repo:** https://github.com/guidance-ai/guidance \n**Docs:** https://github.com/guidance-ai/guidance \n\n**Folder:** `guidance` \n**Wiki-status:** `deep` \n**Updated:** 2026-09-14","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:guidance:scientific-notes","kind":"tech_section","path":"technologies/guidance/README.md","title":"Guidance · Scientific notes","text":"Guidance — Scientific notes\n\nScientific pack: **3 sources**, quotes per source, image copyright register.\n\n→ [`science/README.md`](./science/README.md) · schema [`../SCIENCE.md`](../SCIENCE.md)","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:guidance:what-it-is","kind":"tech_section","path":"technologies/guidance/README.md","title":"Guidance · What it is","text":"Guidance — What it is\n\nGuidance is a **guidance language for controlling LLMs**: compose prompts with Python context managers (`system`/`user`/`assistant`) and constrained generators — free `gen`, regex-constrained generation, `select` over options, and Pydantic/JSON schema generation (`gen_json`). Models are immutable copies (Transformers, LlamaCpp, mocks, and others). Related runtime work lives in `llguidance` (Rust). MIT. Last primary push noted mid-2026 in the pack map — verify activity before locking.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:guidance:when-to-use-skip","kind":"tech_section","path":"technologies/guidance/README.md","title":"Guidance · When to use / skip","text":"Guidance — When to use / skip\n\n- **Use when:** you need **programmable constrained generation** (regex, choice, JSON schema) interleaved with prompt control flow. \n- **Skip when:** simple chat+JSON mode + Pydantic retries suffice (**instructor**), or you already standardize on Outlines grammars. \n- **Prefer instead:** Instructor for API extraction; Outlines for engine-level structured generation guarantees.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:guidance:why-it-matters-here","kind":"tech_section","path":"technologies/guidance/README.md","title":"Guidance · Why it matters here","text":"Guidance — Why it matters here\n\nAgent tool protocols and ledger-bound records need **generation that cannot emit invalid shapes**. Guidance is the research pick when you want a programmable mix of free text and hard constraints at decode time. Prefer **Instructor** for pragmatic multi-provider Pydantic extraction in app code; prefer **Outlines** for grammar/schema-guided decoding especially on self-hosted backends; prefer **LMQL** for query-language + research constraint programming (check maintenance).","quality":null,"needs_deepen":false,"deepen_reason":null}],"corpus_tags":{"category":["Agentic → Structured Output"],"dedication":["open-source"],"feature":["closed-execution-loop","outcome-verification","testing-debugging-optimization","wt-intelligence"],"kind":["map_edge","tech_features","tech_quote","tech_readme","tech_science_source","tech_section"],"needs_deepen":["false"],"quality":["ok"],"section":["Evidence","Features map","GitHub map","How it works","Limits & failure modes","Links","Scientific notes","What it is","When to use / skip","Why it matters here"],"slug":["guidance"],"source_id":["guidance-github","lmql-contrast","outlines-contrast"],"technology":["guidance"]},"mermaid":[{"title":"How it 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We find real-world software engineering to be a rich, sustainable, and challenging testbed for evaluating the next generation of language models.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:hal-the-holistic-agent-leaderboard:hal-github:5f5682f1321c","kind":"tech_quote","path":"technologies/hal-the-holistic-agent-leaderboard/science/sources/hal-github.md","title":"HAL: The Holistic Agent Leaderboard · hal-github","text":"HAL: The Holistic Agent Leaderboard (hal-the-holistic-agent-leaderboard, Agentic → Evaluations). Ragas is available at https://github.com/explodinggradients/ragas.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:hal-the-holistic-agent-leaderboard:hal-github:a7d7b60b3982","kind":"tech_quote","path":"technologies/hal-the-holistic-agent-leaderboard/science/sources/hal-github.md","title":"HAL: The Holistic Agent Leaderboard · hal-github","text":"HAL: The Holistic Agent Leaderboard (hal-the-holistic-agent-leaderboard, Agentic → Evaluations). SWE-bench, an evaluation framework consisting of 2,294 software engineering problems drawn from real GitHub issues and corresponding pull requests across 12 popular Python repositories.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:hal-the-holistic-agent-leaderboard:ragas-contrast:1aa7b0f06810","kind":"tech_quote","path":"technologies/hal-the-holistic-agent-leaderboard/science/sources/ragas-contrast.md","title":"HAL: The Holistic Agent Leaderboard · ragas-contrast","text":"HAL: The Holistic Agent Leaderboard (hal-the-holistic-agent-leaderboard, Agentic → Evaluations). Given a codebase along with a description of an issue to be resolved, a language model is tasked with editing the codebase to address the issue.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:hal-the-holistic-agent-leaderboard:ragas-contrast:8b19066ec3af","kind":"tech_quote","path":"technologies/hal-the-holistic-agent-leaderboard/science/sources/ragas-contrast.md","title":"HAL: The Holistic Agent Leaderboard · ragas-contrast","text":"HAL: The Holistic Agent Leaderboard (hal-the-holistic-agent-leaderboard, Agentic → Evaluations). To this end, we introduce SWE-bench, an evaluation framework consisting of 2,294 software engineering problems drawn from real GitHub issues and corresponding pull requests across 12 popular Python repositories.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:hal-the-holistic-agent-leaderboard:ragas-contrast:e84a23bd1e0b","kind":"tech_quote","path":"technologies/hal-the-holistic-agent-leaderboard/science/sources/ragas-contrast.md","title":"HAL: The Holistic Agent Leaderboard · ragas-contrast","text":"HAL: The Holistic Agent Leaderboard (hal-the-holistic-agent-leaderboard, Agentic → Evaluations). For faithfulness and context relevance, the two annotators agreed in around 95% of cases. For answer relevance, they agreed in around 90% of the cases.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:hal-the-holistic-agent-leaderboard:swebench:5e731df2369d","kind":"tech_quote","path":"technologies/hal-the-holistic-agent-leaderboard/science/sources/swebench.md","title":"HAL: The Holistic Agent Leaderboard · swebench","text":"HAL: The Holistic Agent Leaderboard (hal-the-holistic-agent-leaderboard, Agentic → Evaluations). The results… show that our proposed metrics are much closer aligned with the human judgements than the predictions from the two baselines.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:hal-the-holistic-agent-leaderboard:swebench:de668a767a13","kind":"tech_quote","path":"technologies/hal-the-holistic-agent-leaderboard/science/sources/swebench.md","title":"HAL: The Holistic Agent Leaderboard · swebench","text":"HAL: The Holistic Agent Leaderboard (hal-the-holistic-agent-leaderboard, Agentic → Evaluations). Language models have outpaced our ability to evaluate them effectively, but for their future development it is essential to study the frontier of their capabilities.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_readme:hal-the-holistic-agent-leaderboard","kind":"tech_readme","path":"technologies/hal-the-holistic-agent-leaderboard/README.md","title":"HAL: The Holistic Agent Leaderboard","text":"$57","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_science_source:hal-the-holistic-agent-leaderboard:hal-github","kind":"tech_science_source","path":"technologies/hal-the-holistic-agent-leaderboard/science/sources/hal-github.md","title":"HAL: The Holistic Agent Leaderboard · hal-github","text":"HAL: The Holistic Agent Leaderboard · source hal-github\n\n# HAL repo\n\n| Field | Value |\n|---|---|\n| **ID** | `hal-github` |\n| **Type** | `repo-readme` |\n| **URL** | https://github.com/allenai/holistic-agent-leaderboard |\n| **DOI / arXiv / cite** | HAL |\n| **Authors / Org** | AI2 |\n| **Year** | 2026 |\n| **Evidence** | MODERATE |\n| **Retrieved** | 2026-09-13 |\n\n## Relevance\n\nPrimary mapping.\n\n## Quotes\n\n> \"SWE-bench, an evaluation framework consisting of 2,294 software engineering problems drawn from real GitHub issues and corresponding pull requests across 12 popular Python repositories.\"\n> — hal-the-holistic-agent-leaderboard primary (deduped)\n\n> \"Ragas is available at https://github.com/explodinggradients/ragas.\"\n> — hal-the-holistic-agent-leaderboard primary (deduped)\n\n> \"We find real-world software engineering to be a rich, sustainable, and challenging testbed for evaluating the next generation of language models.\"\n> — hal-the-holistic-agent-leaderboard primary (deduped)\n\n\n## Images\n\n_None attached._","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_science_source:hal-the-holistic-agent-leaderboard:ragas-contrast","kind":"tech_science_source","path":"technologies/hal-the-holistic-agent-leaderboard/science/sources/ragas-contrast.md","title":"HAL: The Holistic Agent Leaderboard · ragas-contrast","text":"$58","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_science_source:hal-the-holistic-agent-leaderboard:swebench","kind":"tech_science_source","path":"technologies/hal-the-holistic-agent-leaderboard/science/sources/swebench.md","title":"HAL: The Holistic Agent Leaderboard · swebench","text":"HAL: The Holistic Agent Leaderboard · source swebench\n\n# SWE-bench\n\n| Field | Value |\n|---|---|\n| **ID** | `swebench` |\n| **Type** | `paper` |\n| **URL** | https://arxiv.org/abs/2310.06770 |\n| **DOI / arXiv / cite** | SWE-bench |\n| **Authors / Org** | Jimenez et al. |\n| **Year** | 2023 |\n| **Evidence** | STRONG |\n| **Retrieved** | 2026-09-13 |\n\n## Relevance\n\nCanonical coding eval peer.\n\n## Quotes\n\n> \"Language models have outpaced our ability to evaluate them effectively, but for their future development it is essential to study the frontier of their capabilities.\"\n> — hal-the-holistic-agent-leaderboard primary (deduped)\n\n> \"The results… show that our proposed metrics are much closer aligned with the human judgements than the predictions from the two baselines.\"\n> — hal-the-holistic-agent-leaderboard primary (deduped)\n\n\n## Images\n\n_None attached._","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:hal-the-holistic-agent-leaderboard:evidence","kind":"tech_section","path":"technologies/hal-the-holistic-agent-leaderboard/README.md","title":"HAL: The Holistic Agent Leaderboard · Evidence","text":"HAL: The Holistic Agent Leaderboard — Evidence\n\n| Claim area | Source id | Strength |\n|---|---|---|\n| HAL harness / leaderboard role | `science/sources/hal-github.md` | MODERATE |\n| Coding-benchmark contrast | `science/sources/swebench.md` | MODERATE |\n| Eval-peer contrast | `science/sources/ragas-contrast.md` | MODERATE |","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:hal-the-holistic-agent-leaderboard:features-map","kind":"tech_section","path":"technologies/hal-the-holistic-agent-leaderboard/README.md","title":"HAL: The Holistic Agent Leaderboard · Features map","text":"HAL: The Holistic Agent Leaderboard — Features map\n\nWhich research features may use this technology: [`features.md`](./features.md) (7 mapped).\n\n","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:hal-the-holistic-agent-leaderboard:github-map","kind":"tech_section","path":"technologies/hal-the-holistic-agent-leaderboard/README.md","title":"HAL: The Holistic Agent Leaderboard · GitHub map","text":"HAL: The Holistic Agent Leaderboard — GitHub map\n\nGitHub repositories for **HAL: The Holistic Agent Leaderboard** (several allowed per technology).\n\n| Role | Repo | ★ | Lang | License | Pushed |\n|---|---|--:|---|---|---|\n| `primary` | [princeton-pli/hal-harness](https://github.com/princeton-pli/hal-harness) ⚠ | 311 | Python | — | 2026-07-01 |\n\n```mermaid\nflowchart LR\n Tech[\"HAL: The Holistic Agent Leaderboard\"]\n Tech -->|primary| hal_harness[\"princeton-pli/hal-harness\"]\n Tech --> Cell[\"Agentic / Evaluations\"]\n Cell --> Index[technologies/GITHUB_MAP.md]\n```\n\n**Repos mapped:** 1 · edit `_source/github-repos.overrides.json` to add more.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:hal-the-holistic-agent-leaderboard:how-it-works","kind":"tech_section","path":"technologies/hal-the-holistic-agent-leaderboard/README.md","title":"HAL: The Holistic Agent Leaderboard · How it works","text":"HAL: The Holistic Agent Leaderboard — How it works\n\n1. Install `hal` from the harness repo (historically recursive clone + conda). \n2. `hal-eval` runs agents against a chosen benchmark with Weave logging. \n3. Traces/costs are recorded; optional `hal-upload` sends encrypted traces for leaderboard integration. \n4. Today: prefer reading **published** leaderboard/reliability pages; treat the harness as archived reference code.\n\n```mermaid\nflowchart TD\n A[Agent under test] --> H[hal-eval CLI]\n H --> B[Benchmark suite]\n B --> W[Weave logs / costs]\n W --> L[HAL leaderboard / HF traces]\n```","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:hal-the-holistic-agent-leaderboard:limits-failure-modes","kind":"tech_section","path":"technologies/hal-the-holistic-agent-leaderboard/README.md","title":"HAL: The Holistic Agent Leaderboard · Limits & failure modes","text":"HAL: The Holistic Agent Leaderboard — Limits & failure modes\n\n- **Archived harness:** do not plan new PRs or expect leaderboard updates via this repo. \n- **Not production observability:** Weave in HAL ≠ company tracing stack. \n- **Contamination risk:** public traces/benchmarks; encrypted upload exists for a reason — still don’t leak private tasks. \n- **Science shelf:** packs reuse SWE-bench / Ragas contrast sources — fine for contrast Evidence, not for claiming active maintenance.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:hal-the-holistic-agent-leaderboard:links","kind":"tech_section","path":"technologies/hal-the-holistic-agent-leaderboard/README.md","title":"HAL: The Holistic Agent Leaderboard · Links","text":"HAL: The Holistic Agent Leaderboard — Links\n\n**URL:** https://hal.cs.princeton.edu/ \n**Repo:** https://github.com/princeton-pli/hal-harness \n**Docs:** https://hal.cs.princeton.edu/ · Reliability Dashboard on same site \n\n**Folder:** `hal-the-holistic-agent-leaderboard` \n**Wiki-status:** `deep` \n**Updated:** 2026-09-13","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:hal-the-holistic-agent-leaderboard:scientific-notes","kind":"tech_section","path":"technologies/hal-the-holistic-agent-leaderboard/README.md","title":"HAL: The Holistic Agent Leaderboard · Scientific notes","text":"HAL: The Holistic Agent Leaderboard — Scientific notes\n\nScientific pack: **3 sources**, quotes per source, image copyright register.\n\n→ [`science/README.md`](./science/README.md) · schema [`../SCIENCE.md`](../SCIENCE.md)","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:hal-the-holistic-agent-leaderboard:what-it-is","kind":"tech_section","path":"technologies/hal-the-holistic-agent-leaderboard/README.md","title":"HAL: The Holistic Agent Leaderboard · What it is","text":"HAL: The Holistic Agent Leaderboard — What it is\n\nHAL is Princeton’s **Holistic Agent Leaderboard** ecosystem: a public leaderboard plus a standardized **`hal-eval` harness** for reproducible agent runs across benchmarks (SWE-bench Verified, USACO, AppWorld, CORE-bench, τ-bench, …). The harness supports local conda/Docker and Azure VMs, Weave logging/cost tracking, and encrypted trace upload to Hugging Face for leaderboard submission.\n\n**Important (primary README, 2026):** leaderboard results are **no longer updated through this harness**; the repo is **archived for historical reference** while work shifts to agent reliability. Public results remain on the HAL site and Reliability Dashboard.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:hal-the-holistic-agent-leaderboard:when-to-use-skip","kind":"tech_section","path":"technologies/hal-the-holistic-agent-leaderboard/README.md","title":"HAL: The Holistic Agent Leaderboard · When to use / skip","text":"HAL: The Holistic Agent Leaderboard — When to use / skip\n\n- **Use when:** calibrating agents against **public multi-benchmark** baselines or studying published HAL traces/methodology. \n- **Skip when:** you need live CI for *your* product scenarios (**promptfoo** / **deepeval** / **agent-evaluation**), RAG metrics (**ragas**), or process mining (**pm4py**). \n- **Prefer instead:** SWE-bench (or peers) directly if you only care about one coding benchmark; Promptfoo for private regression suites.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:hal-the-holistic-agent-leaderboard:why-it-matters-here","kind":"tech_section","path":"technologies/hal-the-holistic-agent-leaderboard/README.md","title":"HAL: The Holistic Agent Leaderboard · Why it matters here","text":"HAL: The Holistic Agent Leaderboard — Why it matters here\n\nBefore locking a coding/tool agent stack, you need **external baselines**, not only private golden sets. HAL is the research pointer for multi-benchmark agent comparison and published traces — use historical harness docs to understand how public scores were produced. It is **not** a production monitor and **not** a substitute for install-specific CI goldens (Promptfoo/DeepEval).","quality":null,"needs_deepen":false,"deepen_reason":null}],"corpus_tags":{"category":["Agentic → Evaluations"],"dedication":["open-source"],"feature":["outcome-verification","r2-30-evaluator-critic","r2-32-eval-harnesses","r2-42-intervention-preassessment","r2-47-trace-maturity-gate","testing-debugging-optimization","wt-test-debug-evaluate"],"kind":["map_edge","tech_features","tech_quote","tech_readme","tech_science_source","tech_section"],"needs_deepen":["false"],"quality":["ok"],"section":["Evidence","Features map","GitHub map","How it works","Limits & failure modes","Links","Scientific notes","What it is","When to use / skip","Why it matters here"],"slug":["hal-the-holistic-agent-leaderboard"],"source_id":["hal-github","ragas-contrast","swebench"],"technology":["hal-the-holistic-agent-leaderboard"]},"mermaid":[{"title":"How it works","code":"flowchart TD\n A[Agent under 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It's designed for AI systems running in real-world enterprise environments.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:haystack:haystack-who:4e4a63a0d1db","kind":"tech_quote","path":"technologies/haystack/science/sources/haystack-who.md","title":"Haystack · haystack-who","text":"Haystack (haystack, Agentic → RAG). Haystack is built for AI developers and engineers creating AI systems that need to be accurate, modular, and transparent.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:haystack:haystack-who:c5a61d535a46","kind":"tech_quote","path":"technologies/haystack/science/sources/haystack-who.md","title":"Haystack · haystack-who","text":"Haystack (haystack, Agentic → RAG). Run Haystack in your own environment, get dedicated support or extend it with a cloud or self-hosted platform to test, deploy, and operate pipelines at scale.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_readme:haystack","kind":"tech_readme","path":"technologies/haystack/README.md","title":"Haystack","text":"$5c","quality":"ok","needs_deepen":false,"deepen_reason":null}],"corpus_tags":{"category":["Agentic → RAG"],"dedication":["open-core"],"feature":["citations","company-ontology","knowledge-grounding-rag","os-company-model-memory","os-unified-cockpit","r2-07-dashboards","r2-35-context-engineering","shadow-schema","wt-data-context"],"kind":["map_edge","tech_features","tech_quote","tech_readme"],"needs_deepen":["false"],"quality":["ok"],"slug":["haystack"],"source_id":["haystack-github","haystack-intro","haystack-who"],"technology":["haystack"]},"mermaid":[{"title":"How it works","code":"flowchart TD\n Docs([Corpus]) --> Store[(Document store)]\n 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review/review.md","title":"Headroom · interface review","text":"$5d","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"company_readme:headroom","kind":"company_review","path":"companies/headroom/README.md","title":"Headroom","text":"# Headroom\n\n**Category:** agentic OS / agent platform research target\n\nApproval calibration apparatus for HITL agents\n\n**Status:** folder scaffold — deepen later\n\n**URL:** https://github.com/turangenesis/headroom","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:headroom:access-vs-authority","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"headroom → access-vs-authority","text":"Technology Headroom (headroom): Headroom is a **human-in-the-loop firewall for AI coding agents** and a **measurement study of when to escalate to humans**. A worker agent proposes actions; a guardian (deterministic rules + LLM risk score) classifies each as **safe / approval-required / blocked**. Risky actions pause via LangGraph `interrupt()` until a human decides; every decision is audit-logged on a dashboard with a calibrati. Maps to research feature «Access vs Authority Separation» (access-vs-authority). Landscape category: Agentic → Guardrails.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:headroom:autonomy-levels","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"headroom → autonomy-levels","text":"Technology Headroom (headroom): Headroom is a **human-in-the-loop firewall for AI coding agents** and a **measurement study of when to escalate to humans**. A worker agent proposes actions; a guardian (deterministic rules + LLM risk score) classifies each as **safe / approval-required / blocked**. 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Landscape category: Agentic → Guardrails.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:headroom:exception-first-participation","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"headroom → exception-first-participation","text":"Technology Headroom (headroom): Headroom is a **human-in-the-loop firewall for AI coding agents** and a **measurement study of when to escalate to humans**. A worker agent proposes actions; a guardian (deterministic rules + LLM risk score) classifies each as **safe / approval-required / blocked**. Risky actions pause via LangGraph `interrupt()` until a human decides; every decision is audit-logged on a dashboard with a calibrati. Maps to research feature «Exception-First Human Participation» (exception-first-participation). Landscape category: Agentic → Guardrails.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:headroom:human-oversight-guardrails","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"headroom → human-oversight-guardrails","text":"Technology Headroom (headroom): Headroom is a **human-in-the-loop firewall for AI coding agents** and a **measurement study of when to escalate to humans**. A worker agent proposes actions; a guardian (deterministic rules + LLM risk score) classifies each as **safe / approval-required / blocked**. Risky actions pause via LangGraph `interrupt()` until a human decides; every decision is audit-logged on a dashboard with a calibrati. Maps to research feature «Human Oversight & Guardrails» (human-oversight-guardrails). Landscape category: Agentic → Guardrails.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:headroom:r2-13-governance-policy","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"headroom → r2-13-governance-policy","text":"Technology Headroom (headroom): Headroom is a **human-in-the-loop firewall for AI coding agents** and a **measurement study of when to escalate to humans**. A worker agent proposes actions; a guardian (deterministic rules + LLM risk score) classifies each as **safe / approval-required / blocked**. Risky actions pause via LangGraph `interrupt()` until a human decides; every decision is audit-logged on a dashboard with a calibrati. Maps to research feature «T13 — Governance, Policy Gates, Trust» (r2-13-governance-policy). Landscape category: Agentic → Guardrails.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:headroom:r2-16-approval-calibration","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"headroom → r2-16-approval-calibration","text":"Technology Headroom (headroom): Headroom is a **human-in-the-loop firewall for AI coding agents** and a **measurement study of when to escalate to humans**. A worker agent proposes actions; a guardian (deterministic rules + LLM risk score) classifies each as **safe / approval-required / blocked**. Risky actions pause via LangGraph `interrupt()` until a human decides; every decision is audit-logged on a dashboard with a calibrati. Maps to research feature «T16 — Approval Policy Calibration» (r2-16-approval-calibration). Landscape category: Agentic → Guardrails.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:headroom:security-identity-governance","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"headroom → security-identity-governance","text":"Technology Headroom (headroom): Headroom is a **human-in-the-loop firewall for AI coding agents** and a **measurement study of when to escalate to humans**. A worker agent proposes actions; a guardian (deterministic rules + LLM risk score) classifies each as **safe / approval-required / blocked**. Risky actions pause via LangGraph `interrupt()` until a human decides; every decision is audit-logged on a dashboard with a calibrati. Maps to research feature «Security, Identity & Governance» (security-identity-governance). Landscape category: Agentic → Guardrails.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:headroom:wt-human-policy","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"headroom → wt-human-policy","text":"Technology Headroom (headroom): Headroom is a **human-in-the-loop firewall for AI coding agents** and a **measurement study of when to escalate to humans**. A worker agent proposes actions; a guardian (deterministic rules + LLM risk score) classifies each as **safe / approval-required / blocked**. Risky actions pause via LangGraph `interrupt()` until a human decides; every decision is audit-logged on a dashboard with a calibrati. Maps to research feature «Workflow Tools — Human & Policy» (wt-human-policy). Landscape category: Agentic → Guardrails.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_features:headroom","kind":"tech_features","path":"technologies/headroom/features.md","title":"Headroom · features","text":"$5e","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:headroom:earned-autonomy-contrast:45394d826fca","kind":"tech_quote","path":"technologies/headroom/science/sources/earned-autonomy-contrast.md","title":"Headroom · earned-autonomy-contrast","text":"Headroom (headroom, Agentic → Guardrails). The Earned Autonomy Protocol is a governance framework for AI systems in consequential domains: trust is earned through evidence, not assumed at deployment.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:headroom:earned-autonomy-contrast:cb1ca2f2320a","kind":"tech_quote","path":"technologies/headroom/science/sources/earned-autonomy-contrast.md","title":"Headroom · earned-autonomy-contrast","text":"Headroom (headroom, Agentic → Guardrails). For developing, debugging, and deploying AI agents and LLM applications, see LangSmith.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:headroom:earned-autonomy-contrast:e839061d4ad6","kind":"tech_quote","path":"technologies/headroom/science/sources/earned-autonomy-contrast.md","title":"Headroom · earned-autonomy-contrast","text":"Headroom (headroom, Agentic → Guardrails). Headroom is a human-in-the-loop firewall for coding agents that measures when to trust the human — oversight as resource allocation, not just classification.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:headroom:headroom-github:ba4cf3b7f564","kind":"tech_quote","path":"technologies/headroom/science/sources/headroom-github.md","title":"Headroom · headroom-github","text":"Headroom (headroom, Agentic → Guardrails). Open Policy Agent (OPA) is an open source, general-purpose policy engine.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:headroom:headroom-github:c7e8b5342c5c","kind":"tech_quote","path":"technologies/headroom/science/sources/headroom-github.md","title":"Headroom · headroom-github","text":"Headroom (headroom, Agentic → Guardrails). LangChain is a framework for building agents and LLM-powered applications.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:headroom:langgraph-interrupt:0213015ca7c3","kind":"tech_quote","path":"technologies/headroom/science/sources/langgraph-interrupt.md","title":"Headroom · langgraph-interrupt","text":"Headroom (headroom, Agentic → Guardrails). Open Policy Agent (OPA) is an open source, general-purpose policy engine that enables unified, context-aware policy enforcement across the entire stack.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:headroom:langgraph-interrupt:e3b42894c831","kind":"tech_quote","path":"technologies/headroom/science/sources/langgraph-interrupt.md","title":"Headroom · langgraph-interrupt","text":"Headroom (headroom, Agentic → Guardrails). The agent engineering platform.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_readme:headroom","kind":"tech_readme","path":"technologies/headroom/README.md","title":"Headroom","text":"$5f","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_science_source:headroom:earned-autonomy-contrast","kind":"tech_science_source","path":"technologies/headroom/science/sources/earned-autonomy-contrast.md","title":"Headroom · earned-autonomy-contrast","text":"$60","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_science_source:headroom:headroom-github","kind":"tech_science_source","path":"technologies/headroom/science/sources/headroom-github.md","title":"Headroom · headroom-github","text":"Headroom · source headroom-github\n\n# turangenesis/headroom\n\n| Field | Value |\n|---|---|\n| **ID** | `headroom-github` |\n| **Type** | `repo-readme` |\n| **URL** | https://github.com/turangenesis/headroom |\n| **DOI / arXiv / cite** | turangenesis/headroom |\n| **Authors / Org** | turangenesis |\n| **Year** | 2026 |\n| **Evidence** | MODERATE |\n| **Retrieved** | 2026-09-13 |\n\n## Relevance\n\nPrimary repo.\n\n## Quotes\n\n> \"Open Policy Agent (OPA) is an open source, general-purpose policy engine.\"\n> — headroom primary (deduped)\n\n> \"LangChain is a framework for building agents and LLM-powered applications.\"\n> — headroom primary (deduped)\n\n\n## Images\n\n_None attached._","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_science_source:headroom:langgraph-interrupt","kind":"tech_science_source","path":"technologies/headroom/science/sources/langgraph-interrupt.md","title":"Headroom · langgraph-interrupt","text":"Headroom · source langgraph-interrupt\n\n# LangGraph interrupt contrast\n\n| Field | Value |\n|---|---|\n| **ID** | `langgraph-interrupt` |\n| **Type** | `docs` |\n| **URL** | https://langchain-ai.github.io/langgraph/ |\n| **DOI / arXiv / cite** | LangGraph contrast |\n| **Authors / Org** | LangChain |\n| **Year** | 2026 |\n| **Evidence** | MODERATE |\n| **Retrieved** | 2026-09-13 |\n\n## Relevance\n\nDurable HITL interrupt vs calibrated spend of oversight.\n\n## Quotes\n\n> \"Open Policy Agent (OPA) is an open source, general-purpose policy engine that enables unified, context-aware policy enforcement across the entire stack.\"\n> — headroom primary (deduped)\n\n> \"The agent engineering platform.\"\n> — headroom primary (deduped)\n\n\n## Images\n\n_None attached._","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:headroom:evidence","kind":"tech_section","path":"technologies/headroom/README.md","title":"Headroom · Evidence","text":"Headroom — Evidence\n\n| Claim area | Source id | Strength |\n|---|---|---|\n| HITL firewall / measurement framing | `science/sources/headroom-github.md` | MODERATE |\n| LangGraph interrupt pattern | `science/sources/langgraph-interrupt.md` | MODERATE |\n| Autonomy / oversight contrast | `science/sources/earned-autonomy-contrast.md` | MODERATE |","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:headroom:features-map","kind":"tech_section","path":"technologies/headroom/README.md","title":"Headroom · Features map","text":"Headroom — Features map\n\nWhich research features may use this technology: [`features.md`](./features.md) (9 mapped).\n\n","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:headroom:github-map","kind":"tech_section","path":"technologies/headroom/README.md","title":"Headroom · GitHub map","text":"Headroom — GitHub map\n\nGitHub repositories for **Headroom** (several allowed per technology).\n\n| Role | Repo | ★ | Lang | License | Pushed |\n|---|---|--:|---|---|---|\n| `primary` | [turangenesis/headroom](https://github.com/turangenesis/headroom) | 2 | Python | MIT | 2026-06-21 |\n\n```mermaid\nflowchart LR\n Tech[\"Headroom\"]\n Tech -->|primary| headroom[\"turangenesis/headroom\"]\n Tech --> Cell[\"Agentic / Guardrails\"]\n Cell --> Index[technologies/GITHUB_MAP.md]\n```\n\n> More human oversight can make an AI agent less safe. Headroom is a human-in-the-loop firewall for coding agents that measures when to trust the human: oversight as resource allocation, not just classification.\n\n**Repos mapped:** 1 · edit `_source/github-repos.overrides.json` to add more.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:headroom:how-it-works","kind":"tech_section","path":"technologies/headroom/README.md","title":"Headroom · How it works","text":"Headroom — How it works\n\nWorker proposes → guardian scores → SAFE runs, BLOCKED denies, APPROVAL pauses for human → resume/abort → append-only audit log. Calibration sweeps the risk threshold to plot missed-danger vs false-alarm under asymmetric cost. MCP mode lets other agents ask Headroom before acting (cooperative enforcement).\n\n1. Run dashboard (`uvicorn headroom.api:app`) or MCP server. \n2. Agent submits actions for review. \n3. Guardian returns allow / block / pending. \n4. Humans approve on the dashboard; logs feed calibration evals.\n\n```mermaid\nflowchart TD\n W[Worker agent] --> G[Guardian rules + LLM]\n G -->|SAFE| Run[Execute]\n G -->|BLOCKED| Deny[Deny]\n G -->|APPROVAL| H[Human interrupt]\n H --> Run\n H --> Deny\n G --> Log[Audit log / dashboard]\n```","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:headroom:limits-failure-modes","kind":"tech_section","path":"technologies/headroom/README.md","title":"Headroom · Limits & failure modes","text":"Headroom — Limits & failure modes\n\n- **Cooperative MCP:** agents must ask — not a hard sandbox/host hook (roadmap notes enforcement ladder). \n- **Tiny star / research scope:** applied measurement project, not a novel-theory paper or enterprise product. \n- **LLM judge cost/noise:** calibration depends on labels and models — re-run on your action set. \n- **Not the ledger:** approvals are gates, not declared company configuration.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:headroom:links","kind":"tech_section","path":"technologies/headroom/README.md","title":"Headroom · Links","text":"Headroom — Links\n\n**URL:** https://turangenesis.github.io/headroom/ \n**Repo:** https://github.com/turangenesis/headroom \n**Docs:** https://github.com/turangenesis/headroom \n\n**Folder:** `headroom` \n**Wiki-status:** `deep` \n**Updated:** 2026-09-14","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:headroom:scientific-notes","kind":"tech_section","path":"technologies/headroom/README.md","title":"Headroom · Scientific notes","text":"Headroom — Scientific notes\n\nScientific pack: **3 sources**, quotes per source, image copyright register.\n\n→ [`science/README.md`](./science/README.md) · schema [`../SCIENCE.md`](../SCIENCE.md)","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:headroom:what-it-is","kind":"tech_section","path":"technologies/headroom/README.md","title":"Headroom · What it is","text":"Headroom — What it is\n\nHeadroom is a **human-in-the-loop firewall for AI coding agents** and a **measurement study of when to escalate to humans**. A worker agent proposes actions; a guardian (deterministic rules + LLM risk score) classifies each as **safe / approval-required / blocked**. Risky actions pause via LangGraph `interrupt()` until a human decides; every decision is audit-logged on a dashboard with a calibration dial. Separately, eval scripts measure safety/utility tradeoffs, label noise (Fleiss’ κ ≈ 0.52 on a persona proxy), and an inverted-U “oversight capacity” story. Also ships as an **MCP server** so external agents can `submit_action_for_review`. MIT; small research repo.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:headroom:when-to-use-skip","kind":"tech_section","path":"technologies/headroom/README.md","title":"Headroom · When to use / skip","text":"Headroom — When to use / skip\n\n- **Use when:** coding/ops agents need **HITL action gates with calibrated escalation**, or you are researching oversight fatigue. \n- **Skip when:** you only need prompt/output validators (**guardrails-ai**, **nemo-guardrails**) or archived scanners (**llm-guard**). \n- **Prefer instead:** NeMo/Guardrails AI for conversational I/O rails; production IAM/policy products for enterprise SSO — Headroom is measurement + firewall research.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:headroom:why-it-matters-here","kind":"tech_section","path":"technologies/headroom/README.md","title":"Headroom · Why it matters here","text":"Headroom — Why it matters here\n\nPolicy gates that escalate *everything* burn reviewer attention until dangerous actions slip through rubber stamps. Headroom’s research value for an agentic OS is treating **escalation as a budgeted resource**, not only a classifier — aligned with human gates on world-changing tools. Prefer **NeMo** / **Guardrails AI** for LLM text/dialog rails; use Headroom when the object of control is **agent actions** (deploy, delete, push) and you need measured HITL thresholds.","quality":null,"needs_deepen":false,"deepen_reason":null}],"corpus_tags":{"category":["Agentic → Guardrails"],"dedication":["open-source"],"feature":["access-vs-authority","autonomy-levels","emergency-control","exception-first-participation","human-oversight-guardrails","r2-13-governance-policy","r2-16-approval-calibration","security-identity-governance","wt-human-policy"],"kind":["company_review","map_edge","tech_features","tech_quote","tech_readme","tech_science_source","tech_section"],"needs_deepen":["false"],"quality":["ok"],"section":["Evidence","Features map","GitHub map","How it works","Limits & failure modes","Links","Scientific notes","What it is","When to use / skip","Why it matters 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It also supports async OpenLLMetry-style logging and framework integrations (LangChain, Vercel AI SDK, and others). Open-sourc. Maps to research feature «Cost, Latency & Operational Economics» (cost-latency-ops). Landscape category: Agentic → Observability.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:helicone:observability-auditability","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"helicone → observability-auditability","text":"Technology Helicone (helicone): Helicone is an **AI gateway and LLM observability** platform: point OpenAI-compatible clients at Helicone’s gateway for logging, cost/latency, sessions/traces, prompt management, playground, and routing/fallbacks across many models — often with a one-line `baseURL` change. It also supports async OpenLLMetry-style logging and framework integrations (LangChain, Vercel AI SDK, and others). Open-sourc. 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Landscape category: Agentic → Observability.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:helicone:r2-14-process-mining","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"helicone → r2-14-process-mining","text":"Technology Helicone (helicone): Helicone is an **AI gateway and LLM observability** platform: point OpenAI-compatible clients at Helicone’s gateway for logging, cost/latency, sessions/traces, prompt management, playground, and routing/fallbacks across many models — often with a one-line `baseURL` change. It also supports async OpenLLMetry-style logging and framework integrations (LangChain, Vercel AI SDK, and others). Open-sourc. Maps to research feature «T14 — Process & Agent Behavior Mining» (r2-14-process-mining). Landscape category: Agentic → Observability.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:helicone:r2-34-decision-lineage","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"helicone → r2-34-decision-lineage","text":"Technology Helicone (helicone): Helicone is an **AI gateway and LLM observability** platform: point OpenAI-compatible clients at Helicone’s gateway for logging, cost/latency, sessions/traces, prompt management, playground, and routing/fallbacks across many models — often with a one-line `baseURL` change. It also supports async OpenLLMetry-style logging and framework integrations (LangChain, Vercel AI SDK, and others). Open-sourc. Maps to research feature «T34 — Decision Lineage as First-Class Data» (r2-34-decision-lineage). Landscape category: Agentic → Observability.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:helicone:r2-38-abm-span-mapping","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"helicone → r2-38-abm-span-mapping","text":"Technology Helicone (helicone): Helicone is an **AI gateway and LLM observability** platform: point OpenAI-compatible clients at Helicone’s gateway for logging, cost/latency, sessions/traces, prompt management, playground, and routing/fallbacks across many models — often with a one-line `baseURL` change. It also supports async OpenLLMetry-style logging and framework integrations (LangChain, Vercel AI SDK, and others). Open-sourc. Maps to research feature «T38 — `secretary_spans` → ABM/XES Schema Mapping» (r2-38-abm-span-mapping). Landscape category: Agentic → Observability.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:helicone:r2-47-trace-maturity-gate","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"helicone → r2-47-trace-maturity-gate","text":"Technology Helicone (helicone): Helicone is an **AI gateway and LLM observability** platform: point OpenAI-compatible clients at Helicone’s gateway for logging, cost/latency, sessions/traces, prompt management, playground, and routing/fallbacks across many models — often with a one-line `baseURL` change. It also supports async OpenLLMetry-style logging and framework integrations (LangChain, Vercel AI SDK, and others). Open-sourc. Maps to research feature «T47 — Complete-Trace Counter + Maturity Gate» (r2-47-trace-maturity-gate). Landscape category: Agentic → Observability.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:helicone:wt-runtime","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"helicone → wt-runtime","text":"Technology Helicone (helicone): Helicone is an **AI gateway and LLM observability** platform: point OpenAI-compatible clients at Helicone’s gateway for logging, cost/latency, sessions/traces, prompt management, playground, and routing/fallbacks across many models — often with a one-line `baseURL` change. It also supports async OpenLLMetry-style logging and framework integrations (LangChain, Vercel AI SDK, and others). Open-sourc. Maps to research feature «Workflow Tools — Runtime» (wt-runtime). Landscape category: Agentic → Observability.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_features:helicone","kind":"tech_features","path":"technologies/helicone/features.md","title":"Helicone · features","text":"$61","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:helicone:helicone-docs:3dc3c62a01fe","kind":"tech_quote","path":"technologies/helicone/science/sources/helicone-docs.md","title":"Helicone · helicone-docs","text":"Helicone (helicone, Agentic → Observability). Helicone is an AI Gateway & LLM observability platform.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:helicone:helicone-docs:990c66ec1760","kind":"tech_quote","path":"technologies/helicone/science/sources/helicone-docs.md","title":"Helicone · helicone-docs","text":"Helicone (helicone, Agentic → Observability). Open source LLM observability platform. One line of code to monitor, evaluate, and experiment.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:helicone:helicone-docs:d6fbb8ec861f","kind":"tech_quote","path":"technologies/helicone/science/sources/helicone-docs.md","title":"Helicone · helicone-docs","text":"Helicone (helicone, Agentic → Observability). Based on OpenTelemetry to increase compatibility and reduce vendor lock-in","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:helicone:helicone-github:9e43ddfea7a5","kind":"tech_quote","path":"technologies/helicone/science/sources/helicone-github.md","title":"Helicone · helicone-github","text":"Helicone (helicone, Agentic → Observability). Langfuse is an open-source LLM engineering platform that helps teams collaboratively debug, analyze, and iterate on their LLM applications.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:helicone:helicone-github:c68b1e1ec81d","kind":"tech_quote","path":"technologies/helicone/science/sources/helicone-github.md","title":"Helicone · helicone-github","text":"Helicone (helicone, Agentic → Observability). Open source LLM observability platform. One line of code to monitor, evaluate, and experiment. YC W23","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:helicone:langfuse-contrast:bca599b9971d","kind":"tech_quote","path":"technologies/helicone/science/sources/langfuse-contrast.md","title":"Helicone · langfuse-contrast","text":"Helicone (helicone, Agentic → Observability). Observability is essential for understanding and debugging LLM applications. Unlike traditional software, LLM applications involve complex, non-deterministic interactions that can be challenging to monitor and debug.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:helicone:langfuse-contrast:d2a98e7364bf","kind":"tech_quote","path":"technologies/helicone/science/sources/langfuse-contrast.md","title":"Helicone · langfuse-contrast","text":"Helicone (helicone, Agentic → Observability). One line of code to monitor, evaluate, and experiment.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_readme:helicone","kind":"tech_readme","path":"technologies/helicone/README.md","title":"Helicone","text":"$62","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_science_source:helicone:helicone-docs","kind":"tech_science_source","path":"technologies/helicone/science/sources/helicone-docs.md","title":"Helicone · helicone-docs","text":"Helicone · source helicone-docs\n\n# Helicone documentation\n\n| Field | Value |\n|---|---|\n| **ID** | `helicone-docs` |\n| **Type** | `docs` |\n| **URL** | https://docs.helicone.ai/ |\n| **DOI / arXiv / cite** | Helicone docs |\n| **Authors / Org** | Helicone |\n| **Year** | 2026 |\n| **Evidence** | MODERATE |\n| **Retrieved** | 2026-09-13 |\n\n## Relevance\n\nDocs.\n\n## Quotes\n\n> \"Open source LLM observability platform. One line of code to monitor, evaluate, and experiment.\"\n> — helicone primary (deduped)\n\n> \"Helicone is an AI Gateway & LLM observability platform.\"\n> — helicone primary (deduped)\n\n> \"Based on OpenTelemetry to increase compatibility and reduce vendor lock-in\"\n> — helicone primary (deduped)\n\n\n## Images\n\n_None attached._","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_science_source:helicone:helicone-github","kind":"tech_science_source","path":"technologies/helicone/science/sources/helicone-github.md","title":"Helicone · helicone-github","text":"Helicone · source helicone-github\n\n# Helicone/helicone\n\n| Field | Value |\n|---|---|\n| **ID** | `helicone-github` |\n| **Type** | `repo-readme` |\n| **URL** | https://github.com/Helicone/helicone |\n| **DOI / arXiv / cite** | Helicone/helicone |\n| **Authors / Org** | Helicone |\n| **Year** | 2026 |\n| **Evidence** | MODERATE |\n| **Retrieved** | 2026-09-13 |\n\n## Relevance\n\nPrimary repo.\n\n## Quotes\n\n> \"Open source LLM observability platform. One line of code to monitor, evaluate, and experiment. YC W23\"\n> — helicone primary (deduped)\n\n> \"Langfuse is an open-source LLM engineering platform that helps teams collaboratively debug, analyze, and iterate on their LLM applications.\"\n> — helicone primary (deduped)\n\n\n## Images\n\n_None attached._","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_science_source:helicone:langfuse-contrast","kind":"tech_science_source","path":"technologies/helicone/science/sources/langfuse-contrast.md","title":"Helicone · langfuse-contrast","text":"Helicone · source langfuse-contrast\n\n# Langfuse contrast\n\n| Field | Value |\n|---|---|\n| **ID** | `langfuse-contrast` |\n| **Type** | `repo-readme` |\n| **URL** | https://github.com/langfuse/langfuse |\n| **DOI / arXiv / cite** | Langfuse contrast |\n| **Authors / Org** | Langfuse |\n| **Year** | 2026 |\n| **Evidence** | MODERATE |\n| **Retrieved** | 2026-09-13 |\n\n## Relevance\n\nPeer LLM observability.\n\n## Quotes\n\n> \"One line of code to monitor, evaluate, and experiment.\"\n> — helicone primary (deduped)\n\n> \"Observability is essential for understanding and debugging LLM applications. Unlike traditional software, LLM applications involve complex, non-deterministic interactions that can be challenging to monitor and debug.\"\n> — helicone primary (deduped)\n\n\n## Images\n\n_None attached._","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:helicone:evidence","kind":"tech_section","path":"technologies/helicone/README.md","title":"Helicone · Evidence","text":"Helicone — Evidence\n\n| Claim area | Source id | Strength |\n|---|---|---|\n| Gateway / observability role | `science/sources/helicone-github.md` | MODERATE |\n| Docs / features | `science/sources/helicone-docs.md` | MODERATE |\n| Contrast vs Langfuse | `science/sources/langfuse-contrast.md` | MODERATE |","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:helicone:features-map","kind":"tech_section","path":"technologies/helicone/README.md","title":"Helicone · Features map","text":"Helicone — Features map\n\nWhich research features may use this technology: [`features.md`](./features.md) (8 mapped).\n\n","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:helicone:github-map","kind":"tech_section","path":"technologies/helicone/README.md","title":"Helicone · GitHub map","text":"$63","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:helicone:how-it-works","kind":"tech_section","path":"technologies/helicone/README.md","title":"Helicone · How it works","text":"Helicone — How it works\n\nClients send completions through Helicone’s AI Gateway (or async OpenLLMetry logging). Helicone stores/analyzes requests; operators inspect sessions, costs, and prompts in the UI.\n\n1. Obtain API key (cloud) or self-host. \n2. Set client `baseURL` to Helicone gateway (or async logger). \n3. Call models as usual; view logs/dashboard. \n4. Optionally version prompts and export analytics.\n\n```mermaid\nflowchart LR\n Client[App / agent SDK] --> GW[Helicone AI Gateway]\n GW --> Providers[100+ model providers]\n GW --> Obs[Logs · cost · sessions]\n```","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:helicone:limits-failure-modes","kind":"tech_section","path":"technologies/helicone/README.md","title":"Helicone · Limits & failure modes","text":"Helicone — Limits & failure modes\n\n- **Proxy in the path:** gateway outages/latency become your outages. \n- **Not case audit:** request logs ≠ propose→gate→execute ledger. \n- **Self-host complexity:** multi-service stack (web, worker, ClickHouse, Supabase, Minio). \n- **Does not replace IAM/policy gates.**","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:helicone:links","kind":"tech_section","path":"technologies/helicone/README.md","title":"Helicone · Links","text":"Helicone — Links\n\n**URL:** https://www.helicone.ai/ \n**Repo:** https://github.com/Helicone/helicone \n**Docs:** https://docs.helicone.ai/ \n\n**Folder:** `helicone` \n**Wiki-status:** `deep` \n**Updated:** 2026-09-14","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:helicone:scientific-notes","kind":"tech_section","path":"technologies/helicone/README.md","title":"Helicone · Scientific notes","text":"Helicone — Scientific notes\n\nScientific pack: **3 sources**, quotes per source, image copyright register.\n\n→ [`science/README.md`](./science/README.md) · schema [`../SCIENCE.md`](../SCIENCE.md)","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:helicone:what-it-is","kind":"tech_section","path":"technologies/helicone/README.md","title":"Helicone · What it is","text":"Helicone — What it is\n\nHelicone is an **AI gateway and LLM observability** platform: point OpenAI-compatible clients at Helicone’s gateway for logging, cost/latency, sessions/traces, prompt management, playground, and routing/fallbacks across many models — often with a one-line `baseURL` change. It also supports async OpenLLMetry-style logging and framework integrations (LangChain, Vercel AI SDK, and others). Open-source (Apache-2.0) with self-host (Docker/Helm) and cloud; SOC2/GDPR claims for enterprise. YC W23.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:helicone:when-to-use-skip","kind":"tech_section","path":"technologies/helicone/README.md","title":"Helicone · When to use / skip","text":"Helicone — When to use / skip\n\n- **Use when:** you want **minimal-code LLM request observability** and/or multi-provider routing. \n- **Skip when:** you need deep agent graph evals (**langfuse**, **phoenix**, **comet-optik**) or pure OTEL instrumentation (**openllmetry**). \n- **Prefer instead:** OpenLLMetry if Datadog/Grafana already own backends.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:helicone:why-it-matters-here","kind":"tech_section","path":"technologies/helicone/README.md","title":"Helicone · Why it matters here","text":"Helicone — Why it matters here\n\nFastest path to **request-level LLM logs** without deep span instrumentation. Prefer Helicone for gateway/proxy adoption; prefer **Langfuse/Phoenix/Opik** for richer agent-tree debugging and eval workflows; prefer **OpenLLMetry** to keep vendor-neutral OTEL into existing ops stacks.","quality":null,"needs_deepen":false,"deepen_reason":null}],"corpus_tags":{"category":["Agentic → Observability"],"dedication":["open-core"],"feature":["cost-latency-ops","observability-auditability","r2-09-self-monitoring","r2-14-process-mining","r2-34-decision-lineage","r2-38-abm-span-mapping","r2-47-trace-maturity-gate","wt-runtime"],"kind":["map_edge","tech_features","tech_quote","tech_readme","tech_science_source","tech_section"],"needs_deepen":["false"],"quality":["ok"],"section":["Evidence","Features map","GitHub map","How it works","Limits & failure modes","Links","Scientific notes","What it is","When to use / skip","Why it matters here"],"slug":["helicone"],"source_id":["helicone-docs","helicone-github","langfuse-contrast"],"technology":["helicone"]},"mermaid":[{"title":"How 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Targets LLM traffic governance in cloud-native stacks. Candidate LLM gateway when preferring CNCF-style gateways over app-level proxies like LiteLLM.. Maps to research feature «APIs, SDKs & MCP Extensibility» (apis-sdks-mcp-extensibility). Landscape category: Agentic → LLM Gateway.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:higress:bring-your-own-model","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"higress → bring-your-own-model","text":"Technology higress (higress): Higress (Alibaba) is an AI-native API gateway on Envoy/Istio patterns with WASM extensibility and AI route features. Targets LLM traffic governance in cloud-native stacks. Candidate LLM gateway when preferring CNCF-style gateways over app-level proxies like LiteLLM.. Maps to research feature «Bring Your Own Model» (bring-your-own-model). Landscape category: Agentic → LLM Gateway.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:higress:cost-latency-ops","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"higress → cost-latency-ops","text":"Technology higress (higress): Higress (Alibaba) is an AI-native API gateway on Envoy/Istio patterns with WASM extensibility and AI route features. Targets LLM traffic governance in cloud-native stacks. Candidate LLM gateway when preferring CNCF-style gateways over app-level proxies like LiteLLM.. Maps to research feature «Cost, Latency & Operational Economics» (cost-latency-ops). Landscape category: Agentic → LLM Gateway.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:higress:deployment-data-residency","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"higress → deployment-data-residency","text":"Technology higress (higress): Higress (Alibaba) is an AI-native API gateway on Envoy/Istio patterns with WASM extensibility and AI route features. Targets LLM traffic governance in cloud-native stacks. Candidate LLM gateway when preferring CNCF-style gateways over app-level proxies like LiteLLM.. Maps to research feature «Deployment & Data Residency» (deployment-data-residency). Landscape category: Agentic → LLM Gateway.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:higress:model-flexibility-routing","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"higress → model-flexibility-routing","text":"Technology higress (higress): Higress (Alibaba) is an AI-native API gateway on Envoy/Istio patterns with WASM extensibility and AI route features. Targets LLM traffic governance in cloud-native stacks. Candidate LLM gateway when preferring CNCF-style gateways over app-level proxies like LiteLLM.. Maps to research feature «Model Flexibility & Routing» (model-flexibility-routing). Landscape category: Agentic → LLM Gateway.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_features:higress","kind":"tech_features","path":"technologies/higress/features.md","title":"higress · features","text":"# Features using `higress`\n\n**Technology:** [higress](./README.md)\n**Category:** Agentic → LLM Gateway\n**Mapped features:** 5\n**Map:** [`../features/_source/feature-technology-map.md`](../features/_source/feature-technology-map.md)\n\nThis technology is a **candidate building block** for the features below (research map — not a claim that the product currently depends on it).\n\n| Feature | Title |\n|---|---|\n| [`apis-sdks-mcp-extensibility`](../features/apis-sdks-mcp-extensibility/README.md) | APIs, SDKs & MCP Extensibility |\n| [`bring-your-own-model`](../features/bring-your-own-model/README.md) | Bring Your Own Model |\n| [`cost-latency-ops`](../features/cost-latency-ops/README.md) | Cost, Latency & Operational Economics |\n| [`deployment-data-residency`](../features/deployment-data-residency/README.md) | Deployment & Data Residency |\n| [`model-flexibility-routing`](../features/model-flexibility-routing/README.md) | Model Flexibility & Routing |\n\n","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:higress:envoy-contrast:76e3ba5b5796","kind":"tech_quote","path":"technologies/higress/science/sources/envoy-contrast.md","title":"higress · envoy-contrast","text":"higress (higress, Agentic → LLM Gateway). Candidate LLM gateway when preferring CNCF-style gateways over app-level proxies like LiteLLM.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:higress:envoy-contrast:8e3bba91f804","kind":"tech_quote","path":"technologies/higress/science/sources/envoy-contrast.md","title":"higress · envoy-contrast","text":"higress (higress, Agentic → LLM Gateway). Higress (Alibaba) is an AI-native API gateway on Envoy/Istio patterns with WASM extensibility and AI route features. Targets LLM traffic governance in cloud-native stacks.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:higress:envoy-contrast:bfd5963645b7","kind":"tech_quote","path":"technologies/higress/science/sources/envoy-contrast.md","title":"higress · envoy-contrast","text":"higress (higress, Agentic → LLM Gateway). **Operating caveats.** Do not let this layer silently become the system of record for company process, authority, or money movement. Those belong in declared configuration and a ledger with human gates. Promote from this research folder to a dependency only after a primary-source read and a failing test that proves the","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:higress:higress-github:7f905628a519","kind":"tech_quote","path":"technologies/higress/science/sources/higress-github.md","title":"higress · higress-github","text":"higress (higress, Agentic → LLM Gateway). Envoy is hosted by the Cloud Native Computing Foundation (CNCF).","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:higress:higress-github:7fd7a2b26204","kind":"tech_quote","path":"technologies/higress/science/sources/higress-github.md","title":"higress · higress-github","text":"higress (higress, Agentic → LLM Gateway). 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→ closed-execution-loop","text":"Technology Instructor (instructor): Instructor gives **reliable structured outputs from LLMs** via Pydantic: define a `response_model`, call `instructor.from_provider(...)`, and get validated typed objects — with automatic retries on validation failure, streaming partials, nested models, and the same API across major providers (OpenAI, Anthropic, Google, Ollama, …). Multi-language ports exist (TS, Go, Ruby, Elixir, Rust). MIT. Autho. Maps to research feature «Closed Execution Loop» (closed-execution-loop). Landscape category: Agentic → Structured Output.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:instructor:job-finance","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"instructor → job-finance","text":"Technology Instructor (instructor): Instructor gives **reliable structured outputs from LLMs** via Pydantic: define a `response_model`, call `instructor.from_provider(...)`, and get validated typed objects — with automatic retries on validation failure, streaming partials, nested models, and the same API across major providers (OpenAI, Anthropic, Google, Ollama, …). Multi-language ports exist (TS, Go, Ruby, Elixir, Rust). MIT. Autho. Maps to research feature «Finance & accounting agents» (job-finance). Landscape category: Agentic → Structured Output.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:instructor:job-insurance","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"instructor → job-insurance","text":"Technology Instructor (instructor): Instructor gives **reliable structured outputs from LLMs** via Pydantic: define a `response_model`, call `instructor.from_provider(...)`, and get validated typed objects — with automatic retries on validation failure, streaming partials, nested models, and the same API across major providers (OpenAI, Anthropic, Google, Ollama, …). Multi-language ports exist (TS, Go, Ruby, Elixir, Rust). MIT. Autho. Maps to research feature «Insurance agents» (job-insurance). Landscape category: Agentic → Structured Output.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:instructor:outcome-verification","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"instructor → outcome-verification","text":"Technology Instructor (instructor): Instructor gives **reliable structured outputs from LLMs** via Pydantic: define a `response_model`, call `instructor.from_provider(...)`, and get validated typed objects — with automatic retries on validation failure, streaming partials, nested models, and the same API across major providers (OpenAI, Anthropic, Google, Ollama, …). Multi-language ports exist (TS, Go, Ruby, Elixir, Rust). MIT. Autho. Maps to research feature «Outcome Verification» (outcome-verification). Landscape category: Agentic → Structured Output.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:instructor:testing-debugging-optimization","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"instructor → testing-debugging-optimization","text":"Technology Instructor (instructor): Instructor gives **reliable structured outputs from LLMs** via Pydantic: define a `response_model`, call `instructor.from_provider(...)`, and get validated typed objects — with automatic retries on validation failure, streaming partials, nested models, and the same API across major providers (OpenAI, Anthropic, Google, Ollama, …). Multi-language ports exist (TS, Go, Ruby, Elixir, Rust). MIT. Autho. Maps to research feature «Testing, Debugging & Optimization» (testing-debugging-optimization). Landscape category: Agentic → Structured Output.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:instructor:wt-intelligence","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"instructor → wt-intelligence","text":"Technology Instructor (instructor): Instructor gives **reliable structured outputs from LLMs** via Pydantic: define a `response_model`, call `instructor.from_provider(...)`, and get validated typed objects — with automatic retries on validation failure, streaming partials, nested models, and the same API across major providers (OpenAI, Anthropic, Google, Ollama, …). Multi-language ports exist (TS, Go, Ruby, Elixir, Rust). MIT. Autho. Maps to research feature «Workflow Tools — Intelligence» (wt-intelligence). Landscape category: Agentic → Structured Output.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_features:instructor","kind":"tech_features","path":"technologies/instructor/features.md","title":"Instructor · features","text":"$68","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:instructor:instructor-docs:34d6c48cb3cc","kind":"tech_quote","path":"technologies/instructor/science/sources/instructor-docs.md","title":"Instructor · instructor-docs","text":"Instructor (instructor, Agentic → Structured Output). Built on top of Pydantic, Instructor provides type-safe data extraction with automatic validation, retries, and streaming support.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:instructor:instructor-docs:b3e3b2fcbc44","kind":"tech_quote","path":"technologies/instructor/science/sources/instructor-docs.md","title":"Instructor · instructor-docs","text":"Instructor (instructor, Agentic → Structured Output). Extract structured data from any LLM with type safety, validation, and automatic retries.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:instructor:instructor-docs:d493a4cfa737","kind":"tech_quote","path":"technologies/instructor/science/sources/instructor-docs.md","title":"Instructor · instructor-docs","text":"Instructor (instructor, Agentic → Structured Output). Instructor for extraction, PydanticAI for agents. Instructor shines when you need fast, schema-first extraction without extra agents.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:instructor:instructor-features:4270643d898c","kind":"tech_quote","path":"technologies/instructor/science/sources/instructor-features.md","title":"Instructor · instructor-features","text":"Instructor (instructor, Agentic → Structured Output). Automatic Retries: Built-in retry logic when validation fails - no more manual error handling","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:instructor:instructor-features:f99be4ac7942","kind":"tech_quote","path":"technologies/instructor/science/sources/instructor-features.md","title":"Instructor · instructor-features","text":"Instructor (instructor, Agentic → Structured Output). Multi-Provider: Works with OpenAI, Anthropic, Google, Mistral, Cohere, Ollama, DeepSeek, and 15+ LLM providers","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:instructor:instructor-github:3325a9019ab6","kind":"tech_quote","path":"technologies/instructor/science/sources/instructor-github.md","title":"Instructor · instructor-github","text":"Instructor (instructor, Agentic → Structured Output). Get reliable JSON from any LLM. Built on Pydantic for validation, type safety, and IDE support.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_readme:instructor","kind":"tech_readme","path":"technologies/instructor/README.md","title":"Instructor","text":"$69","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_science_source:instructor:instructor-docs","kind":"tech_science_source","path":"technologies/instructor/science/sources/instructor-docs.md","title":"Instructor · instructor-docs","text":"Instructor · source instructor-docs\n\n# Instructor documentation home\n\n| Field | Value |\n|---|---|\n| **ID** | `instructor-docs` |\n| **Type** | `docs` |\n| **URL** | https://python.useinstructor.com/ |\n| **DOI / arXiv / cite** | Instructor docs |\n| **Authors / Org** | Jason Liu / Instructor |\n| **Year** | 2026 |\n| **Evidence** | MODERATE |\n| **Retrieved** | 2026-09-13 |\n\n## Relevance\n\nPrimary docs for structured extraction library.\n\n## Quotes\n\n> \"Extract structured data from any LLM with type safety, validation, and automatic retries.\"\n\n\n> \"Built on top of Pydantic, Instructor provides type-safe data extraction with automatic validation, retries, and streaming support.\"\n\n\n> \"Instructor for extraction, PydanticAI for agents. Instructor shines when you need fast, schema-first extraction without extra agents.\"\n\n\n## Images\n\n_None attached._","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_science_source:instructor:instructor-features","kind":"tech_science_source","path":"technologies/instructor/science/sources/instructor-features.md","title":"Instructor · instructor-features","text":"Instructor · source instructor-features\n\n# Key features list\n\n| Field | Value |\n|---|---|\n| **ID** | `instructor-features` |\n| **Type** | `docs` |\n| **URL** | https://python.useinstructor.com/ |\n| **DOI / arXiv / cite** | Instructor key features |\n| **Authors / Org** | Instructor |\n| **Year** | 2026 |\n| **Evidence** | MODERATE |\n| **Retrieved** | 2026-09-13 |\n\n## Relevance\n\nRetry + multi-provider claims.\n\n## Quotes\n\n> \"Automatic Retries: Built-in retry logic when validation fails - no more manual error handling\"\n\n\n> \"Multi-Provider: Works with OpenAI, Anthropic, Google, Mistral, Cohere, Ollama, DeepSeek, and 15+ LLM providers\"\n\n\n## Images\n\n_None attached._","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_science_source:instructor:instructor-github","kind":"tech_science_source","path":"technologies/instructor/science/sources/instructor-github.md","title":"Instructor · instructor-github","text":"Instructor · source instructor-github\n\n# 567-labs/instructor\n\n| Field | Value |\n|---|---|\n| **ID** | `instructor-github` |\n| **Type** | `repo-readme` |\n| **URL** | https://github.com/567-labs/instructor |\n| **DOI / arXiv / cite** | 567-labs/instructor |\n| **Authors / Org** | 567-labs |\n| **Year** | 2026 |\n| **Evidence** | MODERATE |\n| **Retrieved** | 2026-09-13 |\n\n## Relevance\n\nOSS implementation.\n\n## Quotes\n\n> \"Get reliable JSON from any LLM. Built on Pydantic for validation, type safety, and IDE support.\"\n\n## Images\n\n_None attached._","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:instructor:document-management-map","kind":"tech_section","path":"technologies/instructor/README.md","title":"Instructor · Document management map","text":"Instructor — Document management map\n\nDMS/IDP research vendors related to this cell: [`document-management.md`](./document-management.md) (33).\n\n","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:instructor:evidence","kind":"tech_section","path":"technologies/instructor/README.md","title":"Instructor · Evidence","text":"Instructor — Evidence\n\n| Claim area | Source id | Strength |\n|---|---|---|\n| Structured extraction role | `science/sources/instructor-github.md` | MODERATE |\n| Docs / API | `science/sources/instructor-docs.md` | MODERATE |\n| Feature notes | `science/sources/instructor-features.md` | MODERATE |","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:instructor:features-map","kind":"tech_section","path":"technologies/instructor/README.md","title":"Instructor · Features map","text":"Instructor — Features map\n\nWhich research features may use this technology: [`features.md`](./features.md) (6 mapped).\n\n","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:instructor:github-map","kind":"tech_section","path":"technologies/instructor/README.md","title":"Instructor · GitHub map","text":"Instructor — GitHub map\n\nGitHub repositories for **Instructor** (several allowed per technology).\n\n| Role | Repo | ★ | Lang | License | Pushed |\n|---|---|--:|---|---|---|\n| `primary` | [567-labs/instructor](https://github.com/567-labs/instructor) | 13,879 | Python | MIT | 2026-09-11 |\n| `sdk` | [567-labs/instructor-js](https://github.com/567-labs/instructor-js) | 802 | TypeScript | MIT | 2025-01-27 |\n\n```mermaid\nflowchart LR\n Tech[\"Instructor\"]\n Tech -->|primary| instructor[\"567-labs/instructor\"]\n Tech -->|sdk| instructor_js[\"567-labs/instructor-js\"]\n Tech --> Cell[\"Agentic / Structured Output\"]\n Cell --> Index[technologies/GITHUB_MAP.md]\n```\n\n> structured outputs for llms\n\n**Repos mapped:** 2 · edit `_source/github-repos.overrides.json` to add more.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:instructor:how-it-works","kind":"tech_section","path":"technologies/instructor/README.md","title":"Instructor · How it works","text":"Instructor — How it works\n\nPatch or wrap a provider client; each completion requests a Pydantic model. On validation errors, Instructor retries with error context until success or `max_retries`.\n\n1. `pip install instructor`; define Pydantic models. \n2. `client = instructor.from_provider(\"…\")`. \n3. `create(response_model=…, messages=…)`. \n4. Use the typed object; fail closed if retries exhaust.\n\n```mermaid\nflowchart LR\n Text[Natural language] --> Inst[Instructor + provider]\n Schema[Pydantic model] --> Inst\n Inst -->|validated object| App[Agent / ledger writer]\n```","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:instructor:limits-failure-modes","kind":"tech_section","path":"technologies/instructor/README.md","title":"Instructor · Limits & failure modes","text":"Instructor — Limits & failure modes\n\n- **Retries cost tokens** and can still fail — plan a hard abort. \n- **API JSON mode ≠ proof:** provider cooperation varies; not a formal guarantee like some local engines. \n- **Not IAM/policy:** typed output can still be an unauthorized action. \n- **Not the company ledger.**","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:instructor:links","kind":"tech_section","path":"technologies/instructor/README.md","title":"Instructor · Links","text":"Instructor — Links\n\n**URL:** https://python.useinstructor.com/ \n**Repo:** https://github.com/567-labs/instructor \n**Docs:** https://python.useinstructor.com/ \n\n**Folder:** `instructor` \n**Wiki-status:** `deep` \n**Updated:** 2026-09-14","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:instructor:scientific-notes","kind":"tech_section","path":"technologies/instructor/README.md","title":"Instructor · Scientific notes","text":"Instructor — Scientific notes\n\nScientific pack: **3 sources**, quotes per source, image copyright register.\n\n→ [`science/README.md`](./science/README.md) · schema [`../SCIENCE.md`](../SCIENCE.md)","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:instructor:what-it-is","kind":"tech_section","path":"technologies/instructor/README.md","title":"Instructor · What it is","text":"Instructor — What it is\n\nInstructor gives **reliable structured outputs from LLMs** via Pydantic: define a `response_model`, call `instructor.from_provider(...)`, and get validated typed objects — with automatic retries on validation failure, streaming partials, nested models, and the same API across major providers (OpenAI, Anthropic, Google, Ollama, …). Multi-language ports exist (TS, Go, Ruby, Elixir, Rust). MIT. Authors position it for fast schema-first extraction; richer agent runtimes point to PydanticAI.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:instructor:when-to-use-skip","kind":"tech_section","path":"technologies/instructor/README.md","title":"Instructor · When to use / skip","text":"Instructor — When to use / skip\n\n- **Use when:** Python (or ports) need **schema-validated LLM extraction** across providers. \n- **Skip when:** you need logit-level grammar guarantees on open models (**outlines**, **guidance**) or a full agent framework (PydanticAI / LangGraph). \n- **Prefer instead:** Outlines for self-hosted guaranteed JSON; Guidance for interleaved constrained prompting.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:instructor:why-it-matters-here","kind":"tech_section","path":"technologies/instructor/README.md","title":"Instructor · Why it matters here","text":"Instructor — Why it matters here\n\nDefault research pick for **cheap schema gates** before anything hits durable state or side-effecting tools. Prefer **Outlines/Guidance** when you need decode-time guarantees on self-hosted models; prefer **LMQL** for research query/constraint languages; keep Instructor when the install is mostly cloud chat APIs.","quality":null,"needs_deepen":false,"deepen_reason":null}],"corpus_tags":{"category":["Agentic → Structured Output"],"dedication":["open-source"],"feature":["closed-execution-loop","job-finance","job-insurance","outcome-verification","testing-debugging-optimization","wt-intelligence"],"kind":["map_edge","tech_features","tech_quote","tech_readme","tech_science_source","tech_section"],"needs_deepen":["false"],"quality":["ok"],"section":["Document management map","Evidence","Features map","GitHub map","How it works","Limits & failure modes","Links","Scientific notes","What it is","When to use / skip","Why it matters here"],"slug":["instructor"],"source_id":["instructor-docs","instructor-features","instructor-github"],"technology":["instructor"]},"mermaid":[{"title":"How it 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Reliable, large-scale web extraction, now built to be drastically more cost-effective than any of the existing solutions.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:jina-reader:firecrawl-contrast:1a095d051090","kind":"tech_quote","path":"technologies/jina-reader/science/sources/firecrawl-contrast.md","title":"Jina Reader · firecrawl-contrast","text":"Jina Reader (jina-reader, Agentic → Search & Web Access). Media parsing: Parse and extract content from web-hosted PDFs, DOCX, and more","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:jina-reader:firecrawl-contrast:74a0417b11c8","kind":"tech_quote","path":"technologies/jina-reader/science/sources/firecrawl-contrast.md","title":"Jina Reader · firecrawl-contrast","text":"Jina Reader (jina-reader, Agentic → Search & Web Access). 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Statefulness is a first-class product claim.\n\nInteresting when you want managed long-running agents quickly; for a company OS, insist that durable business state still lands in your ledger with exportable audit.\n\n**When to reach for it.** Prefer this component when its subcategory job is the bottleneck (e.g. durable steps, structured outputs, retrieval quality, sandboxed code). Skip it when a thinner primitive already in the install covers the need — YAGNI applies to agent stacks too.\n\n**Operating caveats.** Do not let this layer silently become the system of record for company process, authority, or money movement. Those belong in declared configuration and a ledger with human gates. 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result]\n Peerag2[[ag2]]"},{"title":"GitHub map","code":"flowchart LR\n Tech[\"Julep\"]\n Tech -->|primary| julep[\"julep-ai/julep\"]\n Tech -->|samples| julep_examples[\"julep-ai/julep-examples\"]\n Tech -->|related| julep_assistant[\"julep-ai/julep-assistant\"]\n Tech -->|related| julep_fork[\"julep-ai/julep-fork\"]\n Tech --> Cell[\"Agentic / Frameworks\"]\n Cell --> Index[technologies/GITHUB_MAP.md]"}]},{"kind":"technology","slug":"jupyter-enterprise-gateway","title":"Jupyter Enterprise Gateway","one_liner":"Agentic","path":"technologies/jupyter-enterprise-gateway","tags":{"landscape.layer":{"value":"Agentic","status":null,"evidence_class":"inferred","evidence_ref":"technologies/jupyter-enterprise-gateway/README.md","evidence_note":null},"landscape.subcategory":{"value":"Code Sandbox","status":null,"evidence_class":"inferred","evidence_ref":null,"evidence_note":null},"license_tag":{"value":"Open 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missing document-management.md","severity":2}],"corpus_docs":[{"doc_key":"map_edge:jupyter-enterprise-gateway:browser-computer-use","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"jupyter-enterprise-gateway → browser-computer-use","text":"Technology Jupyter Enterprise Gateway (jupyter-enterprise-gateway): Jupyter Enterprise Gateway is a **web server that gives headless, enterprise access to Jupyter kernels** — including **remote kernels** on distributed clusters (Apache Spark on YARN, IBM Spectrum Conductor, Kubernetes, Docker Swarm, or local to the gateway). It extends Jupyter Kernel Gateway’s websocket mode with multi-tenant remote launch, secure client→gateway→kernel paths, and persistent kernel. Maps to research feature «Browser & Computer Use» (browser-computer-use). Landscape category: Agentic → Code Sandbox.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:jupyter-enterprise-gateway:job-coding","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"jupyter-enterprise-gateway → job-coding","text":"Technology Jupyter Enterprise Gateway (jupyter-enterprise-gateway): Jupyter Enterprise Gateway is a **web server that gives headless, enterprise access to Jupyter kernels** — including **remote kernels** on distributed clusters (Apache Spark on YARN, IBM Spectrum Conductor, Kubernetes, Docker Swarm, or local to the gateway). It extends Jupyter Kernel Gateway’s websocket mode with multi-tenant remote launch, secure client→gateway→kernel paths, and persistent kernel. Maps to research feature «Coding agents» (job-coding). Landscape category: Agentic → Code Sandbox.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:jupyter-enterprise-gateway:r2-06-self-coding","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"jupyter-enterprise-gateway → r2-06-self-coding","text":"Technology Jupyter Enterprise Gateway (jupyter-enterprise-gateway): Jupyter Enterprise Gateway is a **web server that gives headless, enterprise access to Jupyter kernels** — including **remote kernels** on distributed clusters (Apache Spark on YARN, IBM Spectrum Conductor, Kubernetes, Docker Swarm, or local to the gateway). It extends Jupyter Kernel Gateway’s websocket mode with multi-tenant remote launch, secure client→gateway→kernel paths, and persistent kernel. Maps to research feature «T06 — Self-Coding (Observation → Deterministic Scripts)» (r2-06-self-coding). Landscape category: Agentic → Code Sandbox.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:jupyter-enterprise-gateway:sandboxing-isolation","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"jupyter-enterprise-gateway → sandboxing-isolation","text":"Technology Jupyter Enterprise Gateway (jupyter-enterprise-gateway): Jupyter Enterprise Gateway is a **web server that gives headless, enterprise access to Jupyter kernels** — including **remote kernels** on distributed clusters (Apache Spark on YARN, IBM Spectrum Conductor, Kubernetes, Docker Swarm, or local to the gateway). It extends Jupyter Kernel Gateway’s websocket mode with multi-tenant remote launch, secure client→gateway→kernel paths, and persistent kernel. Maps to research feature «Sandboxing & Isolation» (sandboxing-isolation). Landscape category: Agentic → Code Sandbox.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:jupyter-enterprise-gateway:wt-tools-actions","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"jupyter-enterprise-gateway → wt-tools-actions","text":"Technology Jupyter Enterprise Gateway (jupyter-enterprise-gateway): Jupyter Enterprise Gateway is a **web server that gives headless, enterprise access to Jupyter kernels** — including **remote kernels** on distributed clusters (Apache Spark on YARN, IBM Spectrum Conductor, Kubernetes, Docker Swarm, or local to the gateway). It extends Jupyter Kernel Gateway’s websocket mode with multi-tenant remote launch, secure client→gateway→kernel paths, and persistent kernel. Maps to research feature «Workflow Tools — Tools & Actions» (wt-tools-actions). Landscape category: Agentic → Code Sandbox.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_features:jupyter-enterprise-gateway","kind":"tech_features","path":"technologies/jupyter-enterprise-gateway/features.md","title":"Jupyter Enterprise Gateway · features","text":"# Features using `jupyter-enterprise-gateway`\n\n**Technology:** [Jupyter Enterprise Gateway](./README.md)\n**Category:** Agentic → Code Sandbox\n**Mapped features:** 5\n**Map:** [`../features/_source/feature-technology-map.md`](../features/_source/feature-technology-map.md)\n\nThis technology is a **candidate building block** for the features below (research map — not a claim that the product currently depends on it).\n\n| Feature | Title |\n|---|---|\n| [`browser-computer-use`](../features/browser-computer-use/README.md) | Browser & Computer Use |\n| [`job-coding`](../features/job-coding/README.md) | Coding agents |\n| [`r2-06-self-coding`](../features/r2-06-self-coding/README.md) | T06 — Self-Coding (Observation → Deterministic Scripts) |\n| [`sandboxing-isolation`](../features/sandboxing-isolation/README.md) | Sandboxing & Isolation |\n| [`wt-tools-actions`](../features/wt-tools-actions/README.md) | Workflow Tools — Tools & Actions |\n\n","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:jupyter-enterprise-gateway:e2b-contrast:0c858cb59274","kind":"tech_quote","path":"technologies/jupyter-enterprise-gateway/science/sources/e2b-contrast.md","title":"Jupyter Enterprise Gateway · e2b-contrast","text":"Jupyter Enterprise Gateway (jupyter-enterprise-gateway, Agentic → Code Sandbox). Open-source, secure environment with real-world tools for enterprise-grade agents.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:jupyter-enterprise-gateway:e2b-contrast:df13c86ee714","kind":"tech_quote","path":"technologies/jupyter-enterprise-gateway/science/sources/e2b-contrast.md","title":"Jupyter Enterprise Gateway · e2b-contrast","text":"Jupyter Enterprise Gateway (jupyter-enterprise-gateway, Agentic → Code Sandbox). Jupyter Enterprise Gateway enables Jupyter Notebook to launch remote kernels in a distributed cluster, including Apache Spark managed by YARN, IBM Spectrum Conductor, Kubernetes or Docker Swarm.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:jupyter-enterprise-gateway:jeg-docs:36a60836d8f2","kind":"tech_quote","path":"technologies/jupyter-enterprise-gateway/science/sources/jeg-docs.md","title":"Jupyter Enterprise Gateway · jeg-docs","text":"Jupyter Enterprise Gateway (jupyter-enterprise-gateway, Agentic → Code Sandbox). A lightweight, multi-tenant, scalable and secure gateway that enables Jupyter Notebooks to share resources across distributed clusters such as Apache Spark, Kubernetes and others.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:jupyter-enterprise-gateway:jeg-docs:64c8d4104cf5","kind":"tech_quote","path":"technologies/jupyter-enterprise-gateway/science/sources/jeg-docs.md","title":"Jupyter Enterprise Gateway · jeg-docs","text":"Jupyter Enterprise Gateway (jupyter-enterprise-gateway, Agentic → Code Sandbox). E2B provides isolated sandboxes that let agents safely execute code, process data, and run tools.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:jupyter-enterprise-gateway:jeg-github:5a2e3c3e9426","kind":"tech_quote","path":"technologies/jupyter-enterprise-gateway/science/sources/jeg-github.md","title":"Jupyter Enterprise Gateway · jeg-github","text":"Jupyter Enterprise Gateway (jupyter-enterprise-gateway, Agentic → Code Sandbox). Jupyter Enterprise Gateway","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:jupyter-enterprise-gateway:jeg-github:f6c51ddb1b31","kind":"tech_quote","path":"technologies/jupyter-enterprise-gateway/science/sources/jeg-github.md","title":"Jupyter Enterprise Gateway · jeg-github","text":"Jupyter Enterprise Gateway (jupyter-enterprise-gateway, Agentic → Code Sandbox). Sandbox — A fast, secure Linux VM created on demand for your agent, which you can pause and resume as needed","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_readme:jupyter-enterprise-gateway","kind":"tech_readme","path":"technologies/jupyter-enterprise-gateway/README.md","title":"Jupyter Enterprise Gateway","text":"$6c","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_science_source:jupyter-enterprise-gateway:e2b-contrast","kind":"tech_science_source","path":"technologies/jupyter-enterprise-gateway/science/sources/e2b-contrast.md","title":"Jupyter Enterprise Gateway · e2b-contrast","text":"Jupyter Enterprise Gateway · source e2b-contrast\n\n# E2B contrast\n\n| Field | Value |\n|---|---|\n| **ID** | `e2b-contrast` |\n| **Type** | `repo-readme` |\n| **URL** | https://github.com/e2b-dev/E2B |\n| **DOI / arXiv / cite** | E2B contrast |\n| **Authors / Org** | E2B |\n| **Year** | 2026 |\n| **Evidence** | MODERATE |\n| **Retrieved** | 2026-09-13 |\n\n## Relevance\n\nPeer code sandbox.\n\n## Quotes\n\n> \"Open-source, secure environment with real-world tools for enterprise-grade agents.\"\n> — jupyter-enterprise-gateway primary (deduped)\n\n> \"Jupyter Enterprise Gateway enables Jupyter Notebook to launch remote kernels in a distributed cluster, including Apache Spark managed by YARN, IBM Spectrum Conductor, Kubernetes or Docker Swarm.\"\n> — jupyter-enterprise-gateway primary (deduped)\n\n\n## Images\n\n_None attached._","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_science_source:jupyter-enterprise-gateway:jeg-docs","kind":"tech_science_source","path":"technologies/jupyter-enterprise-gateway/science/sources/jeg-docs.md","title":"Jupyter Enterprise Gateway · jeg-docs","text":"Jupyter Enterprise Gateway · source jeg-docs\n\n# Enterprise Gateway documentation\n\n| Field | Value |\n|---|---|\n| **ID** | `jeg-docs` |\n| **Type** | `docs` |\n| **URL** | https://jupyter-enterprise-gateway.readthedocs.io/ |\n| **DOI / arXiv / cite** | JEG docs |\n| **Authors / Org** | Jupyter |\n| **Year** | 2026 |\n| **Evidence** | MODERATE |\n| **Retrieved** | 2026-09-13 |\n\n## Relevance\n\nDocs.\n\n## Quotes\n\n> \"E2B provides isolated sandboxes that let agents safely execute code, process data, and run tools.\"\n> — jupyter-enterprise-gateway primary (deduped)\n\n> \"A lightweight, multi-tenant, scalable and secure gateway that enables Jupyter Notebooks to share resources across distributed clusters such as Apache Spark, Kubernetes and others.\"\n> — jupyter-enterprise-gateway primary (deduped)\n\n\n## Images\n\n_None attached._","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_science_source:jupyter-enterprise-gateway:jeg-github","kind":"tech_science_source","path":"technologies/jupyter-enterprise-gateway/science/sources/jeg-github.md","title":"Jupyter Enterprise Gateway · jeg-github","text":"Jupyter Enterprise Gateway · source jeg-github\n\n# jupyter-server/enterprise_gateway\n\n| Field | Value |\n|---|---|\n| **ID** | `jeg-github` |\n| **Type** | `repo-readme` |\n| **URL** | https://github.com/jupyter-server/enterprise_gateway |\n| **DOI / arXiv / cite** | jupyter-server/enterprise_gateway |\n| **Authors / Org** | Jupyter |\n| **Year** | 2026 |\n| **Evidence** | MODERATE |\n| **Retrieved** | 2026-09-13 |\n\n## Relevance\n\nPrimary repo.\n\n## Quotes\n\n> \"Sandbox — A fast, secure Linux VM created on demand for your agent, which you can pause and resume as needed\"\n> — jupyter-enterprise-gateway primary (deduped)\n\n> \"Jupyter Enterprise Gateway\"\n> — jupyter-enterprise-gateway primary (deduped)\n\n\n## Images\n\n_None attached._","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:jupyter-enterprise-gateway:evidence","kind":"tech_section","path":"technologies/jupyter-enterprise-gateway/README.md","title":"Jupyter Enterprise Gateway · Evidence","text":"Jupyter Enterprise Gateway — Evidence\n\n| Claim area | Source id | Strength |\n|---|---|---|\n| Remote kernels / gateway role | `science/sources/jeg-github.md` | MODERATE |\n| Docs / operators surface | `science/sources/jeg-docs.md` | MODERATE |\n| Contrast vs agent sandboxes | `science/sources/e2b-contrast.md` | MODERATE |","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:jupyter-enterprise-gateway:features-map","kind":"tech_section","path":"technologies/jupyter-enterprise-gateway/README.md","title":"Jupyter Enterprise Gateway · Features map","text":"Jupyter Enterprise Gateway — Features map\n\nWhich research features may use this technology: [`features.md`](./features.md) (5 mapped).\n\n","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:jupyter-enterprise-gateway:github-map","kind":"tech_section","path":"technologies/jupyter-enterprise-gateway/README.md","title":"Jupyter Enterprise Gateway · GitHub map","text":"Jupyter Enterprise Gateway — GitHub map\n\nGitHub repositories for **Jupyter Enterprise Gateway** (several allowed per technology).\n\n| Role | Repo | ★ | Lang | License | Pushed |\n|---|---|--:|---|---|---|\n| `primary` | [jupyter-server/enterprise_gateway](https://github.com/jupyter-server/enterprise_gateway) | 668 | Python | NOASSERTION | 2026-07-14 |\n\n```mermaid\nflowchart LR\n Tech[\"Jupyter Enterprise Gateway\"]\n Tech -->|primary| enterprise_gateway[\"jupyter-server/enterprise_gateway\"]\n Tech --> Cell[\"Agentic / Code Sandbox\"]\n Cell --> Index[technologies/GITHUB_MAP.md]\n```\n\n> A lightweight, multi-tenant, scalable and secure gateway that enables Jupyter Notebooks to share resources across distributed clusters such as Apache Spark, Kubernetes and others.\n\n**Repos mapped:** 1 · edit `_source/github-repos.overrides.json` to add more.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:jupyter-enterprise-gateway:how-it-works","kind":"tech_section","path":"technologies/jupyter-enterprise-gateway/README.md","title":"Jupyter Enterprise Gateway · How it works","text":"Jupyter Enterprise Gateway — How it works\n\nClients talk to the Enterprise Gateway; the gateway’s process-proxy/launcher framework starts kernels locally, round-robin on nodes, or via a configured resource manager. Sessions can persist; communication is secured end-to-end through the gateway.\n\n1. Deploy Enterprise Gateway in the cluster network. \n2. Notebook/agent clients open websocket sessions to the gateway. \n3. Gateway launches remote kernels under the chosen resource manager. \n4. Code executes on the remote kernel; results return through the gateway — not as unrestricted host shell.\n\n```mermaid\nflowchart TD\n C[Notebook / agent client] --> G[Enterprise Gateway]\n G --> K[Remote kernel]\n K --> RM[YARN / K8s / Swarm / local]\n K --> Out[Results]\n Out --> C\n```","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:jupyter-enterprise-gateway:limits-failure-modes","kind":"tech_section","path":"technologies/jupyter-enterprise-gateway/README.md","title":"Jupyter Enterprise Gateway · Limits & failure modes","text":"Jupyter Enterprise Gateway — Limits & failure modes\n\n- **Jupyter-protocol shaped:** not a general Docker sandbox API for non-kernel agents. \n- **Ops weight:** cluster resource managers and network paths are real SRE cost. \n- **Not JupyterHub:** confusing the two leads to the wrong architecture. \n- **Not the ledger:** kernel output is analysis evidence, not company authority.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:jupyter-enterprise-gateway:links","kind":"tech_section","path":"technologies/jupyter-enterprise-gateway/README.md","title":"Jupyter Enterprise Gateway · Links","text":"Jupyter Enterprise Gateway — Links\n\n**URL:** https://jupyter-enterprise-gateway.readthedocs.io/ \n**Repo:** https://github.com/jupyter-server/enterprise_gateway \n**Docs:** https://jupyter-enterprise-gateway.readthedocs.io/ \n\n**Folder:** `jupyter-enterprise-gateway` \n**Wiki-status:** `deep` \n**Updated:** 2026-09-14","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:jupyter-enterprise-gateway:scientific-notes","kind":"tech_section","path":"technologies/jupyter-enterprise-gateway/README.md","title":"Jupyter Enterprise Gateway · Scientific notes","text":"Jupyter Enterprise Gateway — Scientific notes\n\nScientific pack: **3 sources**, quotes per source, image copyright register.\n\n→ [`science/README.md`](./science/README.md) · schema [`../SCIENCE.md`](../SCIENCE.md)","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:jupyter-enterprise-gateway:what-it-is","kind":"tech_section","path":"technologies/jupyter-enterprise-gateway/README.md","title":"Jupyter Enterprise Gateway · What it is","text":"Jupyter Enterprise Gateway — What it is\n\nJupyter Enterprise Gateway is a **web server that gives headless, enterprise access to Jupyter kernels** — including **remote kernels** on distributed clusters (Apache Spark on YARN, IBM Spectrum Conductor, Kubernetes, Docker Swarm, or local to the gateway). It extends Jupyter Kernel Gateway’s websocket mode with multi-tenant remote launch, secure client→gateway→kernel paths, and persistent kernel sessions. Out of the box it supports IPython (Python), IRkernel (R), and Apache Toree (Scala).\n\nIt does **not** manage fleets of Notebook servers — use **JupyterHub** for that. Install via `pip install jupyter_enterprise_gateway` and run `jupyter enterprisegateway`.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:jupyter-enterprise-gateway:when-to-use-skip","kind":"tech_section","path":"technologies/jupyter-enterprise-gateway/README.md","title":"Jupyter Enterprise Gateway · When to use / skip","text":"Jupyter Enterprise Gateway — When to use / skip\n\n- **Use when:** you need **multi-tenant remote Jupyter kernels** on Spark/K8s/Swarm with enterprise gateway controls. \n- **Skip when:** you need agent **cloud sandboxes** for arbitrary commands (**e2b**, **daytona**), or multi-user Notebook *server* orchestration alone (**JupyterHub**). \n- **Prefer instead:** e2b for SDK sandboxes; JupyterHub alongside EG when you also need notebook server farms.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_section:jupyter-enterprise-gateway:why-it-matters-here","kind":"tech_section","path":"technologies/jupyter-enterprise-gateway/README.md","title":"Jupyter Enterprise Gateway · Why it matters here","text":"Jupyter Enterprise Gateway — Why it matters here\n\nSome agent/analysis paths need **governed notebook compute on a cluster**, not a disposable cloud micro-VM. Enterprise Gateway is the Code Sandbox pick when isolation means resource-manager placement and shared Spark/K8s kernels behind a gateway. 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Landscape category: Agentic → IAM.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"map_edge:keycloak:wt-reuse-deploy-govern","kind":"map_edge","path":"features/_source/feature-technology-map.json","title":"keycloak → wt-reuse-deploy-govern","text":"Technology Keycloak (keycloak): Keycloak is **open-source identity and access management**: add authentication to applications and secure services without building your own user store. It provides user federation, strong authentication, user management, fine-grained authorization, and related IAM features. Run from the official distribution (`bin/kc.sh start-dev`) or container (`quay.io/keycloak/keycloak`). CNCF-aligned Apache-2. Maps to research feature «Workflow Tools — Reuse, Deploy & Govern» (wt-reuse-deploy-govern). Landscape category: Agentic → IAM.","quality":null,"needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_features:keycloak","kind":"tech_features","path":"technologies/keycloak/features.md","title":"Keycloak · features","text":"$6d","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:keycloak:keycloak-docs:049bdba03e49","kind":"tech_quote","path":"technologies/keycloak/science/sources/keycloak-docs.md","title":"Keycloak · keycloak-docs","text":"Keycloak (keycloak, Agentic → IAM). Add authentication to applications and secure services with minimum effort. No need to deal with storing users or authenticating users.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:keycloak:keycloak-docs:3d5d26f0385e","kind":"tech_quote","path":"technologies/keycloak/science/sources/keycloak-docs.md","title":"Keycloak · keycloak-docs","text":"Keycloak (keycloak, Agentic → IAM). Users authenticate with Keycloak rather than individual applications. This means that your applications don't have to deal with login forms, authenticating users, and storing users.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:keycloak:keycloak-docs:fbf06c49d737","kind":"tech_quote","path":"technologies/keycloak/science/sources/keycloak-docs.md","title":"Keycloak · keycloak-docs","text":"Keycloak (keycloak, Agentic → IAM). Keycloak has built-in support to connect to existing LDAP or Active Directory servers.","quality":"ok","needs_deepen":false,"deepen_reason":null},{"doc_key":"tech_quote:keycloak:keycloak-github:201592122cd0","kind":"tech_quote","path":"technologies/keycloak/science/sources/keycloak-github.md","title":"Keycloak · keycloak-github","text":"Keycloak (k