Technology · Agentic → Observability · Open Core · wiki:deep
Weave is Weights & Biases’ toolkit for developing generative AI apps: log/debug LM inputs/outputs/traces, run rigorous evaluations, and organize experiment→eval→production artifacts inside the W&B ecosystem. Install via pip install weave, weave.init(...), and @weave.op tracing. Apache-2.0 library with W&B account/cloud gravity (free tier available). Repo notes older “engine/boards” code; current focus is tracing + evaluations.
Prefer Weave when experiments already live in W&B. Prefer Langfuse/Phoenix/Opik for dedicated self-host LLM-ops UIs; OpenLLMetry for OTEL export; Helicone for gateway logging.
Initialize Weave against a W&B project; decorate functions with @weave.op to build trace trees; use Weave evals to compare runs.
pip install weave; create/login W&B. weave.init("project"). Related: arize-phoenix · comet-optik · helicone · topics/09-self-monitoring
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Catalog backlinks — what points here (wiki “what links here”).
Claims below are backed by science sources on disk.
Toolkit / tracing role
wandb/weave · MODERATE
“Based on OpenTelemetry to increase compatibility and reduce vendor lock-in”
Docs
Weave documentation · MODERATE
“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.”
Contrast vs Langfuse
Langfuse contrast · MODERATE
“Langfuse is an open-source LLM engineering platform that helps teams collaboratively debug, analyze, and iterate on their LLM applications.”
Features and peers linked from the catalog map — not a second product surface.
No DM vendor crosswalk edges yet.
Primary repo github.com/wandb/weave · Open Core
technologies/wandb-weave/README.md
9 tags · 14 out · 15 in · 3 artifacts · 1 gaps · 29 corpus docs
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