LangChain

In this catalog it is the Frameworks option when you need to compose multi-step agent loops and tool handoffs. Keep LangChain on the observe/propose side of the loop: useful signals and drafts, with human or policy authority still required before anything irreversible runs.

Developers define agents, tools, and graphs/crews; the framework runs the loop (plan→act→observe), managing memory and handoffs.

1. Define agents, tools, and the collaboration pattern the product documents.
2. Bind model credentials and any memory/checkpointer backends.
3. Run a small multi-step task; confirm tool results re-enter the loop.
4. Only then attach side-effecting tools behind an explicit gate.

Related: langgraph · llamaindex · langsmith · topics/01-architecture · topics/12-central-nervous-system

• Use when: LangChain is the widely adopted framework for composing LLM calls, tools, retrievers, memory, and callbacks into applications and agents, with a huge integration ecosystem matches the bottleneck and you specifically need to compose multi-step agent loops and tool handoffs.
• Skip when: another pack already covers the need with less ops surface, or LangChain would become a second place people retype truth.
• Prefer instead: compare LangGraph, LlamaIndex, LangSmith before locking the cell.

• Ops: confirm license, hosting, and release cadence for LangChain (langchain-ai/langchain) before relying on it in a live corridor.
• Integration: bad credentials, scopes, or schema drift produce empty retrievals or runaway tool/model spend without an obvious UI alarm.
• Scope: Frameworks components support agents; they are not the company ledger or the approval authority.