| name | langchain-code |
| description | Build LangChain agents in Python — create_agent, chat model initialization, tool binding, structured output, and middleware. Use for a single-agent tool-calling loop with a system prompt and a fixed tool list; use langgraph-code instead when the task needs explicit multi-step state-machine control flow, and deepagents-code instead when it needs a pre-assembled filesystem/planning/subagent harness for long-running work. Triggers on "LangChain agent", "create_agent", "chat model", "tool calling agent", "structured output langchain", "bind_tools". |
LangChain
Source: https://docs.langchain.com/oss/python/langchain/overview (fetched 2026-07-06 — re-verify if this content seems stale). Note: docs.langchain.com's top-level llms.txt index is dominated by LangSmith/Agent-Server/Fleet/API-reference content unrelated to this skill — the relevant pages live under oss/python/langchain/* specifically; don't confuse the two when looking for more material.
Core Philosophy
An agent is a model plus a configurable harness: create_agent gives you a minimal, composable base (model, tools, system prompt, optional middleware) rather than a fixed opinionated pipeline. This is the "assemble exactly what you need" layer — see deepagents-code for the pre-assembled, opinionated alternative when the task needs filesystem access, planning, and subagents out of the box.
When to Reach for LangChain vs. Its Siblings
langchain-code (this skill): a single agent, a fixed tool list, optionally structured output — no explicit multi-step state machine needed.langgraph-code: the task needs explicit branching/looping control flow, durable checkpointing, or a pause-for-approval step.deepagents-code: the task is long-running (coding/research) and needs filesystem access, task planning, and subagent delegation pre-assembled rather than composed by hand.
Quick Reference
references/agents.md — create_agent, tool attachment, middleware, structured output, conversation persistence via checkpointer/thread_id.references/models.md — init_chat_model, provider-specific classes, invoke/stream/batch, tool binding at the model level, structured output at the model level.
Gotchas (do not skip)
- Conversation persistence requires an explicit checkpointer, same as LangGraph (
create_agent is itself LangGraph-backed under the hood) — without one, history is discarded between invocations even with a thread_id.
- System prompts are static at agent creation and do not enforce hard rules — for actual policy enforcement (PII redaction, content filters), use middleware, not prompt wording.
- Tool docstrings are load-bearing. The agent introspects a tool function's docstring and type hints to build its schema — a vague docstring produces poor tool-selection behavior, this is not cosmetic.
invoke() blocks until the whole run finishes. For real-time UX on a multi-tool-call run, use streaming (stream_events) instead.
Not Yet Covered Here (fetch before relying on these topics)
This skill's reference files cover create_agent and chat-model configuration in depth. Tool-definition patterns beyond the basics, retrieval/RAG composition, and memory patterns beyond what agents.md's checkpointer section covers were not separately fetched into a reference file this pass — fetch https://docs.langchain.com/oss/python/langchain/overview's current linked pages (the set may have grown) before advising deeply on those topics.
