langchain-dev-guide

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LangChain / LangGraph engineering pitfalls and verified fixes. Covers DeepAgents, OpenAI-compatible model integration (including Chinese provider adapters: DeepSeek, Qwen, GLM, etc.), middleware, streaming, multi-agent orchestration, and other common development issues. Use when hitting unexpected behavior, making architecture decisions, or integrating Chinese LLM providers during LangChain development.

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Sourceob-labs/agentseek

Added on2026-06-11

NPX Install

npx skill4agent add ob-labs/agentseek langchain-dev-guide

SKILL.md Content

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LangChain Dev Guide

A systematic summary of typical issues, non-obvious behaviors, and verified solutions encountered in real engineering with the LangChain / LangGraph ecosystem. Every entry comes from a real development scenario and is organized by category.

[!IMPORTANT] This skill is an engineering practice reference, not an introductory tutorial. Each entry assumes the developer is already familiar with basic LangChain concepts (agent, tool, message, graph).

How to Use

First use the "Scenario Index" below to locate the category file your problem belongs to.
When unsure which category applies, search keywords directly in the "Common Issues Quick Reference".
For ContextSeek / semantic memory: start with contextseek-middleware.md to identify your scenario, then go to contextseek-params.md for specific parameter configuration issues.
Once you find the relevant section, read it in depth — every entry follows the structure Symptom → Cause → Solution → Lessons learned.

Scenario Index

Category	File	Trigger Scenarios
Deep Agents	reference/deepagents.md	Model selection, filesystem backend, disabling the general-purpose sub-agent, file permissions, long-term memory
OpenAI-compatible Model Integration	reference/model-integration.md	Pitfalls when using `ChatOpenAI` against OpenAI-compatible providers, integrating Reasoning models (chain-of-thought / `reasoning_content` )
CN Model Integration	reference/cn-models/README.md	Generating LangChain integration classes for Chinese providers (DeepSeek, Qwen, GLM, Moonshot)
Middleware	reference/middleware.md	Middleware execution order, `state_schema` merging, HITL `resume` values, modifying state from `wrap_model_call`
Streaming Output	reference/streaming.md	Choosing between `stream_events` and `stream` , distinguishing tokens from multiple LLMs, disabling streaming, custom progress events
Multi-Agent Orchestration	reference/multi-agent.md	subagents vs handoffs, tool-per-agent vs dispatch, retrieving subagent state, trimming subagent boilerplate, quickly building handoff setups
Other Common Issues	reference/common-issues.md	High-frequency standalone issues that don't fit the categories above. Currently includes: tools returning data to both the model and the application layer, `with_structured_output` returning None, MCP tools unable to access runtime context
ContextSeek — Use Case Scenarios	reference/contextseek-middleware.md	Agent loses context across sessions, tool call auditing, cross-topic knowledge discovery (dream), SRE provenance / confidence tracing, enterprise knowledge cold-start (DataPlug)
ContextSeek — Parameter & Config Issues	reference/contextseek-params.md	scope isolation, auto_store / record_tool_calls write volume, auto_compact throttling and shutdown, retrieval_tags / min_score filtering, tool_arg_overrides, dream trigger conditions, dream item decay, evidence_chain vs chain_confidence, DataPlug vs ctx.add(), plug() scope priority, auto_dream dual-gate triggering

Common Issues Quick Reference

Keyword / Error	Where to Look
Which model to choose / Deep Agent performing poorly	deepagents issue 1
Filesystem backend / local files / file permissions	deepagents issues 2 / 4
Disabling the default sub-agent / general-purpose	deepagents issue 3
Long-term memory / store	deepagents issue 5
OpenAI-compatible model / `ChatOpenAI` not working	model-integration issue 1
Reasoning model / `reasoning_content` / chain-of-thought lost	model-integration issue 2
Chinese model / CN provider / DeepSeek / Qwen / GLM / Moonshot	cn-models README
`langchain-cn-models` (embedded) / generate integration class	cn-models README
Middleware order messed up / before/after counterintuitive	middleware issue 1
`state_schema` fields not merged / input/output control	middleware issue 2
`interrupt` resume value missing / HITL	middleware issue 3
Modifying state inside `wrap_model_call` has no effect	middleware issue 4
Choosing between `astream_events` and `astream` for streaming	streaming issue 1
Distinguishing token sources across multiple LLMs	streaming issue 2
Disabling streaming for a specific model	streaming issue 3
Custom events from inside a tool not being emitted	streaming issue 4
Multi-agent: subagents vs handoffs	multi-agent issue 1
Single dispatch tool vs one tool per agent	multi-agent issue 2
`interrupt` can't see subagent state	multi-agent issue 3
Too much subagent wrapper boilerplate	multi-agent issue 4
Quickly building a handoff-based multi-agent setup	multi-agent issue 5
Tool returning data to both the model and the app layer / `artifact` / `Command(update=...)`	common-issues issue 1
`with_structured_output` returning None / missing fields	common-issues issue 2
MCP tool can't access `user_id` / `store` / state / API key	common-issues issue 3
Agent loses context across sessions — personal assistant or support bot	contextseek-middleware issue 1
Multi-tool data-pipeline agent — auditing tool call decisions	contextseek-middleware issue 2
Research agent accumulates raw notes — cross-topic pattern discovery	contextseek-middleware issue 3
SRE incident postmortem agent — tracing knowledge confidence and conflicts	contextseek-middleware issue 4
Enterprise knowledge migration — agent is retrieval-ready on day one	contextseek-middleware issue 5