Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add ob-labs/agentseek --skill langchain-dev-guidegit clone --depth 1 https://github.com/ob-labs/agentseekWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/ob-labs/agentseek/langchain-dev-guide)<a href="https://agentmods.dev/skills/ob-labs/agentseek/langchain-dev-guide"><img src="https://agentmods.dev/badge/skills/ob-labs/agentseek/langchain-dev-guide/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/ob-labs/agentseek/langchain-dev-guide"><img src="https://agentmods.dev/badge/skills/ob-labs/agentseek/langchain-dev-guide.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00081 | $0.01598 |
| Opus 5.5 | $0.00032 | $0.00639 |
| Sonnet 5.5 | $0.00016 | $0.00320 |
| Haiku 4.5 | $0.00008 | $0.00160 |
Grade A, and why
langchain-dev-guide scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 73 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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, long SKILL.md truncated by read_file 100-line default |
| Structured Output | reference/structured-output.md | Model-level method selection, create_agent strategies, missing fields, unsupported tool_choice, provider-side 400 errors on forced schema tool selection |
| 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, MCP tools unable to access runtime context, invalid_tool_calls, and dynamic system prompt placeholders |
| 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 |
What ships with it
13 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- reference/cn-models/integration-tests.md 3.0 KB
- reference/cn-models/README.md 4.6 KB
- reference/common-issues.md 14 KB
- reference/contextseek-middleware.md 16 KB
- reference/contextseek-params.md 33 KB
- reference/deepagents.md 14 KB
- reference/middleware.md 14 KB
- reference/model-integration.md 8.4 KB
- reference/multi-agent.md 22 KB
- reference/streaming.md 13 KB
- reference/structured-output.md 9.1 KB
- reference/user-queries.md 4.1 KB
- template/chat_model.py 7.4 KB runs code
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 73 lines · 81 tokens per session scan A d5e651af8d4f
langchain-dev-guide is a skill published in the GitHub repository ob-labs/agentseek (203 stars, last pushed 18d ago), licensed Apache-2.0. It adds 81 tokens to every session and 1,598 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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