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 magnus919/agent-skills --skill langchaingit clone --depth 1 https://github.com/magnus919/agent-skillsWrote 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/magnus919/agent-skills/langchain)<a href="https://agentmods.dev/skills/magnus919/agent-skills/langchain"><img src="https://agentmods.dev/badge/skills/magnus919/agent-skills/langchain/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/magnus919/agent-skills/langchain"><img src="https://agentmods.dev/badge/skills/magnus919/agent-skills/langchain.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- 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 | $0.00040 | $0.01925 |
| Opus 5 | $0.00020 | $0.00962 |
| Sonnet 5 | $0.00008 | $0.00385 |
| Haiku 4.5 | $0.00004 | $0.00193 |
Grade A, and why
langchain 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 7d 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 — 134 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LangChain Expert Skill
LangChain is an MIT-licensed Python framework for building LLM-powered applications. Since v1.0 (October 2025), it provides a layered architecture: high-level chain composition via LCEL (LangChain Expression Language), agent creation via create_agent (running on the LangGraph runtime underneath), and production observability via LangSmith. With 1000+ integrations and 100K+ GitHub stars, it is the most widely adopted LLM orchestration framework.
Key v1.0 change: All new LangChain agents run on the LangGraph runtime. AgentExecutor is in maintenance mode until December 2026. Use create_agent for new agents. Drop to LangGraph directly when you need full state-machine control.
⚠️ CRITICAL: Do NOT use AgentExecutor for new code. It is in maintenance mode until December 2026. Use
create_agent(model, tools, prompt)instead — it generates a LangGraph state machine with streaming, persistence, and observability out of the box.
Core Principles
These principles govern every decision when building with LangChain. Read them before proceeding to the reference guides.
- LCEL is the composition primitive. The pipe operator (
|) chains Runnables. Every component — prompt, model, parser, retriever — implements the Runnable interface. Build everything in LCEL. - Agents run on LangGraph. Since v1.0,
create_agentgenerates a LangGraph state machine underneath. You get streaming, persistence, and observability without writing graph code. Drop to LangGraph when you need branching, cycles, or human-in-the-loop. - RAG is a chain, not a framework.
retriever | prompt | model | parseris the canonical RAG pattern. Document loaders, splitters, and vector stores are all interchangeable components. - LangSmith is production observability. Enable tracing at startup. 89% of production teams use observability — without it, debugging agent behavior is guesswork.
- The ecosystem is the moat. 1000+ integrations mean model providers, vector stores, and tools are swappable with one line. Build against the interface, not the implementation.
What ships with it
16 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.
- evals/evals.json 2.8 KB
- README.md 1.6 KB
- references/agent-patterns.md 4.9 KB
- references/architecture.md 1.3 KB
- references/callbacks.md 3.4 KB
- references/faq-and-troubleshooting.md 2.0 KB
- references/integration-ecosystem.md 2.1 KB
- references/lcel-reference.md 3.6 KB
- references/production-deployment.md 2.0 KB
- references/rag-strategies.md 3.4 KB
- references/validation-audit.md 2.2 KB
- scripts/check-setup.py 924 B runs code
- templates/agent-with-tools.py 789 B runs code
- templates/basic-chain.py 496 B runs code
- templates/production-deploy.py 705 B runs code
- templates/rag-pipeline.py 1.3 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.
- 7d ago First seen · 134 lines · 40 tokens per session scan A 212cb5308625
langchain is a skill published in the GitHub repository magnus919/agent-skills (76 stars, last pushed today), licensed MIT. It adds 40 tokens to every session and 1,925 once invoked, about $0.0002 per session on Opus 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 2026-09-03.
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