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 svngoku/coding-agents-skills --skill langchaingit clone --depth 1 https://github.com/svngoku/coding-agents-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/svngoku/coding-agents-skills/langchain)<a href="https://agentmods.dev/skills/svngoku/coding-agents-skills/langchain"><img src="https://agentmods.dev/badge/skills/svngoku/coding-agents-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/svngoku/coding-agents-skills/langchain"><img src="https://agentmods.dev/badge/skills/svngoku/coding-agents-skills/langchain.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00139 | $0.04427 |
| Opus 5.5 | $0.00056 | $0.01771 |
| Sonnet 5.5 | $0.00028 | $0.00885 |
| Haiku 4.5 | $0.00014 | $0.00443 |
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 6d 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 — 401 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LangChain Skill
Build production AI agents with LangChain v1 (create_agent on top of LangGraph), its middleware
system, built-in MCP support and the LangGraph runtime. Verified against langchain 1.4.6,
langchain-core 1.6, langgraph 1.2 and deepagents 0.7 (Python 3.10+; deepagents needs 3.11+).
Quick Reference
| Topic | File |
|---|---|
| Built-in and custom middleware | references/middleware.md |
| LangGraph graphs, persistence, durability, streaming | references/langgraph.md |
| Subagents, handoffs, skills, routers | references/multi-agent.md |
Deep Agents harness and Managed Deep Agents (mda) |
references/deep-agents.md |
| RAG, embeddings, vector stores, retrievers | references/retrieval.md |
Installation
pip install -U langchain langgraph langchain-anthropic # or langchain-openai, langchain-google-genai, ...
pip install -U "langchain[mcp]" # built-in MCP client (langchain.mcp, beta)
pip install -U deepagents # optional: planning + filesystem + subagents harness
pip install -U langchain-classic # only for legacy chains/retrievers
Quick Agent
from langchain.agents import create_agent
def get_weather(city: str) -> str:
"""Get the current weather for a city."""
return f"Sunny in {city}!"
agent = create_agent(
model="anthropic:claude-sonnet-5",
tools=[get_weather],
system_prompt="You are a helpful assistant.",
)
result = agent.invoke({"messages": [{"role": "user", "content": "Weather in SF?"}]})
print(result["messages"][-1].content)
create_agent returns a compiled LangGraph graph, so invoke, stream, stream_events,
checkpointers, stores and interrupts all work on it. Plain functions with type hints and a
docstring are accepted as tools; use @tool when you need a custom name, schema or ToolRuntime.
What ships with it
10 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.
- eval.yaml 949 B
- graders/check.py 11 KB runs code
- instructions/customer-support-agent.md 2.5 KB
- references/deep-agents.md 22 KB
- references/langgraph.md 8.6 KB
- references/middleware.md 8.4 KB
- references/multi-agent.md 12 KB
- references/retrieval.md 9.4 KB
- rubrics/quality.md 2.4 KB
- solutions/reference-customer-support-agent 2.3 KB
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.
- 6d ago Changed · +1 lines · +17 tokens per session f276cdd54cee
- 7d ago Changed · +133 lines · +73 tokens per session f7c03a7d5004
- 1mo ago First seen · 267 lines · 49 tokens per session scan A e054c4785b34
langchain is a skill published in the GitHub repository svngoku/coding-agents-skills (12 stars, last pushed 3d ago), licensed MIT. It adds 139 tokens to every session and 4,427 once invoked, about $0.0006 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 2026-08-31.
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