Individual skill
langchain-deploy-integration
Deploy a LangChain 1.0 / LangGraph 1.0 app to Cloud Run, Vercel, or LangServe correctly — with timeouts sized for chain length, cold-start mitigation, SSE anti-buffering headers, and Secret Manager over .env. Use when prepping a first production deploy, debugging a stream that hangs behind a proxy, or diagnosing p99 latency spikes. Trigger with "langchain deploy", "langchain cloud run", "langchain vercel python", "langchain langserve", or "langchain docker".
skills/.curated/langchain-deploy-integration/SKILL.md
Source commit 0500b275e963c027f6b410e8c49c0446945c574e
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- Not checked yet
- Last checked
- Nov 12, 2026
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Repository context
- Repository stars (not skill ratings)
- 2,916
- Repository last updated
- Nov 12, 2026
- License
- MIT
Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.
- ai Repository topic
- automation Repository topic
- claude-code Repository topic
- devops Repository topic
- mcp Repository topic
- ai-agents Repository topic
- anthropic Repository topic
- claude-code-plugins Repository topic
- developer-tools Repository topic
- llm Repository topic
- saas Declared skill tag
- langchain Declared skill tag
- langgraph Declared skill tag
- python Declared skill tag
- langchain-1.0 Declared skill tag
- deployment Declared skill tag
- cloud-run Declared skill tag
- vercel Declared skill tag
- langserve Declared skill tag
- devops Derived category
- devops-containers Derived category
Files and excerpt
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Browse files
SKILL.mdcore · 15,754 bytesSKILL.mdcore · 15,754 bytesSKILL.mdcore · 15,754 bytesSKILL.mdcore · 15,754 bytesSKILL.mdcore · 15,754 bytesSKILL.mdcore · 15,754 bytesreferences/cloud-run-deploy.mddoc · 5,424 bytesreferences/dockerfile-and-secrets.mddoc · 7,330 bytesreferences/langserve-patterns.mddoc · 6,619 bytesreferences/one-pager.mddoc · 3,366 bytesreferences/vercel-python-deploy.mddoc · 5,566 bytes
Read excerpt
# LangChain Deploy Integration (Python)
## Overview
An engineer ships a working LangGraph agent to Vercel. Every non-trivial request
returns `FUNCTION_INVOCATION_TIMEOUT`. The Python runtime on Vercel defaults to
a **10-second** cap (P35) — a three-tool agent with one RAG round easily runs
20-40s. Local dev never exposed the wall because `uvicorn` on a laptop has no
timeout. Two fixes apply together and each is load-bearing:
```json
// vercel.json — the baseline cap bump (Pro plan max is 60s, Enterprise 900s)
{ "functions": { "api/chat.py": { "maxDuration": 60 } } }
```
