Deploy a LangChain 1.0 / LangGraph 1.0 app to Cloud Run, Vercel, or LangServe
correctly — timeouts sized for chain length, cold-start mitigation, SSE
anti-buffering headers, Secret Manager over `.env`. Use when prepping first
prod 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", "langchain docker".
Standardmäßig ist der Prompt ausgewählt, der zuerst die Quelle prüft. Sie können zu einem direkten Befehl wechseln oder eine lokale Kopie herunterladen.
Quelldateien prüfen
Lesen Sie SKILL.md und alle von SkillsMP angezeigten Begleitdateien, bevor Sie sich für eine Installation entscheiden.
Mit Codex oder Claude installieren Kopieren Sie diesen Prompt, fügen Sie ihn in Codex, Claude oder einen anderen Assistant ein und lassen Sie die Skill-Seite prüfen und installieren.
Ein direkter Befehl überspringt den Prüf-Prompt. Prüfen Sie die Quelle, bevor Sie ihn ausführen.
Deploy a LangChain 1.0 / LangGraph 1.0 app to Cloud Run, Vercel, or LangServe
correctly — timeouts sized for chain length, cold-start mitigation, SSE
anti-buffering headers, Secret Manager over `.env`. Use when prepping first
prod 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", "langchain docker".
Designed for Claude Code, also compatible with Codex
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:
// vercel.json — the baseline cap bump (Pro plan max is 60s, Enterprise 900s){"functions":{"api/chat.py":{"maxDuration":60}}}
# app/api/chat.py — stream the response so partial output arrives before the capfrom fastapi.responses import StreamingResponse
@app.post("/api/chat")asyncdefchat(req: ChatRequest):
asyncdefgen():
asyncfor chunk in chain.astream(req.input):
yieldf"data: {chunk.model_dump_json()}\n\n"return StreamingResponse(gen(), media_type="text/event-stream",
headers={"X-Accel-Buffering": "no"})
The maxDuration: 60 raises the Vercel-imposed wall; streaming reduces
time-to-first-byte to under a second so the user sees progress even on a
40-second completion. Once the Vercel cap is fixed, the next three walls are:
Cloud Run cold starts ( p99 on Python + LangChain — P36),
secrets leaking via (P37), and SSE streams hanging
because Nginx / Cloud Run buffer the final chunk (P46).
5-15s
.env
docker exec <pod> env
This skill walks through a production-grade multi-stage Dockerfile, Cloud Run
flags for cold-start mitigation, Vercel maxDuration + streaming, LangServe
route mounting with FastAPI lifespan, SSE anti-buffering headers, and Secret
Manager via pydantic.SecretStr. Pin: langchain-core 1.0.x, langgraph 1.0.x,
langserve 1.0.x. Pain-catalog anchors: P35 (Vercel 10s default),
P36 (Cloud Run cold start), P37 (.env leaks), P46 (SSE buffering).
Prerequisites
Python 3.11+ (3.12 preferred for uvicorn startup speed)
For Cloud Run: a GCP project with Secret Manager API enabled
For Vercel: a project with @vercel/python runtime configured
Instructions
Step 1 — Multi-stage Dockerfile with slim runtime and uvicorn
A multi-stage build keeps the runtime image under 400MB, which cuts Cloud Run
cold starts by 2-3 seconds. Use python:3.12-slim as the final stage (not
python:3.12 — that base adds ~900MB for dev tooling that never runs in prod).
# syntax=docker/dockerfile:1.7
FROM python:3.12-slim AS builder
WORKDIR /build
RUN pip install --no-cache-dir uv
COPY pyproject.toml uv.lock ./
RUN uv export --format requirements-txt --no-hashes > requirements.txt \
&& pip wheel --wheel-dir=/wheels -r requirements.txt
FROM python:3.12-slim AS runtime
RUN useradd -m -u 10001 app
WORKDIR /app
COPY --from=builder /wheels /wheels
RUN pip install --no-cache-dir --no-index --find-links=/wheels /wheels/* \
&& rm -rf /wheels
COPY --chown=app:app app/ ./app/
USER app
EXPOSE 8080
ENV PORT=8080 PYTHONUNBUFFERED=1 PYTHONDONTWRITEBYTECODE=1
CMD ["sh", "-c", "uvicorn app.main:app --host 0.0.0.0 --port ${PORT} --workers 1"]
Single worker is correct — Cloud Run handles horizontal scale; in-process
multi-worker just duplicates LangChain client memory. See Dockerfile and
Secrets for the distroless variant
and the .dockerignore hardening for .env files.
Step 2 — Deploy to Cloud Run with cold-start mitigation
Python + LangChain + tiktoken + one embedding model imports take 5-15
seconds (P36). At --min-instances=0, every scale-from-zero request eats that
as user-facing latency. Paying for one always-on instance is usually cheaper
than the lost requests.
gcloud run deploy langchain-api \
--source=. \
--region=us-central1 \
--min-instances=1 \
--max-instances=20 \
--cpu=2 --memory=2Gi \
--cpu-boost \
--no-cpu-throttling \
--timeout=3600 \
--concurrency=80 \
--set-secrets=ANTHROPIC_API_KEY=anthropic-key:latest,OPENAI_API_KEY=openai-key:latest \
--service-account=langchain-api@PROJECT.iam.gserviceaccount.com
# --timeout=3600 is the Cloud Run per-request maximum (1 hour) — needed# because multi-tool LangGraph agents routinely run 1-5 minutes end-to-end.
The load-bearing flags: --min-instances=1 kills cold-start p99 (one always-warm
replica costs ~$15/mo and dominates p99 improvement); --cpu-boost doubles CPU
for the first 10 seconds; --no-cpu-throttling (CPU-always-allocated billing)
keeps astream running between keepalive pings so long LangGraph runs do not
stall at tool boundaries; --concurrency=80 matches typical I/O-bound
workloads (drop to 10 if embedding large docs in-process).
See Cloud Run Deploy for VPC egress, file
secret mounts, revision traffic splitting, and the full cost model.
Step 3 — Vercel Python: maxDuration: 60 + streaming to beat the cap
On Vercel Hobby the max is 10s by default (P35); Pro is 60s, Enterprise
900s. Always set maxDuration explicitly — the default is a trap.
Streaming is not just a UX fix — it is the mitigation for bursts that still
exceed maxDuration. Time-to-first-byte under a second keeps the proxy
considering the request alive; partial content renders on the client; when
the cap finally triggers, the user has already seen most of the answer. The
Vercel entrypoint pattern mirrors the Overview snippet above — pair with the
SSE headers from Step 5.
Edge Runtime is not an option here — @vercel/edge is JavaScript-only.
Anything that imports langchain must run on @vercel/python (serverless,
Node-free Python container). See Vercel Python Deploy
for env vars vs Vercel Secrets, cold-start profiling, and the serverless vs
fluid-compute tradeoff.
Step 4 — LangServe: add_routes + FastAPI lifespan for pool cleanup
LangServe ships typed HTTP routes over any Runnable. The playground path
is invaluable in dev but must be disabled in production — it leaks chain
topology to anyone who can hit the URL. Mount behind a FastAPI lifespan
that closes asyncpg / httpx / Redis pools on revision retirement;
on_shutdown fires too late on Cloud Run and connections leak across
revisions.
# app/main.pyfrom contextlib import asynccontextmanager
from fastapi import FastAPI
from langserve import add_routes
@asynccontextmanagerasyncdeflifespan(app: FastAPI):
app.state.chain = build_chain()
yieldawait db_pool.close()
app = FastAPI(lifespan=lifespan)
add_routes(app, build_chain(), path="/chat",
enable_feedback_endpoint=False,
playground_type="chat"if__debug__elseNone) # None = off in prod
See LangServe Patterns for typed input/output
schemas, auth middleware, and coexisting with raw FastAPI handlers.
Nginx, Cloud Run's load balancer, and Cloudflare all buffer responses by
default. On SSE, buffering means the client never sees the final end event
and LangGraph.astream hangs forever (P46). Two headers plus one response
flush fix it:
Cross-reference: this is the same anti-buffering pattern used by
langchain-langgraph-streaming (L29) — that skill covers the astream_events
event shapes and client reconnection; this skill covers the proxy surface.
If you see the stream work locally but hang in prod, the header is missing on
the upstream response, not the client.
Step 6 — Secret Manager over .env with pydantic.SecretStr
python-dotenv populates os.environ. Anyone with container access runs
docker exec <pod> env and reads every API key in plain text (P37). Mount
secrets from Secret Manager on Cloud Run, Vercel Secrets on Vercel; wrap in
pydantic.SecretStr so repr(), logs, and tracebacks print **********
instead of the value.
from pydantic import SecretStr
from pydantic_settings import BaseSettings, SettingsConfigDict
classSettings(BaseSettings):
model_config = SettingsConfigDict(env_file=None, extra="ignore")
anthropic_api_key: SecretStr
openai_api_key: SecretStr
settings = Settings() # reads real env vars only; never .env in prod
Cloud Run: --set-secrets=VAR=secret-name:latest (Step 2). Vercel:
vercel env add ANTHROPIC_API_KEY production. Never commit .env; add
.env* to .dockerignore so it does not enter the build context.
Cross-reference: this skill sets the deployment boundary. langchain-security-basics
(P18) covers key-rotation cadence, PII log redaction, and the broader threat
model — get the boundary right here, then layer on P18.
Output
Multi-stage Dockerfile, runtime image under 400MB, non-root user, uvicorn entrypoint
Cloud Run deployment with --min-instances=1 --cpu-boost --no-cpu-throttling --timeout=3600 --concurrency=80
vercel.json with maxDuration: 60 plus streaming response for requests that may exceed the cap
LangServe add_routes(app, chain, path="/chat") behind a FastAPI lifespan that closes resource pools on revision retirement
SSE responses with X-Accel-Buffering: no and Cache-Control: no-cache so proxies do not buffer the final end event
Runtime secrets sourced from Secret Manager / Vercel Secrets and wrapped in pydantic.SecretStr; no .env in the image
Platform Decision Table
Platform
Max timeout
Cold start (p99)
SSE default
Cost baseline (1 agent, 1M req/mo)
Pick when
Cloud Run
3600s
5-15s (mitigable with min-instances)
Buffers without header override
~$40-80/mo + min-instance
Long agents, heavy imports, VPC egress, GCP-native
Vercel Python
10s Hobby / 60s Pro / 900s Enterprise
2-5s (warmed)
No buffering when streaming response
~$20/mo Pro plan flat
Fast iteration, Next.js frontend colocation, short agents
Fly.io
No hard cap (per-request soft)
1-3s (always-on VM)
No default buffer
~$30/mo for shared-cpu-1x + 1GB
Need persistent state, low cold start, non-serverless model
Railway
30 min default
2-4s
No default buffer
~$25/mo on Hobby replica
Prototype to production on one platform, Postgres bundled
Self-hosted (k8s + Nginx)
Ingress-controlled
0s (warm pod)
Buffers — requires proxy_buffering off
Variable, fixed infra cost
Compliance constraints, existing k8s, egress control
Cloud Run is the default for most LangChain production deployments — the 3600s
timeout is the only mainstream serverless option long enough for multi-tool
agents, and Secret Manager integration is native.
Error Handling
Error
Cause
Fix
FUNCTION_INVOCATION_TIMEOUT (Vercel)
10s Python default on Hobby, 60s on Pro (P35)
Set maxDuration: 60 in vercel.json; stream the response
504 Gateway Timeout from Cloud Run
Missing --timeout=3600; default is 300s
Redeploy with --timeout=3600 --concurrency=80
p99 latency 10x p95
Cold start from Python + LangChain imports (P36)
--min-instances=1 --cpu-boost, defer tiktoken import to request time
Client hangs forever on SSE stream
Proxy buffering the final end event (P46)
Add X-Accel-Buffering: no and Cache-Control: no-cache headers
docker exec <pod> env shows API keys
python-dotenv leaked .env into os.environ (P37)
Delete .env from image, use Secret Manager + pydantic.SecretStr
RuntimeError: Event loop is closed on shutdown
asyncpg / httpx pool not closed before revision retirement
Move pool lifecycle into FastAPI lifespan context manager
Stream works locally, stalls on Cloud Run
CPU throttling between keepalive pings
Deploy with --no-cpu-throttling (CPU-always-allocated)
ModuleNotFoundError in Vercel build
requirements.txt not generated from pyproject.toml
A: Cloud Run with min-instances, secret mounts, VPC egress
One always-warm replica, two secrets from Secret Manager, egress routed
through a VPC connector for private Postgres. See Cloud Run Deploy
for the full flag set, revision traffic splitting, and the cost model.
B: Vercel with streaming and maxDuration
vercel.json with maxDuration: 60 plus the FastAPI streaming entrypoint
from Step 3. The combination gives a 40-second completion a 60-second wall
and <1s time-to-first-byte. See Vercel Python Deploy
for Edge Runtime limits and the Vercel Secrets vs env var split.
C: LangServe behind FastAPI with lifespan
Single add_routes call mounting /chat and /chat/stream with a shared
connection pool closed via lifespan. Playground disabled in prod via the
__debug__ check. See LangServe Patterns
for typed schemas, auth middleware, and raw-handler coexistence.
