agentmods.dev

deployment-patterns-for-langgraph

A guide to deploying LangGraph workflows as production applications. LangGraph is a framework for building workflows made of connected steps; the guide covers managed, hybrid, and self-hosted deployment options.

In plain words
What is it for?
Use it when deploying LangGraph APIs, background jobs, scheduled tasks, webhooks, task queues, checkpointing, or FastAPI and Docker-based services.
Why use it?
It helps translate a workflow that runs during development into a service that can be called, persisted, scaled, and monitored in production.

Skill for Claude CodeCodex

2 1mo ago A scan 90 tokens original MIT
View the source file postindustria-tech/agentic-toolkit
Install

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.

agentmods
npx agentmods add skills/postindustria-tech/agentic-toolkit/langgraph-dev-deployment-patterns
Any agent
npx skills add postindustria-tech/agentic-toolkit --skill langgraph-dev-deployment-patterns
Clone the repo
git clone --depth 1 https://github.com/postindustria-tech/agentic-toolkit

Made for: Claude Code, Codex.

Per session 90 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 5,634 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00090 $0.05634
Opus 5 $0.00045 $0.02817
Sonnet 5 $0.00018 $0.01127
Haiku 4.5 $0.00009 $0.00563

Measured 2d ago against content hash f6441a4cb96e, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

deployment-patterns-for-langgraph scanned grade A with 1 finding 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 2d ago.

The scan reads SKILL.md. This mod also ships 7 executable files (examples/platform-deployment-demo/client_example.py, examples/platform-deployment-demo/cron_example.py, examples/platform-deployment-demo/src/__init__.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

curl http://localhost:8123/ok # {"ok": true}
plugins/langgraph-dev/skills/langgraph-dev-deployment-patterns/SKILL.md · 757 lines

How it starts

The opening of the file, as written. The whole thing — 757 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Deployment Patterns for LangGraph

Deploy LangGraph workflows as production APIs with managed platforms, FastAPI, async execution, persistence, and observability.

LangGraph Platform offers managed deployment for production applications with built-in persistence, scaling, and monitoring.

Deployment Options

Option Description Plan
Cloud (SaaS) Fully managed by LangChain, deploy from GitHub Plus, Enterprise
Hybrid (BYOC) LangChain control plane + your data plane Enterprise
Self-Hosted Lite Free tier, up to 1M nodes/month Developer
Self-Hosted Enterprise Full platform in your infrastructure Enterprise

Quick Start

pip install "langgraph-cli[inmem]"  # Requires Python 3.11+
langgraph new my-agent --template new-langgraph-project-python
cd my-agent
langgraph dev  # Runs on http://localhost:2024

See LangGraph Platform Deployment Options.

RemoteGraph Client

RemoteGraph provides API parity with CompiledGraph, allowing interaction with deployed graphs using the same methods (invoke(), stream(), get_state()) in both development and production.

Key Benefits:

  • Separation of concerns: Build/test locally, deploy to Platform, call with RemoteGraph
  • Thread-level persistence for stateful conversations
  • Subgraph embedding for modular multi-agent workflows

Basic Usage:

from langgraph.pregel.remote import RemoteGraph

# Connect to deployed graph
remote_graph = RemoteGraph("agent", url="<DEPLOYMENT_URL>")

# Stateful conversation with thread persistence
config = {"configurable": {"thread_id": "user-123"}}
result = await remote_graph.ainvoke(
    {"messages": [{"role": "user", "content": "Hello"}]},
    config=config
)

# Use as subgraph in local composition
from langgraph.graph import StateGraph, START

builder = StateGraph(MessagesState)
builder.add_node("remote_specialist", remote_graph)
builder.add_edge(START, "remote_specialist")
graph = builder.compile()

Read the full file on GitHub · 757 lines

Files

What ships with it

17 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.

Changes

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.

  1. 2d ago First seen · 757 lines · 90 tokens per session scan A f6441a4cb96e

Subscribe to this mod's changes

deployment-patterns-for-langgraph is a skill published in the GitHub repository postindustria-tech/agentic-toolkit (2 stars, last pushed 1mo ago), licensed MIT. It adds 90 tokens to every session and 5,634 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

Backend and APIsDevOps and cloud
Related

Other skills, from other repositories

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

brainstorming

You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.

obra/superpowers · 37 tokens

auto-perf-optimize

Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.

microsoft/vscode · 62 tokens

chat-perf

Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.

microsoft/vscode · 51 tokens

chat-pet-sprite-creation

Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.

microsoft/vscode · 53 tokens

cpu-profile-analysis

Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…

microsoft/vscode · 71 tokens