langgraph

An integration guide for using LangGraph, a framework for building language-model workflows, with MCP, a standard way for AI applications to discover and use tools. It describes connecting LangGraph to one or more ContextForge MCP servers.

In plain words
What is it for?
Use it when creating LangGraph agents that need tools from one or several MCP servers over Streamable HTTP.
Why use it?
It avoids writing custom tool connections and lets the workflow discover the tools exposed by the configured servers.

Agent

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 agents/jetwind/mcp-context-forge/langgraph
Clone the repo
git clone --depth 1 https://github.com/jetwind/mcp-context-forge
Per session 0 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 459 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin 100% copy Near-identical to another mod 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 $0.00000 $0.00459
Opus 5 $0.00000 $0.00230
Sonnet 5 $0.00000 $0.00092
Haiku 4.5 $0.00000 $0.00046

Measured yesterday against content hash bf2b363a4ca4, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

langgraph 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 yesterday.

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.

Origin

This is a copy

100% identical to langgraph — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

docs/docs/using/agents/langgraph.md · 80 lines

How it starts

The opening of the file, as written. The whole thing — 80 lines — stays where its author put it; the contents beside it link to each section on GitHub.

LangGraph Integration with ContextForge

LangGraph is a framework for developing applications powered by language models. Integrating LangGraph with the Model Context Protocol (MCP) allows agents to utilize tools defined across one or more MCP servers, enabling seamless interaction with external data sources and services.


🧰 Key Features

  • Dynamic Tool Access: Connects to MCP servers to fetch available tools in real time.
  • Multi-Server Support: Interact with tools defined on multiple MCP servers simultaneously.
  • Standardized Communication: Utilizes the open MCP standard for consistent tool integration.

🛠 Installation

To use MCP tools in LangGraph, install the langchain-mcp-adapters package:

pip install langchain-mcp-adapters

🔗 Connecting to ContextForge

Here's how to set up a connection to your ContextForge:

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent

client = MultiServerMCPClient(
    {
        "gateway": {
            "url": "http://localhost:4444/mcp",
            "transport": "streamable_http",
        }
    }
)

Replace "http://localhost:4444/mcp" with the URL of your ContextForge.


🤖 Creating an Agent

After setting up the client, you can create a LangGraph agent:

agent = create_react_agent(
    tools=client.get_tools(),
    llm=your_language_model,
)

Replace your_language_model with your configured language model instance.


🧪 Using the Agent

Once the agent is created, you can use it to perform tasks:

response = agent.run("Use the 'weather' tool to get the forecast for Dublin.")
print(response)

📚 Additional Resources

Read the full file on GitHub · 80 lines

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. yesterday First seen · 80 lines · 0 tokens per session scan A bf2b363a4ca4

Subscribe to this mod's changes

langgraph is an agent published in the GitHub repository jetwind/mcp-context-forge (0 stars, last pushed 19d ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 459 tokens. A static security scan graded it A with 0 findings. It is 100% identical to langgraph, differing in 0 lines, and is treated as a copy.

Related

Other agents, from other repositories

Demonstrate

Agent for demonstrating VS Code features.

microsoft/vscode · 10 tokens

playwright-test-generator

Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.

microsoft/playwright · 151 tokens

.NET-Notebook-Migration-Agent

Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.

microsoft/ai-agents-for-beginners · 33 tokens

AVM Owner Triage

Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.

github/awesome-copilot · 61 tokens

Ultimate Transparent Thinking Beast Mode

Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.

github/awesome-copilot · 11 tokens

code-reviewer

Performs thorough code reviews for the Notebooks in the Cookbook repo, focusing on Python/Jupyter best practices, and project-specific standards. Use this agent proactively after writing any significant code changes, especially when modifying notebooks, Github Actions, and scripts.

anthropics/claude-cookbooks · 52 tokens