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 agentmods add agents/jrmatherly/mcp-context-forge/langgraphgit clone --depth 1 https://github.com/jrmatherly/mcp-context-forgeWrote 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/agents/jrmatherly/mcp-context-forge/langgraph)<a href="https://agentmods.dev/agents/jrmatherly/mcp-context-forge/langgraph"><img src="https://agentmods.dev/badge/agents/jrmatherly/mcp-context-forge/langgraph.svg" alt="Measured on agentmods" 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 | $0.00000 | $0.00459 |
| Opus 5 | $0.00000 | $0.00230 |
| Sonnet 5 | $0.00000 | $0.00092 |
| Haiku 4.5 | $0.00000 | $0.00046 |
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.
This is a copy
86% identical to langgraph — 8 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.
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 MCP Gateway
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 MCP Gateway
Here's how to set up a connection to your MCP Gateway:
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 MCP Gateway.
🤖 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
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.
- yesterday First seen · 80 lines · 0 tokens per session scan A 91a1c47744e6
langgraph is an agent published in the GitHub repository jrmatherly/mcp-context-forge (0 stars, last pushed 6mo 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 86% identical to langgraph, differing in 8 lines, and is treated as a copy.
Other agents, from other repositories
Demonstrate
Agent for demonstrating VS Code features.
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.
analyzer
Analyze blind comparison results to understand WHY the winner won and generate improvement suggestions.
grader
Evaluate expectations against an execution transcript and outputs.
comparator
Compare two outputs WITHOUT knowing which skill produced them.
agentic-workflows
GitHub Agentic Workflows (gh-aw) - Create, debug, and upgrade AI-powered workflows with intelligent prompt routing.