> For the complete documentation index, see [llms.txt](https://docs.fastrouter.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.fastrouter.ai/integrations/agents/langgraph.md).
# Langgraph
### What is LangGraph?
[LangGraph](https://www.langchain.com/langgraph) is a framework for building stateful, multi-step agents as graphs. Built by the LangChain team, it adds durable state, cycles, branching, and human-in-the-loop control on top of LangChain's model interfaces.
By routing LangGraph through FastRouter, you get:
* **100+ models** from OpenAI, Anthropic, Google, xAI, Meta, Groq, Mistral, and more through one endpoint
* **Observability** for every request: cost, tokens, latency, and model selection tracked in real time
* **Reliability** through automatic failover across providers, response caching, and intelligent routing
* **Governance** with per-key budgets, rate limits, model restrictions, role-based access, and project isolation
This guide covers connecting LangGraph (Python) to FastRouter using a `ChatOpenAI` model inside a prebuilt ReAct agent.
**Prerequisites**
* A FastRouter.ai account ([sign up](https://fastrouter.ai))
* Python 3.10 or higher
***
#### Quick Start
**Step 1: Create a Project and Virtual Environment**
You'll only need to do this once:
```bash
mkdir my_project
cd my_project
python -m venv .venv
```
Activate the virtual environment. Do this every time you start a new terminal session.
On macOS or Linux:
```bash
source .venv/bin/activate
```
On Windows:
```bash
.venv\Scripts\activate
```
**Step 2: Install LangGraph**
```bash
pip install langchain langgraph langchain-openai
```
**Step 3: Get Your FastRouter API Key**
1. Sign up or log in at [fastrouter.ai](https://fastrouter.ai)
2. Navigate to your project's **Keys** page
3. Click **Create User Key**
4. Copy the key immediately. FastRouter does not display the key again after creation.
Export it in your terminal:
```bash
export FASTROUTER_API_KEY=sk-add-your-key-here
```
**Step 4: Point the Model at FastRouter**
LangGraph uses LangChain chat models, so the standard `ChatOpenAI` class works once you override the base URL. Save this as `graph_example.py`:
```python
import os
from langchain_openai import ChatOpenAI
from langchain.agents import create_agent
llm = ChatOpenAI(
base_url="https://api.fastrouter.ai/api/v1",
api_key=os.environ["FASTROUTER_API_KEY"],
model="openai/gpt-5.2",
)
def get_weather(city: str) -> str:
"""Get the current weather for a city."""
return f"The weather in {city} is 72°F and sunny."
agent = create_agent(llm, tools=[get_weather])
result = agent.invoke({"messages": [{"role": "user", "content": "What's the weather in San Francisco?"}]})
print(result["messages"][-1].content)
```
**Step 5: Run the Agent**
```bash
python graph_example.py
```
The agent calls the `get_weather` tool and responds with the weather. Every request, token count, and cost appears in your [FastRouter Dashboard](https://dashboard.fastrouter.ai/).
***
#### Use LangGraph with 100+ Models
FastRouter uses the `provider/model-name` format. Switch providers by changing the model slug—no new dependencies or credentials:
```python
# Anthropic Claude
model="anthropic/claude-4.5-sonnet"
# Google Gemini
model="google/gemini-3.1-pro-preview"
```
You can give each node in your graph a different model—a fast model for routing nodes and a frontier model for reasoning nodes.
[Explore the full model catalog](https://fastrouter.ai/models)
**Automatic Model Selection**
Let FastRouter pick the best model for each request based on query complexity, domain, and cost:
```python
model="fastrouter/auto"
```
[Explore automatic model selection](https://docs.fastrouter.ai/automatic-model-selection)
***
#### FAQs
**Configuration & Setup**
**Can I use multiple models with the same API key?**
Yes. The API key controls access and budget. Each node can use its own `ChatOpenAI` instance with a different model, all sharing one key.
**Can I restrict a key to only use specific models?**
Yes. When creating or editing a key, use the **Select Models** setting to limit which models the key can access. FastRouter rejects requests to unauthorized models.
**Costs & Budgeting**
**Graphs with cycles can make many model calls. How do I control cost?**
Set a budget and rate limit on the key, and use Dynamic Tags to attribute spend per graph or per run. The Dashboard breaks down costs by project, key, model, and tag.
**Performance & Reliability**
**Does FastRouter add latency to graph execution?**
FastRouter adds near-zero gateway overhead, negligible compared to model inference time.
***
#### Next Steps
* [Explore the full model catalog](https://fastrouter.ai/models)
* [Set up Fallback Models](https://docs.fastrouter.ai/fallback-models) for high availability
* [Configure Alerts](https://docs.fastrouter.ai/alerts) for spend and performance monitoring
* [Run a Free Audit](https://fastrouter.ai/audit) on your existing LLM traffic to identify savings
* [Join the Discord community](https://discord.gg/QfTgEtMyyU)
---
# Agent Instructions
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