diff --git a/README.md b/README.md
index 3bddc8b1a..721df18a3 100644
--- a/README.md
+++ b/README.md
@@ -21,63 +21,55 @@ Train multi-step agents for real-world tasks using GRPO.
-## 🔌 MCP•RL: Teach your agents to master MCP
+## 🦜🔗 LangGraph Integration: Build Smarter Multi-Step Agents
-
+ART's **LangGraph integration** enables you to train sophisticated ReAct-style agents that improve through reinforcement learning. Build agents that reason, use tools, and adapt their behavior over time without manual prompt engineering.
-**MCP•RL** enables you to train agents to effectively use any MCP (Model Context Protocol) server with minimal setup. Simply provide a server URL and MCP•RL will:
+✨ **Key Benefits:**
-1. Automatically discover server tools
-2. Design input tasks that utilize those tools
-3. Train the model to improve performance on the MCP server using RULER
-4. Test on new tasks to validate the trained model
-
-✨ **Key Features:**
-
-- **No labeled data** - MCP•RL learns what tasks a server will be used for by analyzing its tools
-- **General-purpose** - Optimizes models for any MCP server
-- **Strong performance** - Matches or exceeds SOTA performance in 2/3 benchmarks
-- **Easy integration** - No customization of your MCP server required!
+- **Automatic behavior improvement** - Train agents to get better at multi-step reasoning
+- **Tool usage optimization** - Learn when and how to use tools more effectively
+- **Seamless integration** - Drop-in replacement for LangGraph's LLM initialization
+- **RULER compatibility** - Train without hand-crafted reward functions
```python
-from art.rewards import ruler_score_group
+import art
+from art.langgraph import wrap_rollout, init_chat_model
+from langgraph import create_react_agent
-# Specialize a model for NWS MCP server
-MCP_SERVER_URL = "https://server.smithery.ai/@smithery-ai/national-weather-service/mcp"
+# Your existing tools
+tools = [search_inbox, read_email, return_final_answer]
-# Generate training scenarios based on MCP tools
-scenarios = await generate_scenarios(
- num_scenarios=24,
- server_url=MCP_SERVER_URL,
-)
+@wrap_rollout(model)
+async def run_agent(scenario: str) -> art.Trajectory:
+ # Create LangGraph agent with ART's LLM wrapper
+ agent = create_react_agent(init_chat_model(), tools)
-# ...run the agent...
+ result = await agent.ainvoke({"messages": [("user", scenario)]})
+ return art.Trajectory() # Automatically captured
-# Use RULER to assign relative scores to each trajectory
-scored_groups = []
-for group in groups:
- judged_group = await ruler_score_group(group)
- scored_groups.append(judged_group)
-
-# Train the model to improve performance on the MCP server
-await model.train(scored_groups)
+# Train with RULER - no reward engineering needed!
+await art.train(model, reward_function="ruler")
```
+[📖 Learn more about LangGraph integration →](https://art.openpipe.ai/integrations/langgraph-integration) | [🏋️ Try the notebook →](https://colab.research.google.com/github/openpipe/art-notebooks/blob/main/examples/langgraph/art-e-langgraph.ipynb)
+
## ART Overview
ART is an open-source RL framework that improves agent reliability by allowing LLMs to **learn from experience**. ART provides an ergonomic harness for integrating GRPO into any python application. For a quick hands-on introduction, run one of the notebooks below. When you're ready to learn more, check out the [docs](https://art.openpipe.ai).
## 📒 Notebooks
-| Agent Task | Example Notebook | Description | Comparative Performance |
-| ------------------ | -------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| **MCP•RL** | [🏋️ Train agent](https://colab.research.google.com/github/openpipe/art-notebooks/blob/main/examples/mcp-rl/mcp-rl.ipynb) | Qwen 2.5 3B masters the NWS MCP server | [Link coming soon] |
-| **ART•E [RULER]** | [🏋️ Train agent](https://colab.research.google.com/github/openpipe/art-notebooks/blob/main/examples/art-e.ipynb) | Qwen 2.5 7B learns to search emails using RULER | 
[benchmarks](/examples/art-e/art_e/evaluate/display_benchmarks.ipynb) |
-| **2048** | [🏋️ Train agent](https://colab.research.google.com/github/openpipe/art-notebooks/blob/main/examples/2048/2048.ipynb) | Qwen 2.5 3B learns to play 2048 | 
[benchmarks](/examples/2048/benchmark_2048.ipynb) |
-| **Temporal Clue** | [🏋️ Train agent](https://colab.research.google.com/github/openpipe/art-notebooks/blob/main/examples/temporal_clue/temporal-clue.ipynb) | Qwen 2.5 7B learns to solve Temporal Clue | [Link coming soon] |
-| **Tic Tac Toe** | [🏋️ Train agent](https://colab.research.google.com/github/openpipe/art-notebooks/blob/main/examples/tic_tac_toe/tic-tac-toe.ipynb) | Qwen 2.5 3B learns to play Tic Tac Toe | 
[benchmarks](/examples/tic_tac_toe/benchmark_tic_tac_toe.ipynb) |
-| **Codenames** | [🏋️ Train agent](https://colab.research.google.com/github/openpipe/art-notebooks/blob/main/examples/codenames/Codenames_RL.ipynb) | Qwen 2.5 3B learns to play Codenames | 
[benchmarks](/examples/codenames/Codenames_RL.ipynb) |
-| **AutoRL [RULER]** | [🏋️ Train agent](https://colab.research.google.com/github/openpipe/art-notebooks/blob/main/examples/auto_rl.ipynb) | Train Qwen 2.5 7B to master any task | [Link coming soon] |
+| Agent Task | Example Notebook | Description | Comparative Performance |
+| ------------------- | -------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| **ART•E LangGraph** | [🏋️ Train agent](https://colab.research.google.com/github/openpipe/art-notebooks/blob/main/examples/langgraph/art-e-langgraph.ipynb) | Qwen 2.5 7B learns to search emails using LangGraph | [Link coming soon] |
+| **MCP•RL** | [🏋️ Train agent](https://colab.research.google.com/github/openpipe/art-notebooks/blob/main/examples/mcp-rl/mcp-rl.ipynb) | Qwen 2.5 3B masters the NWS MCP server | [Link coming soon] |
+| **ART•E [RULER]** | [🏋️ Train agent](https://colab.research.google.com/github/openpipe/art-notebooks/blob/main/examples/art-e.ipynb) | Qwen 2.5 7B learns to search emails using RULER | [benchmarks](/examples/art-e/art_e/evaluate/display_benchmarks.ipynb) |
+| **2048** | [🏋️ Train agent](https://colab.research.google.com/github/openpipe/art-notebooks/blob/main/examples/2048/2048.ipynb) | Qwen 2.5 3B learns to play 2048 | [benchmarks](/examples/2048/benchmark_2048.ipynb) |
+| **Temporal Clue** | [🏋️ Train agent](https://colab.research.google.com/github/openpipe/art-notebooks/blob/main/examples/temporal_clue/temporal-clue.ipynb) | Qwen 2.5 7B learns to solve Temporal Clue | [Link coming soon] |
+| **Tic Tac Toe** | [🏋️ Train agent](https://colab.research.google.com/github/openpipe/art-notebooks/blob/main/examples/tic_tac_toe/tic-tac-toe.ipynb) | Qwen 2.5 3B learns to play Tic Tac Toe | [benchmarks](/examples/tic_tac_toe/benchmark_tic_tac_toe.ipynb) |
+| **Codenames** | [🏋️ Train agent](https://colab.research.google.com/github/openpipe/art-notebooks/blob/main/examples/codenames/Codenames_RL.ipynb) | Qwen 2.5 3B learns to play Codenames | [benchmarks](/examples/codenames/Codenames_RL.ipynb) |
+| **AutoRL [RULER]** | [🏋️ Train agent](https://colab.research.google.com/github/openpipe/art-notebooks/blob/main/examples/auto_rl.ipynb) | Train Qwen 2.5 7B to master any task | [Link coming soon] |
## 📰 ART News
diff --git a/docs/docs.json b/docs/docs.json
index b27b0b825..e53789dff 100644
--- a/docs/docs.json
+++ b/docs/docs.json
@@ -67,6 +67,12 @@
"features/additional-histories"
]
},
+ {
+ "group": "Integrations",
+ "pages": [
+ "integrations/langgraph-integration"
+ ]
+ },
{
"group": "Tutorials",
"pages": [