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| title: LangGraph Multi-Agent MCTS Demo | |
| emoji: 🌳 | |
| colorFrom: blue | |
| colorTo: green | |
| sdk: gradio | |
| sdk_version: 4.44.0 | |
| app_file: app.py | |
| pinned: false | |
| license: mit | |
| tags: | |
| - multi-agent | |
| - mcts | |
| - reasoning | |
| - langgraph | |
| - ai-agents | |
| - wandb | |
| - experiment-tracking | |
| short_description: Multi-agent reasoning framework with Monte Carlo Tree Search | |
| # LangGraph Multi-Agent MCTS Framework | |
| **Production Demo with Trained Neural Models** - Experience real trained meta-controllers for intelligent agent routing | |
| ## What This Demo Shows | |
| This interactive demo showcases trained neural meta-controllers that dynamically route queries to specialized agents: | |
| ### 🤖 Trained Meta-Controllers | |
| 1. **RNN Meta-Controller** | |
| - GRU-based recurrent neural network | |
| - Learns sequential patterns in agent performance | |
| - Fast inference (~2ms latency) | |
| - Trained on 1000+ synthetic routing examples | |
| 2. **BERT Meta-Controller with LoRA** | |
| - Transformer-based text understanding | |
| - Parameter-efficient fine-tuning with LoRA adapters | |
| - Context-aware routing decisions | |
| - Better generalization to unseen query patterns | |
| ### 🧠 Three Specialized Agents | |
| 1. **HRM (Hierarchical Reasoning Module)** | |
| - Best for: Complex decomposition, multi-level problems | |
| - Technique: Hierarchical planning with adaptive computation | |
| 2. **TRM (Tree Reasoning Module)** | |
| - Best for: Iterative refinement, comparison tasks | |
| - Technique: Recursive refinement with convergence detection | |
| 3. **MCTS (Monte Carlo Tree Search)** | |
| - Best for: Optimization, strategic planning | |
| - Technique: UCB1 exploration with value backpropagation | |
| ### 📊 Key Features | |
| - **Real Trained Models**: Production-ready neural meta-controllers | |
| - **Intelligent Routing**: Models learn optimal agent selection patterns | |
| - **Routing Visualization**: See confidence scores and probability distributions | |
| - **Feature Engineering**: Demonstrates query → features → routing pipeline | |
| - **Performance Metrics**: Track execution time and routing accuracy | |
| ## How to Use | |
| 1. **Enter a Query**: Type your question or select an example | |
| 2. **Select Controller**: Choose RNN (fast) or BERT (context-aware) | |
| 3. **Process Query**: Click "🚀 Process Query" | |
| 4. **Review Results**: | |
| - See which agent the controller selected | |
| - View routing confidence and probabilities | |
| - Examine features used for decision-making | |
| - Check agent execution details | |
| ## Weights & Biases Integration | |
| Track your experiments with **Weights & Biases** for: | |
| - 📈 **Metrics Dashboard**: Visualize consensus scores, execution times, agent performance | |
| - 🔄 **Run Comparison**: Compare different configurations side-by-side | |
| - 📊 **Experiment History**: Track all your queries and results | |
| - 🌳 **MCTS Visualization**: Log tree exploration patterns | |
| ### Setting Up W&B | |
| 1. **Get API Key**: Sign up at [wandb.ai](https://wandb.ai) and get your API key | |
| 2. **Configure Space Secret** (if deploying your own): | |
| - Go to Space Settings → Repository secrets | |
| - Add: `WANDB_API_KEY` = your API key | |
| 3. **Enable in UI**: | |
| - Expand "Weights & Biases Tracking" accordion | |
| - Check "Enable W&B Tracking" | |
| - Set project name (optional) | |
| - Set run name (optional, auto-generated if empty) | |
| 4. **View Results**: After processing, click the W&B run URL to see your dashboard | |
| ### Logged Metrics | |
| - **Per Agent**: Confidence, execution time, response length, reasoning steps | |
| - **MCTS**: Best value, visits, tree depth, top actions with UCB1 scores | |
| - **Consensus**: Score, level (high/medium/low), number of agents | |
| - **Performance**: Total processing time | |
| - **Artifacts**: Full JSON results, tree visualizations | |
| ## Example Queries | |
| - "What are the key factors to consider when choosing between microservices and monolithic architecture?" | |
| - "How can we optimize a Python application that processes 10GB of log files daily?" | |
| - "Should we use SQL or NoSQL database for a social media application with 1M users?" | |
| - "How to design a fault-tolerant message queue system?" | |
| ## Technical Details | |
| ### Architecture | |
| ``` | |
| Query Input | |
| │ | |
| ├─→ HRM Agent (Hierarchical Decomposition) | |
| │ ├─ Component Analysis | |
| │ └─ Structured Synthesis | |
| │ | |
| ├─→ TRM Agent (Iterative Refinement) | |
| │ ├─ Initial Response | |
| │ ├─ Clarity Enhancement | |
| │ └─ Validation Check | |
| │ | |
| └─→ MCTS Engine (Strategic Search) | |
| ├─ Selection (UCB1) | |
| ├─ Expansion | |
| ├─ Simulation | |
| └─ Backpropagation | |
| │ | |
| ▼ | |
| Consensus Scoring | |
| │ | |
| ▼ | |
| Final Synthesized Response | |
| ``` | |
| ### MCTS Algorithm | |
| The Monte Carlo Tree Search implementation uses: | |
| - **UCB1 Selection**: `Q(s,a) + C * sqrt(ln(N(s)) / N(s,a))` | |
| - **Progressive Widening**: Controls branching factor | |
| - **Domain-Aware Actions**: Contextual decision options | |
| - **Value Backpropagation**: Updates entire path statistics | |
| ### Consensus Calculation | |
| ``` | |
| consensus = average_confidence * agreement_factor | |
| agreement_factor = max(0, 1 - std_deviation * 2) | |
| ``` | |
| High consensus (>70%) indicates agents agree on approach. | |
| Low consensus (<40%) suggests uncertainty or conflicting strategies. | |
| ## Demo Scope | |
| This demonstration focuses on **meta-controller training and routing**: | |
| - ✅ **Real Trained Models**: Production RNN and BERT controllers | |
| - ✅ **Actual Model Loading**: PyTorch and HuggingFace Transformers | |
| - ✅ **Feature Engineering**: Query analysis → feature vectors | |
| - ✅ **Routing Visualization**: See controller decision-making | |
| - ⚠️ **Simplified Agents**: Agent responses are mocked for demo purposes | |
| - ⚠️ **No Live LLM Calls**: Agents don't call actual LLMs (to reduce latency/cost) | |
| ## Full Production Framework | |
| The complete repository includes all production features: | |
| - ✅ **Neural Meta-Controllers**: RNN and BERT with LoRA (deployed here!) | |
| - ✅ **Agent Implementations**: Full HRM, TRM, and MCTS with PyTorch | |
| - ✅ **Training Pipeline**: Data generation, training, evaluation | |
| - ✅ **LLM Integration**: OpenAI, Anthropic, LM Studio support | |
| - ✅ **RAG Systems**: ChromaDB, FAISS, Pinecone vector stores | |
| - ✅ **Observability**: OpenTelemetry tracing, Prometheus metrics | |
| - ✅ **Storage**: S3 artifact storage, experiment tracking | |
| - ✅ **CI/CD**: Automated testing, security scanning, deployment | |
| **GitHub Repository**: [ianshank/langgraph_multi_agent_mcts](https://github.com/ianshank/langgraph_multi_agent_mcts) | |
| ## Technical Stack | |
| - **Python**: 3.11+ | |
| - **UI**: Gradio 4.x | |
| - **ML Frameworks**: PyTorch 2.1+, Transformers, PEFT (LoRA) | |
| - **Models**: GRU-based RNN, BERT-mini with LoRA adapters | |
| - **Architecture**: Neural meta-controller + multi-agent system | |
| - **Experiment Tracking**: Weights & Biases (optional) | |
| - **Numerical**: NumPy | |
| ## Research Applications | |
| This framework demonstrates concepts applicable to: | |
| - Complex decision-making systems | |
| - AI-assisted software architecture decisions | |
| - Multi-perspective problem analysis | |
| - Strategic planning with uncertainty | |
| ## Citation | |
| If you use this framework in research, please cite: | |
| ```bibtex | |
| @software{langgraph_mcts_2024, | |
| title={LangGraph Multi-Agent MCTS Framework}, | |
| author={Your Name}, | |
| year={2024}, | |
| url={https://github.com/ianshank/langgraph_multi_agent_mcts} | |
| } | |
| ``` | |
| ## License | |
| MIT License - See repository for details. | |
| --- | |
| **Built with** LangGraph, Gradio, and Python | **Demo Version**: 1.0.0 | |