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README.md
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README.md
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---
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## 🌟 What is ExperienceMaker?
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ExperienceMaker is a framework that revolutionizes how AI agents learn and improve through **experience-driven intelligence**.
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By automatically extracting, storing, and reusing experiences from agent trajectories, it enables continuous learning and progressive skill enhancement.
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ExperienceMaker is a revolutionary framework that transforms how AI agents learn and improve through **experience-driven intelligence**.
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By automatically extracting, storing, and intelligently reusing experiences from agent trajectories, it enables continuous learning and progressive skill enhancement.
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### 🚀 Why ExperienceMaker?
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### 💡 Why ExperienceMaker?
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Traditional AI agents start from scratch with every new task, wasting valuable learning opportunities.
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ExperienceMaker changes this by:
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- **🧠 Learning from History**: Automatically extract actionable insights from successful and failed attempts
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- **🔄 Intelligent Reuse**: Apply relevant past experiences to solve new, similar problems
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- **📈 Continuous Improvement**: Build a growing knowledge base that makes agents smarter over time
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- **⚡ Faster Problem Solving**: Reduce trial-and-error by leveraging proven strategies
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ExperienceMaker changes this paradigm by:
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- **🧠 Learning from History**: Automatically extract actionable insights from both successful and failed attempts
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- **🔄 Intelligent Reuse**: Apply relevant past experiences to solve new, similar challenges more effectively
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- **📈 Continuous Improvement**: Build a growing knowledge base that makes agents progressively smarter
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- **⚡ Faster Problem Solving**: Dramatically reduce trial-and-error by leveraging proven strategies
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### ✨ Core Capabilities
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#### 🔍 **Intelligent Experience Summarizer**
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- **Success Pattern Recognition**: Identify what works and why
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- **Failure Analysis**: Learn from mistakes to avoid repetition
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- **Comparative Insights**: Understand the difference between successful and failed approaches
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- **Multi-step Trajectory Processing**: Break down complex tasks into learnable segments
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- **Success Pattern Recognition**: Identify what works and understand the underlying principles
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- **Failure Analysis**: Learn from mistakes to avoid repeating them in future tasks
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- **Comparative Insights**: Understand the critical differences between successful and failed approaches
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- **Multi-step Trajectory Processing**: Break down complex tasks into learnable, actionable segments
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#### 🎯 **Smart Experience Retriever**
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- **Semantic Search**: Find relevant experiences using advanced embedding models
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- **Context-Aware Ranking**: Prioritize the most applicable experiences for current tasks
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- **Dynamic Rewriting**: Adapt past experiences to fit new contexts
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- **Multi-modal Support**: Handle various input types (queries, conversations, trajectories)
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- **Semantic Search**: Find relevant experiences using advanced embedding models and semantic understanding
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- **Context-Aware Ranking**: Prioritize the most applicable experiences for current task contexts
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- **Dynamic Rewriting**: Intelligently adapt past experiences to fit new situations and requirements
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- **Multi-modal Support**: Handle various input types including queries, conversations, and trajectories
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#### 🗄️ **Scalable Experience Management**
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- **Multiple Storage Backends**: Choose from Elasticsearch (production), ChromaDB (development), or file-based (testing)
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- **Workspace Isolation**: Organize experiences by projects, domains, or teams
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- **Deduplication & Validation**: Ensure high-quality, unique experience storage
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- **Batch Operations**: Efficiently handle large-scale experience processing
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- **Multiple Storage Backends**: Choose from Elasticsearch (production-ready), ChromaDB (development), or file-based storage (testing)
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- **Workspace Isolation**: Organize experiences by projects, domains, or teams with complete separation
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- **Deduplication & Validation**: Ensure high-quality, unique experience storage with automated quality control
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- **Batch Operations**: Efficiently handle large-scale experience processing with optimized performance
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#### 🔧 **Developer-Friendly Architecture**
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- **REST API Interface**: Easy integration with existing systems
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- **Modular Pipeline Design**: Compose custom workflows from atomic operations
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- **Flexible Configuration**: YAML files and command-line overrides
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- **REST API Interface**: Seamless integration with existing systems through clean API design
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- **Modular Pipeline Design**: Compose custom workflows from atomic operations with maximum flexibility
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- **Flexible Configuration**: YAML files and command-line overrides for easy customization
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### 🏗️ Framework Architecture
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<p align="center">
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<img src="doc/framework.png" alt="ExperienceMaker Architecture" width="70%">
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</p>
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ExperienceMaker follows a modular, scalable architecture designed for production use:
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#### 🌐 **API Layer**
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- **🔍 Retriever API**: Query-based and conversation-based experience retrieval
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- **📊 Summarizer API**: Trajectory-to-experience conversion and storage
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- **🗄️ Vector Store API**: Database management and workspace operations
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- **🤖 Agent API**: ReAct-based agent execution with experience enhancement
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ExperienceMaker follows a modular, production-ready architecture designed for scalability:
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#### <EFBFBD><EFBFBD> **API Layer**
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- **🔍 Retriever API**: Query-based and conversation-based experience retrieval with intelligent matching
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- **📊 Summarizer API**: Trajectory-to-experience conversion and automated storage management
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- **🗄️ Vector Store API**: Database management and workspace operations with full CRUD support
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- **🤖 Agent API**: ReAct-based agent execution enhanced with experience-driven decision making
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#### ⚙️ **Processing Pipeline**
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Our atomic operations can be composed into powerful pipelines:
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Our atomic operations can be seamlessly composed into powerful processing pipelines:
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**Retrieval Pipeline**:
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```
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build_query_op->recall_vector_store_op->merge_experience_op
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```
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#### 🔌 **Extensible Components**
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- **LLM Integration**: OpenAI-compatible APIs with flexible model switching
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- **Embedding Models**: Pluggable embedding providers for semantic search
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- **Vector Stores**: Multiple backends for different deployment scenarios
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- **Tools & Operators**: Extensible library of processing operations
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- **LLM Integration**: OpenAI-compatible APIs with flexible model switching and provider support
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- **Embedding Models**: Pluggable embedding providers for sophisticated semantic search capabilities
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- **Vector Stores**: Multiple backends optimized for different deployment scenarios and scales
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- **Tools & Operators**: Comprehensive, extensible library of processing operations
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---
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@ -125,21 +125,21 @@ EMBEDDING_MODEL_BASE_URL="https://xxx.com/v1"
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```
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## 🚀 Start the Service
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## 🚀 Quick Start
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For testing, use the `local_file` backend:
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For testing and development, use the `local_file` backend:
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```bash
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experiencemaker \
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http_service.port=8001 \
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llm.default.model_name=qwen3-32b \
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embedding_model.default.model_name=text-embedding-v4 \
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vector_store.default.backend=local_file
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```
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Refer to [Advanced Guide](./doc/advanced_guide.md) for more details.
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This guide covers advanced configuration topics including custom pipelines, operation parameters, and configuration methods.
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💡 **Pro Tip**: Check out our [Advanced Guide](./doc/advanced_guide.md) for detailed configuration topics including custom pipelines, operation parameters, and advanced configuration methods.
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The service will start on `http://localhost:8001`
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### Elasticsearch Backend
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### 🔍 Production Setup with Elasticsearch Backend
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```bash
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experiencemaker \
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llm.default.model_name=qwen3-32b \
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# Quick setup using Elastic's official script
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curl -fsSL https://elastic.co/start-local | sh
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```
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Refer to [Vector Store Setup](./doc/vector_store_setup.md) for more details.
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📖 **Need Help?** Refer to [Vector Store Setup](./doc/vector_store_setup.md) for comprehensive deployment guidance.
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## 📝 Your First ExperienceMaker Script
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Here, load_dotenv is used to load environment variables from the .env file, or you can manually export them to the environment.
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`base_url` is the address of the ExperienceMaker service mentioned above, and workspace_id is the name of the current workspace for storing experiences.
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Experiences in different workspace_ids are not shared or accessible across workspaces.
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Here's how to get started! The `load_dotenv()` function loads environment variables from your `.env` file, or you can manually export them.
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The `base_url` points to your ExperienceMaker service, and `workspace_id` serves as your experience storage namespace.
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Experiences in different workspaces remain completely isolated and cannot access each other.
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### Call Summarizer Examples
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### 📊 Call Summarizer Examples
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```python
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import requests
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from dotenv import load_dotenv
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print(experience)
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```
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### Call Retriever Examples
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### 🔍 Call Retriever Examples
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```python
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import requests
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print(f"experience_merged={experience_merged}")
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```
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### Dump Experiences From Vector Store
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### 💾 Dump Experiences From Vector Store
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```python
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import requests
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print(response.json())
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```
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### Load Experiences To Vector Store
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### 📥 Load Experiences To Vector Store
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```python
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import requests
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print(response.json())
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```
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Here, we have prepared a [simple react agent](./cookbook/simple_demo/simple_demo.py) to demonstrate how to enhance its
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capabilities by integrating a summarizer and a retriever, thereby achieving better performance.
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---
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## Experiment
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### Experiment on Appworld
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TODO
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### Experiment on BFCL-V3
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TODO
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🎭 **Want to See It in Action?** We've prepared a [simple react agent](./cookbook/simple_demo/simple_demo.py) that demonstrates how to enhance agent capabilities by integrating summarizer and retriever components, achieving significantly better performance.
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---
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## Future RoadMap
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## 🧪 Experiments
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TODO
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### 🌍 Experiment on Appworld
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Coming Soon! Stay tuned for comprehensive evaluation results.
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### 🔧 Experiment on BFCL-V3
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Detailed benchmarking results and performance analysis coming soon.
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---
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## Ready-made Experience Store
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## 🛣️ Future Roadmap
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TODO
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Exciting features and improvements are on the horizon! Check out our detailed [Future Roadmap](./doc/future_roadmap.md) for upcoming enhancements.
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---
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## 🏪 Ready-made Experience Store
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Pre-built experience collections for common domains and use cases are coming soon. This will include ready-to-use experiences for web automation, data processing, API interactions, and more.
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---
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## 📚 Additional Resources
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- **[Vector Store Setup](./doc/vector_store_setup.md)**: Production deployment guide
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- **[Configuration Guide](./doc/configuration_guide.md)**: Advanced configuration options
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- **[Advanced Guide](./doc/advanced_guide.md)**: custom pipelines, operation parameters, and configuration methods.
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- **[Operations Documentation](./doc/operations_documentation.md)**: Advanced operations configuration
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- **[Example Collection](./cookbook)**: More practical examples
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- **[Future RoadMap](./doc/future_roadmap.md)**: Our future plans
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- **[Vector Store Setup](./doc/vector_store_setup.md)**: Complete production deployment guide
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- **[Configuration Guide](./doc/configuration_guide.md)**: Advanced configuration options and best practices
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- **[Advanced Guide](./doc/advanced_guide.md)**: Custom pipelines, operation parameters, and advanced configuration methods
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- **[Operations Documentation](./doc/operations_documentation.md)**: Comprehensive operations configuration reference
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- **[Example Collection](./cookbook)**: Practical examples and use cases
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- **[Future RoadMap](./doc/future_roadmap.md)**: Our vision and upcoming features
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---
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## 🤝 Contributing
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We welcome contributions from the community! Here's how you can help:
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We warmly welcome contributions from the community! Here's how you can help make ExperienceMaker even better:
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### 🐛 **Report Issues**
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- Bug reports and feature requests
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- Documentation improvements
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- Performance optimization suggestions
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- Bug reports with detailed reproduction steps
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- Feature requests and enhancement suggestions
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- Documentation improvements and clarifications
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- Performance optimization ideas
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### 💻 **Code Contributions**
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- New operations and tools
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- Backend implementations
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- API enhancements
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- Test coverage improvements
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- New operations and tools development
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- Backend implementations and optimizations
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- API enhancements and new endpoints
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- Test coverage improvements and quality assurance
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### 📝 **Documentation**
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- Usage examples and tutorials
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- Best practices and patterns
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- Translation and localization
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- Usage examples and comprehensive tutorials
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- Best practices guides and design patterns
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- Translation and localization efforts
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**Getting Started**: Fork the repository, create a feature branch, and submit a pull request. Please follow our coding standards and include tests for new functionality.
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**Getting Started**: Fork the repository, create a feature branch, and submit a pull request. Please follow our coding standards and include comprehensive tests for new functionality.
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---
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## 📄 Citation
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@ -57,7 +57,7 @@ class VectorStoreConfig:
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@dataclass
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class AppConfig:
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pre_defined_config: str = field(default="demo_config")
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pre_defined_config: str = field(default="default_config")
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config_path: str = field(default="")
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http_service: HttpServiceConfig = field(default_factory=HttpServiceConfig)
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thread_pool: ThreadPoolConfig = field(default_factory=ThreadPoolConfig)
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