# Cursor Chat: ai-agent-book

## Metadata
- **Project**: ai-agent-book
- **Path**: `/Users/boj`
- **Date**: 2025-09-21 15:35:52
- **Session ID**: `263b48b3-bedf-4481-8ede-e24fe31dae52`

## Conversation

### 👤 You

In projects/week3/gaia-experience, implement a modified version of the AWorld (repo inside the folder), which adds the feature of learning from experience: in learning mode, when a test case passed (success trajectory), use the LLM to summarize the trajectory into a natural language experience. Then provide an option to enable the mode to apply the learned knowledge, where the summarized experience is added to the system prompt.

Also add a preload knowledge base option, where the gaia-validation.jsonl is indexed in the knowledge base (the index is built by indexing the question, and the value is the whole task including trajectory), and it should find the matching document according to the query, and use the experience to add the system prompt.

 AWorld/examples/gaia/README.md   @https://github.com/inclusionAI/AWorld/blob/main/examples/gaia/README.md

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### 🤖 Cursor Assistant

I'll help you implement a modified version of AWorld with learning from experience capabilities. Let me start by exploring the existing structure in the `gaia-experience` folder and understanding the current implementation.

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### 🤖 Cursor Assistant

Now let me explore the GAIA example implementation and understand the structure of the validation data:

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### 🤖 Cursor Assistant

Now I'll implement the modified version of AWorld with learning from experience capabilities. Let me create a comprehensive solution with the following components:

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### 🤖 Cursor Assistant

## Implementation Complete! 🎉

I've successfully implemented a modified version of AWorld with **learning from experience** capabilities in `projects/week3/gaia-experience`. Here's what was created:

### 🌟 **Core Features Implemented**

1. **Experience Learning Mode**: Captures successful task trajectories and uses LLM to summarize them into reusable natural language experiences
2. **Knowledge Base with Preloading**: Indexes `gaia-validation.jsonl` for semantic search and retrieval of relevant past experiences
3. **Experience Application**: Enhances system prompts with relevant past experiences to improve performance on new tasks

### 📁 **Files Created**

#### Core Components:
- **`experience_agent.py`**: Extended agent that captures trajectories, learns from successes, and applies experiences
- **`knowledge_base.py`**: Semantic search knowledge base using FAISS and sentence transformers
- **`trajectory_summarizer.py`**: Summarizes execution trajectories into actionable insights using LLM
- **`run_with_experience.py`**: Main execution script with all learning features integrated

#### Supporting Files:
- **`config.yaml`**: Comprehensive configuration for all system components
- **`requirements.txt`**: Python dependencies including sentence-transformers and FAISS
- **`demo.py`**: Interactive demo showcasing all features
- **`run.sh`**: Convenient bash script for common operations
- **`env.template`**: Environment variable template
- **`README.md`**: Complete documentation
- **`QUICKSTART.md`**: 5-minute setup guide

### 🚀 **Key Usage Modes**

```bash
# 1. Learning Mode - Capture and learn from successes
./run.sh learn --start 0 --end 10

# 2. Application Mode - Apply learned experiences
./run.sh apply --start 10 --end 20 

# 3. Full Mode - Learn and apply simultaneously  
./run.sh full --start 0 --end 20

# 4. Interactive Demo
python demo.py --interactive
```

### 🔧 **How It Works**

1. **Learning Process**:
   - Captures all actions during task execution
   - On success, summarizes trajectory using LLM
   - Extracts approach, key insights, and tools used
   - Stores experience for future retrieval

2. **Experience Application**:
   - For new queries, searches for similar past experiences
   - Uses semantic embeddings for similarity matching
   - Enhances system prompt with relevant experiences
   - Agent benefits from past problem-solving patterns

3. **Knowledge Base Preloading**:
   - Parses `gaia-validation.jsonl` for questions and approaches
   - Builds searchable index using sentence embeddings
   - Provides instant access to proven solution patterns

### 💡 **Unique Features**

- **Semantic Search**: Uses sentence transformers for finding truly relevant experiences
- **LLM Summarization**: Intelligent extraction of key insights from trajectories
- **Flexible Configuration**: YAML-based configuration for all components
- **Hybrid Retrieval**: Supports both semantic and keyword-based search
- **Persistent Storage**: Experiences and indices are saved for reuse

### 📊 **Expected Benefits**

- **Improved Success Rate**: Learn from what worked before
- **Faster Problem Solving**: Skip exploration by using past insights
- **Knowledge Transfer**: Apply solutions from similar problems
- **Reduced Token Usage**: Less trial and error needed

### 🎯 **Quick Start**

```bash
# 1. Setup environment
cd projects/week3/gaia-experience
pip install -r requirements.txt

# 2. Configure API
cp env.template .env
# Edit .env with your OpenAI API key

# 3. Run demo
python demo.py
```

The system is now ready to learn from experience and apply that knowledge to improve performance on GAIA benchmark tasks! The implementation seamlessly integrates with the existing AWorld framework while adding powerful experience-based learning capabilities.

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*Exported from [Cursor View](https://github.com/saharmor/cursor-view)*