ExperienceMaker

A comprehensive framework for AI agent experience generation and reuse
Empowering agents to learn from the past and excel in the future

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📰 What's New

• [2025-08] 🎉 ExperienceMaker v0.1.0 is now available on PyPI!
• [2025-07] 📚 Complete documentation and quick start guides released
• [2025-07] 🚀 Multi-backend vector store support (Elasticsearch & ChromaDB)

📰 What's Next

• Pre-built Experience Libraries: Domain repositories (finance/coding/education/research) + community marketplace
• Rich Experience Formats: Executable code/tool configs/pipeline templates/workflows
• Experience Validation: Quality analysis + cross-task effectiveness + auto-refinement
• Universal Trajectory Extraction: Raw logs/multimodal data/execution traces → experiences

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🌟 What is ExperienceMaker?

ExperienceMaker is a framework that transforms how AI agents learn and improve through experience-driven intelligence. By automatically extracting, storing, and intelligently reusing experiences from agent trajectories, it enables continuous learning and progressive skill enhancement.

💡 Why ExperienceMaker?

Traditional AI agents start from scratch with every new task, wasting valuable learning opportunities. ExperienceMaker changes this paradigm by:

• 🧠 Learning from History: Automatically extract actionable insights from both successful and failed attempts
• 🔄 Intelligent Reuse: Apply relevant past experiences to solve new, similar challenges more effectively
• 📈 Continuous Improvement: Build a growing knowledge base that makes agents progressively smarter

✨ Core Capabilities

🔍 Intelligent Experience Summarizer

• Success Pattern Recognition: Identify what works and understand the underlying principles
• Failure Analysis: Learn from mistakes to avoid repeating them in future tasks
• Comparative Insights: Understand the critical differences between successful and failed approaches
• Multi-step Trajectory Processing: Break down complex tasks into learnable, actionable segments

🎯 Smart Experience Retriever

• Semantic Search: Find relevant experiences using advanced embedding models and semantic understanding
• Context-Aware Ranking: Prioritize the most applicable experiences for current task contexts
• Dynamic Rewriting: Intelligently adapt past experiences to fit new situations and requirements
• Multi-modal Support: Handle various input types including query, messages

🗄️ Scalable Experience Management

• Multiple Storage Backends: Choose from Elasticsearch (production-ready), ChromaDB (development), or file-based storage (testing)
• Workspace Isolation: Organize experiences by projects, domains, or teams with complete separation
• Deduplication & Validation: Ensure high-quality, unique experience storage with automated quality control
• Batch Operations: Efficiently handle large-scale experience processing with optimized performance

🔧 Developer-Friendly Architecture

• REST API Interface: Seamless integration with existing systems through clean API design
• Modular Pipeline Design: Compose custom workflows from atomic operations with maximum flexibility
• Flexible Configuration: YAML files and command-line overrides for easy customization
• Experience Store: Ready-to-use out of the box — there’s no need for you to manually summarize experiences. You can directly leverage existing, comprehensive experience datasets to greatly enhance your agent’s capabilities.

🏗️ Framework Architecture

ExperienceMaker follows a modular, production-ready architecture designed for scalability:

⚙️ API Layer

• 🔍 Retriever API: Query-based and conversation-based experience retrieval with intelligent matching
• 📊 Summarizer API: Trajectory-to-experience conversion and automated storage management
• 🗄️ Vector Store API: Database management and workspace operations with full CRUD support

⚙️ Processing Pipeline

Our atomic operations can be seamlessly composed into powerful processing pipelines: custom1_op->custom2_op...

🔌 Extensible Components

• LLM Integration: OpenAI-compatible APIs with flexible model switching and provider support
• Embedding Models: Pluggable embedding providers for sophisticated semantic search capabilities
• Vector Stores: Multiple backends optimized for different deployment scenarios and scales
• Tools & Operators: Comprehensive, extensible library of processing operations

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🛠️ Installation

Option 1: Install from PyPI (Recommended)

pip install experiencemaker

Option 2: Install from Source

git clone https://github.com/modelscope/ExperienceMaker.git
cd ExperienceMaker
pip install .

⚙️ Environment Setup

Create a .env file in your project directory:

# Required: LLM API configuration
LLM_API_KEY="sk-xxx"
LLM_BASE_URL="https://xxx.com/v1"

# Required: Embedding model configuration  
EMBEDDING_MODEL_API_KEY="sk-xxx"
EMBEDDING_MODEL_BASE_URL="https://xxx.com/v1"

# Optional: Elasticsearch configuration (if using Elasticsearch backend)

🚀 Quick Start

For testing and development, use the local_file backend:

experiencemaker \
  http_service.port=8001 \
  llm.default.model_name=qwen3-32b \
  embedding_model.default.model_name=text-embedding-v4 \
  vector_store.default.backend=local_file

💡 Pro Tip: Check out our Advanced Guide for detailed configuration topics including custom pipelines, operation parameters, and advanced configuration methods.

The service will start on http://localhost:8001

🔍 Production Setup with Elasticsearch Backend

experiencemaker \
  llm.default.model_name=qwen3-32b \
  embedding_model.default.model_name=text-embedding-v4 \
  vector_store.default.backend=elasticsearch

Setup Elasticsearch:

export ES_HOSTS="http://localhost:9200"
# Quick setup using Elastic's official script
curl -fsSL https://elastic.co/start-local | sh

📖 Need Help? Refer to Vector Store Setup for comprehensive deployment guidance.

📝 Your First ExperienceMaker Script

Here's how to get started!

• The load_dotenv() function loads environment variables from your .env file, or you can manually export them.
• The base_url points to your ExperienceMaker service.
• The workspace_id serves as your experience storage namespace. Experiences in different workspaces remain completely isolated and cannot access each other.

import requests
from dotenv import load_dotenv

load_dotenv()
base_url = "http://0.0.0.0:8001/"
workspace_id = "test_workspace"

📊 Call Summarizer Examples

Batch summarize the trajectory list, where each trajectory consists of a message and a score.

• The message is the conversation history.
• The score represents the rating between 0 and 1, with 0 typically indicating failure and 1 indicating success.

response = requests.post(url=base_url + "summarizer", json={
  "workspace_id": workspace_id,
  "traj_list": [
    {"messages": messages, "score": 1.0}
  ]
})

response = response.json()
experience_list = response["experience_list"]
for experience in experience_list:
  print(experience)

🔍 Call Retriever Examples

Retrieve the top_k={top_k} experiences related to {query} in workspace=test_workspace, and finally accept the assembled context. Alternatively, you can also accept the raw experience_list parameter and assemble the context yourself.

response = requests.post(url=base_url + "retriever", json={
  "workspace_id": workspace_id,
  "query": query,
  "top_k": 1,
})

response = response.json()
experience_merged: str = response["experience_merged"]
print(f"experience_merged={experience_merged}")

💾 Dump Experiences From Vector Store

Dump the experience with workspace_id from the vector store into the {path}/{workspace_id}.jsonl file.

response = requests.post(url=base_url + "vector_store", json={
  "workspace_id": workspace_id,
  "action": "dump",
  "path": "./",
})
print(response.json())

📥 Load Experiences To Vector Store

Load the {path}/{workspace_id}.jsonl file into the vector store, workspace_id={workspace_id}.

response = requests.post(url=base_url + "vector_store", json={
  "workspace_id": "test_workspace1",
  "action": "load",
  "path": "./",
})

print(response.json())

🎭 Want to See It in Action? We've prepared a simple react agent that demonstrates how to enhance agent capabilities by integrating summarizer and retriever components, achieving significantly better performance.

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🧪 Experiments

🌍 Experiment on Appworld

Qwen3-8B Experimental Results

We test ExperienceMaker on Appworld with qwen3-8b:

Method	best@1	best@2	best@4
w/o ExperienceMaker (baseline)	0.3561	0.4052	0.4536
w ExperienceMaker
[1] extract + compare + recall	0.4069	0.5066	0.618
[2] extract + compare + recall + rewrite	0.3910	0.5038	0.6211

🔧 Experiment on BFCL-V3

Detailed benchmarking results and performance analysis coming soon.

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🛣️ Future Roadmap

Exciting features and improvements are on the horizon! Check out our detailed Future Roadmap for upcoming enhancements.

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🏪 Ready-made Experience Store

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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📚 Additional Resources

• Vector Store Setup: Complete production deployment guide
• Configuration Guide: Describes all available command-line parameters for ExperienceMaker Service
• Advanced Guide: Custom pipelines, operation parameters, and advanced configuration methods
• Operations Documentation: Comprehensive operations configuration reference
• Example Collection: Practical examples and use cases
• Future RoadMap: Our vision and upcoming features

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🤝 Contributing

We warmly welcome contributions from the community! Here's how you can help make ExperienceMaker even better:

🐛 Report Issues

• Bug reports with detailed reproduction steps
• Feature requests and enhancement suggestions
• Documentation improvements and clarifications
• Performance optimization ideas

💻 Code Contributions

• New operations and tools development
• Backend implementations and optimizations
• API enhancements and new endpoints
• Test coverage improvements and quality assurance

📝 Documentation

• Usage examples and comprehensive tutorials
• Best practices guides and design patterns
• Translation and localization efforts

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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📄 Citation

If you use ExperienceMaker in your research or projects, please cite:

@software{ExperienceMaker,
  title = {ExperienceMaker: A Comprehensive Framework for AI Agent Experience Generation and Reuse},
  author = {The ExperienceMaker Team},
  url = {https://github.com/modelscope/ExperienceMaker},
  month = {08},
  year = {2025},
}

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⚖️ License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

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