Multiagentic Retriever
A powerful, extensible framework for building multiagentic retrieval systems that combine multiple AI agents to enhance information retrieval and knowledge discovery.
Features
- Multi-Agent Architecture: Coordinate multiple specialized retrieval agents
- Flexible Retrieval: Support for various retrieval methods and data sources
- Extensible Design: Easy to add new agents and retrieval strategies
- Async Support: Built for high-performance concurrent operations
- Rich Configuration: Comprehensive configuration options for different use cases
Quick Start
pip install multiagentic-retriever
from multiagentic_retriever import MultiAgentRetriever, Agent
# Create agents
web_agent = Agent(name="web", retriever="web_search")
doc_agent = Agent(name="documents", retriever="vector_search")
# Initialize the multi-agent retriever
retriever = MultiAgentRetriever(agents=[web_agent, doc_agent])
# Perform retrieval
results = await retriever.retrieve("What is quantum computing?")
Architecture Overview
The framework consists of several key components:
- Agents: Individual retrieval agents with specialized capabilities
- Retrievers: Backend retrieval implementations (vector search, web search, etc.)
- Orchestrator: Coordinates agent interactions and result aggregation
- Configuration: Flexible configuration system for different deployment scenarios
Use Cases
- Research Assistance: Combine academic papers, web sources, and documentation
- Customer Support: Integrate knowledge bases, FAQs, and real-time information
- Content Discovery: Multi-source content aggregation and recommendation
- Decision Support: Gather information from multiple specialized sources
Getting Started
Ready to dive in? Check out our Getting Started Guide to begin building your first multiagentic retrieval system.
- GitHub: Report issues and contribute
- Documentation: Comprehensive guides and API reference
- Examples: Real-world usage examples and tutorials