Welcome to LRS-Agents Documentation

LRS-Agents enables AI agents to automatically adapt when tools fail through Active Inference and precision tracking.

Getting Started

• Installation
  • Requirements
  • Basic Installation
  • Installation with Optional Dependencies
  • Installation from Source
  • Verify Installation
  • Configuration
  • Docker Installation
  • Kubernetes Deployment
  • Troubleshooting
  • Getting Help
  • Next Steps
• Quickstart
  • Overview
  • Step 1: Install LRS-Agents
  • Step 2: Set API Key
  • Step 3: Create Your First Tool
  • Step 4: Create an LRS Agent
  • Step 5: Run a Task
  • Understanding the Output
  • What Just Happened?
  • Key Concepts
  • Next Steps
  • Common Patterns
  • Troubleshooting
  • Further Reading
• Core Concepts
  • Active Inference
  • The Free Energy Principle
  • Precision Tracking
  • Hierarchical Precision
  • Prediction Errors
  • Tool Lenses
  • Policy Selection
  • Adaptation
  • Multi-Agent Concepts
  • Putting It All Together
  • Key Takeaways
  • Next Steps

User Guides

• LangChain Integration
  • Overview
  • Quick Start
  • Using LangChain Tools
  • Common LangChain Tools
  • Using LangChain Agents with LRS
  • LangChain + LRS Hybrid
  • Using LangChain Chains
  • LangGraph Integration
  • Error Handling
  • Best Practices
  • Complete Example
  • Troubleshooting
  • Next Steps
• OpenAI Assistants Integration
  • Overview
  • Quick Start
  • Using Assistants for Policy Generation
  • Precision-Adaptive Temperature
  • Using Built-in Assistant Tools
  • Complete Agent Example
  • Handling Long-Running Operations
  • Error Handling
  • Best Practices
  • Cost Optimization
  • Troubleshooting
  • Next Steps
• AutoGPT Integration
  • Overview
  • Quick Start
  • Converting AutoGPT Commands to LRS Tools
  • Common AutoGPT Commands
  • Complete Research Agent Example
  • Handling AutoGPT Loops
  • Precision Dynamics in Research
  • Monitoring and Logging
  • Best Practices
  • Troubleshooting
  • Comparison with Standard AutoGPT
  • Next Steps
• Production Deployment
  • Overview
  • Architecture
  • Docker Deployment
  • Kubernetes Deployment
  • Monitoring
  • Database Management
  • Security
  • Performance Optimization
  • Health Checks
  • Troubleshooting
  • Checklist
  • Next Steps
  • Further Reading

API Reference

• Core Components
  • Precision Tracking
  • Tool Lenses
  • Tool Registry
  • Free Energy
• Inference API
  • Meta-Cognitive Prompting
  • LLM Policy Generator
  • Hybrid Evaluator
• Integration API
  • LangGraph
  • LangChain Adapter
  • OpenAI Assistants
  • AutoGPT Adapter
• Monitoring API
  • State Tracking
  • Structured Logging
  • Dashboard (Optional)

Theory

• Active Inference
  • Overview
  • The Core Principle
  • Active Inference for AI Agents
  • Mathematical Framework
  • How LRS-Agents Implements Active Inference
  • Active Inference vs Other Approaches
  • Theoretical Foundations
  • Real-World Applications
  • Limitations and Open Questions
  • Mathematical Details
  • Further Reading
  • Next Steps
• Free Energy
  • Overview
  • What is Expected Free Energy?
  • Epistemic Value Calculation
  • Pragmatic Value Calculation
  • Total Expected Free Energy
  • Precision-Weighted Selection
  • Adaptive G Evaluation
  • Hybrid G Evaluation
  • Edge Cases and Special Scenarios
  • Implementation Details
  • Further Reading
  • Next Steps
• Precision Dynamics
  • Overview
  • What is Precision?
  • Precision Updates
  • Precision-Dependent Behavior
  • Adaptation Trigger
  • Hierarchical Precision
  • Precision Collapse
  • Social Precision
  • Precision Dynamics in Practice
  • Mathematical Details
  • Common Pitfalls
  • Future Directions
  • Further Reading
  • Next Steps

Indices and tables

• Index

• Module Index

• Search Page