How It Works
🚀 Core Agent Features
Agents
Fundamental agent concepts and creation
Tasks
Task definition and management
Tools
Agent tools and capabilities
Memory
Basic memory systems
🧠 Advanced Intelligence
Advanced Memory
Quality-based, entity, user, and graph memory systems
Model Router
Intelligent LLM selection based on task requirements
Model Capabilities
Model feature detection and comparison
RouterAgent
Dynamic model selection and cost optimization
🔄 Workflow & Process
Process Workflows
Conditional execution with decision nodes
Handoffs
Agent-to-agent task delegation
Approval System
Human-in-the-loop approval for critical operations
Routing
Dynamic task routing and workflow branching
📊 Data & Knowledge
Knowledge Bases
Static reference information for agents
RAG
Retrieval Augmented Generation
Chunking Strategies
Document chunking for efficient retrieval
Context Management
Automatic token limit handling
📈 Monitoring & Performance
Telemetry
Performance tracking and monitoring
Display Callbacks
Comprehensive callback system for UI
AgentOps
Third-party monitoring integration
Latency Tracking
Performance optimization metrics
🎯 Specialized Agents
AutoAgents
Automatically created and managed agents
Mini Agents
Lightweight, focused AI agents
Code Agent
Specialized agent for code generation
Math Agent
Mathematical computation specialist
🔒 Safety & Control
Guardrails
Safety constraints and validation
Approval System
Human oversight for critical actions
Session Management
Stateful interactions with persistence
Input Handling
Safe input processing and validation
🎨 UI & Integration
UI Overview
User interface options
Gradio Interface
Web-based agent interfaces
Streamlit Apps
Interactive agent applications
MCP Integration
Model Context Protocol support
🚄 Advanced Features
Async Processing
Asynchronous agent execution
Parallelization
Concurrent task execution
Multimodal
Text, image, and audio processing
Reasoning
Chain-of-thought reasoning
🛠️ Developer Tools
CLI
Command-line interface
Testing
Testing agent systems
Local Development
Development environment setup
API Reference
Complete API documentation
📚 Learning Resources
Course
Comprehensive agent development course
Examples
Real-world implementation examples
Playbook
Best practices and patterns
Video Tutorials
Video guides and demonstrations
🌟 Feature Highlights
Most Popular Features
- Memory Systems - Store and retrieve information across agent interactions
- Model Router - Automatically select the best LLM for each task
- RAG Integration - Connect agents to your knowledge bases
- Display Callbacks - Create rich, interactive UIs
- Approval System - Add human oversight to critical operations
Latest Additions
- Telemetry - Comprehensive performance monitoring
- Context Window Management - Automatic handling of token limits
- Graph Memory - Neo4j integration for complex relationships
- MCP Support - Model Context Protocol for tool integration
Enterprise Features
- Session Management - Multi-user support with persistence
- Guardrails - Safety constraints and validation
- Telemetry - Performance tracking and analytics
- Approval Workflows - Human-in-the-loop controls
Getting Started
Choose Your Path
- New to PraisonAI? Start with the Quickstart Guide
- Building Agents? Check out Agent Profiles
- Advanced User? Explore Advanced Memory or Model Router
Pick Your Features
Select features based on your use case:
- Chatbots: Memory, Sessions, Display Callbacks
- Research: RAG, Knowledge, Chunking
- Automation: Approval, Guardrails, Process
- Analysis: Telemetry, Model Router, Context Management
Build & Deploy
- Use the CLI for quick prototyping
- Add UI for user interaction
- Deploy with monitoring enabled
- Scale with async and parallelization
This features overview is continuously updated as new capabilities are added to PraisonAI. Check back regularly or follow our GitHub repository for the latest updates.
Best Practices
Start with core agent features
Start with core agent features
Master agents, tasks, and tools before layering memory, RAG, or multi-agent workflows.
Enable observability early
Enable observability early
Add telemetry and context monitoring in staging so production issues are traceable.
Pick features by use case
Pick features by use case
Chatbots need sessions and memory; research agents need RAG and chunking — avoid enabling everything at once.
Follow the Quickstart first
Follow the Quickstart first
Run the Quickstart guide, then return here to explore advanced capabilities incrementally.