Frameworks for GenAI Development

LangChain

• Python & JavaScript frameworks for building LLM applications
• https://python.langchain.com/
• https://js.langchain.com/
• Features:
  • Chains and Agents
  • Document loading and splitting
  • Vector store integration
  • Memory management
  • Structured output parsing

LlamaIndex

• Framework for connecting custom data with LLMs
• https://www.llamaindex.ai/
• Features:
  • Data ingestion and indexing
  • Query interface
  • Advanced RAG capabilities
  • Structured data handling

Haystack

• End-to-end framework for building NLP applications
• https://haystack.deepset.ai/
• Features:
  • Question answering
  • Document search
  • Text generation
  • Summarization

AutoGen

• Framework for building multi-agent systems
• https://microsoft.github.io/autogen/
• Features:
  • Multi-agent conversations
  • Task automation
  • Code generation and execution
  • Custom agent creation

CrewAI

• Framework for orchestrating role-playing AI agents
• https://docs.crewai.com/
• Features:
  • Role-based agents
  • Task planning
  • Agent collaboration
  • Process automation

SWE Kit

• Comprehensive toolkit for AI-powered software development
• Core Features:
  • Code generation and refactoring
  • Automated documentation generation
  • Test case creation and management
  • Code review assistance
  • Architecture pattern recommendations
  • Performance optimization suggestions
  • Security vulnerability detection
  • API design assistance
• Development Workflows:
  • Intelligent code completion
  • Context-aware refactoring
  • Automated code quality checks
  • Smart debugging suggestions
  • Design pattern implementation
• Integration Capabilities:
  • Multiple IDE support
  • Version control systems
  • CI/CD pipeline integration
  • Code analysis tools
  • Popular development frameworks
• Documentation: https://composio.dev/swe-kit/

Agentarium

• Open-source framework for building and managing AI agents
• Key Features:
  • Multi-agent environment support
  • Real-time agent interaction visualization
  • Built-in debugging and monitoring tools
  • Customizable agent behaviors and roles
  • Environment simulation capabilities
  • Easy integration with popular LLM providers
  • Extensible plugin architecture
  • Memory management system
• Use Cases:
  • Multi-agent simulations
  • Agent behavior testing
  • Collaborative problem-solving
  • Agent interaction research
• Repository: https://github.com/Thytu/Agentarium

LangGraph

• Framework for building stateful agent workflows
• https://github.com/langchain-ai/langgraph
• Features:
  • Agent orchestration
  • State management
  • Workflow automation

Semantic Kernel

• Microsoft’s AI orchestration framework
• https://learn.microsoft.com/en-us/semantic-kernel/overview/
• Features:
  • AI orchestration
  • Plugin architecture
  • Memory and context management
  • Multi-modal AI support

Additional Frameworks

RAG-specific

• GraphRAG - Graph-based RAG framework
• ChromaDB - Embedding database with RAG capabilities
• Weaviate - Vector database with RAG support
• 7 Open Source Libraries for Retrieval Augmented Generation (RAG)

Agent-specific

• BabyAGI - Task-driven autonomous agent framework
• SuperAGI - Autonomous AI agent framework

Framework Selection Guide

Consider these factors when choosing a framework:

• Use case requirements
• Programming language preference
• Learning curve
• Community support
• Integration capabilities
• Deployment options
• Cost and licensing
• Performance requirements