| Tool/Framework | Key Strengths | Best For | Links |
|---|---|---|---|
| LlamaIndex | Connection to varied data sources | RAG workflows and data-centered LLM applications | GitHub · Docs · Website |
| Semantic-Kernel | C# modular SDK | .NET-based LLM integration | GitHub · Docs |
| LiteLLM | Multi-API LLM support | Unified access across LLM providers | GitHub · Docs |
| AutoGen | Agentic AI pipelines | Building agent workflows | GitHub · Docs |
| LMQL | Constraint-guided outputs, structured control | Fine-grained LLM interactions | GitHub · Website |
| Guidance | Structured output control | Programmatic prompt engineering | GitHub |
| Outlines | Output templating and structure | Reliable structured generation | GitHub · Docs |
| GPT-Engineer | CLI-based codegen experimentation | Quick prototype scripts | GitHub |
| Langfuse | Web-based prompt iteration and comparison | Rapid prompt development | GitHub · Website |
| DSPy | Programming foundation models | Optimizing LLM pipelines | GitHub · Docs |
| Tool/Framework | Key Strengths | Best For | Links |
|---|---|---|---|
| CrewAI | Multi-agent orchestration with role-based AI | Building collaborative AI agent teams | GitHub · Docs · Website |
| LangGraph | Stateful multi-actor applications with cycles | Complex, graph-based agent workflows | GitHub · Docs |
| Haystack | Production-ready NLP pipelines | End-to-end search and QA systems | GitHub · Docs · Website |
| txtai | Semantic search and workflows | Building AI-powered search engines | GitHub · Docs |
| Instructor | Structured output validation with Pydantic | Type-safe LLM responses | GitHub · Docs |
| Marvin | Lightweight AI engineering toolkit | Quick AI-powered Python functions | GitHub · Docs |
| Tool/Framework | Key Strengths | Best For | Links |
|---|---|---|---|
| AgentGPT | Autonomous agents with goal-based execution | Auto-GPT style agent systems | GitHub · Website |
| SuperAGI | Infrastructure for autonomous agents | Production agent deployment | GitHub · Docs · Website |
| AutoGPT | Self-prompting autonomous agents | Experimental autonomous systems | GitHub · Docs |
| BabyAGI | Task-driven autonomous agent | Simple agent implementations | GitHub |
| Langroid | Multi-agent programming framework | Building agent conversations | GitHub · Docs |
| Tool/Framework | Key Strengths | Best For | Links |
|---|---|---|---|
| Vertex AI | Google Cloud's managed ML platform | Enterprise-scale LLM deployment | Docs · Website |
| AWS Bedrock | AWS managed foundation models | AWS-native LLM applications | Docs · Website |
| Azure OpenAI | Microsoft's enterprise OpenAI offering | Enterprise Azure integrations | Docs · Website |
| Modal | Serverless cloud functions for ML | Quick LLM API deployment | GitHub · Docs · Website |
| Replicate | Run models via API | Easy model hosting and inference | Docs · Website |
| Tool/Framework | Key Strengths | Best For | Links |
|---|---|---|---|
| Chroma | Open-source vector database | Building RAG applications | GitHub · Docs · Website |
| Weaviate | Vector search with GraphQL | Semantic search systems | GitHub · Docs · Website |
| Pinecone | Managed vector database | Production vector search | Docs · Website |
| Qdrant | High-performance vector search | Fast similarity search | GitHub · Docs · Website |
| Milvus | Scalable vector database | Large-scale vector operations | GitHub · Docs · Website |
| Tool/Framework | Key Strengths | Best For | Links |
|---|---|---|---|
| Phoenix (Arize) | LLM observability and tracing | Debugging and monitoring LLMs | GitHub · Docs |
| LangSmith | LangChain's debugging platform | LangChain app development | Docs · Website |
| Weights & Biases | Experiment tracking for LLMs | ML experiment management | GitHub · Docs · Website |
| PromptLayer | Prompt management and versioning | Prompt engineering workflows | GitHub · Docs · Website |
| Helicone | LLM observability and analytics | Production monitoring | GitHub · Docs · Website |
| Tool/Framework | Key Strengths | Best For | Links |
|---|---|---|---|
| Flowise | Visual LLM app builder (no-code) | Quick prototyping without coding | GitHub · Docs · Website |
| Langflow | Drag-and-drop LangChain builder | Visual workflow creation | GitHub · Docs · Website |
| Chainlit | Python framework for chat UIs | Building conversational interfaces | GitHub · Docs · Website |
| Streamlit | Rapid UI prototyping | Data science + LLM demos | GitHub · Docs · Website |
| Gradio | Quick ML model interfaces | Sharing LLM demos | GitHub · Docs · Website |
| Tool/Framework | Key Strengths | Best For | Links |
|---|---|---|---|
| Axolotl | Fine-tuning various architectures | Custom model training | GitHub · Docs |
| Unsloth | Fast and memory-efficient fine-tuning | Quick LoRA/QLoRA training | GitHub · Docs |
| PEFT (Hugging Face) | Parameter-efficient fine-tuning | LoRA, adapter training | GitHub · Docs |
| TRL | Transformer reinforcement learning | RLHF implementations | GitHub · Docs |
| LLaMA-Factory | Easy-to-use LLM fine-tuning | One-stop training solution | GitHub |
| Tool/Framework | Key Strengths | Best For | Links |
|---|---|---|---|
| RAGAS | RAG evaluation framework | Testing retrieval quality | GitHub · Docs |
| TruLens | LLM app evaluation | Assessing app performance | GitHub · Docs |
| DeepEval | Unit testing for LLMs | CI/CD for LLM apps | GitHub · Docs |
| UpTrain | LLM observability and evaluation | Production monitoring | GitHub · Docs · Website |
- "Getting Started in 10 Minutes" - Quick setup tutorials for each framework
- "What is X?" - Explaining core concepts and use cases
- "Building Your First RAG App" - Step-by-step project tutorials
- "LLM Framework Comparison" - Side-by-side feature comparisons
- "Multi-Agent Systems Deep Dive" - CrewAI vs AutoGen vs LangGraph
- "Production RAG Best Practices" - Using vector databases effectively
- "Monitoring LLM Applications" - Phoenix, LangSmith, Helicone comparison
- "Fine-tuning on a Budget" - Using Unsloth and Axolotl
- "Building Production-Grade LLM Apps" - Complete architecture walkthrough
- "Custom Agent Workflows" - Advanced LangGraph patterns
- "Performance Optimization" - Benchmarking and tuning LLM applications
- "Enterprise Deployment" - Using AWS Bedrock, Azure OpenAI, or Vertex AI
- 🔥 "Framework Friday" - Weekly deep dive into a different framework
- 🎯 "Use Case Spotlight" - Solving real-world problems with LLMs
- 🆚 "X vs Y" - Framework comparison battles
- 🏗️ "Build With Me" - Live coding sessions
- 📈 "From Zero to Production" - Complete project series
- ✅ Multi-agent systems and orchestration
- ✅ RAG implementation and optimization
- ✅ Fine-tuning open-source models
- ✅ Production deployment patterns
- ✅ Cost optimization strategies
- ✅ No-code/Low-code LLM tools
- ✅ LLM observability and monitoring
- Framework installation and setup guides
- Best practices and design patterns
- Troubleshooting common issues
- Performance benchmarking
- Security and privacy considerations
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Last Updated: September 2025
Maintained by: AI Content Creators Community
Star this guide and keep it updated with the latest LLM frameworks! ⭐