🧬 Feature Overview (Compact)

graph TD
   LLMChain --> Chains
   LLMChain --> Agents
   LLMChain --> Providers
   LLMChain --> Memory
   LLMChain --> Retrieval
   LLMChain --> Monitoring
   LLMChain --> UI
   LLMChain --> Plugins
   LLMChain --> Deploy
   Chains --> Workflows
   Agents --> Orchestration
   Providers --> AIProviders
   Retrieval --> RetrieverStore
   Monitoring --> Tracking
   UI --> VisualUI
   Plugins --> PluginSystem
   Deploy --> APICLI

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LLMChain: Professional Modular AI Framework

LLMChain is a professional, modular framework for building advanced LLM-powered applications and agents. It offers robust orchestration, broad AI provider support, compositional chains, memory, retrievers, monitoring, experiment tracking, enterprise features, and a modern UI.

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🚀 Key Features

• Modular Chains: Compose flexible workflows (QA, summarization, map-reduce, sequential, multi-prompt, etc.)
• Agent Orchestration: Pluggable strategies, subagents, voting, cascading, and more
• AI LLM Providers: OpenAI, Anthropic, Google, Cohere, HuggingFace, DeepSeek, Groq, Ollama, Mistral, Azure, VertexAI, Replicate, Fireworks, Together, Perplexity, MosaicML, PaLM, Bedrock, SageMaker, Clarifai, Petals, AlephAlpha, Forefront, Writer, Yandex, Baidu, Qianfan, Zhipu, Baichuan, ERNIE, Spark, DashScope, Moonshot, Zero, Qwen, Yi, MiniMax, DeepInfra, BaiduCloud, SparkDesk, BaiduWenxin, ErnieBot, Baichuan2, Qwen2, Yi1, Yi34, MiniChain, Moon
• Embeddings & Vector Stores: In-memory, semantic, Chroma, Pinecone, Weaviate, and more
• Retrievers: Simple, multi-query, document compression, rerankers, conversational QA
• Memory: Generic, chat, episodic, retrieval-augmented
• Prompt Engineering: Templates, manager, multi-prompt routing
• Monitoring & Evaluation: Logger, evaluator, experiment tracker, distributed tracing
• Enterprise Features: Security, scaling, environment config, .env loader
• UI Tools: Visual chain builder, dashboards
• Plugin System: Extend with custom tools, retrievers, chains
• Production Deployment: FastAPI server, CLI, Docker
• Comprehensive Documentation: API reference, tutorials, migration guides

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🏗️ Architecture Diagram

graph TD
    UserInput -->|Prompt| Chain
    Chain -->|LLM Call| LLM[LLM Provider]
    Chain -->|Retrieve| Retriever
    Retriever -->|Search| VectorStore
    Chain -->|Memory| Memory
    Chain -->|Callback| Monitoring
    LLM -->|Embedding| Embedding
    Chain -->|Agent| AgentOrchestrator
    AgentOrchestrator -->|Strategy| SubAgent
    Monitoring -->|Log| Logger
    Monitoring -->|Evaluate| Evaluator
    Chain -->|Deploy| FastAPI
    Chain -->|Deploy| CLI
    Chain -->|Plugin| PluginManager
    PluginManager -->|Extend| Plugin
    Chain -->|Prompt| PromptManager
    Chain -->|Config| EnvConfig
    Chain -->|Load| DotenvLoader
    Chain -->|Trace| DistributedTracing
    Chain -->|Scale| Scaling
    Chain -->|Secure| Security
    Chain -->|Docs| Documentation
    Chain -->|UI| UI[Visual Builder]
    UI -->|Dashboard| Dashboard
    Chain -->|Experiment| ExperimentTracker
    Chain -->|Debug| Debugger

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🤖 AI LLM Providers & Workflow

flowchart LR
    UserInput --> Chain
    Chain --> LLMProviders
    LLMProviders[AI LLM Providers: OpenAI, Anthropic, Google, Cohere, HuggingFace, etc.]
    Chain --> VectorStore
    Chain --> Retriever
    Chain --> Memory
    Chain --> Monitoring
    Chain --> ExperimentTracker
    Chain --> UI
    Chain --> PluginManager
    Chain --> FastAPI
    Chain --> CLI
    Chain --> Documentation
    Chain --> Security
    Chain --> Scaling
    Chain --> DistributedTracing
    Chain --> EnvConfig
    Chain --> DotenvLoader
    Chain --> Debugger
    Chain --> PromptManager
    Chain --> AgentOrchestrator
    AgentOrchestrator --> SubAgent
    UI --> Dashboard

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⚡ Quick Start

1. Install dependencies:

   pip install fastapi openai

2. Run the FastAPI server:

   python llmchain/deployment/server.py

3. Use the CLI:

   python llmchain/deployment/cli.py "Your prompt here"

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🧩 Ecosystem & Extensibility

• Add new LLM providers, retrievers, vector stores, plugins, and chains easily
• Integrate with enterprise features, monitoring, and experiment tracking
• Visual builder and dashboard for rapid prototyping
• Comprehensive API reference and tutorials

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🤝 Contributing

• Fork the repo, create a branch, and submit a pull request
• Add tests for new features
• Follow the modular structure for new components
• See CONTRIBUTING.md for details

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🌐 Community

• Issues and discussions are welcome on GitHub
• Contributions, feedback, and feature requests encouraged

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📄 License

MIT License