The best LazyLLM alternatives

GenAI_Agents

NirDiamant/GenAI_Agents

50+ tutorials and implementations for Generative AI Agent techniques, from basic conversational bots to complex multi-agent systems.

Choose GenAI_Agents if…

• you would rather work in Jupyter Notebook than Python
• you want the more widely starred option (24.8k)
• you specifically need agentic-ai or ai

Shares agents, langchain, llm, llms with LazyLLM.

langchain

langchain-ai/langchain

The agent engineering platform.

Choose langchain if…

• MIT License suits your use better than Apache License 2.0
• you want the more widely starred option (148.9k)
• you specifically need ai or ai-agents

Shares agents, framework, langchain, llm with LazyLLM.

langgraph

langchain-ai/langgraph

Build resilient agents.

Choose langgraph if…

• MIT License suits your use better than Apache License 2.0
• you want the more widely starred option (44.4k)
• you specifically need ai or ai-agents

Shares agents, framework, langchain, llm with LazyLLM.

agents-towards-production

NirDiamant/agents-towards-production

End-to-end, code-first tutorials for building production-grade GenAI agents. From prototype to enterprise deployment.

Choose agents-towards-production if…

• you would rather work in Jupyter Notebook than Python
• you want the more widely starred option (21.8k)
• you specifically need agent or agent-framework

Shares agents, llm, llms, rag with LazyLLM.

Hands-On-AI-Engineering

Sumanth077/Hands-On-AI-Engineering

A curated collection of practical AI projects implementing OCR systems, RAG, AI agents, and other AI use cases.

Choose Hands-On-AI-Engineering if…

• you specifically need ai or ai-agents

Shares agents, llms, rag with LazyLLM.

llama_index

run-llama/llama_index

LlamaIndex is the document processing platform for AI

Choose llama_index if…

• MIT License suits your use better than Apache License 2.0
• you want the more widely starred option (52.8k)
• you specifically need application or fine-tuning

Shares agents, data, framework, llamaindex with LazyLLM.

haystack

deepset-ai/haystack

Open-source AI orchestration framework for building context-engineered, production-ready LLM applications. Design modular pipelines and agent workflows with explicit control over retrieval, routing, memory, and generation. Built for scalable agents, RAG, multimodal applications, semantic search, and conversational systems.

Choose haystack if…

• you want the more widely starred option (26.9k)
• you specifically need agent-framework or agentic-ai

Shares agents, framework, llm, rag with LazyLLM.

ai-agents-from-zero

didilili/ai-agents-from-zero

🚀 2026 最系统的 AI Agent 速成指南｜智能体实战教程 · 完整学习路径 + 实战项目 + 面试题库 · 对标大模型应用开发工程师岗位 · 覆盖LangChain / LangGraph / Coze / Dify / MCP / skills / LLM / RAG / 提示词 · 企业级部署与微调 · 从0到企业级落地 + 从学习到上线项目 + 面试准备一体化

Choose ai-agents-from-zero if…

• MIT License suits your use better than Apache License 2.0
• you want something your agent can install directly rather than a library to wire up yourself
• you specifically need agent or agent-framework

Shares ai-agent, langchain, llm, rag with LazyLLM.

agentic-rag-for-dummies

GiovanniPasq/agentic-rag-for-dummies

A modular Agentic RAG built with LangGraph — learn Retrieval-Augmented Generation Agents in minutes.

Choose agentic-rag-for-dummies if…

• you would rather work in Jupyter Notebook than Python
• MIT License suits your use better than Apache License 2.0
• you specifically need agent or agentic-ai

Shares agents, langchain, llm, rag with LazyLLM.

AdalFlow

SylphAI-Inc/AdalFlow

AdalFlow: The library to build & auto-optimize LLM applications.

Choose AdalFlow if…

• MIT License suits your use better than Apache License 2.0
• you specifically need agent or ai

Shares framework, llm, rag with LazyLLM.

mem0

mem0ai/mem0

The Memory Layer for AI Agents - Drop-in memory infrastructure for AI agents and apps. Context that persists. Built for production.

Choose mem0 if…

• you want the more widely starred option (68.3k)
• you specifically need agentic-memory or agentic-memory-system

Shares agents, llm, rag with LazyLLM.

GenAI-Showcase

mongodb-developer/GenAI-Showcase

MongoDB's Generative AI Showcase: an exhaustive collection of examples and sample applications covering Retrieval-Augmented Generation (RAG), AI agents, and industry-specific use cases.

Choose GenAI-Showcase if…

• you would rather work in Jupyter Notebook than Python
• MIT License suits your use better than Apache License 2.0
• you specifically need artificial-intelligence or atlas

Shares agents, llms, rag with LazyLLM.