https://github.com/langchain-ai/langchain

🦜🔗 Build context-aware reasoning applications
https://github.com/langchain-ai/langchain

Last synced: 5 days ago
JSON representation

🦜🔗 Build context-aware reasoning applications

• Host: GitHub
• URL: https://github.com/langchain-ai/langchain
• Owner: langchain-ai
• License: mit
• Created: 2022-10-17T02:58:36.000Z (about 2 years ago)
• Default Branch: master
• Last Pushed: 2024-10-29T16:57:47.000Z (2 months ago)
• Last Synced: 2024-10-29T17:14:42.906Z (2 months ago)
• Language: Jupyter Notebook
• Homepage: https://python.langchain.com
• Size: 339 MB
• Stars: 94,094
• Watchers: 688
• Forks: 15,197
• Open Issues: 787
• Metadata Files:
  • Readme: README.md
  • Contributing: .github/CONTRIBUTING.md
  • License: LICENSE
  • Code of conduct: .github/CODE_OF_CONDUCT.md
  • Citation: CITATION.cff
  • Security: SECURITY.md

Awesome Lists containing this project

• awesome_open_llms - langchain
• awesome - langchain-ai/langchain - 🦜🔗 Build context-aware reasoning applications (Jupyter Notebook)
• awesome - langchain-ai/langchain - 🦜🔗 Build context-aware reasoning applications (Jupyter Notebook)
• awesome-ai-sdks - GitHub
• my-awesome - langchain-ai/langchain - 01 star:97.4k fork:15.8k 🦜🔗 Build context-aware reasoning applications (Jupyter Notebook)
• awesome-systematic-trading - LangChain - ai/langchain?style=social) - Building applications with LLMs through composability (Basic Components / Fundamental libraries)
• my-awesome-starred - langchain-ai/langchain - 🦜🔗 Build context-aware reasoning applications (Jupyter Notebook)
• awesome-ml-python-packages - LangChain
• Awesome-Tool-Learning - LangChain - ai/langchain) (📱 Applications / 🧪 Evalution)
• Awesome-AITools - Github - ai/langchain?style=social)|免费| (精选文章 / GPT-LLMs应用)
• awesome-repositories - langchain-ai/langchain - 🦜🔗 Build context-aware reasoning applications (Jupyter Notebook)
• awesome-github-star - langchain - ai | 63463 | (Python)
• ai-game-devtools - LangChain
• stars - langchain-ai/langchain - 🦜🔗 Build context-aware reasoning applications (Python)
• awesome-LLM-resourses - LangChain - aware reasoning applications. (推理 Inference)
• awesome-llm-json - LangChain
• awesome-deliberative-prompting - [>code - ai/langchain) (Tools and Frameworks / Reflection and Meta-Cognition)
• awesome-production-machine-learning - LangChain - ai/langchain.svg?style=social) - LangChain assists in building applications with LLMs through composability. (Industry Strength NLP)
• awesome-agents - langchain - aware reasoning applications | framework | 94,476 | 15,279 | 760 | 480 | 0 days, 12 hrs, 15 mins | 680 | MIT License | (Table of Open-Source AI Agents Projects)
• awesome-agents - langchain - aware reasoning applications | framework | 94,476 | 15,279 | 760 | 480 | 0 days, 12 hrs, 15 mins | 680 | MIT License | (Table of Open-Source AI Agents Projects)
• jimsghstars - langchain-ai/langchain - 🦜🔗 Build context-aware reasoning applications (Jupyter Notebook)
• awesome-ccamel - langchain-ai/langchain - 🦜🔗 Build context-aware reasoning applications (Jupyter Notebook)
• awesome-langchain-zh - LangChain - ai/langchain?style=social): 原始的🐍 (LangChain框架)
• awesome-genai - Langchain - Build context-aware reasoning applications. [](https://github.com/langchain-ai/langchain/network/members) [](https://github.com/langchain-ai/langchain/stargazers) (Tools & Frameworks / Development Frameworks)
• awesome-genai - Langchain - Build context-aware reasoning applications. [](https://github.com/langchain-ai/langchain/network/members) [](https://github.com/langchain-ai/langchain/stargazers) (Tools & Frameworks / Development Frameworks)
• awesome-llm-and-aigc - LangChain
• awesome-llm-and-aigc - LangChain
• awesome-generative-ai-data-scientist - LangChain
• awesome-generative-ai-meets-julia-language - LangChain - The best option for building applications on top of LLMs (eg, Chains, Agents). It has a lot of adapters for common models, databases, and other services. (Must-Know Python Projects / Generative AI - Previous Generation)
• awesome - langchain-ai/langchain - 🦜🔗 Build context-aware reasoning applications (Jupyter Notebook)
• awesome - langchain-ai/langchain - 🦜🔗 Build context-aware reasoning applications (Jupyter Notebook)
• awesome-ai-papers - [LangChain - rag/)\]\[[LangChain Hub](https://smith.langchain.com/hub)\]\[[langgraph](https://github.com/langchain-ai/langgraph)\] (NLP / 3. Pretraining)
• awesome-ai-papers - [LangChain - rag/)\]\[[LangChain Hub](https://smith.langchain.com/hub)\]\[[langgraph](https://github.com/langchain-ai/langgraph)\] (NLP / 3. Pretraining)
• alan_awesome_llm - LangChain - aware reasoning applications. (推理 Inference)
• alan_awesome_llm - LangChain - aware reasoning applications. (推理 Inference)
• awesome-engineering - Langchain
• awesome-engineering - Langchain
• awesome-data-engineer - LangChain
• awesome-data-engineer - LangChain
• awesome_ai_agents - LangChain Agents - Build context-aware reasoning applications. (Building / Frameworks)
• awesome_ai_agents - LangChain - LangChain is a framework enabling context-aware reasoning applications with integrated libraries, templates, and developer tools [github](https://github.com/langchain-ai/langchain) (Learning / Repositories)
• redis-ai-resources - LangChain Python
• redis-ai-resources - LangChain Python
• AiTreasureBox - langchain-ai/langchain - 01-07_97776_70](https://img.shields.io/github/stars/langchain-ai/langchain.svg)|⚡ Building applications with LLMs through composability ⚡| (Repos)
• Awesome-LLMOps - LangChain - ai/langchain.svg) |  |  | ⚡ Building applications with LLMs through composability ⚡ | | (LLMOps)
• Awesome-LLMOps - LangChain - ai/langchain.svg) |  |  | ⚡ Building applications with LLMs through composability ⚡ | | (LLMOps)
• Awesome-LLM4Security - langchain - 6B的微调模型对该事件进行分类。然后，根据分类后的结果，使用对应的prompt模板对ChatGLM-pro大模型进行请求，大模型根据向量库中的知识以及自身的逻辑推理能力，生成该类事件的分析研判流程。接着，Agent 根据事件的分析研判流程通过调用工具、访问大模型、请求网络等方式来对该事件进行研判。最后，根据分析的结果输出研判结论以及形成对应的研判报告。 | [AutoMSS](https://github.com/1700111005/autoMSS) | (资源列表 / 项目)
• Awesome-LLM4Security - langchain - 6B的微调模型对该事件进行分类。然后，根据分类后的结果，使用对应的prompt模板对ChatGLM-pro大模型进行请求，大模型根据向量库中的知识以及自身的逻辑推理能力，生成该类事件的分析研判流程。接着，Agent 根据事件的分析研判流程通过调用工具、访问大模型、请求网络等方式来对该事件进行研判。最后，根据分析的结果输出研判结论以及形成对应的研判报告。 | [AutoMSS](https://github.com/1700111005/autoMSS) | (资源列表 / 项目)