ActionFlow: Agent Workflows with Prompts and Tools. 快速构建你自己的Agent工作流

Create your own ChatGPT with Retrieval-Augmented-Generation workshop

AI powered OpenSource tool for college students

🪢 Open source LLM engineering platform: Observability, metrics, evals, prompt management, playground, datasets. Integrates with LlamaIndex, Langchain, OpenAI SDK, LiteLLM, and more. 🍊YC W23

Java version of LangChain

Browser extension and framework to use and build AI assistants for any web application

The all-in-one LLM developer platform: prompt management, evaluation, human feedback, and deployment all in one place.

A framework for building custom AI Copilots 🤖 in-app AI chatbots, in-app AI Agents, & AI-powered Textareas.

The PDF Chatbot project uses advanced NLP models and Unstructured.io for parsing complex PDFs, enabling streamlined extraction and querying of information, including tables, graphs, and images, through a user-friendly interface.

AI Native Data App Development framework with AWEL(Agentic Workflow Expression Language) and Agents

Web application to help develop scraping techniques to create context in LLM prompts.

The production toolkit for LLMs. Observability, prompt management and evaluations.

Retrieval-Augmented Generation on PDF for Free, Integrated with Memory to recall previous interactions, it operates as a sophisticated lang-chain application.

Framework to bring LLM applications to production

Library containing common python modules for all devops code generators like pipeline code generator, infrastructure code generator, database code generator, deployment code generator, container image code generator, container deployment code generator, etc.

Langchain-Chatchat（原Langchain-ChatGLM, Qwen 与 Llama 等）基于 Langchain 与 ChatGLM 等语言模型的 RAG 与 Agent 应用 | Langchain-Chatchat (formerly langchain-ChatGLM), local knowledge based LLM (like ChatGLM, Qwen and Llama) RAG and Agent app with langchain

The open source platform for AI-native application development.

Argilla is a collaboration platform for AI engineers and domain experts that require high-quality outputs, full data ownership, and overall efficiency.

Easyest and lazyest way for building multi-agent LLMs applications.

LangChain for Go, the easiest way to write LLM-based programs in Go