Langchain Playground
Explores and demonstrates various LLM-related tools and integrations, including LangChain.js, LangGraph, Slack, and the Model Context Protocol.
About
This project serves as a comprehensive testing ground and demonstration for building applications with large language models. It showcases integrations with core LLM frameworks like LangChain.js and LangGraph for advanced workflow orchestration, enabling multi-step processes. The tool offers both REST API endpoints and a Slack bot interface for interacting with diverse language models and custom workflows, exemplified by a New Relic log analysis system and a Retriever-Augmented Generation (RAG) implementation for querying documents. It also highlights the integration of Model Context Protocol (MCP) tools and support for various LLM providers and services.
Key Features
- Supports multiple LLM providers including OpenAI, Ollama, and Groq
- Features a Retriever-Augmented Generation (RAG) system for document querying
- Implements advanced workflows like New Relic log analysis using LangGraph
- 4 GitHub stars
- Provides both REST API endpoints and Slack bot functionality
- Integrates LangChain.js and LangGraph for LLM application development
Use Cases
- Developing and testing complex LLM-powered workflows with LangGraph
- Building interactive Slack bots capable of advanced conversational AI
- Implementing and experimenting with Retriever-Augmented Generation (RAG) systems