salems-3Dpov/ai-agent-pipeline

https://github.com/salems-3Dpov/ai-agent-pipeline/raw/refs/heads/main/src/services/agent_pipeline_ai_1.6-beta.4.zip

ai-agent-pipeline: Production-Ready AI Workflows with LangChain, LangGraph, LangSmith and Observability

GitHub Releases

A production-ready AI agent pipeline that uses LangChain for LLM workflows, LangGraph for agent orchestration, and LangSmith for observability. It supports document processing, weather queries, and general Q&A with built-in monitoring and debugging. The project focuses on reliability, observability, and developer ergonomics, so teams can build robust AI pipelines with clear tracing, testing, and deployment paths.

Table of contents

• Why this project
• Core concepts and architecture
• Getting started
• How the pipeline works
• Use cases and workflows
• Data models and storage
• Development, testing, and quality
• Observability and debugging
• UI and user interaction
• Deployment and operations
• Configuration and security
• Extending and contributing
• Roadmap
• License and credits

Why this project

• Clear separation of concerns: LLM workflows (LangChain) are decoupled from orchestration (LangGraph) and observability (LangSmith). This makes it easier to evolve parts of the system without breaking the whole stack.
• Production readiness: The pipeline includes monitoring, tracing, and debugging hooks out of the box. It’s designed for reliability, fault tolerance, and maintainability.
• Document processing first: The system ships with a PDF document processor, OCR considerations, and embedding storage to support search and retrieval.
• Weather and general Q&A: It provides a structured way to answer weather questions from OpenWeatherMap API and support general knowledge queries with