Awesome GitHub RepositoriesLLM Application Orchestration

LangChain is a framework for building applications that chain large language models with external data sources and third-party tools. It serves as an orchestrator for autonomous agents that use language models to plan and execute multi-step tasks, while providing a toolkit for linking interoperable AI components into sequences to prototype complex model behaviors. The project provides a model agnostic integration layer, allowing users to switch between different language model providers using a standardized interface. It also includes tools for observability and evaluation to track the perfor

LangChain is an orchestration framework designed for building, managing, and deploying applications powered by large language models. It provides a unified integration layer that normalizes disparate model provider APIs into a consistent set of primitives, enabling developers to build complex, multi-step AI workflows that manage state, memory, and tool execution. The project distinguishes itself through a durable execution runtime that maintains persistent state across long-running processes by checkpointing progress to external storage. It models agent workflows as directed graphs, allowing

Kotaemon is an orchestration framework designed for building modular, agentic workflows that integrate document processing, retrieval-augmented generation, and multi-step reasoning. It provides a comprehensive platform for developing document-based question answering systems, allowing users to chain language models, prompt templates, and external tools into complex, automated pipelines. The system distinguishes itself through a highly modular architecture that emphasizes component-based composition and schema-driven data exchange. It supports autonomous agents capable of decomposing complex q

JARVIS is a system for large language model task orchestration, deployment management, and automation benchmarking. It utilizes a task orchestrator to decompose complex requests into actionable steps and coordinates various expert models to synthesize final responses. The project includes an AI model deployment manager to handle the local deployment of expert models across different hardware scales. It further provides an AI workflow API consisting of web endpoints used to trigger automated task workflows and retrieve results from model selection stages. The framework incorporates an automat

This project is a technical curriculum and development guide focused on large language model prompt engineering, fine-tuning, and the creation of retrieval augmented generation applications. It serves as a comprehensive resource for developers to master crafting precise instructions and textual patterns to improve the quality and predictability of model outputs. The material covers the end-to-end workflow of adapting open-source models to specific datasets and integrating language models with vector databases to generate responses based on private information. It also provides a systematic ap

This repository is a comprehensive set of tutorials and examples for building software powered by large language models. It serves as an application development guide and a prompt engineering framework, providing instructional content for integrating model logic with user interfaces and external data sources. The project provides technical walkthroughs for specialized workflows, including the implementation of retrieval augmented generation using vector databases and semantic search. It includes guidance on adapting pre-trained model weights through fine-tuning with private datasets and the o

Strix is an automated security research and vulnerability scanning platform that leverages language models to orchestrate complex security analysis tasks. It functions as a comprehensive framework for penetration testing and continuous security integration, allowing users to embed automated vulnerability research directly into development pipelines or execute it within isolated, containerized environments. The platform distinguishes itself through a multi-agent orchestration engine that coordinates specialized autonomous agents to perform parallel security assessments. By integrating LLM-agno

LangChainJS is an AI agent orchestrator and application framework designed for building autonomous systems that use large language models to plan and execute tasks. It serves as an integration library that connects language models with tools, memory, and external data sources to create context-aware logic and complex workflows. The project provides a provider-agnostic interface and model provider abstraction, allowing applications to switch between different language model providers without rewriting core logic. It includes a toolkit for retrieval augmented generation, utilizing retrievers to

Rowboat is an LLM orchestration platform and multimodal AI agent framework. It coordinates large language models with external tools, automated web monitoring, and local data vaults to execute actions and retrieve real-time information. The system operates as a local-first knowledge base, converting meeting notes and emails into a linked markdown knowledge graph. It functions as an automated market intelligence tool that tracks competitors and trends across the web to maintain updated information summaries. The platform covers a broad range of productivity and automation capabilities, includ

llm-universe is a structured learning resource and technical guide focused on the development of large language model applications. It serves as a curriculum for mastering model orchestration, the creation of autonomous conversational agents, and the implementation of retrieval-augmented generation systems. The project provides detailed instructions on connecting model APIs with memory and tools to create execution chains. It specifically covers the construction of retrieval pipelines, including the process of cleaning raw documents, generating embeddings, and integrating vector databases to

OpenLLM is a framework for deploying, managing, and scaling open-source large language models

Promptflow es un framework de desarrollo y orquestador para construir aplicaciones impulsadas por modelos de lenguaje de gran tamaño (LLM). Funciona como un conjunto de herramientas para diseñar, orquestar y desplegar flujos de trabajo de IA vinculando prompts, código Python personalizado y modelos de lenguaje en secuencias ejecutables. El proyecto se distingue por un diseñador visual de flujos de trabajo de IA que permite la creación de grafos acíclicos dirigidos de nodos lógicos. Proporciona un entorno dedicado de ingeniería de prompts para el versionado y comparación de plantillas, junto con un seguimiento de ejecución con estado para registrar llamadas a funciones y valores de variables para la depuración paso a paso. La plataforma cubre una amplia superficie de capacidades, incluyendo la generación aumentada por recuperación (RAG) mediante búsquedas en bases de datos vectoriales y pipelines de evaluación basados en métricas para pruebas por lotes y control de calidad. Gestiona todo el ciclo de vida desde el desarrollo hasta la producción mediante despliegue en contenedores, servicio de endpoints de flujo de trabajo y gestión segura de conexiones para credenciales de API. Se proporciona una interfaz de línea de comandos (CLI) y un SDK para la validación de flujos de trabajo y la integración en pipelines de CI/CD automatizados.

This project is a comprehensive Node.js software development kit designed for integrating large language models into applications. It serves as a foundational client for interacting with REST and WebSocket services, enabling developers to implement chat functionality, multimodal content generation, and autonomous agent orchestration. The library provides a structured framework for defining executable tools and enforcing JSON schemas, ensuring that model outputs remain programmatically compatible with downstream systems. The SDK distinguishes itself through its robust request orchestration and

mcp-use is a development framework designed for building, deploying, and managing servers, clients, and autonomous agents using the Model Context Protocol. It provides a comprehensive toolkit for creating servers that expose custom tools, data resources, and prompts to compatible AI agents. The project distinguishes itself by offering a complete lifecycle for protocol-based applications, including a dedicated hosting platform for production servers and a compliance validator to ensure servers meet marketplace publishing requirements. It also features an observability suite for tracing protoco

langchaingo is an LLM application framework for Go designed for building language model-powered applications and autonomous agents. It serves as an orchestration library and tool integration framework that allows developers to link prompt sequences and model calls into complex, multi-step workflows. The project provides a toolkit for implementing retrieval-augmented generation pipelines by processing unstructured documents and retrieving relevant context via vector search. It includes a dedicated integration layer for indexing high-dimensional embeddings and performing similarity searches acr