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Tools for chaining model calls, managing state, and coordinating complex agentic workflows.
Explore 15 awesome GitHub repositories matching artificial intelligence & ml · LLM Application Orchestration. Refine with filters or upvote what's useful.
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
Implements tools for chaining model calls and coordinating complex agentic workflows.
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
Coordinates complex agentic workflows by chaining model calls and managing state across multi-step processes.
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
Chains language models, prompt templates, and external tools into complex, multi-step reasoning and data processing pipelines.
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
Coordinates large language models to plan complex requests and synthesize responses from multiple expert models.
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
Provides a comprehensive guide to chaining model calls and managing complex agentic workflows for AI applications.
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
Implements tools for chaining model calls and coordinating complex agentic workflows.
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
Coordinates complex security scan workflows by chaining model calls and managing agentic state.
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
Offers tools for chaining model calls, managing state, and coordinating complex agentic workflows.
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
Coordinates large language models with external tools and local data to manage complex agentic workflows.
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
Provides a curriculum for orchestrating model calls, managing memory, and coordinating agentic workflows into functional applications.
OpenLLM is a framework for deploying, managing, and scaling open-source large language models
Chains multiple language models together to build complex automated pipelines and multi-step reasoning tasks.
Promptflow 是一个用于构建大语言模型驱动应用的开发框架和编排器。它是一套通过将提示词(prompts)、自定义 Python 代码和语言模型链接成可执行序列来设计、编排和部署 AI 工作流的工具。 该项目的特色在于其可视化 AI 工作流设计器,允许创建逻辑节点的有向无环图(DAG)。它提供了一个专门的提示工程环境用于模板版本控制和对比,并具备状态化执行追踪功能,可记录函数调用和变量值以进行分步调试。 该平台涵盖了广泛的功能,包括通过向量数据库查找实现的检索增强生成(RAG),以及用于批量测试和质量保证的指标驱动评估流水线。它通过容器化部署、工作流端点服务以及 API 凭据的安全连接管理,处理从开发到生产的全生命周期。 项目提供了命令行界面(CLI)和 SDK,用于工作流验证并集成到自动化 CI/CD 流水线中。
Coordinates complex agentic workflows by chaining model calls and managing execution state.
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
Provides a comprehensive development kit for integrating and orchestrating large language models.
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
Provides Python and TypeScript SDKs to scaffold and implement Model Context Protocol tools and resources.
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
Provides tools for chaining model calls and coordinating complex agentic workflows.