Awesome AI Agents › Language Agent Frameworks
LangChain is a comprehensive framework designed to facilitate the development of applications powered by large language models (LLMs). It provides a standardized interface that allows developers to seamlessly integrate various components such as models, embeddings, vector stores, and retrievers, enabling the creation of sophisticated AI applications with ease. The framework emphasizes interoperability, allowing users to swap out models and tools as needed to adapt to evolving technologies and optimize application performance. One of the key features of LangChain is its ability to augment real-time data by connecting LLMs to diverse data sources and external or internal systems. This capability is supported by a vast library of integrations with model providers, tools, and vector stores, making it highly versatile for different use cases. LangChain also supports advanced customization and agent orchestration through complementary tools like LangGraph, which offers a low-level framework for building controllable agent workflows with features such as long-term memory and human-in-the-loop processes. The ecosystem around LangChain includes additional products like LangSmith, which aids in evaluating and debugging agent performance, and the LangGraph Platform, which facilitates the deployment and scaling of agents with stateful workflows. These tools collectively provide a robust environment for developing, testing, and deploying LLM-powered applications in production. LangChain is well-documented with tutorials, how-to guides, conceptual explanations, and an API reference, making it accessible for developers at various skill levels. It is trusted by major companies and supports a wide range of AI application scenarios, from simple integrations to complex agent orchestration. Overall, LangChain stands out as a future-proof solution for building context-aware reasoning applications leveraging the power of large language models.
https://github.com/langchain-ai/langchain
LangGraph is a low-level orchestration framework for building resilient, controllable AI language agents with customizable architectures, long-term memory, and real-time streaming support, used by major companies and integrated with the LangChain ecosystem.
Semantic Kernel is an enterprise-ready orchestration framework by Microsoft that enables developers to build and deploy intelligent AI agents and multi-agent systems using cutting-edge large language model technology.
BISHENG is an open-source LLM DevOps platform designed for enterprise AI applications, offering powerful workflow orchestration, security features, and high-precision document parsing to support complex intelligent business solutions.
VoltAgent is an open-source TypeScript framework for building and orchestrating AI agents powered by Large Language Models, enabling developers to create scalable, customizable, and maintainable AI applications with modular components and visual monitoring.
Swarms is an enterprise-grade, production-ready multi-agent orchestration framework designed to enable complex AI workflows with high scalability, flexibility, and robust developer and security features.
LangGraph.js is a low-level orchestration framework for building customizable, reliable, and controllable language agents as graphs, enabling complex AI workflows with features like long-term memory, human-in-the-loop, and real-time streaming support.
PHP framework for creating and orchestrating AI agents, covering LLM providers, tools, RAG, MCP, event-driven workflows with checkpointing and human-in-the-loop, streaming UI protocols and monitoring.
Go framework for production agent systems providing LLM agents, type-safe graph workflows, tool calling, session and memory state, knowledge retrieval, evaluation and OpenTelemetry observability.