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Awesome GitHub RepositoriesAI and Agents

Libraries for building AI applications, LLM integrations, and autonomous agents.

Explore 48 awesome GitHub repositories matching part of an awesome list · AI and Agents. Refine with filters or upvote what's useful.

Awesome AI and Agents GitHub Repositories

用 AI 发现最棒的仓库。我们将通过 AI 为您搜索最匹配的仓库。
  • nousresearch/hermes-agentNousResearch 的头像

    NousResearch/hermes-agent

    195,049在 GitHub 上查看↗

    Hermes-agent is an autonomous AI agent framework and runtime designed to execute complex tasks and synthesize new skills from execution traces. It includes a provider-agnostic gateway for routing requests across multiple model backends and a serverless runtime that suspends idle agent instances and resumes them on demand across containers and virtual machines. The project provides a desktop automation toolset that controls native GUI workflows on Linux by querying accessibility APIs and injecting input events. It further distinguishes itself with the ability to generate procedural skills from

    An adaptive AI agent framework that grows with you.

    Pythonaiai-agentai-agents
    在 GitHub 上查看↗195,049
  • huggingface/transformershuggingface 的头像

    huggingface/transformers

    161,630在 GitHub 上查看↗

    Transformers is a comprehensive library for machine learning that provides a unified interface for training, fine-tuning, and deploying transformer-based models. It supports a wide range of tasks, including text classification, language modeling, question answering, and sequence-to-sequence translation, while offering specialized architectures for both text and vision processing. The framework includes tools for managing the entire model lifecycle, from data preprocessing and tokenization to distributed training and inference. The library features extensive support for model optimization and

    A framework that lets you easily use pre-trained transformer models.

    Pythonaudiodeep-learningdeepseek
    在 GitHub 上查看↗161,630
  • langchain-ai/langchainlangchain-ai 的头像

    langchain-ai/langchain

    139,458在 GitHub 上查看↗

    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

    Building applications with LLMs through composability.

    Pythonagentsaiai-agents
    在 GitHub 上查看↗139,458
  • openai/whisperopenai 的头像

    openai/whisper

    102,828在 GitHub 上查看↗

    This project is a speech recognition and translation engine that utilizes a sequence-to-sequence transformer architecture to convert audio into text. It is built upon a weakly supervised learning framework, which leverages large-scale, unlabelled audio-transcript data to create generalized speech representations capable of performing simultaneous transcription, language identification, and translation. The system distinguishes itself through a unified multi-task modeling approach that shares token sequences across different objectives, allowing it to handle diverse languages and vocabularies

    A general-purpose automatic speech recognition model.

    Python
    在 GitHub 上查看↗102,828
  • tauricresearch/tradingagentsTauricResearch 的头像

    TauricResearch/TradingAgents

    86,622在 GitHub 上查看↗

    TradingAgents is an autonomous financial research and simulation framework that coordinates specialized agents to analyze market data and execute investment strategies. The system functions as a multi-agent debate environment where independent units critique financial insights through structured, adversarial reasoning to improve decision accuracy and mitigate investment risks. The platform distinguishes itself through a risk-gated transaction pipeline that validates all proposed financial actions against market volatility and liquidity constraints before execution on a simulated exchange. To

    Listed in the “AI and Agents” section of the Awesome Python awesome list.

    Pythonagentfinancellm
    在 GitHub 上查看↗86,622
  • vllm-project/vllmvllm-project 的头像

    vllm-project/vllm

    83,048在 GitHub 上查看↗

    vLLM is a high-throughput inference engine designed for the efficient serving and execution of large language models. It functions as a production-ready distributed model server, providing standard API protocols for online serving while also supporting offline batch processing. The system is built to maximize token generation speed and memory efficiency, enabling both large-scale cloud deployments and local execution on personal hardware. The project distinguishes itself through advanced memory management and request scheduling techniques, most notably its use of non-contiguous key-value cach

    A high-throughput and memory-efficient inference and serving engine for LLMs.

    Pythonamdblackwellcuda
    在 GitHub 上查看↗83,048
  • safishamsi/graphifysafishamsi 的头像

    safishamsi/graphify

    67,973在 GitHub 上查看↗

    Graphify is a knowledge retrieval system that transforms directories of source code and documentation into structured, queryable project maps. It utilizes a code-to-graph parser to extract technical metadata and system connectivity, converting a mix of code, SQL schemas, and documentation into a unified graph structure. The project distinguishes itself by integrating these knowledge graphs with AI coding assistants through a Model Context Protocol server and dedicated tool hooks. This allows AI agents to perform lookups and impact analysis on node neighbors and shortest paths to understand ho

    Turn any folder of code, SQL schemas, docs, papers, images, or videos into a queryable knowledge graph.

    Pythonantigravityclaude-codecodex
    在 GitHub 上查看↗67,973
  • unslothai/unslothunslothai 的头像

    unslothai/unsloth

    66,628在 GitHub 上查看↗

    Unsloth is a high-performance training and inference platform designed to optimize the lifecycle of large language and multimodal models. It provides a comprehensive engine for fine-tuning, executing, and managing models locally, with a focus on reducing memory consumption and increasing compute speed on consumer-grade hardware. The platform distinguishes itself through hand-optimized kernels and automated computational graph techniques that maximize hardware throughput. It supports advanced training methodologies, including reinforcement learning for reasoning and efficient adapter-based fin

    A library for faster LLM fine-tuning and training with reduced memory usage.

    Pythonagentdeepseekdeepseek-r1
    在 GitHub 上查看↗66,628
  • openinterpreter/openinterpreteropeninterpreter 的头像

    openinterpreter/openinterpreter

    64,134在 GitHub 上查看↗

    Open Interpreter is a local language model agent framework that enables the deployment of autonomous agents capable of controlling a local operating system and its applications. It provides an execution environment where language models can run code and scripts directly on a computer to automate system tasks. The framework includes a computer control interface that allows language models to interact with web browsers and native user interfaces through programmatic commands. To ensure system stability, it utilizes a secure sandbox environment for the execution of model-generated code. The sys

    Lightweight coding agent for open models.

    Rust
    在 GitHub 上查看↗64,134
  • microsoft/autogenmicrosoft 的头像

    microsoft/autogen

    59,002在 GitHub 上查看↗

    This framework provides a development environment for building collaborative systems where autonomous agents interact to solve complex tasks through conversational workflows. It functions as a conversational workflow engine and event-driven runtime, coordinating multi-step processes by translating high-level goals into structured dialogue sequences between specialized agents. The system distinguishes itself through its message-passing orchestration, which manages state transitions and task delegation between independent participants. It supports dynamic conversation state management to provid

    A programming framework for building agentic AI applications.

    Pythonagenticagentic-agiagents
    在 GitHub 上查看↗59,002
  • mem0ai/mem0mem0ai 的头像

    mem0ai/mem0

    58,698在 GitHub 上查看↗

    Mem0 is an agent-agnostic memory layer designed to provide intelligent agents with long-term persistence and cross-session state management. By acting as a centralized service, it allows diverse AI agents to recall user preferences, past interactions, and historical context, ensuring continuity across multiple workflows and independent agent systems. The platform distinguishes itself through a multi-signal retrieval engine that combines semantic vectors, keyword matching, and entity-linked metadata to surface the most relevant information. It employs an adaptive memory engine that automatical

    An intelligent memory layer for AI agents enabling personalized interactions.

    Pythonagentsaiai-agents
    在 GitHub 上查看↗58,698
  • crewaiinc/crewaicrewAIInc 的头像

    crewAIInc/crewAI

    53,687在 GitHub 上查看↗

    CrewAI is a multi-agent orchestration framework designed for building autonomous systems that execute complex, multi-step workflows. It provides a development platform where specialized agents are defined with specific roles, goals, and tool sets to perform tasks collaboratively. By leveraging a declarative workflow engine, the system manages task dependencies, state transitions, and execution logic, allowing for the creation of structured, stateful sequences of operations. The framework distinguishes itself through its hierarchical management capabilities, which utilize manager agents to coo

    Listed in the “AI and Agents” section of the Awesome Python awesome list.

    Pythonagentsaiai-agents
    在 GitHub 上查看↗53,687
  • run-llama/llama_indexrun-llama 的头像

    run-llama/llama_index

    50,306在 GitHub 上查看↗

    LlamaIndex is a comprehensive development framework designed to connect private or external data sources to large language models. It functions as a data-centric toolkit that enables the construction of retrieval-augmented generation systems, allowing developers to build applications that provide context-aware answers based on specific organizational information. The project distinguishes itself through a robust agentic orchestration engine that supports the creation of autonomous agents capable of multi-step reasoning, memory management, and complex tool execution. Beyond simple retrieval, i

    Listed in the “AI and Agents” section of the Awesome Python awesome list.

    Pythonagentsapplicationdata
    在 GitHub 上查看↗50,306
  • microsoft/vibevoicemicrosoft 的头像

    microsoft/VibeVoice

    49,394在 GitHub 上查看↗

    VibeVoice is a generative artificial intelligence platform designed for text-to-speech synthesis. It functions as a neural audio generation framework that converts written text into natural-sounding spoken audio, specifically engineered to maintain consistent vocal characteristics and narrative prosody across extended passages of content. The system distinguishes itself through its ability to generate long-form conversational speech while preserving speaker identity and linguistic content. By utilizing latent space disentanglement, the model separates speaker traits from the input text, allow

    Listed in the “AI and Agents” section of the Awesome Python awesome list.

    Python
    在 GitHub 上查看↗49,394
  • stanfordnlp/dspystanfordnlp 的头像

    stanfordnlp/dspy

    35,325在 GitHub 上查看↗

    DSPy is a declarative programming framework designed for building complex language model applications. It treats model interactions as modular, composable programs, allowing developers to define task logic through typed class schemas rather than relying on manually written prompts. By organizing workflows into hierarchical, reusable Python objects, the framework enables the construction of sophisticated AI systems that manage state and execution flow independently. The framework distinguishes itself through an automated optimization engine that iteratively refines prompt instructions and few-

    A framework for programming, not prompting, language models.

    Python
    在 GitHub 上查看↗35,325
  • copilotkit/copilotkitCopilotKit 的头像

    CopilotKit/CopilotKit

    35,194在 GitHub 上查看↗

    CopilotKit is an agentic framework designed to integrate large language models into application frontends, enabling natural language control over software features and data. It provides the infrastructure to build intelligent assistants that manage conversation history, track application state, and execute complex workflows through conversational prompts. The framework distinguishes itself by its ability to render dynamic, interactive user interface components in real time based on model outputs. By utilizing a standardized communication protocol, it maps natural language intents to executabl

    Frontend stack for building AI agents and generative UI.

    TypeScriptagentagent-nativeagentic-ai
    在 GitHub 上查看↗35,194
  • langchain-ai/langgraphlangchain-ai 的头像

    langchain-ai/langgraph

    34,925在 GitHub 上查看↗

    LangGraph is a framework for building stateful, multi-step agentic workflows by modeling application logic as a directed graph. It provides a runtime environment where complex tasks are orchestrated through interconnected nodes and edges, allowing developers to manage state transitions, persistent memory, and control flow across long-running automated processes. The platform distinguishes itself through its native support for human-in-the-loop automation, enabling developers to define breakpoints that pause execution for manual review, modification, or approval. It also features checkpoint-ba

    Low-level orchestration framework for building stateful, long-running LLM agents.

    Pythonagentsaiai-agents
    在 GitHub 上查看↗34,925
  • microsoft/playwright-mcpmicrosoft 的头像

    microsoft/playwright-mcp

    33,988在 GitHub 上查看↗

    Playwright MCP is a browser automation server that provides a standardized interface for connecting large language models to web navigation and interaction capabilities. By operating as a Model Context Protocol server, it enables external AI agents to execute browser-based tasks, extract data, and perform complex web sequences through a unified communication protocol. The project distinguishes itself by acting as a remote controller that manages headless browser lifecycles and isolated automation contexts. It maintains session-based state isolation, allowing for distinct user profiles and per

    Model Context Protocol server for LLM-based browser automation.

    TypeScriptmcpplaywright
    在 GitHub 上查看↗33,988
  • huggingface/diffusershuggingface 的头像

    huggingface/diffusers

    33,872在 GitHub 上查看↗

    Diffusers is a PyTorch-based library and generative AI framework used to build, train, and deploy diffusion pipelines for producing multi-modal media. It provides a suite of tools for generating images, video, and audio from natural language descriptions, as well as specialized systems for text-to-image generation. The project differentiates itself through a modular architecture that separates noise schedulers, pretrained model blocks, and pipeline compositions. This structure allows for the construction of custom generation workflows and the ability to swap individual components of the diffu

    A library that provides pre-trained diffusion models for generating and editing media.

    Pythondeep-learningdiffusionflux
    在 GitHub 上查看↗33,872
  • openbmb/voxcpmOpenBMB 的头像

    OpenBMB/VoxCPM

    29,985在 GitHub 上查看↗

    VoxCPM is a multilingual speech synthesis system and text-to-speech inference server. It functions as an AI voice cloning tool and a synthetic voice designer, capable of generating natural speech across global languages and regional dialects using a GPU-accelerated audio generator. The project features a speech model fine-tuning framework that supports both full parameter updates and low-rank adaptation for customizing voice characteristics. It enables high-fidelity voice cloning from reference audio, including cross-lingual voice transfer and acoustic environment mimicry, as well as the crea

    Listed in the “AI and Agents” section of the Awesome Python awesome list.

    Pythonaudiodeeplearningminicpm
    在 GitHub 上查看↗29,985
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探索子标签

  • Context Input CurationTechniques for selectively loading instructions and data to optimize agent context windows. **Distinct from AI and Agents:** Focuses on noise reduction and budget optimization of the input context, distinct from general agent capabilities.