Awesome Ai Agent

Awesome AI Agents [](https://awesome.re)

> Build and deploy autonomous and multi-agent systems powered by large language models (LLMs).

Contents

• [Cloud Platforms for Agents](#cloud-platforms-for-agents)
• [Context Processing](#context-processing)
• [Embedding models](#embedding-models)
• [Transformers](#transformers)
• [Foundation Models Providers](#foundation-models-providers)
• [Inference Providers](#inference-providers)
• [Interoperability Protocols](#interoperability-protocols)
• [Local LLM Tools](#local-llm-tools)
• [Observability](#observability)
• [Orchestration Frameworks](#orchestration-frameworks)
• [Sandboxes](#sandboxes)
• [Learning resources](#learning-resources)
• [Google](#google)
• [Huggin Face](#huggin-face)
• [LangChain](#langchain)
• [Microsoft](#microsoft)

Cloud Platforms for Agents

• Amazon Bedrock - The AWS platform for building generative AI applications and agents.
• Vertex AI Agent Builder - A suite of Google Cloud products designed to build, scale, and manage AI agents in production environments.

Context Processing

Embedding models

• Harrier OSS - A family of multilingual text embedding models developed by Microsoft.
• OpenAI Embedding Models - Large and small models developed by OpenAI.

Transformers

• Huggin Face Transformers - HF library for Transformers with hundreds of models.

Foundation Models Providers

• Anthropic Claude - Foundational models such as Haiku, Opus, and Sonnet.
• Google DeepMind - The Gemini family and Gemma open models, spanning multimodal and lightweight use cases.
• Meta LLaMA - A family of open-weight language models designed for developers and researchers, supporting fine-tuning, adaptation, and deployment across a broad ecosystem.
• Open AI - Frontier and specialized models for text, image, speech-to-speech, text-to-speech and transcription tasks.

Inference Providers

• Cerebras - High-performance AI inference infrastructure focused on large-scale workloads and low-latency execution.
• Cohere - Enterprise-oriented language models and inference APIs for NLP and retrieval-based applications.
• Fal - Platform for running and fine-tuning generative media models (image, video, audio) using serverless and on-demand GPU infrastructure.
• Hyperbolic - Open-access cloud platform for running and serving AI models.
• Featherless - Infrastructure for deploying and serving open-weight models with minimal setup.
• Fireworks - Inference platform for open-source models with optimization for performance, scalability, and customization.
• Groq - Low-latency inference platform powered by custom hardware for deterministic model execution.
• HF Inference - Serverless inference APIs provided by Hugging Face for deploying and consuming machine learning models.
• Novita - Unified API platform for accessing and deploying multiple models and running agent-based workflows.
• Nscale - Infrastructure provider covering compute, storage, and deployment for AI systems across environments.
• ovhOVH AI Endpoints - Managed APIs for integrating and serving machine learning and generative AI models.
• Public AI - Open-source and nonprofit initiative providing shared infrastructure for public AI model access and experimentation.
• Replicate - Platform for running, deploying, and fine-tuning models via API-based workflows.
• SambaNova - AI inference systems built on specialized hardware and software for large-scale model execution.
• Scaleway - Cloud platform supporting the deployment and scaling of AI models and applications.
• Together AI - Platform for training, fine-tuning, and serving open and research-driven AI models.
• WaveSpeedAI - Infrastructure for accelerating generative media workloads, particularly image and video models.
• Zai - Platform providing access to conversational AI and agent-based systems.

Interoperability Protocols

• Agent2Agent (A2A) - An open standard designed to enable seamless communication and collaboration between AI agents.
• Agent Payments Protocol (AP2) - An open protocol for the emerging Agent Economy. It enables secure, reliable, and interoperable agent commerce for developers, merchants, and the payments industry.
• Model Context Protocol - (MCP) - An open-source standard for connecting AI applications to external systems.

Local LLM Tools

• DiffusionBee - Desktop application for running generative models locally, with a focus on image generation.
• Docker Model Runner - Tooling for managing, running, and deploying AI models within Docker-based environments.
• Draw Things - Application for running image generation models locally, with support for offline workflows.
• Jan - Local-first AI assistant designed to run models privately on user devices.
• JellyBox - Environment for running AI models locally with full offline support.
• Lemonade - Open-source local AI runtime for deploying and interacting with models on personal hardware.
• Local AI - Self-hosted AI stack for running language models, agents, and related workloads locally.
• llama.cpp - Lightweight inference engine in C/C++ for running large language models on local hardware.
• LM Studio - Desktop interface for discovering, running, and interacting with local language models.
• MLX LM - Python library for inference and fine-tuning of language models on Apple Silicon using MLX.
• Ollama - Tool for running and managing language models locally with a simplified CLI and API.
• SGLang - High-performance framework for serving language and multimodal models.
• Unsloth - Toolkit for running and fine-tuning models locally, with support for offline environments.
• vLLM - Inference and serving engine optimized for throughput and memory efficiency in LLM workloads.

Observability

• LangSmith Platform - Framework-agnostic platform for monitoring, evaluating, and debugging LLM applications and agents.

Orchestration Frameworks

• Deep Agents - Open-source agent framework for long-running tasks, with support for planning, context management, and multi-agent coordination.
• Google Agent Development Kit (ADK) - Framework for building AI agents with a model-agnostic and deployment-agnostic design.
• LangChain - Open-source framework providing abstractions, integrations, and tooling for building LLM-powered applications.
• LangGraph - Low-level orchestration framework for building and running stateful, long-lived agent workflows.
• Microsoft Agent Framework - Framework for developing agent-based systems, supporting both simple interactions and multi-agent workflows with graph-based orchestration in .NET and Python.

Sandboxes

• Amazon Bedrock AgentCore - Managed environment for deploying and running AI agents with support for multiple models and frameworks.
• Daytona - Infrastructure for executing AI-generated code in isolated and reproducible environments.
• Modal Sandboxes - Serverless container-based environments for running AI-generated code with support for dynamic configuration and GPU workloads.
• Runloop - Ephemeral development environments for executing code in isolation, with support for agent-based workflows and evaluation pipelines.

Learning resources

Google

• 5-Day AI Agents Intensive Course with Google - Learn guide so anyone can explore the foundations, architecture and practical development of AI agents.
• 5-Day Gen AI Intensive Course with Google - Learning guide for exploring the fundamental technologies and techniques behind Generative AI.

Huggin Face

• AI Agents Course - This free course will take you on a journey, from beginner to expert, in understanding, using and building AI agents.
• MCP Course - This free course, built in partnership with Anthropic, will take you on a journey, from beginner to informed, in understanding, using, and building applications with MCP.

LangChain

• Ambient Agents with LangGraph - Build your own ambient agent to manage your email. You’ll learn the fundamentals of LangGraph as you build an email assistant from scratch, and use LangSmith to evaluate its performance.
• Building Reliable Agents - Take an agent from first run to production-ready system through iterative cycles of improvement with LangSmith, the agent engineering platform for observing and evaluating agents.
• Deep Agents - Learn the fundamental characteristics of Deep Agents and how to implement your own Deep Agent for complex, long-running tasks.
• Deep Research with LangGraph - Build your own deep research agent to handle research tasks. Learn how to use LangGraph to build a multi-agent system, then use LangSmith to evaluate its performance.
• Introduction to Agent Observability & Evaluations - Learn the essentials of agent observability & evaluations with LangSmith. Continuously improve your agents with LangSmith's tools for observability, evaluation, and prompt engineering.
• Introduction to LangChain - Python - Learn how to build AI agents with LangChain. Get started quickly using pre-built architectures and model integrations, then debug your agents with LangSmith Observability.
• Introduction to LangGraph - Python - Learn the basics of LangGraph, the framework helps developers add better precision and control into agentic workflows.
• Quickstart courses - Collection of quickstart courses about LangChain, LangGraph and LangSmith.

Microsoft

• AI Agents for Beginners - A course teaching everything you need to know to start building AI Agents.
• MCP for Beginners - Learn MCP with Hands-on Code Examples in C#, Java, JavaScript, Rust, Python, and TypeScript.
• Python + Agentes: Creando agentes y flujos de IA - \[Spanish version] A series that explores the foundational concepts behind building AI agents in Python using the Microsoft Agent Framework.
• Python + Agents: Building AI agents and workflows - \[English version] A series that explores the foundational concepts behind building AI agents in Python using the Microsoft Agent Framework.

Contributing

Your contributions and suggestions are heartily welcome. Please check the [Contributing Guidelines](contributing.md) for more details.

Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: Smithech
• Source: Smithech/awesome-ai-agent
• License: CC0-1.0

Install and usage instructions live in the source repository linked above.