Open Source Python AI Agents
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Python AI Agents
View 507 business solutionsBrowse free open source Python AI Agents and projects below. Use the toggles on the left to filter open source Python AI Agents by OS, license, language, programming language, and project status.
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Enterprise-grade ITSM, for every businessFreshservice is an intuitive, AI-powered platform that helps IT, operations, and business teams deliver exceptional service without the usual complexity. Automate repetitive tasks, resolve issues faster, and provide seamless support across the organization. From managing incidents and assets to driving smarter decisions, Freshservice makes it easy to stay efficient and scale with confidence.
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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AutoGPT
Powerful tool that lets you create and run intelligent agents
AutoGPT is an experimental open-source application showcasing the capabilities of the GPT-4 language model. This program, driven by GPT-4, chains together LLM "thoughts", to autonomously achieve whatever goal you set. As one of the first examples of GPT-4 running fully autonomously, AutoGPT pushes the boundaries of what is possible with AI. -
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Agent S2
Agent S: an open agentic framework that uses computers like a human
Simular's Agent S2 represents a leap forward in the development of computer-use agents, capable of autonomously interacting with a range of devices and interfaces. By integrating specialized AI models, Agent S2 delivers state-of-the-art performance, whether on desktop systems or smartphones. Through modular architecture, it efficiently handles complex tasks, such as navigating UIs, performing low-level actions like text selection, and executing high-level strategies like planning. Additionally, the system's proactive hierarchical planning allows for real-time adaptation, making it an ideal solution for businesses seeking to streamline operations and automate digital workflows. Agent S2 is designed with flexibility, enabling seamless scaling for future applications and tasks. -
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Agent Zero
Agent Zero AI framework
Agent Zero is not a predefined agentic framework. It is designed to be dynamic, organically growing, and learning as you use it. Agent Zero is fully transparent, readable, comprehensible, customizable and interactive. Agent Zero uses the computer as a tool to accomplish its (your) tasks. Agents can communicate with their superiors and subordinates, asking questions, giving instructions, and providing guidance. Instruct your agents in the system prompt on how to communicate effectively. The terminal interface is real-time streamed and interactive. You can stop and intervene at any point. If you see your agent heading in the wrong direction, just stop and tell it right away. There is a lot of freedom in this framework. You can instruct your agents to regularly report back to superiors asking for permission to continue. You can instruct them to use point-scoring systems when deciding when to delegate subtasks. Superiors can double-check subordinates' results and disputes. -
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DB-GPT
Revolutionizing Database Interactions with Private LLM Technology
DB-GPT is an experimental open-source project that uses localized GPT large models to interact with your data and environment. With this solution, you can be assured that there is no risk of data leakage, and your data is 100% private and secure. -
Picsart Enterprise Background Removal API for Stunning eCommerce VisualsWith our Remove Background API tool, you can access the transformative capabilities of automation , which will allow you to turn any photo asset into compelling product imagery. With elevated visuals quality on your digital platforms, you can captivate your audience, and therefore achieve higher engagement and sales.
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TapeAgents
A framework that facilitates all stages of LLM development
TapeAgents is a framework that facilitates all stages of the Large Language Model (LLM) agent development lifecycle, providing tools for building, testing, and deploying AI agents. -
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Browser Use
Make websites accessible for AI agents
Browser-Use is a framework that makes websites accessible for AI agents, enabling automated interactions and data extraction from web pages. -
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CrewAI
Framework for orchestrating role-playing, autonomous AI agents
Framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks. The power of AI collaboration has too much to offer. CrewAI is designed to enable AI agents to assume roles, share goals, and operate in a cohesive unit - much like a well-oiled crew. Whether you're building a smart assistant platform, an automated customer service ensemble, or a multi-agent research team, CrewAI provides the backbone for sophisticated multi-agent interactions. -
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OpenHands
Open-source autonomous AI software engineer
Welcome to OpenHands (formerly OpenDevin), an open-source autonomous AI software engineer who is capable of executing complex engineering tasks and collaborating actively with users on software development projects. Use AI to tackle the toil in your backlog, so you can focus on what matters: hard problems, creative challenges, and over-engineering your dotfiles We believe agentic technology is too important to be controlled by a few corporations. So we're building all our agents in the open on GitHub, under the MIT license. Our agents can do anything a human developer can: they write code, run commands, and use the web. We're partnering with AI safety experts like Invariant Labs to balance innovation with security. -
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c/ua
c/ua is the Docker Container for Computer-Use AI Agents
Cua is a Docker-based framework that facilitates the deployment and management of computer-use AI agents. It provides a sandboxed environment where agents can perform tasks on macOS and Linux virtual machines, supporting various AI models and ensuring safe execution. Cua is particularly useful for developers looking to test and run AI agents in controlled settings. -
Crowdtesting That Delivers | TesteumTesteum connects your software, app, or website to a worldwide network of testers, delivering detailed feedback in under 48 hours. Ensure functionality and refine UX on real devices, all at a fraction of traditional costs. Trusted by startups and enterprises alike, our platform streamlines quality assurance with actionable insights.
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mcp-use
A solution to build and deploy MCP agents and applications
mcp-use is an open source development platform offering SDKs, cloud infrastructure, and a developer-friendly control plane for building, managing, and deploying AI agents that leverage the Model Context Protocol (MCP). It enables connection to multiple MCP servers, each exposing specific tool capabilities like browsing, file operations, or specialized integrations, through a unified MCPClient. Developers can create custom agents (via MCPAgent) that dynamically select the most appropriate server for each task using configurable pipelines or a built-in server manager. It simplifies authentication, access control, audit logging, observability, sandboxed runtime environments, and deployment workflows, whether self-hosted or managed, making MCP development production-ready. With integrations for popular frameworks like LangChain (Python) and LangChain.js (TypeScript), mcp-use accelerates the creation of tool-enabled AI agents. -
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uAgents
A fast and lightweight framework for creating decentralized agents
uAgents is a library developed by Fetch.ai that allows for creating autonomous AI agents in Python. With simple and expressive decorators, you can have an agent that performs various tasks on a schedule or takes action on various events. -
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ChemCrow
Chemcrow
ChemCrow is an AI-powered framework designed to assist in chemical research and discovery. It integrates AI models with chemical knowledge bases to provide intelligent recommendations for synthesis planning, reaction prediction, and material discovery. This tool helps automate and accelerate research in computational chemistry and drug development. -
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DSPy
DSPy: The framework for programming—not prompting—language models
Developed by the Stanford NLP Group, DSPy (Declarative Self-improving Python) is a framework that enables developers to program language models through compositional Python code rather than relying solely on prompt engineering. It facilitates the construction of modular AI systems and provides algorithms for optimizing prompts and weights, enhancing the quality and reliability of language model outputs. -
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Open Autonomy
A framework for the creation of autonomous agent services
Open Autonomy is a framework that enables the development of autonomous economic agents (AEAs) capable of operating independently in various economic contexts. -
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CAMEL AI
Finding the Scaling Law of Agents. A multi-agent framework
The rapid advancement of conversational and chat-based language models has led to remarkable progress in complex task-solving. However, their success heavily relies on human input to guide the conversation, which can be challenging and time-consuming. This paper explores the potential of building scalable techniques to facilitate autonomous cooperation among communicative agents and provide insight into their "cognitive" processes. To address the challenges of achieving autonomous cooperation, we propose a novel communicative agent framework named role-playing. Our approach involves using inception prompting to guide chat agents toward task completion while maintaining consistency with human intentions. We showcase how role-playing can be used to generate conversational data for studying the behaviors and capabilities of chat agents, providing a valuable resource for investigating conversational language models. -
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cognee
Deterministic LLMs Outputs for AI Applications and AI Agents
We build for developers who need a reliable, production-ready data layer for AI applications. Cognee implements scalable, modular data pipelines that allow for creating the LLM-enriched data layer using graph and vector stores. Cognee acts a semantic memory layer, unveiling hidden connections within your data and infusing it with your company's language and principles. This self-optimizing process ensures ultra-relevant, personalized, and contextually aware LLM retrievals. Any kind of data works; unstructured text or raw media files, PDFs, tables, presentations, JSON files, and so many more. Add small or large files, or many files at once. We map out a knowledge graph from all the facts and relationships we extract from your data. Then, we establish graph topology and connect related knowledge clusters, enabling the LLM to "understand" the data. -
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AgentOps
Python SDK for agent monitoring, LLM cost tracking, benchmarking, etc.
Industry-leading developer platform to test and debug AI agents. We built the tools so you don't have to. Visually track events such as LLM calls, tools, and multi-agent interactions. Rewind and replay agent runs with point-in-time precision. Keep a full data trail of logs, errors, and prompt injection attacks from prototype to production. Native integrations with the top agent frameworks. -
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BISHENG
BISHENG is an open LLM devops platform for next generation apps
BISHENG is an open LLM application DevOps platform, focusing on enterprise scenarios. It has been used by a large number of industry-leading organizations and Fortune 500 companies. "Bi Sheng" was the inventor of movable type printing, which played a vital role in promoting the transmission of human knowledge. We hope that BISHENG can also provide strong support for the widespread implementation of intelligent applications. Everyone is welcome to participate. -
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GPTme
Your agent in your terminal, equipped with local tools
GPTMe is a personal AI chatbot designed for self-reflection, journaling, and productivity, using GPT models to generate personalized insights and responses. -
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Griptape
Python framework for AI workflows and pipelines with chain of thought
The Griptape framework provides developers with the ability to create AI systems that operate across two dimensions: predictability and creativity. For predictability, Griptape enforces structures like sequential pipelines, DAG-based workflows, and long-term memory. To facilitate creativity, Griptape safely prompts LLMs with tools (keeping output data off prompt by using short-term memory), which connects them to external APIs and data stores. The framework allows developers to transition between those two dimensions effortlessly based on their use case. Griptape not only helps developers harness the potential of LLMs but also enforces trust boundaries, schema validation, and tool activity-level permissions. By doing so, Griptape maximizes LLMs’ reasoning while adhering to strict policies regarding their capabilities. -
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Haystack
Haystack is an open source NLP framework to interact with your data
Apply the latest NLP technology to your own data with the use of Haystack's pipeline architecture. Implement production-ready semantic search, question answering, summarization and document ranking for a wide range of NLP applications. Evaluate components and fine-tune models. Ask questions in natural language and find granular answers in your documents using the latest QA models with the help of Haystack pipelines. Perform semantic search and retrieve ranked documents according to meaning, not just keywords! Make use of and compare the latest pre-trained transformer-based languages models like OpenAI’s GPT-3, BERT, RoBERTa, DPR, and more. Pick any Transformer model from Hugging Face's Model Hub, experiment, find the one that works. Use Haystack NLP components on top of Elasticsearch, OpenSearch, or plain SQL. Boost search performance with Pinecone, Milvus, FAISS, or Weaviate vector databases, and dense passage retrieval. -
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LangGraph
Build resilient language agents as graphs
LangGraph is a library for building stateful, multi-actor applications with LLMs, used to create agent and multi-agent workflows. Compared to other LLM frameworks, it offers these core benefits: cycles, controllability, and persistence. LangGraph allows you to define flows that involve cycles, essential for most agentic architectures, differentiating it from DAG-based solutions. As a very low-level framework, it provides fine-grained control over both the flow and state of your application, crucial for creating reliable agents. Additionally, LangGraph includes built-in persistence, enabling advanced human-in-the-loop and memory features. -
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Nerve
The Simple Agent Development Kit
Nerve is a developer-friendly Agent Development Kit (ADK) that utilizes YAML and a CLI to define, run, orchestrate, and evaluate LLM-driven agents. It supports declarative setups, tool integration, workflow pipelines, and both MCP client and server roles. -
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Parlant
The behavior guidance framework for customer-facing LLM agents
Parlant is a lightweight speech-to-text and text-to-speech framework designed for real-time AI-driven voice applications. -
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SuperAGI
A dev-first open source autonomous AI agent framework
An open-source autonomous AI framework to enable you to develop and deploy useful autonomous agents quickly & reliably. Join a community of developers constantly contributing to make SuperAGI better. Access your agents through a graphical user interface. Interact with agents by giving them input, permissions, etc. Agents typically learn and improve their performance over time with feedback loops. Run multiple agents simultaneously to improve efficiency and productivity. Connect to multiple Vector DBs to enhance your agent’s performance. Each agent is unique, use different models of your choice. Get insights into your agent’s performance and optimize accordingly. Control token usage to manage costs effectively. Enable your agents to learn and adapt by storing their memory. Get notified when agents get stuck in the loop, and provide proactive resolution. Read and store files generated by Agents.
