Open Source Python Agentic AI Tools
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Python Agentic AI Tools
View 102 business solutionsBrowse free open source Python Agentic AI Tools and projects below. Use the toggles on the left to filter open source Python Agentic AI Tools by OS, license, language, programming language, and project status.
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Enterprise Job Scheduling SoftwareManaging your sprawling data center and cloud with disparate native schedulers creates chaos. Achieve unparalleled control and efficiency over your entire IT environment with JAMS job orchestration tools. JAMS provides the singular, centralized platform required to overcome the complexities of disparate native schedulers. Automate, secure, and govern all your workloads, eliminating fragmented control, compliance risks, and operational bottlenecks. JAMS streamlines operations and ensures audit-ready history, transforming your enterprise automation with confidence and precision.
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No-code email and landing page creationBuilt for speed and collaboration, Knak streamlines campaign production with modular templates, real-time editing, simple collaboration, and seamless integrations with leading MAPs like Adobe Marketo Engage, Salesforce Marketing Cloud, Oracle Eloqua, and more. Whether you're supporting global teams or launching fast-turn campaigns, Knak helps you go from brief to build in minutes—not weeks. Say goodbye to bottlenecks and hello to marketing agility.
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MemPalace
The highest-scoring AI memory system ever benchmarked
MemPalace is an open-source AI memory system designed to solve one of the most persistent limitations of large language models: the loss of context between sessions. Instead of relying on summarization or selective extraction like most memory tools, it takes a radically different approach by storing conversations in their entirety and making them retrievable through structured organization and semantic search. The system is inspired by the classical “memory palace” mnemonic technique, organizing information into hierarchical spaces such as wings, rooms, and halls, which allows AI agents to navigate past knowledge in a more contextual and intuitive way. It operates fully locally using tools like ChromaDB, meaning it requires no API keys, cloud services, or external dependencies once installed. MemPalace emphasizes fidelity over compression, preserving full conversational history to maintain reasoning, nuance, and decision-making context that is typically lost in other systems. -
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OpenJarvis
Personal AI, On Personal Devices
OpenJarvis is an open-source framework designed to build personal AI agents that run primarily on local devices rather than relying on cloud infrastructure. Developed as part of the Intelligence Per Watt research initiative, it focuses on improving the efficiency and practicality of on-device AI systems. The framework provides shared primitives for building local-first agents, along with evaluation tools that measure performance using metrics such as energy consumption, latency, cost, and accuracy. OpenJarvis integrates with local inference engines like Ollama, vLLM, SGLang, and llama.cpp to run language models directly on personal hardware. It also includes a learning loop that allows models to improve over time using locally generated interaction traces. By prioritizing local execution and efficiency, OpenJarvis aims to provide a foundation for privacy-preserving personal AI assistants. -
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Hermes Agent
The agent that grows with you
Hermes Agent is a fully open-source autonomous AI agent designed to run persistently on your own machine or server, becoming more capable the longer it operates by learning from experience and building reusable procedural skills. Rather than functioning as a stateless chatbot, it maintains long-term memory across sessions and can generate searchable “Skill Documents” that capture how it solved complex tasks so it doesn’t start from scratch each time. The agent interfaces with messaging platforms like Telegram, Discord, Slack, and WhatsApp through a single gateway process, and also offers an interactive terminal user interface with history, autocomplete, and streamable tool output. It supports scheduled automation in natural language, allowing users to set up recurring tasks such as daily briefings or system audits that it runs unattended. -
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Claude Skills
Public repository for Agent Skills
Claude Skills is a public repository that showcases and serves as a collection of skills — modular, reusable packages of instructions, scripts, and resources that Claude and other compatible agents can dynamically discover and load to extend their capabilities on specialized tasks. Rather than relying on handcrafted prompts every time, Skills teach an AI agent procedural knowledge and task-specific workflows so it can apply that expertise reliably, whether the task involves document creation, data analysis, design generation, or technical automation. Each Skill lives in its own directory with a SKILL.md file containing metadata and instructions, and can include supplemental scripts or assets that the agent uses to perform complex operations when relevant. -
Powering the next decade of business messaging | Twilio MessagingXBuild unique experiences across SMS, MMS, Facebook Messenger, and WhatsApp – with our unified messaging APIs.
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Claw Code
AI agent harness for AI coding agents
Claw Code is an open-source AI agent harness project focused on building better tools for orchestrating and managing autonomous coding agents. It originated as a clean-room reimplementation inspired by the architecture of Claude Code, aiming to replicate core concepts without using proprietary code. The project provides a Python-based foundation for experimenting with agent workflows, tool integration, and task execution pipelines. It emphasizes harness engineering—how agents are structured, how they interact with tools, and how they maintain context during execution. The system is being actively expanded, with a Rust-based runtime in development to improve performance and memory safety. Overall, Claw Code serves as a research-driven platform for advancing agent-based software development systems. -
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DeerFlow
Deep Research framework, combining language models with tools
DeerFlow is an open-source, community-driven “deep research” framework / multi-agent orchestration platform developed by ByteDance. It aims to combine the reasoning power of large language models (LLMs) with automated tool-use — such as web search, web crawling, Python execution, and data processing — to enable complex, end-to-end research workflows. Instead of a monolithic AI assistant, DeerFlow defines multiple specialized agents (e.g. “planner,” “searcher,” “coder,” “report generator”) that collaborate in a structured workflow, allowing tasks like literature reviews, data gathering, data analysis, code execution, and final report generation to be largely automated. It supports asynchronous task coordination, modular tool integration, and orchestrates the data flow between agents — making it suitable for large-scale or multi-stage research pipelines. Users can deploy it locally or on server infrastructure, integrate custom tools, and benefit from its flexible configuration. -
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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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OpenManus
Open-source AI agent framework
OpenManus is an open-source AI agent framework designed to autonomously execute complex, multi-step tasks by combining reasoning, planning, and tool use. It enables developers to build agents that can think, act, and iterate toward goals rather than simply responding to prompts. The platform emphasizes task decomposition, allowing agents to break down objectives into smaller steps and execute them sequentially or recursively. OpenManus supports integration with external tools, APIs, and environments, making it suitable for real-world automation workflows. It is built to be flexible and extensible, enabling customization of agent behaviors, tools, and reasoning strategies. Overall, OpenManus provides a foundation for creating more capable, autonomous AI systems that can handle dynamic and goal-driven tasks. -
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AutoResearchClaw
Autonomous research from idea to paper. Chat an Idea. Get a Paper 🦞
AutoResearchClaw is an open-source framework designed to automatically generate full academic research papers from a single idea or topic. Built in Python, it orchestrates a multi-stage research pipeline that gathers literature, formulates hypotheses, runs experiments, analyzes results, and writes the final paper. The system retrieves real academic references from sources such as arXiv and Semantic Scholar to ensure credible citations. It can automatically generate code for experiments, run them in a sandbox environment, and analyze the results with statistical methods. The platform also uses multi-agent debate and automated peer review processes to refine research findings and improve paper quality. By combining literature discovery, experimentation, and writing automation, AutoResearchClaw aims to turn research ideas into conference-ready papers with minimal human intervention. -
E-commerce Fulfillment For Scaling BrandsFlowspace delivers fulfillment excellence by pairing powerful software and on-the-ground logistics know-how. Our platform provides automation, real-time control, and reliability beyond traditional 3PL capabilities—so you can scale smarter, faster, and easier.
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nanobot
🐈 nanobot: The Ultra-Lightweight Clawdbot / OpenClaw
nanobot is an ultra-lightweight personal AI assistant designed to deliver powerful agent capabilities without unnecessary complexity. Built in just ~4,000 lines of clean, readable code, it offers a minimalist alternative to heavyweight agent frameworks while retaining core intelligence and extensibility. nanobot is optimized for speed and efficiency, enabling fast startup times and low resource usage across environments. Its research-ready architecture makes it easy for developers to understand, customize, and extend for experimentation or production use. With simple one-click deployment and a straightforward CLI, users can get a working AI assistant running in minutes. Inspired by Clawdbot but radically simplified, nanobot proves that capable AI agents don’t need massive codebases. -
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CoPaw
Your Personal AI Assistant; easy to install, deploy on local or coud
CoPaw is a personal AI assistant designed to run on your own machine or in the cloud, giving you full control over memory, models, and data. Built by the AgentScope team, it connects to multiple chat platforms—including DingTalk, Feishu, QQ, Discord, iMessage, and more—through a single unified assistant. CoPaw supports both cloud-based LLM providers and fully local models such as llama.cpp, MLX, and Ollama, allowing you to operate without API keys if preferred. It includes a browser-based Console for chatting, configuring models, managing memory, and extending capabilities with custom skills. With built-in cron scheduling, heartbeat check-ins, and extensible skill loading, CoPaw grows with your workflow over time. Easy installation options—including pip, one-line scripts, Docker, and cloud deployment—make it accessible for both developers and non-technical users. -
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OpenClaw Medical Skills
The largest open-source medical AI skills library for OpenClaw
OpenClaw-Medical-Skills is an open-source library that provides a large collection of specialized medical capabilities designed for the OpenClaw AI agent ecosystem. The project organizes domain-specific “skills” that enable autonomous agents to perform tasks related to biomedical research, healthcare analysis, and clinical data interpretation. Each skill is packaged as a modular component that can be integrated into an OpenClaw-based AI assistant, allowing the agent to perform expert-level reasoning and workflows in medical contexts. Instead of relying on general-purpose language model responses, the repository equips AI agents with structured instructions and tools tailored to medical knowledge and datasets. This modular design allows developers and researchers to build AI systems that can access specialized medical reasoning processes, retrieve relevant biomedical information, and generate structured outputs suitable for analysis or downstream processing. -
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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 Development Kit (ADK)
Open-source, code-first Python toolkit for building, evaluating, etc.
ADK (Android Device Key) Python is a reference implementation by Google for working with Android attestation keys in Python. It facilitates the integration of Android attestation features into backends or systems that require verification of device identity and integrity. This is especially important in high-security applications where verifying that a device is genuine and uncompromised is critical. ADK Python helps developers verify hardware-backed keys, work with JSON Web Tokens (JWT), and integrate with Android’s Key Attestation infrastructure. -
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OpenMontage
World's first open-source, agentic video production system
OpenMontage is an open-source, agent-driven video production system that transforms AI coding assistants into fully automated multimedia creation pipelines. Instead of focusing on a single capability such as text-to-video generation, it treats video production as a structured, multi-stage workflow that mirrors how a real production team operates, including research, scripting, asset generation, editing, and final rendering. The system orchestrates a large collection of tools and models through coordinated pipelines, enabling an AI agent to autonomously gather information, write scripts, generate visuals, synthesize voiceovers, and assemble a complete video output. One of its defining characteristics is its modular and extensible architecture, which allows users to mix and match different providers, including both cloud APIs and local models, depending on performance, cost, or privacy needs. -
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Eigent
The Open Source Cowork Desktop to Unlock Your Exceptional Productivity
Eigent is an open-source cowork desktop application designed to help you build, manage, and deploy a custom AI workforce. It enables multiple specialized AI agents to collaborate in parallel, turning complex workflows into automated, end-to-end tasks. Built on the CAMEL-AI multi-agent framework, Eigent emphasizes productivity, flexibility, and transparent system design. You can run Eigent fully locally for maximum privacy and data control, or choose a cloud-connected experience for quick access. The platform supports a wide range of AI models and integrates powerful tools through the Model Context Protocol (MCP). With human-in-the-loop controls and enterprise-ready features, Eigent balances automation with oversight and security. -
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Langroid
Harness LLMs with Multi-Agent Programming
Given the remarkable abilities of recent Large Language Models (LLMs), there is an unprecedented opportunity to build intelligent applications powered by this transformative technology. The top question for any enterprise is: how best to harness the power of LLMs for complex applications? For technical and practical reasons, building LLM-powered applications is not as simple as throwing a task at an LLM system and expecting it to do it. Effectively leveraging LLMs at scale requires a principled programming framework. In particular, there is often a need to maintain multiple LLM conversations, each instructed in different ways, and "responsible" for different aspects of a task. -
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graphify
AI coding assistant skill (Claude Code, Codex, OpenCode, OpenClaw)
graphify is a data visualization and transformation tool designed to convert structured or semi-structured data into graph-based representations, enabling better understanding of relationships and dependencies. It focuses on building visual models such as nodes and edges that represent entities and their connections, making complex datasets easier to interpret. The system likely supports dynamic updates, allowing graphs to evolve as data changes or new inputs are introduced. It is particularly useful in domains such as network analysis, knowledge graphs, and system architecture visualization. The architecture emphasizes flexibility, enabling users to customize how data is mapped and displayed. It may also include analytical features to explore patterns, clusters, or anomalies within the graph. Overall, graphify serves as a bridge between raw data and visual insight. -
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/last30days
Claude Code skill that researches any topic across Reddit + X
/last30days is a specialized Claude Code skill designed to research current trends and practices across Reddit, X, and the wider web from the last 30 days, synthesize that data, and produce copy-paste-ready prompts or summaries that reflect what the community is actually talking about now. Rather than returning generic model responses, it intelligently analyzes social media and community discussions to identify what’s genuinely trending or working in practice across topics ranging from prompt techniques to tool usage or cultural trends. This makes it particularly useful for prompt engineers, content creators, and developers who want up-to-date prompts and insights that align with the most recent consensus and shared best practices in fast-moving fields like AI tooling. -
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OpenAgents
AI Agent Networks for Open Collaboration
OpenAgents is an ambitious open-source framework for building AI Agent Networks where multiple autonomous AI agents can discover, connect, and collaborate on shared tasks within an extensible, protocol-agnostic ecosystem. The project’s goal is to provide foundational networking infrastructure that lets diverse agents—built using different large language models or tools—interoperate and work together toward complex goals. Agents on OpenAgents can exchange information, share capabilities, execute collaborative workflows, and grow networks without being tied to a single vendor or model provider. It supports integration with popular large language model providers and agent frameworks, giving developers flexibility in how they assemble and scale agent networks. Together with OpenAgents Studio and a plugin ecosystem, users can launch interactive networks quickly, configure agent behaviors, and observe collaborative outcomes in real time. -
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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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MetaGPT
The Multi-Agent Framework
The Multi-Agent Framework: Given one line Requirement, return PRD, Design, Tasks, Repo. Assign different roles to GPTs to form a collaborative software entity for complex tasks. MetaGPT takes a one-line requirement as input and outputs user stories / competitive analysis/requirements/data structures / APIs / documents, etc. Internally, MetaGPT includes product managers/architects/project managers/engineers. It provides the entire process of a software company along with carefully orchestrated SOPs. -
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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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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.
