Open Source Python Agentic AI Tools - Page 7
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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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Gemini 3 and 200+ AI Models on One PlatformBuild generative AI apps with Vertex AI. Switch between models without switching platforms.
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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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OpenSandbox
OpenSandbox is a general-purpose sandbox platform for AI applications
OpenSandbox is a general purpose sandbox platform designed to securely run and isolate AI applications and untrusted workloads in controlled environments. The project focuses on providing a unified sandbox API that simplifies the process of executing code safely across different runtime backends. It supports multiple programming languages through SDKs, allowing developers to integrate sandbox capabilities into their systems without building custom isolation layers. The platform is built to work with container technologies such as Docker and Kubernetes, enabling scalable and production ready deployments. OpenSandbox is particularly useful for AI agents, code execution services, and any scenario where untrusted code must be executed safely. Its architecture emphasizes flexibility, security boundaries, and operational consistency across environments. Overall, the project aims to standardize sandbox execution for modern AI and cloud native workflows. -
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Petri
An alignment auditing agent capable of exploring alignment hypothesis
Petri is an open-source alignment auditing agent that lets researchers rapidly test concrete safety hypotheses against target models using realistic, multi-turn scenarios. Instead of building bespoke evals, Petri automatically generates audit environments from seed “special instructions,” orchestrates an auditor model to probe a target model, and simulates tool use and rollbacks to surface risky behaviors. Each interaction transcript is then scored by a judge model using a consistent rubric so results are comparable across runs and models. The system supports major model APIs and comes with starter seeds and judge dimensions, enabling minutes-to-insight workflows for questions like reward hacking, self-preservation, or eval awareness. Petri is designed for parallel exploration: it spins many audits in flight, aggregates findings, and highlights transcripts that deserve human review. -
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Potpie
Create custom engineering agents for your codebase
Potpie is an AI-powered data analysis tool that automates the exploration and visualization of datasets, assisting users in uncovering insights without extensive coding. -
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TaskWeaver
A code-first agent framework for seamlessly planning analytics tasks
TaskWeaver is a multi-agent AI framework designed for orchestrating autonomous agents that collaborate to complete complex tasks. -
Train ML Models With SQL You Already KnowBuild and deploy ML models using familiar SQL. Automate data prep with built-in Gemini. Query 1 TB and store 10 GB free monthly.
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UCP Python SDK
The official Python SDK for UCP
UCP Python SDK repository for the Universal Commerce Protocol (UCP) delivers an official Python client library that simplifies building UCP-compliant applications in Python. UCP itself is a modern, open-source standard that empowers seamless commerce interactions between platforms, AI agents, merchants, and payment providers without requiring bespoke integrations for every participant in the commerce ecosystem. This SDK provides Pydantic models for UCP schemas, making it easy for Python developers to construct, validate, and serialize protocol messages and data structures according to the UCP specification. By adhering to the official protocol standards, applications built on this SDK can participate in tasks like capability discovery, checkout flows, order management, and more, while remaining interoperable across different UCP implementations and surfaces. -
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XAgent
An Autonomous LLM Agent for Complex Task Solving
XAgent is an AI-driven autonomous agent framework capable of handling multi-step tasks across different domains. It enables AI agents to perform decision-making, task planning, and self-learning based on user-defined objectives, making it ideal for automation and research applications. -
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airda
airda(Air Data Agent
airda(Air Data Agent) is a multi-smart body for data analysis, capable of understanding data development and data analysis needs, understanding data, generating data-oriented queries, data visualization, machine learning and other tasks of SQL and Python codes. -
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ex-skill
Distill your ex into an AI Skill
ex-skill is an experimental AI tooling project that allows users to transform personal memories, particularly past relationships, into interactive AI “skills” that replicate the communication style, personality, and behavioral patterns of a specific individual. The system works by ingesting various forms of personal data such as chat logs, social media content, photos, and user-provided descriptions, then structuring this information into a layered representation that combines memory and persona modeling. It is designed to run within Claude Code environments, where users can generate, manage, and interact with these personalized AI entities through command-based interfaces. The project emphasizes emotional realism by reconstructing conversational tone, habits, and contextual memories, enabling interactions that feel consistent with the original person. -
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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. -
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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verl-agent
Designed for training LLM/VLM agents via RL
verl-agent is an open-source reinforcement learning framework designed to train large language model agents and vision-language model agents for complex interactive environments. Built as an extension of the veRL reinforcement learning infrastructure, the project focuses on enabling scalable training for agents that perform multi-step reasoning and decision-making tasks. The framework supports multi-turn interactions between agents and their environments, allowing the system to receive feedback after each step and adjust its strategy accordingly. This step-wise interaction model makes it possible to train agents to operate in long-horizon scenarios where decisions depend on cumulative context and previous outcomes. Developers can configure memory modules that determine how historical information is stored and incorporated into each step of the reasoning process. -
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Shinkai: Local AI Agents
Shinkai allows you to create advanced AI (local) agents effortlessly
Shinkai is a free, open-source AI platform that lets anyone create powerful AI agents without coding. These agents can collaborate with each other, handle complex tasks, and operate in decentralized crypto environments. Key Features: - No-Code Agent Creation - Build specialized agents (trading bots, sentiment trackers, etc.) with simple descriptions - Multi-Agent Collaboration - Agents work together to solve complex problems - Crypto Integration - Built-in support for decentralized payments and transactions - Flexible AI Models - Choose from cloud models (GPT-4, Claude) or run locally - Universal Compatibility - Works with Model Context Protocol (MCP) for cross-platform integration - Local Security - Crypto keys and computations stay on your device Shinkai transforms AI from single-task tools into collaborative, autonomous systems that can operate in decentralized networks while maintaining privacy and security. -
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AgentPilot
A versatile workflow automation platform to create AI workflows
AgentPilot is a versatile workflow automation platform designed to help users create, organize, and execute AI-driven workflows. It supports everything from simple tasks using a single large language model (LLM) to complex multi-step processes. The platform features a user-friendly interface that allows for real-time interaction with workflows, and it supports flexible configurations, including branching workflows and customizable user interfaces. Users can also schedule tasks based on natural language time expressions and integrate various tools to enhance their workflows. -
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AI Agent Deep Dive
AI Agent Source Code Deep Research Report
AI Agent Deep Dive is a comprehensive educational repository designed to provide a deep and structured understanding of how modern AI agents work, focusing on architecture, workflows, and real-world implementation patterns. It breaks down complex concepts such as planning, tool usage, memory management, and multi-step reasoning into digestible explanations and practical examples. The project is organized as a learning resource rather than a standalone framework, making it particularly useful for developers who want to move beyond surface-level prompt engineering into full agent system design. It explores how agents interact with environments, execute tasks, and maintain context over time, highlighting both strengths and limitations of current approaches. The repository likely includes diagrams, annotated code samples, and conceptual walkthroughs that mirror real production systems. -
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AIBuildAI
An AI agent that automatically builds AI models
AI-Build-AI is an open-source framework focused on enabling autonomous systems that can design, generate, and improve AI applications with minimal human intervention. The project explores recursive AI development, where models are used not only as tools but as builders capable of constructing other AI systems, workflows, or components. It provides a structured environment for orchestrating agents that can plan, execute, and refine tasks such as code generation, system design, and iterative improvement loops. The framework is designed to support experimentation with self-improving AI pipelines, allowing developers to test concepts like automated architecture search or adaptive system evolution. It integrates multiple components including prompt management, execution control, and feedback loops to ensure that generated outputs can be evaluated and improved over time. -
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Agent Skills
Specification and documentation for Agent Skills
agentskills is the specification and documentation repository for the Agent Skills open format, which defines a standardized way to package capabilities that AI agents can discover and use. A “skill” is treated as a foldered bundle containing instructions, optional scripts, and supporting resources, so agents can reliably apply a workflow or expertise area when it becomes relevant. The central goal is portability: you can write a skill once and reuse it across different agent runtimes and developer tools that implement the format. This repo serves as the canonical reference for how skills should be structured, what metadata they should include, and how an SDK can load and apply them consistently. It also includes supporting materials like guides and examples so builders can create skills that are predictable, testable, and shareable with teams. -
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Agentic Data Scientist
An end-to-end Data Scientist
Agentic Data Scientist is an experimental AI-driven research framework that orchestrates data science workflows through autonomous agents that can reason, plan, and execute complex analytics tasks. Unlike traditional scripted pipelines, this project lets AI agents break down high-level research goals into sub-tasks such as data acquisition, cleaning, modeling, evaluation, and reporting, with minimal human direction. Each agent is designed to independently call functions, interact with data sources, and adapt to uncertainties during processing, enabling iterative refinement of models without manual coordination. The framework supports interoperability with existing data tools and libraries, letting the agents leverage libraries like pandas, scikit-learn, and visualization frameworks to perform real computations rather than mock demonstrations. -
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AppWorld
World of apps for benchmarking interactive coding agent
AppWorld is a framework developed by Stony Brook University's NLP group to simulate environments for training and evaluating dialogue agents in task-oriented applications. -
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CUDA Agent
Large-Scale Agentic RL for High-Performance CUDA Kernel Generation
CUDA Agent is a research-driven agentic reinforcement learning system designed to automatically generate and optimize high-performance CUDA kernels for GPU workloads. The project addresses the long-standing challenge that efficient CUDA programming typically requires deep hardware expertise by training an autonomous coding agent capable of iterative improvement through execution feedback. Its architecture combines large-scale data synthesis, a skill-augmented CUDA development environment, and long-horizon reinforcement learning to build intrinsic optimization capability rather than relying on simple post-hoc tuning. The system operates in a ReAct-style loop where the agent profiles baseline implementations, writes CUDA code, compiles it in a sandbox, and iteratively refines performance. CUDA-Agent has demonstrated strong benchmark results, achieving high pass rates and significant speedups compared with compiler baselines such as torch.compile. -
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Hello-Agents
Building an Intelligent Agent from Scratch
Hello Agents is an open educational project designed to teach developers how to understand, design, and build AI-native agents from the ground up through structured tutorials and practical examples. The project focuses on guiding learners beyond superficial framework usage toward deeper comprehension of agent architecture, reasoning loops, and real-world implementation patterns. It walks users through core concepts such as ReAct-style reasoning, tool usage, memory handling, and multi-step task execution, enabling hands-on experimentation with modern LLM-powered agent systems. The repository is structured as a progressive learning path, combining theory, exercises, and runnable code so users can incrementally build more capable agents. Its goal is to demystify agent engineering and help developers move from simple prompt scripts to robust autonomous systems. -
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InfiAgent
Build your own Cowork, AI Scientist and other SoTA Agents
infiAgent is an open-source AI agent framework for building powerful, long-running autonomous agents capable of tackling complex tasks without collapsing under growing context or tool invocation histories. Designed as a “Multi-Level Agent” (MLA) system, it externalizes persistent state to the file system so that agents can operate over unlimited runtime without the need for token-intensive context compression, enabling workflows such as research paper drafting, experiments, coding, and document generation to run reliably. The framework uses a serial multi-agent hierarchy where specialized agents coordinate in tree-structured paths for clear task delegation and minimal tool conflicts, while batch file operations and persistent workspaces ensure reproducibility and traceability. It aims to solve real-world challenges in long-horizon reasoning and execution, offering configuration-driven customization so that users can define domain-specific agents like research assistants. -
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InstantCharacter
Personalize Any Characters with a Scalable Diffusion Transformer
InstantCharacter is a tuning-free diffusion transformer framework created by Tencent Hunyuan / InstantX team, which enables generating images of a specific character (subject) from a single reference image, preserving identity and character features. Uses adapters, so full fine-tuning of the base model is not required. Demo scripts and pipeline API (via infer_demo.py, pipeline.py) included. It works by adapting a base image generation model with a lightweight adapter so that you can produce character-preserving generations in various downstream tasks (e.g. changing pose, clothing, scene) without needing full model fine-tuning. Works with huggingface/transformers/diffusers ecosystems. -
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Live Agent Studio
Open source AI Agents hosted on the oTTomator Live Agent Studio
Live Agent Studio is a curated repository of open-source AI agents associated with the oTTomator Live Agent Studio platform, showcasing a variety of agent implementations that illustrate how autonomous and semi-autonomous tools can be constructed using modern AI frameworks. Each agent in the collection is designed for a specific use case — such as content summarization, task automation, travel planning, or RAG workflows — and is provided with the code or configuration needed to explore and extend it on your own, making the repository both a learning resource and a practical starting point for real projects. The repository is community focused, with sample agents like tweet generators, smart selectors, research assistants, and multi-tool workflows that show how agents can integrate with tools like n8n or custom Python code. Because it’s tied to the broader Live Agent Studio ecosystem, users can experiment with deploying and using these agents in a hosted environment. -
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MedgeClaw
Open-source AI research assistant for biomedicine
MedgeClaw is a specialized AI-powered research assistant tailored for biomedical and scientific workflows, built on top of OpenClaw and Claude Code architectures. It integrates a large library of domain-specific skills, enabling it to perform complex analyses in areas such as genomics, drug discovery, and clinical research. The system connects conversational interfaces with computational environments, allowing users to initiate research tasks through messaging platforms while the backend executes analyses using tools like R and Python. It includes a real-time dashboard that displays progress, generated code, and outputs, providing transparency throughout the research process. MedgeClaw also supports reproducibility by generating structured reports and maintaining consistent environments through containerization. Its architecture combines conversational AI, automated pipelines, and scientific tooling into a unified workflow. -
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SalesGPT
Context-aware AI Sales Agent to automate sales outreach
This repo is an implementation of a context-aware AI Agent for Sales using LLMs and can work across voice, email and texting (SMS, WhatsApp, WeChat, Weibo, Telegram, etc.). SalesGPT is context-aware, which means it can understand what stage of a sales conversation it is in and act accordingly. Moreover, SalesGPT has access to tools, such as your own pre-defined product knowledge base, significantly reducing hallucinations. -
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Softaworks Agent Skills
A curated collection of skills for AI coding agents
The Softaworks Agent Toolkit is a comprehensive collection of agent skills, commands, and sub-agents designed to augment AI coding assistants like Claude Code, Codex, and Cursor with practical workflow capabilities. It packages broad categories of modular skills that help with development automation, documentation creation, planning, architecture, testing, and soft professional workflows. Beyond simple skills, it also includes agents and CLI slash commands that help developers automate common tasks such as pattern finding, diagram generation, requirement drafting, and daily standup preparation. The toolkit’s modular design follows the Agent Skills format, making it easy for users to install only what’s needed via CLI installers or plugin marketplaces. Because the set spans from low-level utilities like dependency updaters to higher-level planning and communication aids, it can streamline many aspects of a developer’s day-to-day work.
