Open Source Python AI Agents - Page 2
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Python AI Agents
View 796 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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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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SafeClaw
Chat with it via text and voice
SafeClaw is an open-source, entirely local alternative to cloud-based AI assistants like OpenClaw, enabling users to build a personal assistant that runs on their own machine without incurring API usage charges or exposing data to third-party services. It emphasizes privacy and predictability by using traditional programming, rule-based intent parsing, and established machine learning tools rather than large language models, meaning there are no per-token API costs and deterministic behavior. The assistant offers features such as voice control using fully local speech-to-text (Whisper) and text-to-speech (Piper) capabilities, news aggregation with extractive summarization, and smart home or Bluetooth device control. SafeClaw supports multiple channels, including CLI and Telegram, and avoids prompt injection risk because it doesn’t rely on LLMs for core operations. -
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autoresearch
AI agents autonomously run and improve ML experiments overnight
autoresearch is an experimental framework that enables AI agents to autonomously conduct machine learning research by iteratively modifying and training models. Created by Andrej Karpathy, the project allows an agent to edit the model training code, run short experiments, evaluate results, and repeat the process without human intervention. Each experiment runs for a fixed five-minute training window, enabling rapid iteration and consistent comparison across architectural or hyperparameter changes. The system centers on a simple workflow where the agent modifies a single training file while human researchers guide the process through a program.md instruction file. Designed to run on a single GPU, it keeps the research loop minimal and self-contained to make autonomous experimentation practical. Over time, the agent logs experiments, evaluates improvements, and gradually evolves the model through automated trial-and-error. -
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cognee
Deterministic LLMs Outputs for AI Applications and AI Agents
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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AIHawk
AIHawk aims to easy job hunt process by automating job applications
AIHawk is an AGPL‑licensed AI agent focused on automating job applications. It scrapes job listings from corporate sites (or LinkedIn in forks) and uses LLMs to generate tailored applications, streamlining the process across multiple platforms—dubbed “revolutionary” by mainstream tech outlets. -
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Agent Starter Pack
Ship AI Agents to Google Cloud in minutes, not months
Agent Starter Pack is a production-focused framework that provides pre-built templates and infrastructure for rapidly developing and deploying generative AI agents on Google Cloud. It is designed to eliminate the complexity of moving from prototype to production by bundling essential components such as deployment pipelines, monitoring, security, and evaluation tools into a single package. Developers can create fully functional agent projects with a single command, generating both backend and frontend structures along with deployment-ready configurations. The framework supports multiple agent architectures, including ReAct, retrieval-augmented generation, and multi-agent systems, allowing flexibility across use cases. It integrates tightly with Google Cloud services like Vertex AI, Cloud Run, and Terraform-based infrastructure provisioning, enabling scalable and reliable deployments. -
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AutoCoder
A long-running autonomous coding agent powered by the Claude Agent
Autocoder is an experimental auto-generation engine that transforms high-level prompts or structured descriptions into functioning source code, models, or systems with minimal manual intervention. Rather than hand-writing boilerplate or repetitive patterns, users supply a specification—such as a description of a feature, a function prototype, or a module outline—and Autocoder fills in complete implementations that compile and run. It is built to support iterative refinement: after generating an initial draft, you can provide feedback or corrections, and the system will adjust the output to match evolving intentions. The core idea is to accelerate software production while preserving correctness and readability, minimizing the cognitive overhead that comes from switching between concept and implementation. Its architecture typically integrates language models with static analysis and template logic so that generated code is not only syntactically valid but also idiomatic and testable. -
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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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GPT Researcher
LLM based autonomous agent that does online comprehensive research
Say Hello to GPT Researcher, your AI agent for rapid insights and comprehensive research. GPT Researcher is the leading autonomous agent that takes care of everything from accurate source gathering to organization of research results. -
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GenericAgent
Self-evolving autonomous agent framework
The GenericAgent project is a flexible framework for building autonomous AI agents that can operate across diverse tasks and environments. It is designed around modularity, allowing developers to define agents with interchangeable components such as tools, memory systems, and reasoning strategies. The architecture emphasizes generality, enabling the same agent framework to be adapted for different domains including coding, research, and task automation. It integrates with modern language models to provide planning, execution, and iterative reasoning capabilities, making it suitable for complex workflows. The project also focuses on extensibility, allowing developers to plug in custom tools or APIs and tailor agent behavior to specific use cases. By abstracting common agent patterns, it reduces the overhead of building agent systems from scratch. Overall, GenericAgent provides a foundation for scalable and reusable AI agent development. -
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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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Android Use
Automate native Android apps with AI using accessibility APIs
android-action-kernel is an open source Python library designed to let AI agents control and automate native Android applications running on real devices or emulators. It fills a gap in automation tooling by focusing on mobile-first workflows where traditional browser or desktop-based automation doesn’t work; such as logistics, gig work, field operations, and other industries reliant on phones or tablets. The project works by using Android’s accessibility API to extract structured UI state (as XML) from the device, which is then fed to a large language model (LLM) like OpenAI’s models for decision-making, and actions are executed via the Android Debug Bridge (ADB). This approach bypasses expensive vision-based models and provides faster, cheaper automation with fine-grained interaction capabilities (for example, tapping buttons, typing text, navigating screens). -
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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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Get Physics Done (GPD)
The first open-source agentic AI physicist
Get Physics Done (GPD) is an open-source project designed to accelerate scientific research in physics by leveraging modern computational tools and automation techniques. It aims to simplify the process of performing simulations, calculations, and experimental analysis by providing structured workflows that integrate computational physics methods with reproducible research practices. The project focuses on reducing the friction involved in setting up experiments, running simulations, and analyzing results, allowing researchers to focus more on scientific insight rather than infrastructure. It emphasizes automation and reproducibility, ensuring that experiments can be easily replicated and extended by other researchers. The framework is adaptable to different areas of physics, making it suitable for both theoretical and applied research scenarios. -
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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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IntentKit
An open and fair framework for everyone to build AI agents
IntentKit is a natural language understanding (NLU) library focused on intent recognition and entity extraction, enabling developers to build conversational AI applications. -
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TrustGraph
Deploy reasoning AI agents powered by agentic graph RAG in minutes
TrustGraph is an AI-driven framework designed to assess and visualize trust relationships within networks, aiding in the analysis of trustworthiness and influence among entities. -
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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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AgentUniverse
agentUniverse is a LLM multi-agent framework
AgentUniverse is a multi-agent AI framework that enables coordination between multiple intelligent agents for complex task execution and automation. -
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Composio
Composio equip's your AI agents & LLMs
Empower your AI agents with Composio - a platform for managing and integrating tools with LLMs & AI agents using Function Calling. Equip your agent with high-quality tools & integrations without worrying about authentication, accuracy, and reliability in a single line of code. -
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Continuous Claude v3
Context management for Claude Code. Hooks maintain state via ledgers
Continuous Claude v3 is a persistent, multi-agent development environment built around the Claude Code CLI that aims to overcome the limitations of standard LLM context windows. Rather than relying on a single session’s context, Continuous Claude uses mechanisms like ledgers, YAML handoffs, and a memory system to preserve and recall state across multiple sessions, ensuring that learned insights and plans are not lost when context compaction occurs. The project orchestrates many specialized agents and skills—109 skills and 32 agents—so that complex coding tasks can be broken down, analyzed, and executed collaboratively by different components. It also includes a layered code analysis pipeline to reduce token usage and maintain relevant context efficiently. This continuous learning environment enables workflows such as bug fixing, refactoring, planning, and exploratory investigation while minimizing the need to re-explain context manually. -
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FinRobot
An Open-Source AI Agent Platform for Financial Analysis using LLMs
FinRobot is an open-source AI framework focused on automating financial data workflows by combining data ingestion, feature engineering, model training, and automated decision-making pipelines tailored for quantitative finance applications. It provides developers and quants with structured modules to fetch market data, process time series, generate technical indicators, and construct features appropriate for machine learning models, while also supporting backtesting and evaluation metrics to measure strategy performance. Built with modularity in mind, FinRobot allows users to plug in custom models — from classical algorithms to deep learning architectures — and orchestrate components in pipelines that can run reproducibly across experiments. The framework also tends to include automation layers for deployment, enabling trained models to operate in live or simulated environments with scheduled re-training and risk controls in place. -
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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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MineContext
MineContext is your proactive context-aware AI partner
MineContext is an open-source, proactive AI assistant designed to capture, understand, and leverage a user’s digital context in order to provide meaningful insights, summaries, and productivity support. The system continuously collects contextual data from sources such as screenshots and user activity, then processes and organizes this information into structured knowledge that can be reused later. Unlike traditional chat-based assistants, MineContext operates in the background and delivers proactive outputs such as daily summaries, task suggestions, and contextual reminders without requiring explicit prompts. It is built around a context engineering framework that manages the full lifecycle of data, including capture, processing, storage, retrieval, and consumption. The platform emphasizes privacy through a local-first architecture, allowing users to keep their data stored and processed on their own device rather than relying on external cloud services. -
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Multi-Agent Particle Envs
Code for a multi-agent particle environment used in a paper
Multiagent Particle Environments is a lightweight framework for simulating multi-agent reinforcement learning tasks in a continuous observation space with discrete action settings. It was originally developed by OpenAI and used in the influential paper Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments. The environment provides simple particle-based worlds with simulated physics, where agents can move, communicate, and interact with each other. Scenarios are designed to model cooperative, competitive, and mixed interactions among agents, making it useful for testing algorithms in multi-agent settings. The project includes built-in scenarios such as navigation to landmarks, cooperative tasks, and adversarial setups. Although archived, its concepts and code structure remain foundational for more advanced libraries like PettingZoo, which extended and maintained this environment. -
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Open Interface
Control Any Computer Using LLMs
Open Interface is a cross-platform application that allows users to control their computers using large language models (LLMs). By sending user requests to an LLM backend, it determines the necessary steps and executes them by simulating keyboard and mouse inputs. The system can adjust its actions based on real-time feedback, providing a self-driving computer experience.
