Open Source Agentic AI Tools - Page 8
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Agentic AI Tools
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AlphaClaw
OpenClaw harness and fleet manager.
AlphaClaw is an open-source management layer and deployment harness built on top of OpenClaw to simplify running and maintaining AI agents. It provides a browser-based interface that replaces manual CLI workflows with an intuitive setup and management experience. Designed for reliability, AlphaClaw includes a self-healing watchdog system that detects crashes, performs repairs, and automatically restarts the agent environment. It integrates with multiple platforms such as Slack, Discord, Telegram, and Google Workspace, enabling seamless multi-agent orchestration. The system also includes Git-backed version control and prompt hardening to ensure agents remain consistent, auditable, and well-behaved over time. AlphaClaw is ideal for developers and teams who want production-ready AI agents without the complexity of manual infrastructure management. -
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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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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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AutoGen
An Open-Source Programming Framework for Agentic AI
AutoGen is an open-source programming framework for building AI agents and facilitating cooperation among multiple agents to solve tasks. AutoGen aims to provide an easy-to-use and flexible framework for accelerating development and research on agentic AI, like PyTorch for Deep Learning. It offers features such as agents that can converse with other agents, LLM and tool use support, autonomous and human-in-the-loop workflows, and multi-agent conversation patterns. AutoGen provides multi-agent conversation framework as a high-level abstraction. With this framework, one can conveniently build LLM workflows. AutoGen offers a collection of working systems spanning a wide range of applications from various domains and complexities. AutoGen supports enhanced LLM inference APIs, which can be used to improve inference performance and reduce cost. -
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Better Agents
Standards for building agents, better
Better Agents is a command-line tool and framework designed to standardize the development of AI agents and improve the workflow for building production-ready agent systems. The project provides a structured set of best practices and templates that help developers organize their agent projects in a way that promotes maintainability, scalability, and reliability. Rather than being a full execution framework itself, Better-Agents focuses on enhancing coding assistants and agent development tools by embedding standardized guidelines into the development process. The system generates structured project files, including configuration documents that define the architecture, roles, and capabilities of the agent system. By following these conventions, developers can ensure that their agents adhere to widely accepted design patterns and operational standards. -
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Claude Agent SDK
Programmatically build AI agents with Claude Code's capabilities
Claude Agent SDK (TypeScript) is an official TypeScript/JavaScript software development kit from Anthropic that makes it easier to build AI agents using the same agent architecture that powers Claude Code and other Claude-based autonomous systems. It provides abstractions and tools that let developers programmatically create, configure, and run intelligent agents that can read files, run commands, edit source code, manage sessions, and execute workflows without having to implement the agent loop manually — the SDK handles much of the complexity for you. Since it’s designed to tap into Claude Code’s capabilities, it integrates tightly with the underlying agent runtime, giving access to built-in tools like file system access, command execution, and code editing, and enabling features such as custom agents, hooks, and subagents. -
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Clawith
OpenClaw for Teams
Clawith is an AI-driven agent system focused on enabling intelligent task execution, coordination, and interaction across complex environments. It is designed to function as a control layer where agents can perform actions, manage workflows, and respond dynamically to changing conditions. The system likely emphasizes integration with external tools and services, allowing agents to extend their capabilities beyond internal reasoning. Its architecture suggests support for multi-agent collaboration, enabling distributed problem-solving and task delegation. It may also include monitoring and control features to ensure that agent behavior remains aligned with user goals. The project reflects a broader trend toward building AI systems that act as autonomous operators rather than passive assistants. Overall, Clawith serves as a foundation for building advanced, action-oriented AI workflows. -
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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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Coze Studio
An AI agent development platform with all-in-one visual tools
Coze Studio is ByteDance’s open‑source, visual AI agent development platform. It offers no-code/low-code workflows to build, debug, and deploy conversational agents, integrating prompting, RAG-based knowledge bases, plugin systems, and workflow orchestration. Developed in Go (backend) and React/TypeScript (frontend), it uses a containerized microservices architecture suitable for enterprise deployment. -
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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Cybergod
A program that can do anything to earn money without human operators
AGI Computer Control is an experimental autonomous software system designed to operate independently and generate income without human intervention. It aims to simulate artificial general intelligence (AGI) by leveraging evolutionary algorithms, deep active inference, and other advanced AI techniques. The project explores the boundaries of machine autonomy and self-directed behavior in computational environments. -
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Dendrite
Tools to build web AI agents that can authenticate
Dendrite Python SDK is a toolkit for building web AI agents that can authenticate, interact with, and extract data from any website, facilitating web automation tasks. -
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Diplomacy Cicero
Code for Cicero, an AI agent that plays the game of Diplomacy
The project is the codebase for an AI agent named Cicero developed by Facebook Research. It is designed to play the board game Diplomacy by combining open-domain natural language negotiation with strategic planning. The repository includes training code, model checkpoints, and infrastructure for both language modelling (via the ParlAI framework) and reinforcement learning for strategy agents. It supports two variants: Cicero (which handles full “press” negotiation) and Diplodocus (a variant focused on no-press diplomacy) as described in the README. The codebase is implemented primarily in Python with performance-critical components in C++ (via pybind11 bindings) and is configured to run in a high‐GPU cluster environment. Configuration is managed via protobuf files to define tasks such as self-play, benchmark agent comparisons, and RL training. The project is now archived and read-only, reflecting that it is no longer actively developed but remains publicly available for research use. -
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E2B Infra
Infrastructure for AI code interpreting that's powering E2B
E2B Infra is an infrastructure management tool that simplifies the deployment and scaling of applications across cloud environments, focusing on automation and efficiency. -
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EdgeChains
EdgeChains.js is Full-Stack GenAI library
EdgeChains.js is a full-stack generative AI library that provides front-end, back-end, APIs, prompt management, and distributed computing capabilities, with core prompts and chains managed declaratively in Jsonnet. At EdgeChains, we take a unique approach to Generative AI - we think Generative AI is a deployment and configuration management challenge rather than a UI and library design pattern challenge. We build on top of a tech that has solved this problem in a different domain - Kubernetes Config Management - and bring that to Generative AI. Edgechains is built on top of jsonnet, originally built by Google based on their experience managing a vast amount of configuration code in the Borg infrastructure. -
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Eko
Build Production-ready Agentic Workflow with Natural Language
Eko (Eko Keeps Operating) is a JavaScript framework designed for building production-ready agent-based workflows using natural language commands. It allows developers to create automated agents that can handle complex workflows in both computer and browser environments. With a focus on high development efficiency, Eko simplifies the creation of multi-step workflows, enabling users to integrate and automate tasks across platforms. It provides a unified interface for managing agents, offering features such as web resource access and high task complexity handling. Eko is open-source and can be used to execute tasks like browser automation, system operations, and software testing. -
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Factory AI
Agent-Native Software Development
Factory is an agent-native software development platform designed to automate and accelerate the entire software engineering lifecycle by leveraging AI agents that can understand, modify, and manage codebases in real time. At its core, the system introduces the concept of “Droids,” specialized AI agents that operate across development environments such as the command line, web interfaces, project management tools, and communication platforms, enabling seamless interaction with engineering workflows. Rather than acting as a simple coding assistant, Factory is positioned as a full command center for development, where AI agents can autonomously write code, review pull requests, debug issues, and even coordinate tasks across tools like GitHub, Jira, or Slack. The platform integrates deeply with existing repositories and infrastructure, allowing it to analyze source code, monitor logs, and use contextual data from observability systems to make informed decisions. -
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Gradient Bang
Gradient Bang is an online multiplayer universe
Gradient Bang is an experimental open-source project developed within the Pipecat ecosystem that reimagines AI interaction as a persistent, multiplayer simulation where users and large language models coexist inside a shared virtual environment. Rather than functioning as a traditional application or API, it is conceptualized as an “online multiplayer universe” in which participants can explore, trade, battle, and collaborate while interacting with AI agents as active entities within the system. The project serves both as a prototype and a conceptual playground for testing how conversational AI systems behave when embedded into dynamic, game-like environments rather than static chat interfaces. It leverages the broader Pipecat architecture for multimodal and conversational AI orchestration, meaning that interactions can potentially extend beyond text into voice, events, and real-time systems. -
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Hindsight
Hindsight: Agent Memory That Learns
Hindsight is an advanced, open-source memory system for AI agents designed to enable long-term learning, reasoning, and consistency across interactions by treating memory as a first-class component of intelligence rather than a simple retrieval layer. It addresses one of the core limitations of modern AI agents, which is their inability to retain and meaningfully use past experiences over time, by introducing a structured, biomimetic memory architecture inspired by how human memory works. Instead of relying solely on vector similarity or basic retrieval techniques, Hindsight organizes information into distinct categories such as facts, experiences, beliefs, and observations, allowing agents to differentiate between raw data and inferred knowledge. The system operates through three core mechanisms—retain, recall, and reflect—which respectively handle storing information, retrieving relevant context, and generating new insights based on accumulated experience. -
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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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Lecca.io
Lecca.io | AI Agents & Automations
Lecca.io is an AI platform that allows you to configure and deploy Large Language Models (LLMs) equipped with powerful tools and workflows. Build, customize, and automate your AI agents with ease. -
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Nimbalyst
Run multiple Codex and Claude Code AI sessions
Crystal is an open-source project focused on building a lightweight and flexible system for managing structured data, workflows, or automation pipelines, typically oriented toward developer productivity and extensible backend tooling. It is designed with modularity in mind, allowing developers to define reusable components and compose them into larger workflows that can adapt to different use cases. The project emphasizes simplicity and clarity, making it easier to understand and extend compared to heavier enterprise frameworks. Crystal often leverages modern programming practices and clean architecture principles to ensure maintainability and scalability as projects grow. It can be used as a foundation for building internal tools, automation systems, or data processing pipelines, depending on how developers configure its components. The system is particularly useful for teams that want control over their infrastructure without relying on overly complex or opinionated platforms. -
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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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Open Multi-Agent
One runTeam() call from goal to result
Open Multi-Agent is a flexible framework designed to enable the creation and coordination of multiple AI agents working together to solve complex tasks through collaboration. It focuses on distributing responsibilities across specialized agents, each handling a specific part of a problem, such as planning, execution, or validation. The system emphasizes modularity, allowing developers to define agent roles, communication protocols, and workflows. It supports iterative collaboration, where agents exchange information and refine outputs collectively. The architecture is designed to be extensible, enabling integration with external tools and APIs to expand agent capabilities. It is particularly useful for research, automation, and development workflows that require multiple perspectives or stages of processing. Overall, open-multi-agent provides a foundation for building scalable and cooperative AI systems. -
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OpenAI Agents SDK
A lightweight, powerful framework for multi-agent workflows
The OpenAI Agents Python SDK is a powerful yet lightweight framework for developing multi-agent workflows. This framework enables developers to create and manage agents that can coordinate tasks autonomously, using a set of instructions, tools, guardrails, and handoffs. The SDK allows users to configure workflows in which agents can pass control to other agents as necessary, ensuring dynamic task management. It also includes a built-in tracing system for tracking, debugging, and optimizing agent activities. -
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Plandex
AI driven development in your terminal
Plandex is an AI-powered project planning and scheduling tool that optimizes resource allocation and workflow efficiency using predictive algorithms.
