Search Results for "agent"
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Showing 60 open source projects for "agent"
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Goose Developer Agent
Goose is a developer agent that operates from your command line
Goose is a developer agent that supercharges your software development by automating an array of coding tasks directly within your terminal or IDE. Guided by you, it can intelligently assess your project's needs, generate the required code or modifications, and implement these changes on its own. Goose can interact with a multitude of tools via external APIs such as Jira, GitHub, Slack, infrastructure and data pipelines, and more -- if your task uses a shell command or can be carried out by a Python script, Goose can do it for you too! ... -
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CodiumAI PR-Agent
AI-Powered tool for automated pull request analysis
...You can try GPT-4 powered PR-Agent, on your public GitHub repository, instantly. Just mention @CodiumAI-Agent and add the desired command in any PR comment. The agent will generate a response based on your command. -
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bu-agent-sdk
An agent is just a for-loop
The bu-agent-sdk from the Browser Use project is a minimalistic Python framework that defines an AI agent as a simple loop of tool calls, aiming to keep abstractions low so developers can build autonomous agents without unnecessary complexity. At its core, the agent loop repeatedly queries a large language model, interprets its output, and executes defined “tools” — functions annotated with task names — to perform actions, allowing the agent to complete tasks like arithmetic, decision-making, or domain-specific work. ... -
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nature-skills
Skill that conforms to the academic expression and scientific research
nature-skills is an open-source collection of AI agent skills and structured workflows designed to enhance autonomous reasoning and tool usage in AI assistants. The project organizes reusable “skills” that agents can invoke for different operational contexts, helping AI systems perform specialized tasks more consistently and effectively. It appears to focus on modularity and interoperability, allowing skills to be combined, extended, or integrated into broader agent ecosystems. ... -
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Neuro SAN Studio
A playground for neuro-san
Neuro SAN Studio is a development environment and playground for building, testing, and deploying multi-agent AI systems using the Neuro SAN framework. It provides a hands-on interface where users can design agent networks, run experiments, and observe how multiple agents collaborate to solve complex tasks. The platform is built around a data-driven approach, where entire agent systems can be defined using configuration files rather than extensive code, making it accessible to both developers and domain experts. ... -
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AG-UI
The Agent-User Interaction Protocol
AG-UI is an open, lightweight protocol for connecting AI agents to user-facing applications through a standardized event-based interface. It is designed to make agent behavior visible, interactive, and controllable inside real-time front-end experiences. Instead of treating an AI agent as a black-box chat endpoint, AG-UI defines structured events for messages, tool calls, state changes, lifecycle updates, and user interactions. This makes it easier for developers to build agent-powered apps that stream progress, request human input, update UI state, and coordinate complex workflows. ... -
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Make It heavy
A Python framework that emulates Grok Heavy functionality
...The framework uses OpenRouter for model access and can also run in single-agent mode for simpler tasks. Overall, it is useful for users who want broader research coverage, richer reasoning diversity, and structured multi-agent responses from one command-line workflow. -
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Antigravity Awesome Skills
The Ultimate Collection of 700+ Agentic Skills for Claude Code
Antigravity Awesome Skills is a playful yet practical repository that curates a set of clever, expressive, and sometimes whimsical AI agent skill templates designed to help users bootstrap agent behavior quickly. Rather than focusing on production-grade systems, it provides creative and high-impact skills that demonstrate how agents can be used to automate tasks, generate content, assist with daily operations, or integrate into larger workflows with minimal configuration. ... -
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SIA
AI framework to autonomously improve the performance of any AI system
SIA is a self-improving AI framework designed to improve the performance of models or agents on benchmark tasks. It uses an iterative loop where a meta-agent creates or updates a task-specific target agent, while a feedback agent studies results and proposes improvements. The framework can refine both the harness around the task and the agent implementation itself. It is aimed at research and experimentation across tasks such as machine learning benchmarks, legal classification, code optimization, and scientific workflows. ... -
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OWL
Optimized Workforce Learning for General Multi-Agent Assistance
OWL (Optimized Workforce Learning) is a sophisticated open-source framework built on the CAMEL-AI ecosystem for orchestrating teams of AI agents to collaboratively solve complex, real-world tasks with dynamic planning and automation capabilities. Unlike single-agent systems, it treats task completion as a collaborative workforce where agents take on specialized roles (planning, execution, analysis) and coordinate via a modular multi-agent architecture that supports flexible teamwork across domains. OWL delivers state-of-the-art performance on benchmarks like GAIA and emphasizes real-time decision-making, web automation, rich search integration, document parsing, and multi-tool workflows, making it suitable for tasks ranging from information retrieval to interactive automation. -
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colleague-skill
Transform a cold separation into a warm Skill
...The system likely integrates with broader agent frameworks, enabling seamless inclusion in multi-agent environments. It emphasizes communication and coordination, ensuring that agents can contribute meaningfully to shared objectives. This makes it particularly useful for complex projects where multiple perspectives or iterative refinement are needed. -
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GPT-Image2-Skill
GPT Image 2 prompt gallery, image prompt library, agentic skill
...It can be installed as an agent skill for supported runtimes or used through a local CLI. The repository is designed for practical reuse, showing both how prompts are structured and how they can be executed. Its value is in combining inspiration, examples, and runnable tooling for repeatable AI image creation. -
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GPTImage2Skill
GPT Image 2 prompt gallery, image prompt library, agentic skill
...Its gallery is organized into category files so an agent can load only the relevant prompt references instead of overwhelming the context window. It also includes installation paths for skill-capable environments such as Claude Code, Codex, OpenClaw, and other agent runtimes. Overall, it is useful as both a learning resource for prompt structure and a practical toolkit for repeatable image generation workflows. -
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PydanticAI
Agent Framework / shim to use Pydantic with LLMs
When I first found FastAPI, I got it immediately. I was excited to find something so innovative and ergonomic built on Pydantic. Virtually every Agent Framework and LLM library in Python uses Pydantic, but when we began to use LLMs in Pydantic Logfire, I couldn't find anything that gave me the same feeling. PydanticAI is a Python Agent Framework designed to make it less painful to build production-grade applications with Generative AI. Built by the team behind Pydantic (the validation layer of the OpenAI SDK, the Anthropic SDK, LangChain, LlamaIndex, AutoGPT, Transformers, CrewAI, Instructor, and many more). -
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claude-code-best-practice
Practice made claude perfect
...By combining conceptual guidance with concrete examples and configuration patterns, the project helps teams move from experimental AI usage toward more production-ready agent orchestration. -
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TradingAgents
Chinese Financial Trading Framework Based on Multi-Agent LLM
TradingAgents-CN is a Chinese-enhanced, multi-agent LLM framework aimed at building financial analysis and trading-oriented workflows, with an emphasis on collaboration between specialized agents rather than a single monolithic prompt. It organizes market-related tasks into roles and stages so different agents can contribute research, reasoning, aggregation, and decision support in a structured pipeline. -
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FlowLens MCP
Open-source MCP server that gives your coding agent
...It works together with a companion browser extension: when a user reproduces a bug or a complicated UI interaction, the extension captures a rich session log, including screen/video recording, network traffic, console logs, DOM events, storage changes, and more, and exports it. The MCP server then loads this captured “flow” and exposes it to the AI agent via the Model Context Protocol (MCP), letting the agent examine, search, filter, and reason about the session just as a human developer would, without needing the agent to re-run the flow or rely on minimal reproduction data (logs, screenshots). -
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Shepherd
Runtime substrate that turns agent's execution into a Git-like trace
...Each result can be reviewed, executed separately, selected, merged, released, or discarded. Tasks are declared as Python functions whose signatures and documentation define the agent contract and required resources. Read-only and read-write repository grants are enforced through native operating-system sandboxing on supported macOS and Linux environments. Runs can be forked, replayed, reverted, and supervised, enabling counterfactual experiments, optimization, and training workflows. Shepherd also retains machine-readable records and supports offline deterministic examples, but its APIs may change while the project remains in alpha. -
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AI Berkshire
AI-era Berkshire: a value investing research framework
...The project is meant to improve research depth, decision discipline, and analytical consistency compared with asking a general AI model for a one-off stock opinion. It uses parallel agent analysis, adversarial viewpoints, financial rigor checks, and repeatable report formats to reduce shallow or overly balanced conclusions. The framework covers company research, earnings review, industry screening, portfolio thinking, management analysis, and investment checklists. It is best understood as a research and decision-support system rather than a source of financial advice. -
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PaperSpine
Motivation-driven skill for learning from strong academic papers
PaperSpine is a motivation-driven writing skill suite for academic papers, reports, reviews, and technical manuscripts. It is built for AI tools such as Codex, Claude Code, and OpenClaw, where the agent can follow structured writing workflows. The project asks the agent to study target formats and strong examples before drafting or revising. It emphasizes the central motivation of a paper, helping writers connect claims, structure, evidence, citations, and revisions into a coherent argument. The suite includes specialized skills for research, citation, rewriting, LaTeX, auditing, translation, humanization, and updates. ... -
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Trellis AI
All-in-one AI framework & toolkit for Claude Code & Cursor
Trellis is an advanced workflow and agent orchestration framework designed for building, managing, and scaling intelligent applications that coordinate numerous autonomous components. At its core, Trellis lets developers define units of work — called tasks or agents — and compose them into rich workflows that can operate with concurrency, conditional logic, and dynamic branching, all without sacrificing readability or control. -
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Patchwork
Automate code reviews, patching and documentation
Patchwork automates development gruntwork like PR reviews, bug fixing, security patching, and more using a self-hosted CLI agent and your preferred LLMs. -
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Claude Code Templates
CLI tool for configuring and monitoring Claude Code
...The templates and components are organized by workflow so teams can adopt best practices for testing, deployment, documentation, DevOps automation, and more, all powered by Claude’s agent SDK. Interactive installation commands and a marketplace-style interface make it easy to browse and apply components, and the tool also includes development utilities like analytics and conversation monitoring. -
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Tactical RMM
A remote monitoring & management tool, built with Django, Vue and Go
Tactical RMM is a remote monitoring & management tool, built with Django and Vue. It uses an agent written in golang and integrates with MeshCentral. Demo database resets every hour. A lot of features are disabled for obvious reasons due to the nature of this app. Teamviewer-like remote desktop control. Real-time remote shell. Remote file browser (download and upload files). Remote command and script execution (batch, powershell and python scripts). -
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All RL Algorithms from Scratch
Implementation of all RL algorithms in a simpler way
...Its goal is to help learners understand how major reinforcement learning algorithms work under the hood instead of hiding the logic behind large frameworks. The project includes notebooks for value-based methods, policy-gradient methods, actor-critic algorithms, model-based learning, multi-agent reinforcement learning, planning, and hierarchical approaches. Implemented topics include Q-learning, SARSA, Expected SARSA, Dyna-Q, REINFORCE, PPO, A2C, A3C, DDPG, SAC, TRPO, DQN, MADDPG, QMIX, HAC, MCTS, and PlaNet. The code prioritizes clarity, experimentation, and mathematical intuition over production speed. A companion cheat sheet gives learners a quick reference for formulas, pseudocode, and key concepts.
