AI Agent Tools

cc-switch39.4k★

Cc-switch wraps them into a single Tauri-based GUI. Cross-platform, open source, and free. Consider it a launcher that lets you switch between agents without context-switching between terminals. Setup is straightforward: download the app, configure your API keys, and pick which agents you want active. It doesn't add intelligence on top of the agents themselves. It's a convenience layer. The value is entirely in the unified interface and the ability to compare agent outputs side-by-side. Solo developers who already use multiple coding agents will get the most out of this. Teams probably don't need it since most teams standardize on one agent. If you only use one coding agent, there's nothing here for you. The catch: it's a wrapper, not a product. If the underlying agents change their CLI interfaces (which they do, frequently), cc-switch breaks until someone updates the integration. You're adding a dependency on a third-party GUI for tools that already work fine in a terminal.

goose37.2k★

Goose does that. Built by Block (the company behind Square and Cash App), it's a local AI agent that uses any LLM you point it at and has full access to your development environment. The key differentiator: extensibility. Goose uses a plugin system where you can add capabilities, called 'toolkits', for specific tasks. Need it to manage your Kubernetes cluster? Deploy to AWS? Run your CI pipeline? Add the toolkit. It's designed to be the agent framework that grows with your workflow. Apache 2.0. Backed by a major tech company, not a weekend project. The catch: Goose needs an LLM, and the quality of its work depends entirely on which model you use. With Claude or GPT-4, it's impressive. With smaller local models, it struggles on complex tasks. Also: giving an AI agent full access to your terminal is powerful but risky. Always review what it's doing, especially with destructive commands.

LibreChat35.3k★

LibreChat is a self-hosted AI chat interface that connects to multiple LLM providers (OpenAI, Anthropic, Google, local models) through a single unified UI. Self-host it and your team gets a unified chat UI that works with whatever models you're paying for (or running locally). No vendor lock-in. MIT license. Multi-model conversations (start with GPT-4, switch to Claude mid-chat), file uploads, code interpreter, plugins, conversation search, and user management are all built in. Docker Compose setup gets you running in minutes. Fully free to self-host. No paid tier, no gated features. You bring your own API keys. Running it locally with Ollama means zero API costs. Self-hosting ops: moderate. Docker Compose handles most of it, but you need MongoDB for the backend. Updates are frequent (active development), which means staying current takes attention. Figure 2-3 hours/month. Solo: self-host, connect your API keys, done. Small teams: add user accounts, share a single deployment. Growing teams: works well but you'll want to think about rate limiting per user. Large orgs: evaluate security hardening. It's not built for enterprise compliance out of the box. The catch: the feature velocity is both a strength and a risk. Breaking changes happen. And while it supports many providers, the quality of each integration varies. OpenAI is rock-solid, others can lag behind.

NemoClaw18.5k★

NemoClaw runs OpenClaw (the open source coding agent) inside NVIDIA's OpenShell sandbox with managed inference, solving the real security risk of agents executing arbitrary code on your machine. Your agent gets GPU-accelerated model inference through NVIDIA's infrastructure while staying sandboxed. This is NVIDIA saying 'run your coding agents on our hardware, securely.' You get the performance of NVIDIA GPUs for inference without managing the infrastructure yourself. The sandbox prevents the agent from doing anything destructive to your system. Apache 2.0 licensed. The catch: this ties you to NVIDIA's ecosystem. You need NVIDIA hardware or their cloud infrastructure, no running this on Apple Silicon or AMD GPUs. It's OpenClaw-specific, so Claude Code and Cursor users are out. And 'managed inference' is a gateway to NVIDIA's paid compute. The tool is free but the GPU time may not be.

codex-plugin-cc12.0k★

OpenAI's Codex agent packaged as a Claude Code skill plugin. It lets you invoke Codex from inside Claude Code to review code or delegate tasks, connecting two AI coding agents so they can collaborate. Useful if you want a second opinion from a different model without switching tools. The integration is straightforward: install the skill, and you can ask Claude Code to hand off specific tasks to Codex. Code review is the primary use case, where having two different models look at the same code catches more issues than either alone. The catch: requires both Claude Code and OpenAI API access, so you're paying for two AI services to talk to each other. The value proposition only makes sense if you're already invested in both ecosystems. For most developers, one AI coding tool is enough.

skills6.9k★

This turns Sahil Lavingia's entire Minimalist Entrepreneur methodology into executable Claude Code skills. Nine skills you install in your terminal: validate your idea, scope an MVP, find first customers, set pricing, and more. Instead of reading a book and trying to apply it, you invoke a skill and your AI walks through Sahil's exact framework applied to your specific situation. It's interactive: it asks you questions, processes your answers, and gives you structured output. The skills are well-structured and the methodology is proven (Gumroad was built on it). The catch: this is a business methodology, not a technical tool. The quality of the output depends entirely on how good your inputs are. And it's one person's framework; if you disagree with the minimalist approach, you'll disagree with the advice. No pricing page for the skills themselves: they're free, but the book is $17 on Amazon if you want the full context.

prompt-master4.7k★

Prompt Master writes the prompts for you. It's a Claude Code skill that generates accurate, context-aware prompts for any AI tool, optimized so you waste fewer tokens and get better output on the first try. The value proposition is simple: instead of trial-and-error with different prompt phrasings, you describe what you want and Prompt Master generates the prompt that actually works. It retains full context and memory across your session, so each prompt builds on what came before. MIT licensed. The catch: this is a skill that writes prompts for other AI tools, so you're adding an extra LLM call before every interaction. If your prompts are already working fine, this is overhead. And 'accurate prompts' is a bold claim. Prompt engineering is still more art than science, and what works for one model may not work for another.

gsd-24.5k★

GSD-2 is a framework for keeping AI agents on track through long, complex tasks by giving them structured context and spec-driven goals. If you've used AI coding agents and watched them lose the plot halfway through a big refactor, this is the fix. The core idea is 'context engineering': you define specs that describe what the agent should build, break work into phases, and the framework ensures the agent always has the right information at the right time. Specs look like structured documents with acceptance criteria, constraints, and dependencies. Instead of the agent drowning in its own conversation history, GSD-2 feeds it focused context windows that keep each step scoped and grounded. The catch: growing fast, but the API is still evolving. Docs are catching up. This is a bet on a concept (structured agent orchestration) rather than a stable production tool. If you need something battle-tested today, look at established agent frameworks. If you want to experiment with the next wave of agent reliability, this is worth watching.

byterover-cli4.2k★

ByteRover adds a persistent memory layer that travels with you. It works as a CLI tool that sits alongside Claude Code, Codex, or any agent that reads context files. It's a portable brain for your coding assistant. Install it globally, run `brv init` in your project, and it creates a structured memory store. The agent can read and write to it during sessions, building up project knowledge over time. It stores things like architecture decisions, coding conventions, and task history. The data lives on your machine in JSON files. This solves a real problem for developers who spend the first 5 minutes of every AI session re-explaining their project. Solo developers and small teams get the most value. The memory is project-scoped, so each repo gets its own context. The catch: you're trusting a third-party tool to manage context that feeds directly into your AI agent. If the memory format drifts from what agents expect, or if the project goes unmaintained, you've got stale context files that might do more harm than good. And Claude Code already has its own CLAUDE.md convention for project context, so the overlap is real.

aiac3.8k★

AIAC uses LLMs to generate infrastructure-as-code: Kubernetes manifests, Dockerfiles, CI/CD configs, all from plain English prompts. Instead of looking up the exact syntax for an AWS security group or a Helm chart values file, you describe what you want and AIAC produces the code. Go, Apache 2.0. It's a CLI tool that connects to OpenAI, Amazon Bedrock, or Ollama (for local models). You run `aiac get terraform for an s3 bucket with versioning enabled` and it returns the HCL. Supports Terraform, Pulumi, CloudFormation, Ansible, Docker, Kubernetes, GitHub Actions, and more. Fully free as a tool, but you pay for the LLM API calls behind it. Using OpenAI's GPT-4, that's roughly $0.01-0.10 per generation depending on complexity. Using Ollama with a local model, it's free but quality varies. Solo developers: useful for scaffolding infrastructure you don't write every day. Saves the 20 minutes of docs-reading for unfamiliar providers. Small to medium teams: helpful for standardizing templates, but review everything it generates; LLMs hallucinate resource attributes. The catch: zero star velocity and the homepage URL points to a Wikipedia article about LLMs, which is not confidence-inspiring. The generated code needs human review; treat it like a first draft, not a production artifact. And if you're already using GitHub Copilot or Claude in your editor, you get this same capability without a separate tool.

openclaw-control-center3.6k★

This gives you a local control center with full visibility. You get a dashboard that shows what OpenClaw is doing in real time, how much each task costs, and lets you set guardrails. It turns OpenClaw from 'fire and pray' into something you can actually trust and control. You see every API call, every decision branch, every token spent. You set budget limits, approve expensive operations, and kill tasks that go off the rails. MIT licensed, TypeScript. The catch: this is OpenClaw-specific. If you're using Claude Code, Cursor, or Codex, this does nothing for you. And 'control center' implies oversight, but you still need to understand what you're looking at. It surfaces the data, it doesn't interpret it for you. Early stage, so expect UI rough edges.

awesome-codex-subagents3.5k★

This is a curated collection of 130+ subagents you can plug in. It's essentially an app store for Codex, where each subagent handles a specific development task: testing, documentation, security review, database migration, and dozens more. You don't build these yourself. You browse the collection, pick the ones relevant to your workflow, and add them to your Codex setup. It's a community effort to make Codex more useful across different development scenarios. The catch: this is a curated list, not a framework. Quality varies across the 130+ entries. Some are polished, some are experiments. And it's Codex-specific. It doesn't with Claude Code, Cursor, or other AI coding tools.

chrome-cdp-skill2.8k★

This skill connects your agent to your live Chrome via the Chrome DevTools Protocol (CDP). Your agent can read pages, click buttons, fill forms, and navigate, in the browser you're already using. The difference from tools like Playwright is that this connects to existing tabs. Your agent can interact with pages where you're already authenticated, see what you see, and do what you'd do manually. MIT licensed, JavaScript. The catch: giving an AI agent access to your live browser session with all your logged-in accounts is a real security consideration. The agent can see everything you can see, including sensitive data in open tabs. There's no permission model beyond 'full access.' And CDP connections can be fragile; Chrome updates can break the protocol.

engram2.3k★

Engram gives it persistent memory. It's a Go binary with SQLite and full-text search that any AI agent can read and write to, so context survives across sessions. It works via MCP server, HTTP API, or CLI, meaning it's agent-agnostic. Claude Code, Codex, OpenClaw, or anything else that speaks HTTP can use it. Your agent writes memories during a session and reads them back next time. Full-text search (FTS5) means it retrieves relevant context, not just raw dumps. MIT licensed, Go. The catch: persistent memory is only useful if the agent writes good memories. Garbage in, garbage out. If the agent stores irrelevant context, it pollutes future sessions. SQLite is great for single-user but won't scale to a team sharing one memory store. And the MCP protocol is still young; not every agent supports it natively.

dbskill2.2k★

This is a prompt library that gives Claude structured frameworks for doing it. Picture pre-built consulting templates that turn Claude into a business analyst. What's free: Everything. It's a collection of Claude Code skills (prompt files) you drop into your project. No install, no dependencies, no account. The real value here is the structure. Instead of prompting Claude from scratch every time you need a SWOT analysis or process audit, these skills give it a repeatable framework. The prompts are well-organized and cover common business diagnostics. The catch: it's brand new (almost all growth in the last week), the description is partially in Chinese, and the license is listed as 'Other' which means you should read it before using commercially. The skills are also opinionated. They assume a specific diagnostic methodology that may not match how you work. And since these are just prompt files, the barrier to building your own is low.

Qclaw2.0k★

Qclaw is a GUI wrapper for OpenClaw that removes the command-line barrier. If you want to use AI coding tools but the terminal feels intimidating, Qclaw puts a graphical interface on top of OpenClaw's capabilities. Chinese-language interface, built for users who prefer visual interaction over command-line workflows. It translates OpenClaw's CLI operations into clickable buttons and forms. The catch: Chinese-language only, and it wraps another tool rather than providing standalone functionality. You still need OpenClaw installed underneath. If you're comfortable with a terminal, OpenClaw directly is more flexible. And because it depends on another project's API, breaking changes upstream can break Qclaw.

notte1.9k★

Notte lets AI agents interact with websites the way a person would (clicking buttons, filling forms, navigating pages) but through a structured API instead of raw browser automation. If you're building an AI agent that needs to do things on the web (book appointments, fill out forms, scrape dynamic content), Notte handles the browser part. The key difference from regular browser automation (Playwright, Selenium): Notte translates web pages into a format LLMs can understand. Instead of your agent parsing raw HTML, it gets a structured representation of what's on the page and what actions are available. The LLM decides what to do, Notte executes it. Early stage. The concept is strong but the project is young. There's a hosted API (pricing on their site suggests usage-based tiers) and you can self-host the Python package. The catch: you're betting on a small team maintaining a tool that sits between your AI agent and the entire web. Browser automation is fragile by nature. Sites change, CAPTCHAs block, rate limits hit. Notte abstracts some of that pain but can't eliminate it. For production agent workflows, compare against Browser Use and Playwright with your own LLM integration.

codex-console1.8k★

Codex-console is an integrated control panel for that workflow. Task management, batch processing, data export, auto-upload, log viewing, and packaging in one place. Built in Python with MIT license. The project provides compatibility fixes and experience optimizations for managing multiple concurrent AI coding sessions. The catch: the README and documentation are entirely in Chinese. If you don't read Chinese, you'll be navigating the tool through translation or code reading. The project is a console/dashboard wrapper. It doesn't do the AI work itself, it just helps you manage it. And at with limited English documentation, community support outside Chinese-speaking developers will be thin.

claude-peers-mcp1.7k★

Claude-peers-mcp is an MCP server that lets multiple Claude Code instances message each other in real time. If you run parallel Claude sessions (one on the frontend, one on the backend, one writing tests), this creates a direct communication channel between them so they can coordinate without you copy-pasting context between terminals. The way it works: you spin up the MCP server, connect each Claude Code instance to it, and they can send and receive messages from each other ad-hoc, like a group chat between your AI assistants. One session can ask another about an API contract it just wrote, or flag a dependency change that affects the other's work. It removes you as the bottleneck in multi-agent workflows. The catch: very new and tightly coupled to Claude Code's MCP ecosystem. It does not work with other AI agents or coding assistants. Coordinating AI sessions is still experimental territory, so expect rough edges and limited documentation. If you only run one Claude session at a time, you do not need this.

MiroFish-Offline1.7k★

MiroFish-Offline runs multi-agent AI simulations and predictions completely offline using a Neo4j knowledge graph and Ollama for local LLM inference. It's a simulation engine where multiple AI agents interact, predict outcomes, and build up a knowledge base over time. This is niche but powerful for scenarios like market simulation, scenario planning, or research where you can't send data to external APIs. Everything runs locally: the database, the models, the agents. The catch: AGPL-3.0 license (if you modify it and offer it as a service, you must open source your changes). Requires Neo4j and Ollama running locally. That's a real setup commitment. And 'offline multi-agent simulation' is a small but growing niche.

boxlite1.7k★

Boxlite gives you lightweight sandboxes. Each sandbox is a stateful micro-VM with hardware isolation, snapshots, and an API to control it. Picture giving every AI agent its own disposable computer. The project is open source under Apache 2.0 and self-hosting is free. It's early but growing fast. The 'agent sandboxing' space is heating up as AI agents get more autonomous and need safer execution environments. The catch: this is emerging technology. The documentation and ecosystem are still maturing. Running Firecracker-based micro-VMs requires Linux with KVM support. No macOS, no Windows natively. And the question of whether you need full VM isolation versus Docker containers depends on your threat model. For most use cases, Docker is simpler. Boxlite is for when you can't trust the code being executed.

OpenSquirrel1.3k★

OpenSquirrel is a native desktop app that puts Codex, Cursor, and OpenCode in one window so you stop losing track of what each one is doing. A control plane for your AI coding agents, built in Rust with the GPUI framework. What's free: Everything. MIT licensed, fully open source. No paid tier, no cloud service. The pitch is honest: you're squirrely, you jump between agents, and you need a way to see them all at once without alt-tabbing through six terminal windows. The Rust/GPUI foundation means it's fast and native, not an Electron wrapper eating 2GB of RAM. The catch: this is early, so it's not battle-tested yet. GPUI (Zed's UI framework) is relatively new itself, so you're building on new foundations. If you only use one AI coding tool, this adds zero value. It's specifically for the multi-agent workflow that a growing number of developers are adopting.

wewrite1.3k★

Wewrite is a Claude Code skill that handles trending topic research, topic selection, article writing, SEO optimization, and publishing. Built for the Chinese content market. The pipeline goes from identifying trending topics on Chinese social platforms to generating articles optimized for WeChat's distribution algorithm. It covers the entire workflow that content teams typically do manually across multiple tools. The catch: Chinese-language tool for a Chinese platform. If you don't publish on WeChat, this is not for you. AI-generated content at scale raises quality questions regardless of platform, and WeChat has its own content policies around automated publishing that you need to understand before running this at volume.

mcp-brasil1.2k★

This MCP server wraps 41 Brazilian public APIs into one standardized interface your agent can query. The smart part: it doesn't dump all 200+ tools on your agent at once. BM25 search filters to show only relevant tools per query, and a query planner can combine multiple APIs in a single call. 24 of the APIs need no authentication at all. The remaining ones use 2 optional API keys you get with free registration. MIT licensed. Built in Python with async httpx, Pydantic v2, and rate limiting with backoff. The catch: this is Brazil-specific. If you're not working with Brazilian data, there's nothing here for you. And wrapping government APIs means you inherit their reliability issues: downtime, rate limits, and data quality are the API provider's problem, not Floci's. The project is brand new and maintained by what appears to be a single developer.

Claw3D1.2k★

Claw3D visualizes AI agent activity as a 3D command center you can watch in real time. Agents sit at desks, review code, run standups, and collaborate in an isometric environment you can watch in real time. Picture a visual mission control for your AI workforce. Each agent gets a customizable 3D avatar with a persistent profile. The office has rooms, navigation, animations, and event-driven activity cues. When an agent starts a code review, you see it happen spatially. Built on OpenClaw, MIT licensed. It's early (just hit open source), but the community is already building on it. The catch: this is a visualization layer, not an orchestration framework. Your agents still need something to make them work; Claw3D just shows you what they're doing. And '3D virtual office' is a concept that sounds cooler than it might be useful day-to-day. If you don't need visual monitoring, this adds complexity for aesthetics.

vm01.1k★

VM0 runs AI coding agents in isolated cloud sandboxes on a schedule. Describe a workflow in natural language, point it at a repo, and it executes in a Firecracker microVM with full Claude Code compatibility. Think of it as cron for AI agents, with sandboxing built in. The platform gives you persistence (resume, fork, version sessions), observability (logs, metrics, network visibility), and integration with 35,000+ skills via the skills.sh ecosystem. Self-hosting means running the Firecracker VM infrastructure yourself, which is a real infrastructure commitment. Solo developers who want to automate repetitive coding tasks (daily CI fixes, dependency updates, report generation) get the most value here. Teams running multiple agents benefit from the orchestration layer. The catch: this is very early stage. The license isn't a standard OSS license, the docs are sparse, and you're building on a startup's roadmap. The managed cloud is the realistic path for most users, and pricing for that isn't finalized yet.

tool-ui612★

Tool-ui gives you pre-built React components for rendering those tool calls and their results inside a conversation UI. This is a UI component library specifically for AI tool-calling interfaces. Instead of building your own "here's what the AI did" rendering from scratch, you get components that display tool invocations, streaming results, and error states. It's TypeScript, React-based, and designed to plug into assistant-ui (the parent project's chat framework). Completely free. MIT license, and growing fast. Solo developers building AI chat products will save significant time here. If you're already using assistant-ui for your chat interface, this is the natural add-on. Small teams building internal AI tools get a polished UX without designing tool-call rendering from scratch. The catch: it's nascent. means a small community, potential breaking changes, and limited battle-testing. Tightly coupled to the assistant-ui ecosystem. If you're using a different chat framework, integration will take work.