AI Agent Tools

gstack92.5k★

GStack gives you 15 opinionated tools and 9 workflow skills that do exactly that. Install it, and suddenly your terminal has slash commands for code review, shipping, browser automation, QA testing, and retrospectives. Garry Tan (YC CEO) built this for his own workflow and claims 10K lines of code and 100 PRs per week using it, in weeks. The tools include a persistent Chromium browser daemon for visual testing, plan review that catches issues before you write code, and one-command deploys. Everything lives inside .claude/; nothing touches your PATH, nothing runs in the background except the browser daemon. TypeScript core with some Go for performance. The catch: this is one person's extremely opinionated workflow. If your development style doesn't match Garry's, you'll fight the tools instead of using them. And it's Claude Code only: no Cursor, no Codex, no other agent.

cc-switch65.2k★

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.

caveman57.2k★

Caveman strips the fluff from Claude Code responses. Install it with one command, activate with /caveman, and your AI assistant drops the pleasantries, hedging, and filler words while keeping full technical accuracy. Average savings: 65% fewer output tokens. Three intensity levels: Lite keeps it professional but terse, Full drops articles and uses fragments, Ultra goes telegraphic. Code blocks, error messages, git commits, and technical terms pass through untouched. Only the natural language gets compressed. A companion tool (caveman-compress) rewrites your CLAUDE.md and memory files to cut input tokens too. Works across 40+ AI coding agents, not just Claude Code. Cursor, Copilot, Windsurf, Cline, Codex, all supported. Heavy token users will feel the difference in both speed and cost. The catch: it started as a meme (Kevin from The Office) but the benchmarks are real, backed by a 2026 arxiv paper. Ultra mode can be hard to read. And the savings are output tokens only, so your thinking/reasoning costs stay the same.

mempalace51.8k★

MemPalace gives AI assistants a memory that actually works. It stores complete conversation histories locally using ChromaDB vector search, organized into a "memory palace" hierarchy of wings, rooms, halls, and closets. No cloud dependency, no subscriptions, no data leaving your machine. It scores 96.6% recall on LongMemEval benchmarks, which puts it ahead of every other memory system tested. The architecture stores verbatim conversations rather than lossy summaries, which is the key insight. Most AI memory systems compress your history into summaries and lose critical detail. MemPalace keeps everything and retrieves it semantically. Setup is straightforward: Python package, local ChromaDB instance, and an MCP server that plugs into Claude, ChatGPT, Cursor, or Gemini. Solo developers and power users get the most value here. Daily AI assistant users who want persistent context across sessions without paying for a cloud service won't find a better option right now. Teams could use it individually but there's no shared memory layer. The catch: it's local-only by design. Working across multiple machines means your memory palace doesn't follow you. And ChromaDB adds a real storage footprint once your conversation history grows.

mempalace51.8k★

MemPalace stores your AI conversation history verbatim and searches it semantically. Every Claude session, every project file, indexed locally. The structure is a metaphor: projects become wings, topics become rooms, so you can scope searches instead of querying a flat blob. It publishes 96.6% recall on LongMemEval with no LLM in the loop, and the benchmarks are reproducible from the repo. Install is pip plus pointing it at a directory. ChromaDB is the default backend, embeddings run on CPU with a 300MB model, no API key required. The MCP server exposes 29 tools so Claude Code can read and write the palace directly during a session. Solo developers using Claude Code heavily: install it. The 'wake-up' command that loads relevant context for a new session is the pitch and it works. Small teams: each engineer runs their own palace, there is no shared knowledge layer yet. The catch: it's about two weeks old. The benchmarks are real but the operational track record is not. Breaking changes will happen, and fast-growing projects attract impostor domains. The README has a scam alert for a reason.

goose44.9k★

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.

career-ops43.8k★

Career-Ops turns Claude Code into a full job search command center. Paste a job URL, get a structured A-F evaluation against your CV with weighted scoring across 10 dimensions. It generates tailored, ATS-optimized PDF resumes per application. One developer used it to evaluate 740+ offers and land a Head of Applied AI role. The system gets smarter over time. It builds an interview story bank in STAR format, scans 45+ pre-configured company portals (Anthropic, OpenAI, Vercel, n8n), and can batch-evaluate 10+ offers in parallel using sub-agents. The Go-based TUI dashboard lets you browse your entire pipeline from the terminal. Solo job seekers who already pay for Claude Code: this is a force multiplier. It does not replace your judgment, it structures it. The human-in-the-loop design means AI evaluates, you decide. The catch: requires a Claude Code subscription to function, so "free" comes with a real asterisk. The pre-configured portals lean heavily toward AI and tech companies. First evaluations are rough until you feed it enough context about yourself.

LibreChat36.8k★

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.

oh-my-claudecode33.2k★

oh-my-claudecode is a plugin that turns Claude Code into a multi-agent team coordinator. You define a pipeline of specialized agents (planner, reviewer, tester) and let them pass work between themselves without you driving each step. MIT-licensed, installs through the Claude Code marketplace or as an npm CLI. No server to run. It lives inside your Claude Code session and extends the built-in agent tool. "Autopilot" mode runs tasks end-to-end. "Ralph" mode cycles through review loops. If you have Gemini or Codex CLIs installed, it routes compatible work to them to save Anthropic tokens. Solo developers get a structured way to run long-running tasks without micromanaging each step. Small teams get a shared pattern for agent pipelines instead of everyone rolling their own. There is no paid tier and no cloud version. The catch: it is moving fast, so breaking changes happen. Multi-agent pipelines also burn tokens faster than a single prompt, and if you do not trust the autopilot, you will review more code than you write.

multica26.7k★

Multica turns AI coding agents into actual team members. Assign GitHub issues to agents like Claude Code, Codex, or OpenCode and they autonomously write code, report progress, and submit results. A task board for humans and AI agents working side by side. The platform runs a Next.js frontend with a Go backend and PostgreSQL. Self-hosting is the real play here: Apache 2.0 license, Docker Compose setup, and full control over your agent compute. WebSocket updates give you live visibility into what each agent is doing. The skill compounds system lets you build reusable instructions that agents share across tasks. Development teams already using AI coding agents benefit most. Teams juggling multiple agents across repos who want a unified dashboard instead of terminal tabs will find Multica fills that gap. Solo developers probably don't need the orchestration layer. The catch: the managed cloud pricing isn't published, and self-hosting means running Go, Postgres, and the agent runtimes yourself. You also need API keys for each AI provider your agents use.

NemoClaw20.3k★

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-cc18.1k★

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.

browser-harness11.9k★

Browser Harness gives LLMs raw access to Chrome through a single WebSocket connection. No abstraction layer, no pre-built recipes, just direct CDP (Chrome DevTools Protocol) control. When the agent encounters something it cannot do, it writes new helper functions mid-task. Self-healing browser automation. The entire codebase is under 600 lines of Python. Connect to Chrome with remote debugging enabled, and your agent can navigate, click, fill forms, extract data, and extend its own capabilities on the fly. From the same team that built the browser-use framework, this is the stripped-down version for agents that need complete freedom. Developers building AI agents that interact with websites: this is the thinnest possible layer between your LLM and a real browser. The free tier at cloud.browser-use.com gives you 3 concurrent remote browsers for testing without managing Chrome instances. The catch: "complete freedom" means no guardrails. Your agent can navigate anywhere, click anything, submit forms. You need your own safety layer if you are pointing this at production accounts.

skills8.6k★

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-master7.3k★

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-27.3k★

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.

hermes-webui6.4k★

Hermes WebUI is a browser frontend for Hermes Agent, a self-hosted autonomous AI agent that holds memory across sessions, runs scheduled jobs, and integrates with messaging platforms. Free and MIT-licensed. Setup is moderate. You bring your own LLM API key (OpenAI, Anthropic, Google, DeepSeek, OpenRouter, others) and run the agent plus WebUI on your own hardware or VPS. Once running, the agent persists conversation context, learns from interactions, and can be triggered on a schedule. The web UI mirrors the CLI experience without locking you out when you close the terminal. For solo developers and small teams who want an AI agent that isn't tied to ChatGPT or Claude.ai, this is a real option. Your conversations, your memory, your hardware. The cost is your LLM API bill, which can climb fast if the agent is making frequent calls. Solo: probably $10 to $50 per month in API spend depending on usage. The catch is that "autonomous AI agent" is doing a lot of work in the description. These systems still hallucinate, still drift, still need supervision. Don't wire it into anything destructive without guardrails.

byterover-cli4.7k★

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.

claude-obsidian4.6k★

Claude Obsidian turns your Obsidian vault into an autonomous knowledge engine powered by Claude Code. Instead of passive AI chat, it actively reads sources you drop in, extracts entities and concepts, creates cross-referenced wiki pages, and maintains a session context cache so the next conversation picks up where you left off. Ten skill commands cover everything from ingestion to vault linting to autonomous web research. Setup is a git clone and a shell script. The vault structure works directly in Obsidian with no plugin conflicts. It supports six wiki modes (Website, GitHub, Business, Personal, Research, Book/Course) and runs an 8-category vault linter that catches orphan notes, dead links, stale claims, and missing cross-references. Batch ingestion runs through parallel agents. Solo knowledge workers who already use Obsidian and Claude Code get a structured workflow for turning raw sources into an organized, interlinked wiki. The hot.md context cache is a clever solution to Claude Code's session boundary problem. The catch: you need a Claude Code subscription to run any of it, so it's free software that requires paid infrastructure. Heavy ingestion sessions burn through Claude Code context fast. The quality depends on prompt engineering that could break with model updates.

awesome-codex-subagents4.5k★

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.

dbskill4.5k★

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.

openclaw-control-center3.9k★

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.

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.

engram3.4k★

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.

chrome-cdp-skill3.0k★

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.

Qclaw2.7k★

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.

diagram-design2.3k★

Diagram-design is a Claude Code skill for editorial-quality diagrams. Architecture sketches, flowcharts, sequence diagrams, quadrants, pyramids. 13 types total, all rendered as self-contained HTML and SVG with no JavaScript or build step. The output is opinionated: low density, restrained color, accent used sparingly on the one or two things that matter. It looks like something a design team made, not generic AI output. Install is clone or plugin, then tell Claude to onboard it to your website and it pulls your brand palette and typography from your homepage. After that every diagram uses your colors. The skill activates automatically when you ask for a diagram. Solo writers and technical bloggers: install it. Small teams with a brand style guide: install it team-wide and onboard to your site. Large teams with a design system: the editorial constraints may conflict with your existing design language, evaluate first. The catch: the plugin route puts the skill in a cache that updates overwrite, so style-guide customizations get wiped unless you clone the repo and symlink. And the opinionated style is the whole pitch. If you want Mermaid-style diagrams, this is the wrong tool.

MiroFish-Offline2.1k★

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.

codex-console2.1k★

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.

boxlite2.0k★

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.

claude-peers-mcp2.0k★

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.

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.

wewrite1.9k★

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-brasil1.5k★

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.

Claw3D1.5k★

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.

gitlab-mcp1.5k★

GitLab MCP fills the gap Anthropic left open. GitHub has an official MCP server for AI coding assistants. GitLab does not. This community-built server connects Claude Code, Cursor, Copilot, VS Code, and Codex to your GitLab instance, exposing merge requests, issues, pipelines, wiki, releases, and labels as callable tools. Setup is simple for local use: one npx command plus a personal access token. Self-hosted GitLab works fine with a custom API URL. For team deployments, there's a Docker image with OAuth2 support and multi-user remote authorization. Four auth methods cover everything from quick local testing to production multi-tenant setups. Solo developers on GitLab get AI coding assistant integration that was previously GitHub-only. Teams running self-hosted GitLab get the same MCP capabilities without migrating to GitHub. There's a read-only mode toggle for safety if you want to prevent the AI from making changes. The catch: community-maintained, not official GitLab or Anthropic. Feature parity depends on one maintainer keeping up with GitLab's API surface. The multi-user OAuth setup requires a public HTTPS endpoint and pre-registered GitLab app, which is non-trivial.

agent-governance-toolkit1.5k★

Agent Governance Toolkit puts deterministic policy enforcement between your AI agents and the actions they take. Every tool call, resource access, and inter-agent message gets evaluated against policy before execution. Not prompt-based safety (which fails 27% of the time in red-team tests) but application-layer enforcement with a 0% violation rate. Works with any agent framework: LangChain, CrewAI, AutoGen, AWS Bedrock, Google ADK, Azure AI, and 20+ others. Ships with a CLI (`agt`), governance dashboard, and covers all 10 OWASP Agentic risks. SDKs for Python, TypeScript, Rust, Go, and dotnet. Sub-millisecond policy checks. Free and open source under MIT. Solo devs building agents should use this from day one. Teams running agents in production need this or something like it. There is no excuse for shipping autonomous agents without action-level governance. The catch: this is still in public preview, so expect breaking changes before GA. It governs agent actions, not model outputs. For prompt-level safety, you still need a separate content moderation layer.

OpenSquirrel1.4k★

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.

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.

chromex1.0k★

Chromex is a Chrome side-panel extension that connects your browser to OpenAI's Codex through a local native messaging bridge. Summarize pages, work across tabs and screenshots, edit images, transcribe voice, and run browser-control workflows with visible in-page indicators. MIT licensed. Setup is heavier than a typical extension: clone the repo, `npm install && npm run build`, run `install-native-host.mjs`, then load the unpacked extension at `chrome://extensions`. The architecture (Chrome extension to native host to local bridge to codex app-server) keeps your API key local; raw keys aren't stored in extension storage. Pick this if you live in Chrome, already run Codex, and want one assistant that sees the page you're on. Solo: free, you pay only for Codex tokens. Small teams: same. Large teams or non-Codex shops: skip; this is built around Codex specifically. The catch: Codex-only. Switch to Claude or Gemini for your CLI agent and Chromex doesn't follow. The native bridge is only as polished as the project, which is small and early. For a more mature Chrome AI assistant, Sider and Monica have more features and broader model support.

tool-ui668★

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.