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Optional extensions add a bug-fixing flow (assess, fix, test) and an idea assessment flow that ends in a go, clarify or stop decision, with reports saved as Markdown under .specify/. It has integrations for dozens of agents, including GitHub Copilot, Claude Code, Codex CLI, Cursor, Gemini CLI and Zed, and you can customize it with extensions, presets and workflows.", "best_for": [ "Teams that want a spec before code", "Keeping agent work reviewable step by step", "Using one process across several coding agents" ], "use_when": "You want a coding agent to work from a written specification, plan and task list rather than one prompt, with artifacts you review at each step.", "skip_if": "Your changes are small enough that a single prompt does the job. 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Its standard model interface lets you swap models as you experiment without rebuilding the app. An equivalent JS/TS library, LangChain.js, is available, and it pairs with LangGraph for lower-level orchestration and LangSmith for evals and tracing.", "best_for": [ "Apps that need many model or data integrations", "Switching between model providers", "Prototyping LLM apps in Python or JS" ], "use_when": "You want ready-made integrations for models, vector stores and retrievers, and a single interface for switching providers.", "skip_if": "You need fine-grained control over agent flow and state; the README points to LangGraph for advanced customization and agent orchestration.", "install": { "kind": "pip", "label": "Install with pip", "snippet": "pip install -U langchain", "source_url": "https://docs.langchain.com/oss/python/langchain/install" }, "platforms": [], "date_added": "2026-10-14", "source_url": "https://github.com/langchain-ai/langchain#readme", "picks_rank_weight": 2, "editor_note": null, "category_label": "Agent frameworks", "github_url": "https://github.com/langchain-ai/langchain", "page_url": "https://agenttooldrop.com/tools/langchain", "markdown_url": "https://agenttooldrop.com/tools/langchain.md", "signals": { "status": "ok", "fetched_at": "2026-10-15T01:19:00.002Z", "full_name": "langchain-ai/langchain", "stars": 147836, "forks": 24803, "open_issues": 644, "license_spdx": "MIT", "pushed_at": "2026-10-14T17:28:05Z", "archived": false, "default_branch": "master", "latest_release": { "tag": "langchain-core==1.6.10", "published_at": "2026-10-12T14:14:03Z" } }, "drop_score": 81, "drop_score_components": { "curation": 0.667, "recency": 0.869, "recency_push": 0.961, "recency_release": 0.778, "traction": 0.939, "basis": "signals" } }, { "slug": "openhands", "name": "OpenHands", "url": "https://www.openhands.dev/", "github": "OpenHands/OpenHands", "category": "coding-agent", "tags": [ "self-hosted", "docker", "acp", "automation", "web-ui" ], "pricing": "open-source", "license_note": "MIT", "effect": "Runs OpenHands, Claude Code, Codex and other ACP agents from a self-hosted control center", "why": "The OpenHands repo now ships Agent Canvas, a self-hosted UI that runs the open-source OpenHands agent and can also drive Claude Code, Codex, Gemini CLI, OpenCode or any ACP-compatible agent. Agents run on Agent Server backends on your laptop, in Docker containers, on VMs or in OpenHands Cloud, and you switch between backends from one frontend. Automations run on a schedule or on webhook events and connect to Slack, GitHub, Linear and Notion.", "best_for": [ "Teams running always-on agents on their own servers", "Running several agents in isolated Docker containers", "Scheduled and webhook-driven agent automations" ], "use_when": "You want coding agents on a server or in Docker sandboxes so they keep working when your laptop is closed, and you want to trigger them from Slack or GitHub.", "skip_if": "You just want one agent in your terminal or editor; the npm install runs the agent server directly on your machine with full filesystem access unless you use the Docker option, and it needs Node.js 24 and uv.", "install": { "kind": "npm", "label": "Install Agent Canvas with npm", "snippet": "npm install -g @openhands/agent-canvas\nagent-canvas", "source_url": "https://github.com/OpenHands/OpenHands#readme" }, "platforms": [ "macos", "linux", "windows", "docker" ], "date_added": "2026-10-14", "source_url": "https://github.com/OpenHands/OpenHands#readme", "editor_note": "Repo renamed from All-Hands-AI/OpenHands and now hosts Agent Canvas; the agent SDK and Agent Server live in OpenHands/software-agent-sdk.", "picks_rank_weight": 2, "category_label": "Coding agents", "github_url": "https://github.com/OpenHands/OpenHands", "page_url": "https://agenttooldrop.com/tools/openhands", "markdown_url": "https://agenttooldrop.com/tools/openhands.md", "signals": { "status": "ok", "fetched_at": "2026-10-15T01:19:03.049Z", "full_name": "OpenHands/OpenHands", "stars": 90921, "forks": 12051, "open_issues": 986, "license_spdx": "MIT", "pushed_at": "2026-10-15T01:17:12Z", "archived": false, "default_branch": "main", "latest_release": { "tag": "v1.27.1", "published_at": "2026-10-12T19:33:56Z" } }, "drop_score": 78, "drop_score_components": { "curation": 0.667, "recency": 0.893, "recency_push": 0.992, "recency_release": 0.795, "traction": 0.803, "basis": "signals" } }, { "slug": "cline", "name": "Cline", "url": "https://cline.bot/", "github": "cline/cline", "category": "coding-agent", "tags": [ "vscode", "jetbrains", "cli", "mcp", "bring-your-own-model" ], "pricing": "open-source", "license_note": "Apache-2.0; the JetBrains plugin is not open source", "effect": "Runs an approval-based coding agent in VS Code, JetBrains, the terminal or a desktop app with any model", "why": "Cline edits files across a project, runs terminal commands and reacts to their output, and in VS Code and JetBrains each edit appears as a diff with checkpoints so you can undo its work. Plan mode explores the codebase and proposes a strategy before Act mode carries it out, and every edit and command needs your approval unless you turn on auto-approve. It works with Anthropic, OpenAI, Gemini, OpenRouter, Bedrock, Ollama, LM Studio and any OpenAI-compatible API, and extends through MCP servers, SDK plugins and .clinerules files.", "best_for": [ "VS Code and JetBrains users", "Developers who want to approve each action", "Bring-your-own-model or local-model setups" ], "use_when": "You want an agent inside your editor that asks before each change, and you want to pick the model provider yourself.", "skip_if": "You need every client to be open source (the JetBrains plugin is not), or you want an agent that runs without per-step approvals by default.", "install": { "kind": "npm", "label": "Install the CLI with npm", "snippet": "npm i -g cline", "source_url": "https://github.com/cline/cline#readme" }, "platforms": [ "vscode", "jetbrains", "macos", "windows" ], "date_added": "2026-10-14", "source_url": "https://github.com/cline/cline#readme", "picks_rank_weight": 2, "editor_note": null, "category_label": "Coding agents", "github_url": "https://github.com/cline/cline", "page_url": "https://agenttooldrop.com/tools/cline", "markdown_url": "https://agenttooldrop.com/tools/cline.md", "signals": { "status": "ok", "fetched_at": "2026-10-15T01:18:55.371Z", "full_name": "cline/cline", "stars": 70336, "forks": 7658, "open_issues": 1662, "license_spdx": "Apache-2.0", "pushed_at": "2026-10-15T01:15:30Z", "archived": false, "default_branch": "main", "latest_release": { "tag": "desktop-v0.0.46", "published_at": "2026-10-13T15:03:59Z" } }, "drop_score": 78, "drop_score_components": { "curation": 0.667, "recency": 0.927, "recency_push": 0.992, "recency_release": 0.861, "traction": 0.788, "basis": "signals" } }, { "slug": "litellm", "name": "LiteLLM", "url": "https://docs.litellm.ai", "github": "BerriAI/litellm", "category": "model-api", "tags": [ "gateway", "proxy", "unified-api", "openai-compatible", "python", "self-hosted" ], "pricing": "open-source", "license_note": "MIT; enterprise/ directory under the BerriAI Enterprise license", "effect": "Calls 100+ LLM providers in the OpenAI format, as a Python SDK or a self-hosted AI gateway", "why": "LiteLLM gives one interface to OpenAI, Anthropic, Gemini, Bedrock, Azure and many other providers using the OpenAI request format, so you can swap providers without rewriting code. The Python SDK includes a Router with retry and fallback logic across deployments. The proxy server adds virtual keys, per-project spend tracking, guardrails, load balancing and an admin dashboard, and it can also act as an MCP gateway and an A2A agent gateway.", "best_for": [ "Platform teams centralizing LLM access", "Multi-provider apps with fallbacks", "Tracking spend per project or key" ], "use_when": "You call several model providers and want one OpenAI-format API, or a central gateway with keys, budgets and logging for a team.", "skip_if": "You use a single provider and its own SDK covers your needs. Features such as SSO sit under the commercial enterprise license.", "install": { "kind": "pip", "label": "Install with uv", "snippet": "uv add litellm", "source_url": "https://github.com/BerriAI/litellm#readme" }, "platforms": [ "docker" ], "date_added": "2026-10-14", "source_url": "https://github.com/BerriAI/litellm#readme", "picks_rank_weight": 2, "editor_note": null, "category_label": "Models and APIs", "github_url": "https://github.com/BerriAI/litellm", "page_url": "https://agenttooldrop.com/tools/litellm", "markdown_url": "https://agenttooldrop.com/tools/litellm.md", "signals": { "status": "ok", "fetched_at": "2026-10-15T01:19:00.790Z", "full_name": "BerriAI/litellm", "stars": 61264, "forks": 12343, "open_issues": 5599, "license_spdx": "NOASSERTION", "pushed_at": "2026-10-15T01:18:30Z", "archived": false, "default_branch": "main", "latest_release": { "tag": "v1.105.1", "published_at": "2026-10-14T04:33:53Z" } }, "drop_score": 77, "drop_score_components": { "curation": 0.667, "recency": 0.951, "recency_push": 0.992, "recency_release": 0.911, "traction": 0.727, "basis": "signals" } }, { "slug": "anthropic-skills", "name": "Anthropic Agent Skills", "url": "https://github.com/anthropics/skills", "github": "anthropics/skills", "category": "devtool", "tags": [ "agent-skills", "claude", "claude-code", "examples", "documents" ], "pricing": "open-source", "license_note": "Set per skill: most are Apache-2.0; docx, pdf, pptx and xlsx are source-available under Anthropic's terms", "effect": "Provides Anthropic's example Agent Skills, the document skills behind Claude, and the skills spec", "why": "Skills are folders with a SKILL.md file of instructions and metadata, plus optional scripts and resources, that Claude loads dynamically for specialized tasks. This repo collects Anthropic's examples across creative, technical and enterprise work (for example mcp-builder, webapp-testing, skill-creator and frontend-design), the docx, pdf, pptx and xlsx skills that power Claude's document features, a skill template and the Agent Skills specification. In Claude Code you can add the repo as a plugin marketplace and install document-skills or example-skills.", "best_for": [ "Learning how to write a skill", "Adding document skills to Claude Code", "Starting a new skill from a template" ], "use_when": "You are writing your own skills and want working patterns to copy, or you want Anthropic's document and example skills in Claude Code.", "skip_if": "You want production-ready behavior: Anthropic says these skills are for demonstration and education and may differ from what Claude does. The document skills' license forbids copying them outside Anthropic's services or making derivative works.", "install": { "kind": "cli", "label": "Add as a Claude Code plugin marketplace", "snippet": "/plugin marketplace add anthropics/skills\n/plugin install document-skills@anthropic-agent-skills\n/plugin install example-skills@anthropic-agent-skills", "source_url": "https://github.com/anthropics/skills#readme" }, "platforms": [ "claude-code" ], "date_added": "2026-10-14", "source_url": "https://github.com/anthropics/skills", "picks_rank_weight": 2, "editor_note": null, "category_label": "Devtools", "github_url": "https://github.com/anthropics/skills", "page_url": "https://agenttooldrop.com/tools/anthropic-skills", "markdown_url": "https://agenttooldrop.com/tools/anthropic-skills.md", "signals": { "status": "ok", "fetched_at": "2026-10-15T01:18:53.715Z", "full_name": "anthropics/skills", "stars": 181173, "forks": 21442, "open_issues": 1411, "license_spdx": null, "pushed_at": "2026-10-09T18:45:52Z", "archived": false, "default_branch": "main", "latest_release": null }, "drop_score": 73, "drop_score_components": { "curation": 0.667, "recency": 0.589, "recency_push": 0.589, "recency_release": 0.589, "traction": 0.97, "basis": "signals" } }, { "slug": "browser-use", "name": "Browser Use", "url": "https://browser-use.com", "github": "browser-use/browser-use", "category": "browser-agent", "tags": [ "python", "browser-automation", "web-agent", "cli", "cloud-browser" ], "pricing": "open-source", "license_note": "MIT", "effect": "Lets an LLM drive a real browser to complete multi-step web tasks from Python code or a CLI", "why": "Browser Use is an open-source browser agent: you give it a task in plain language and a model, and it opens a browser, works through the site and returns a result. It works with OpenAI, Anthropic and Google models or local models through Ollama, accepts custom tools registered as Python functions, and can drive a local Chrome profile, a remote browser over CDP or a Browser Use Cloud browser. A CLI gives existing agents such as Claude Code, Codex and Cursor browser access, and an integration implements Claude's browser toolset with Browser Use as the driver.", "best_for": [ "Python apps that automate tasks on real websites", "Giving a coding agent browser access", "Projects that may move to hosted browsers later" ], "use_when": "You want an agent that completes tasks on live websites, such as forms, bookings and lookups, from your own Python code with your choice of model.", "skip_if": "You only need fixed, scripted browser steps with no model in the loop. The library needs Python 3.11 or later, and stealth browsers, proxies and the BU2 model are paid Browser Use Cloud services.", "install": { "kind": "pip", "label": "Install with uv", "snippet": "uv add browser-use", "source_url": "https://github.com/browser-use/browser-use#readme" }, "platforms": [ "claude-code", "codex", "cursor" ], "date_added": "2026-10-14", "source_url": "https://github.com/browser-use/browser-use#readme", "picks_rank_weight": 2, "editor_note": null, "category_label": "Browser and computer use", "github_url": "https://github.com/browser-use/browser-use", "page_url": "https://agenttooldrop.com/tools/browser-use", "markdown_url": "https://agenttooldrop.com/tools/browser-use.md", "signals": { "status": "ok", "fetched_at": "2026-10-15T01:18:54.464Z", "full_name": "browser-use/browser-use", "stars": 118001, "forks": 13076, "open_issues": 547, "license_spdx": "MIT", "pushed_at": "2026-10-09T17:36:28Z", "archived": false, "default_branch": "main", "latest_release": { "tag": "0.13.11", "published_at": "2026-10-07T04:23:18Z" } }, "drop_score": 69, "drop_score_components": { "curation": 0.667, "recency": 0.52, "recency_push": 0.586, "recency_release": 0.455, "traction": 0.879, "basis": "signals" } }, { "slug": "mastra", "name": "Mastra", "url": "https://mastra.ai/", "github": "mastra-ai/mastra", "category": "agent-framework", "tags": [ "typescript", "workflows", "memory", "rag", "mcp", "evals" ], "pricing": "open-source", "license_note": "Apache-2.0; code in ee/ directories under the Mastra Enterprise License (source-available)", "effect": "Builds TypeScript agents and graph-based workflows with memory, RAG, evals and model routing", "why": "Mastra is a TypeScript framework for agents and workflows that plugs into React, Next.js and Node apps or runs as a standalone server. It connects to 40+ model providers through one interface, has a graph-based workflow engine with .then(), .branch() and .parallel(), and can suspend an agent or workflow for human input and resume from stored state. It also covers conversation history, RAG, observational memory, MCP server authoring, evals and observability.", "best_for": [ "TypeScript and Next.js teams", "Agents with human approval steps", "Apps that need memory and RAG in one stack" ], "use_when": "You want agents, workflows, memory and evals in one TypeScript framework instead of assembling separate libraries.", "skip_if": "You work in Python. Features in ee/ directories (for example auth) need a Mastra enterprise license for production use.", "install": { "kind": "npm", "label": "Create a project with npm", "snippet": "npm create mastra@latest", "source_url": "https://github.com/mastra-ai/mastra#readme" }, "platforms": [], "date_added": "2026-10-14", "source_url": "https://github.com/mastra-ai/mastra#readme", "picks_rank_weight": 2, "editor_note": null, "category_label": "Agent frameworks", "github_url": "https://github.com/mastra-ai/mastra", "page_url": "https://agenttooldrop.com/tools/mastra", "markdown_url": "https://agenttooldrop.com/tools/mastra.md", "signals": { "status": "ok", "fetched_at": "2026-10-15T01:19:01.555Z", "full_name": "mastra-ai/mastra", "stars": 28824, "forks": 2984, "open_issues": 573, "license_spdx": "NOASSERTION", "pushed_at": "2026-10-15T01:13:07Z", "archived": false, "default_branch": "main", "latest_release": { "tag": "@mastra/core@1.77.0", "published_at": "2026-10-14T08:29:32Z" } }, "drop_score": 69, "drop_score_components": { "curation": 0.667, "recency": 0.959, "recency_push": 0.992, "recency_release": 0.926, "traction": 0.455, "basis": "signals" } }, { "slug": "playwright-mcp", "name": "Playwright MCP", "url": "https://github.com/microsoft/playwright-mcp", "github": "microsoft/playwright-mcp", "category": "mcp-server", "tags": [ "browser", "automation", "playwright", "accessibility-tree", "testing" ], "pricing": "open-source", "license_note": "Apache-2.0", "effect": "Lets an agent drive a real browser with Playwright using accessibility snapshots, not screenshots", "why": "Microsoft's MCP server exposes Playwright browser automation as tools that act on structured accessibility snapshots, so no vision model is needed and actions avoid the ambiguity of screenshot-based clicking. It runs Chrome, Firefox, WebKit or Edge, headed or headless, can attach to your logged-in browser through the Playwright extension, and has opt-in capabilities for network, storage, DevTools, PDF, vision and test assertions. The README positions MCP for long-running loops that need persistent browser state, and points coding agents to the more token-efficient Playwright CLI with skills.", "best_for": [ "Exploratory web automation by agents", "Self-healing tests and long-running browser workflows", "MCP clients without built-in browser control" ], "use_when": "Your agent needs to navigate, fill forms and inspect pages across many steps while keeping the same browser session.", "skip_if": "You use a coding agent and care about context size; Microsoft suggests Playwright CLI with skills for that. It is not a security boundary, so keep it away from sessions or data you would not hand to the model.", "install": { "kind": "cli", "label": "Add to Claude Code", "snippet": "claude mcp add playwright npx @playwright/mcp@latest", "source_url": "https://github.com/microsoft/playwright-mcp#readme" }, "platforms": [ "any-mcp-client", "claude-code", "claude-desktop", "codex", "cursor", "gemini-cli", "vscode", "windsurf", "docker" ], "date_added": "2026-10-14", "source_url": "https://github.com/microsoft/playwright-mcp#readme", "picks_rank_weight": 3, "editor_note": null, "category_label": "MCP servers", "github_url": "https://github.com/microsoft/playwright-mcp", "page_url": "https://agenttooldrop.com/tools/playwright-mcp", "markdown_url": "https://agenttooldrop.com/tools/playwright-mcp.md", "signals": { "status": "ok", "fetched_at": "2026-10-15T01:19:03.689Z", "full_name": "microsoft/playwright-mcp", "stars": 38153, "forks": 3249, "open_issues": 0, "license_spdx": "Apache-2.0", "pushed_at": "2026-10-08T17:51:59Z", "archived": false, "default_branch": "main", "latest_release": { "tag": "v0.0.83", "published_at": "2026-09-28T23:17:11Z" } }, "drop_score": 68, "drop_score_components": { "curation": 1, "recency": 0.366, "recency_push": 0.531, "recency_release": 0.202, "traction": 0.576, "basis": "signals" } }, { "slug": "vercel-ai-sdk", "name": "Vercel AI SDK", "url": "https://ai-sdk.dev/", "github": "vercel/ai", "category": "agent-framework", "tags": [ "typescript", "react", "nextjs", "streaming", "structured-output", "model-agnostic" ], "pricing": "open-source", "license_note": "Apache-2.0", "effect": "Gives TypeScript apps one API for text, structured output, tool-calling agents and chat UI hooks", "why": "The AI SDK is a provider-agnostic TypeScript toolkit with a unified API across providers such as OpenAI, Anthropic and Google. generateText covers text and Zod-typed structured output, ToolLoopAgent runs tool-calling agents, and AI SDK UI provides framework-agnostic hooks for chat and generative UIs in Next.js, React, Svelte and Vue. By default it routes model strings through the Vercel AI Gateway, and you can install provider packages to connect to providers directly.", "best_for": [ "Next.js and React apps", "Streaming chat UIs", "Switching model providers in TypeScript" ], "use_when": "You are building an AI feature or agent into a web app and want the model calls and the chat UI from one library.", "skip_if": "You are not on JavaScript or TypeScript; it needs Node.js 22+. If you do not want the default Vercel AI Gateway routing, plan to install provider packages such as @ai-sdk/openai.", "install": { "kind": "npm", "label": "Install with npm", "snippet": "npm install ai", "source_url": "https://github.com/vercel/ai#readme" }, "platforms": [], "date_added": "2026-10-14", "source_url": "https://github.com/vercel/ai#readme", "picks_rank_weight": 2, "editor_note": null, "category_label": "Agent frameworks", "github_url": "https://github.com/vercel/ai", "page_url": "https://agenttooldrop.com/tools/vercel-ai-sdk", "markdown_url": "https://agenttooldrop.com/tools/vercel-ai-sdk.md", "signals": { "status": "ok", "fetched_at": "2026-10-15T01:19:06.303Z", "full_name": "vercel/ai", "stars": 27320, "forks": 5335, "open_issues": 1369, "license_spdx": "NOASSERTION", "pushed_at": "2026-10-15T01:01:39Z", "archived": false, "default_branch": "main", "latest_release": { "tag": "@ai-sdk/workflow-harness@1.0.151", "published_at": "2026-10-14T04:19:38Z" } }, "drop_score": 67, "drop_score_components": { "curation": 0.667, "recency": 0.95, "recency_push": 0.991, "recency_release": 0.91, "traction": 0.409, "basis": "signals" } }, { "slug": "gemini-cli", "name": "Gemini CLI", "url": "https://geminicli.com/", "github": "google-gemini/gemini-cli", "category": "coding-agent", "tags": [ "cli", "terminal", "google", "gemini", "mcp" ], "pricing": "open-source", "license_note": "Apache-2.0", "effect": "Brings Gemini models into the terminal to query and edit codebases, run shell commands and search the web", "why": "Gemini CLI is a terminal agent with built-in file, shell, web fetch and Google Search grounding tools, and it adds MCP servers configured in ~/.gemini/settings.json. It reads project context from GEMINI.md, can checkpoint and resume sessions, and has a headless mode with JSON and streaming JSON output for scripts. 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Retrieval combines semantic search, BM25 keyword matching and entity linking, with time-aware ranking for questions about current state versus past events. You can run it as a library from pip or npm, as a self-hosted server with a dashboard and API keys through Docker Compose, or on the managed Mem0 Platform.", "best_for": [ "Personalized assistants and support bots", "Remembering user preferences across sessions", "Starting with a library and moving to a managed service later" ], "use_when": "Your agent should remember facts about users or its own past actions between conversations without you designing the storage and retrieval.", "skip_if": "You want memory with no LLM or embedding calls: Mem0 requires an LLM and defaults to OpenAI models. 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GitHub hosts a remote version at api.githubcopilot.com/mcp with OAuth or personal access token auth, and the same server runs locally from a Docker image or a Go binary. Tools are grouped into toolsets you can enable selectively, and a read-only mode removes every tool that makes changes.", "best_for": [ "Agents that triage issues or review pull requests", "Investigating CI failures from the editor", "Teams that want a hosted server with OAuth" ], "use_when": "Your agent needs to read or act on GitHub repositories, issues, pull requests or workflows as part of its task.", "skip_if": "You do not use GitHub. Remote OAuth depends on each host configuring a GitHub App or OAuth App, so some clients need a personal access token, and GitHub Enterprise Server needs extra setup.", "install": { "kind": "cli", "label": "Add the remote server to Claude Code", "snippet": "claude mcp add-json github '{\"type\":\"http\",\"url\":\"https://api.githubcopilot.com/mcp\",\"headers\":{\"Authorization\":\"Bearer YOUR_GITHUB_PAT\"}}'", "source_url": "https://github.com/github/github-mcp-server/blob/main/docs/installation-guides/install-claude.md" }, "platforms": [ "any-mcp-client", "claude-code", "claude-desktop", "codex", "cursor", "gemini-cli", "vscode", "jetbrains", "windsurf", "zed", "docker" ], "date_added": "2026-10-14", "source_url": "https://github.com/github/github-mcp-server#readme", "picks_rank_weight": 2, "editor_note": null, "category_label": "MCP servers", "github_url": "https://github.com/github/github-mcp-server", "page_url": "https://agenttooldrop.com/tools/github-mcp-server", "markdown_url": "https://agenttooldrop.com/tools/github-mcp-server.md", "signals": { "status": "ok", "fetched_at": "2026-10-15T01:18:58.505Z", "full_name": "github/github-mcp-server", "stars": 33617, "forks": 5172, "open_issues": 367, "license_spdx": "MIT", "pushed_at": "2026-10-14T16:17:48Z", "archived": false, "default_branch": "main", "latest_release": { "tag": "v2.0.2", "published_at": "2026-10-08T11:33:15Z" } }, "drop_score": 65, "drop_score_components": { "curation": 0.667, "recency": 0.737, "recency_push": 0.956, "recency_release": 0.518, "traction": 0.545, "basis": "signals" } }, { "slug": "pydantic-ai", "name": "Pydantic AI", "url": "https://pydantic.dev/docs/ai/overview/", "github": "pydantic/pydantic-ai", "category": "agent-framework", "tags": [ "python", "type-safe", "structured-output", "durable-execution", "model-agnostic", "opentelemetry" ], "pricing": "open-source", "license_note": "MIT", "effect": "Runs a typed Python agent loop with validated tools and outputs, and models swapped by a string", "why": "Pydantic AI gives you an agent whose tools and outputs are typed: tool arguments are validated before your code runs, and a run is guaranteed to return the output type you declared. 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Automation runs on Puppeteer and waits for action results automatically. A --slim mode trims the tool set for basic browser tasks, and a separate CLI works without MCP.", "best_for": [ "Debugging front-end bugs with an agent", "Performance analysis of web pages", "Checking console and network errors after a code change" ], "use_when": "You want your coding agent to check its changes in a real Chrome instance, reading console, network and performance data the way you would in DevTools.", "skip_if": "You need Firefox or WebKit; only Chrome and Chrome for Testing are officially supported. Usage statistics go to Google by default (opt out with --no-usage-statistics), and performance tools may send trace URLs to the CrUX API unless you pass --no-performance-crux.", "install": { "kind": "mcp-json", "label": "Add to your MCP client config", "snippet": "{\n \"mcpServers\": {\n \"chrome-devtools\": {\n \"command\": \"npx\",\n \"args\": [\n \"-y\",\n \"chrome-devtools-mcp@latest\"\n ]\n }\n }\n}", "source_url": "https://github.com/ChromeDevTools/chrome-devtools-mcp#readme" }, "platforms": [ "any-mcp-client", "claude-code", "codex", "cursor", "gemini-cli", "vscode", "jetbrains", "windsurf" ], "date_added": "2026-10-14", "source_url": "https://github.com/ChromeDevTools/chrome-devtools-mcp#readme", "editor_note": "The README now titles the project \"Chrome DevTools for agents\"; the npm package is still chrome-devtools-mcp.", "picks_rank_weight": 2, "category_label": "MCP servers", "github_url": "https://github.com/ChromeDevTools/chrome-devtools-mcp", "page_url": "https://agenttooldrop.com/tools/chrome-devtools-mcp", "markdown_url": "https://agenttooldrop.com/tools/chrome-devtools-mcp.md", "signals": { "status": "ok", "fetched_at": "2026-10-15T01:18:55.094Z", "full_name": "ChromeDevTools/chrome-devtools-mcp", "stars": 53551, "forks": 6623, "open_issues": 186, "license_spdx": "Apache-2.0", "pushed_at": "2026-10-14T18:13:50Z", "archived": false, "default_branch": "main", "latest_release": { "tag": "chrome-devtools-mcp-v1.10.1", "published_at": "2026-09-23T10:37:16Z" } }, "drop_score": 63, "drop_score_components": { "curation": 0.667, "recency": 0.54, "recency_push": 0.964, "recency_release": 0.117, "traction": 0.682, "basis": "signals" } }, { "slug": "e2b", "name": "E2B", "url": "https://e2b.dev", "github": "e2b-dev/E2B", "category": "sandbox", "tags": [ "code-execution", "cloud", "python", "typescript", "computer-use", "self-hosted" ], "pricing": "open-source", "license_note": "Apache-2.0", "effect": "Runs AI-generated code in isolated cloud sandboxes you start and control from Python or JavaScript", "why": "E2B gives agent tasks their own isolated sandbox in the cloud, started with Sandbox.create() from the JavaScript or Python SDK, where code can run shell commands and programs. The Code Interpreter SDK adds runCode/run_code for executing snippets, and the Desktop SDK adds mouse, keyboard, screenshot and desktop streaming APIs for computer-use agents. The runtime is open source and can be self-hosted with Terraform on AWS, Google Cloud, Azure or a general Linux machine.", "best_for": [ "Running untrusted model-generated code", "Code interpreter and data analysis features", "Computer-use agents that need a desktop" ], "use_when": "Your agent writes code or shell commands that must run somewhere isolated from your own machines and data.", "skip_if": "You do not want to sign up for an E2B API key or operate the Terraform-based self-hosted runtime.", "install": { "kind": "npm", "label": "Install the JavaScript SDK", "snippet": "npm i e2b", "source_url": "https://github.com/e2b-dev/E2B#readme" }, "platforms": [], "date_added": "2026-10-14", "source_url": "https://github.com/e2b-dev/E2B#readme", "picks_rank_weight": 2, "editor_note": null, "category_label": "Sandboxes", "github_url": "https://github.com/e2b-dev/E2B", "page_url": "https://agenttooldrop.com/tools/e2b", "markdown_url": "https://agenttooldrop.com/tools/e2b.md", "signals": { "status": "ok", "fetched_at": "2026-10-15T01:18:57.040Z", "full_name": "e2b-dev/E2B", "stars": 14355, "forks": 1112, "open_issues": 91, "license_spdx": "Apache-2.0", "pushed_at": "2026-10-14T23:38:59Z", "archived": false, "default_branch": "main", "latest_release": { "tag": "@e2b/code-interpreter-python@2.11.0", "published_at": "2026-10-13T15:02:49Z" } }, "drop_score": 63, "drop_score_components": { "curation": 0.667, "recency": 0.923, "recency_push": 0.985, "recency_release": 0.861, "traction": 0.273, "basis": "signals" } }, { "slug": "arize-phoenix", "name": "Arize Phoenix", "url": "https://arize.com/phoenix/", "github": "Arize-ai/phoenix", "category": "eval-observability", "tags": [ "tracing", "evals", "opentelemetry", "self-hosted", "python" ], "pricing": "free", "license_note": "Elastic License 2.0 (source-available)", "effect": "Traces LLM and agent apps over OpenTelemetry and runs evals, datasets and experiments in a local UI", "why": "Phoenix collects traces through OpenTelemetry-based OpenInference instrumentation, with integrations for frameworks such as the OpenAI Agents SDK, Claude Agent SDK, LangGraph and Vercel AI SDK across Python, JavaScript, Java and Go. On top of traces it offers LLM-based evals, versioned datasets, experiments, a prompt playground and prompt management. It runs locally with `phoenix serve`, in a container or on Kubernetes, and exposes a remote MCP endpoint so Claude Code, Cursor and other MCP clients can query traces, datasets and experiments.", "best_for": [ "Local trace debugging during development", "OpenTelemetry-based stacks", "Self-hosted eval and experiment tracking" ], "use_when": "You want an observability and eval UI on your own machine or infrastructure, instrumented with OpenTelemetry rather than a vendor-specific SDK.", "skip_if": "You need an OSI open source license or want to offer it as a hosted service; the Elastic License 2.0 forbids that. For a managed platform Arize points to its separate Arize AX product.", "install": { "kind": "pip", "label": "Install with pip", "snippet": "pip install arize-phoenix\nphoenix serve", "source_url": "https://github.com/Arize-ai/phoenix#readme" }, "platforms": [ "docker" ], "date_added": "2026-10-14", "source_url": "https://github.com/Arize-ai/phoenix#readme", "editor_note": "Phoenix is under the Elastic License 2.0, so it is listed as free rather than open-source.", "picks_rank_weight": 2, "category_label": "Evals and observability", "github_url": "https://github.com/Arize-ai/phoenix", "page_url": "https://agenttooldrop.com/tools/arize-phoenix", "markdown_url": "https://agenttooldrop.com/tools/arize-phoenix.md", "signals": { "status": "ok", "fetched_at": "2026-10-15T01:18:54.330Z", "full_name": "Arize-ai/phoenix", "stars": 11839, "forks": 1203, "open_issues": 1150, "license_spdx": "NOASSERTION", "pushed_at": "2026-10-15T00:26:42Z", "archived": false, "default_branch": "main", "latest_release": { "tag": "arize-phoenix-v20.21.4", "published_at": "2026-10-14T16:12:17Z" } }, "drop_score": 63, "drop_score_components": { "curation": 0.667, "recency": 0.972, "recency_push": 0.989, "recency_release": 0.956, "traction": 0.227, "basis": "signals" } }, { "slug": "crewai", "name": "CrewAI", "url": "https://crewai.com/", "github": "crewAIInc/crewAI", "category": "agent-framework", "tags": [ "python", "multi-agent", "workflows", "role-based", "cli" ], "pricing": "open-source", "license_note": "MIT", "effect": "Orchestrates teams of role-based agents (Crews) and event-driven workflows (Flows) in Python", "why": "CrewAI is a standalone Python framework with its own primitives for agents, tasks, crews, flows, tools and orchestration. Crews let role-based agents collaborate and delegate tasks autonomously, while Flows give event-driven control with state management and conditional branching, and the two can be combined. The crewai CLI scaffolds a project where agents and tasks are defined in JSONC files and run with crewai run.", "best_for": [ "Multi-agent teams with defined roles", "Business process automation", "Mixing autonomous agents with deterministic steps" ], "use_when": "You want several specialized agents with clear roles and tasks, wrapped in deterministic, event-driven workflows in regular Python.", "skip_if": "You are not on Python (it requires Python 3.10 to 3.13). It collects anonymous telemetry by default (disable with OTEL_SDK_DISABLED=true), and the managed control plane, CrewAI AMP, is a separate commercial product.", "install": { "kind": "cli", "label": "Install the CLI with uv", "snippet": "uv tool install crewai", "source_url": "https://github.com/crewAIInc/crewAI#readme" }, "platforms": [], "date_added": "2026-10-14", "source_url": "https://github.com/crewAIInc/crewAI#readme", "picks_rank_weight": 1, "editor_note": null, "category_label": "Agent frameworks", "github_url": "https://github.com/crewAIInc/crewAI", "page_url": "https://agenttooldrop.com/tools/crewai", "markdown_url": "https://agenttooldrop.com/tools/crewai.md", "signals": { "status": "ok", "fetched_at": "2026-10-15T01:18:56.261Z", "full_name": "crewAIInc/crewAI", "stars": 59696, "forks": 8728, "open_issues": 694, "license_spdx": "MIT", "pushed_at": "2026-10-14T20:08:49Z", "archived": false, "default_branch": "main", "latest_release": { "tag": "1.15.28", "published_at": "2026-10-14T04:47:00Z" } }, "drop_score": 63, "drop_score_components": { "curation": 0.333, "recency": 0.941, "recency_push": 0.971, "recency_release": 0.912, "traction": 0.712, "basis": "signals" } }, { "slug": "fastmcp", "name": "FastMCP", "url": "https://gofastmcp.com", "github": "PrefectHQ/fastmcp", "category": "devtool", "tags": [ "python", "mcp", "framework", "server", "client" ], "pricing": "open-source", "license_note": "Apache-2.0", "effect": "Builds MCP servers, clients and interactive apps in Python from plain decorated functions", "why": "You declare a tool as a Python function and FastMCP generates the schema, validation and documentation; when you connect to a server by URL, it handles transport negotiation, authentication and the protocol lifecycle. Besides servers and clients it supports apps, which give tools interactive UIs rendered in the conversation. 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Tools are grouped into features (database, debugging, development, Edge Functions, account, docs, branching, storage) that you can restrict, and you can scope the server to one project or run it in read-only mode, where SQL runs as a read-only Postgres user. The npm package also exports tool schemas for the Vercel AI SDK MCP client and a handler for serving the tools from your own endpoint.", "best_for": [ "Apps built on Supabase", "Letting a coding agent inspect schemas and apply migrations", "Debugging with project logs and advisors" ], "use_when": "Your app runs on Supabase and you want the agent to read schemas, run SQL, apply migrations or check logs from your editor.", "skip_if": "You do not use Supabase, or you cannot keep it away from production data: Supabase warns about prompt injection, recommends project scoping and read-only mode, and says not to give the server to your end users. 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Plan Mode researches the codebase, asks clarifying questions and waits for your approval before building, and the same Agent, Plan and Ask modes are available in the Cursor CLI. 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It runs in VS Code, Visual Studio, JetBrains IDEs, Eclipse and Xcode, in a terminal CLI, in the GitHub Copilot app, on github.com and in GitHub Mobile, and it accepts custom instructions, agent skills, custom agents and MCP servers. Organization admins control access, feature policies, file exclusions and audit logs.", "best_for": [ "Teams whose work already lives on GitHub", "Organizations that need admin policies and audit logs", "Developers spread across many IDEs" ], "use_when": "Your code, issues and pull requests are on GitHub and you want agent work and code review tied into that workflow.", "skip_if": "Your code is not on GitHub; the CLI also requires an active Copilot subscription, and premium features and larger AI credit allowances need a paid plan.", "install": { "kind": "npm", "label": "Install Copilot CLI with npm", "snippet": "npm install -g @github/copilot", "source_url": "https://docs.github.com/en/copilot/how-tos/copilot-cli/set-up-copilot-cli/install-copilot-cli" }, "platforms": [ "vscode", "jetbrains", "web", "macos", "linux", "windows" ], "date_added": "2026-10-14", "source_url": "https://docs.github.com/en/copilot/get-started/about-github-copilot", "picks_rank_weight": 2, "editor_note": null, "category_label": "Coding agents", "github_url": null, "page_url": "https://agenttooldrop.com/tools/github-copilot", "markdown_url": "https://agenttooldrop.com/tools/github-copilot.md", "signals": null, "drop_score": 57, "drop_score_components": { "curation": 0.667, "recency": 0.5, "recency_push": 0.5, "recency_release": 0.5, "traction": 0.5, "basis": "neutral" } }, { "slug": "openrouter", "name": "OpenRouter", "url": "https://openrouter.ai", "github": null, "category": "model-api", "tags": [ "router", "unified-api", "openai-compatible", "multi-provider", "fallbacks", "hosted" ], "pricing": "paid", "license_note": "Proprietary hosted service", "effect": "Routes requests to hundreds of models across providers through one OpenAI-compatible API endpoint", "why": "OpenRouter exposes hundreds of models behind a single `/api/v1/chat/completions` endpoint, handles fallbacks automatically and picks the most cost-effective provider for each request. Existing OpenAI SDK code works by pointing the base URL at OpenRouter, and there are also typed client SDKs for TypeScript and Python plus an Agent SDK. It passes through provider pricing without markup, charges a fee when you buy credits, and supports bringing your own provider keys.", "best_for": [ "Switching between models without code changes", "Comparing models from many providers", "Apps that need provider fallbacks" ], "use_when": "You want one API key and one endpoint for models from many providers, with routing and fallbacks handled for you.", "skip_if": "You only use one provider and already have direct access. Usage is billed through prepaid credits, with a fee on credit purchases and on bring-your-own-key usage above the free allowance.", "install": { "kind": "pip", "label": "Install the Python SDK", "snippet": "pip install openrouter", "source_url": "https://openrouter.ai/docs/quickstart" }, "platforms": [], "date_added": "2026-10-14", "source_url": "https://openrouter.ai/docs/quickstart", "picks_rank_weight": 2, "editor_note": null, "category_label": "Models and APIs", "github_url": null, "page_url": "https://agenttooldrop.com/tools/openrouter", "markdown_url": "https://agenttooldrop.com/tools/openrouter.md", "signals": null, "drop_score": 57, "drop_score_components": { "curation": 0.667, "recency": 0.5, "recency_push": 0.5, "recency_release": 0.5, "traction": 0.5, "basis": "neutral" } }, { "slug": "goose", "name": "goose", "url": "https://goose-docs.ai/", "github": "aaif-goose/goose", "category": "coding-agent", "tags": [ "cli", "desktop-app", "rust", "mcp", "acp" ], "pricing": "open-source", "license_note": "Apache-2.0", "effect": "Runs a general-purpose AI agent on your machine as a desktop app, CLI or embeddable API", "why": "goose is a local agent for code as well as research, writing, automation and data analysis, available as a desktop app for macOS, Linux and Windows, a CLI and an API. It works with 15+ providers including Anthropic, OpenAI, Google, Ollama, OpenRouter, Azure and Bedrock, and can use existing Claude, ChatGPT or Gemini subscriptions through ACP. 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You compose modules with plain Python control flow into pipelines, RAG systems or agent loops. Optimizers such as GEPA, MIPROv2 and BootstrapFewShot take examples and a scoring function and tune the program's prompts and weights, and the result can be saved and reloaded.", "best_for": [ "Multi-step LLM pipelines", "Teams with labeled examples and a metric", "Replacing brittle prompt templates" ], "use_when": "You want LLM behavior defined in code and improved systematically against a metric rather than by editing prompts by hand.", "skip_if": "You only need a single prompt or one structured extraction call. 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It has first-class support for Amazon Bedrock, Anthropic, OpenAI and Gemini plus other and custom providers, and hooks let you intercept any step of the loop. Strands harness (create_harness() in Python, createHarness() in TypeScript) gives a fully assembled agent with preset defaults.", "best_for": [ "Replacing a hand-rolled agent loop", "Amazon Bedrock users", "Mixed Python and TypeScript stacks" ], "use_when": "You want to own the agent loop in your own process, with lifecycle limits, hooks and tracing built in and the option to swap model providers.", "skip_if": "You want a hosted agent platform rather than a library. 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A hosted endpoint works keyless for scrape, search and parse within rate limits, and signing in or adding an API key unlocks the full tool set. You can also run it locally with npx or Docker and point it at a self-hosted Firecrawl API with FIRECRAWL_API_URL.", "best_for": [ "Turning URLs into clean markdown or schema-shaped JSON", "Crawling many pages under one site", "Pages that need a click before they can be read" ], "use_when": "Your agent needs content from known URLs, a whole site or a search query, in a format it can read directly.", "skip_if": "You want to drive a browser session step by step from your own client: each interact call runs to completion and returns control. Crawl, map and agent tools need a Firecrawl account or API key.", "install": { "kind": "mcp-json", "label": "Add the hosted server (OAuth sign-in)", "snippet": "{\n \"mcpServers\": {\n \"firecrawl\": {\n \"type\": \"http\",\n \"url\": \"https://mcp.firecrawl.dev/v2/mcp-oauth\"\n }\n }\n}", "source_url": "https://docs.firecrawl.dev/mcp-server" }, "platforms": [ "any-mcp-client", "cursor", "windsurf", "vscode", "claude-desktop", "docker" ], "date_added": "2026-10-14", "source_url": "https://github.com/firecrawl/firecrawl-mcp-server", "editor_note": "Moved from mendableai/firecrawl-mcp-server to firecrawl/firecrawl-mcp-server.", "picks_rank_weight": 2, "category_label": "MCP servers", "github_url": "https://github.com/firecrawl/firecrawl-mcp-server", "page_url": "https://agenttooldrop.com/tools/firecrawl-mcp", "markdown_url": "https://agenttooldrop.com/tools/firecrawl-mcp.md", "signals": { "status": "ok", "fetched_at": "2026-10-15T01:18:58.351Z", "full_name": "firecrawl/firecrawl-mcp-server", "stars": 7604, "forks": 906, "open_issues": 164, "license_spdx": "MIT", "pushed_at": "2026-10-15T01:15:56Z", "archived": false, "default_branch": "main", "latest_release": { "tag": "v3.2.1", "published_at": "2025-09-26T07:31:17Z" } }, "drop_score": 46, "drop_score_components": { "curation": 0.667, "recency": 0.496, "recency_push": 0.992, "recency_release": 0, "traction": 0.136, "basis": "signals" } }, { "slug": "letta", "name": "Letta", "url": "https://www.letta.com", "github": "letta-ai/letta-code", "category": "memory", "tags": [ "stateful-agents", "cli", "typescript", "desktop-app", "self-improving" ], "pricing": "open-source", "license_note": "Apache-2.0", "effect": "Runs stateful agents that keep memory and identity across sessions and rewrite their own memory over time", "why": "Letta (formerly MemGPT) is now built as Letta Code, an agent harness whose agents keep memory, identity and conversation history and improve by rewriting their own memory blocks, skills and prompts. All context, including memory blocks, is tracked in git (MemFS), and agents can search past messages, call subagents and run on schedules. You reach the same agent from a terminal UI, a desktop app for macOS, Windows and Linux, chat.letta.com, or Slack, Telegram and Discord, with agent state stored in Letta Cloud or kept local.", "best_for": [ "Long-running personal or team agents", "Agents that should learn from past sessions", "Using one agent across terminal, desktop and chat apps" ], "use_when": "You want a ready-made agent that remembers and adapts over long periods, rather than a memory library to wire into your own agent.", "skip_if": "You need the older Letta V1 API server, which is retired to the repo's archive branch. 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It supports custom and MCP tools plus built-in bash, python, web browsing and computer tools, can evaluate external agents like Claude Code, Codex CLI and Gemini CLI, and sandboxes untrusted model code in Docker, Kubernetes, Modal and other systems. 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Stripe requires human confirmation before some write actions, such as refunds and outbound payments. The stripe/ai repo also holds the Agent Toolkit for Python and TypeScript (OpenAI Agents SDK, LangChain, CrewAI, Vercel AI SDK), a local MCP package, billing integrations for the Vercel AI SDK and native OpenAI, Anthropic and Gemini SDKs, and Stripe agent skills and plugins.", "best_for": [ "Products built on Stripe", "Agent frameworks that need Stripe tools via function calling", "Looking up Stripe docs while integrating" ], "use_when": "You build on Stripe and want a coding agent or your own agent to query and change Stripe objects, or to answer integration questions from the Stripe docs.", "skip_if": "You do not use Stripe. Write tools act on a real Stripe account, so Stripe advises turning on tool approval in your client and using restricted API keys to limit what the toolkit can do.", "install": { "kind": "cli", "label": "Add to Claude Code", "snippet": "claude mcp add --transport http stripe https://mcp.stripe.com/", "source_url": "https://docs.stripe.com/mcp" }, "platforms": [ "any-mcp-client", "claude-code", "cursor", "codex", "vscode", "claude-desktop" ], "date_added": "2026-10-14", "source_url": "https://github.com/stripe/ai", "editor_note": "Moved from stripe/agent-toolkit to stripe/ai, which now also holds Stripe AI SDK integrations, skills and agent plugins.", "picks_rank_weight": 1, "category_label": "MCP servers", "github_url": "https://github.com/stripe/ai", "page_url": "https://agenttooldrop.com/tools/stripe-agent-toolkit", "markdown_url": "https://agenttooldrop.com/tools/stripe-agent-toolkit.md", "signals": { "status": "ok", "fetched_at": "2026-10-15T01:19:05.533Z", "full_name": "stripe/ai", "stars": 1941, "forks": 362, "open_issues": 100, "license_spdx": "MIT", "pushed_at": "2026-10-15T00:44:30Z", "archived": false, "default_branch": "main", "latest_release": null }, "drop_score": 44, "drop_score_components": { "curation": 0.333, "recency": 0.99, "recency_push": 0.99, "recency_release": 0.99, "traction": 0.03, "basis": "signals" } }, { "slug": "daytona", "name": "Daytona", "url": "https://www.daytona.io", "github": null, "category": "sandbox", "tags": [ "code-execution", "cloud", "python", "typescript", "snapshots", "computer-use" ], "pricing": "paid", "license_note": "Hosted service; SDKs Apache-2.0 and CLI AGPL-3.0 (daytona/clients)", "effect": "Runs AI-generated code in hosted sandboxes with their own kernel, filesystem and network stack", "why": "Daytona sandboxes are full computers with a dedicated kernel, filesystem, network stack and allocated vCPU, RAM and disk, which agents create and control through SDKs for TypeScript, Python, Ruby, Go and Java, an API and a CLI. Operations cover sandbox lifecycle, file system, process and code execution and git, and stateful snapshots let agent work persist across sessions. The docs also cover computer use, an MCP server, and SSH, VNC and web terminal access, and the pricing page lists GPU options.", "best_for": [ "Agents that need sandboxes that persist across sessions", "SDK users in Ruby, Go or Java as well as Python and TypeScript", "Sandboxed workloads that need GPUs" ], "use_when": "You want hosted, isolated machines for agent code execution and prefer usage-based billing to running the infrastructure yourself.", "skip_if": "You need to self-host or audit the platform source: core development moved to a private codebase and the public repo is archived. Billing is pay as you go per vCPU, memory and storage hour.", "install": { "kind": "pip", "label": "Install the Python SDK", "snippet": "pip install daytona", "source_url": "https://www.daytona.io/docs/" }, "platforms": [], "date_added": "2026-10-14", "source_url": "https://www.daytona.io/docs/", "editor_note": "The open-source daytonaio/daytona repo was archived in June 2026; official SDKs, CLI and MCP tools now live in daytona/clients.", "picks_rank_weight": 1, "category_label": "Sandboxes", "github_url": null, "page_url": "https://agenttooldrop.com/tools/daytona", "markdown_url": "https://agenttooldrop.com/tools/daytona.md", "signals": null, "drop_score": 43, "drop_score_components": { "curation": 0.333, "recency": 0.5, "recency_push": 0.5, "recency_release": 0.5, "traction": 0.5, "basis": "neutral" } }, { "slug": "stagehand", "name": "Stagehand", "url": "https://www.stagehand.dev", "github": "browserbase/stagehand", "category": "browser-agent", "tags": [ "typescript", "python", "go", "browser-automation", "sdk" ], "pricing": "open-source", "license_note": "MIT", "effect": "Adds natural-language act, observe and extract calls to Playwright-style browser code in TS, Python and Go", "why": "Stagehand mixes ordinary browser code (goto, click, locator) with three AI calls: act performs an action described in plain language, observe returns real selectors, and extract returns data validated against a Zod or Pydantic schema. Because observe hands back selectors, you can fill credentials yourself without sending them to the model, and the README says act, observe and extract refresh how an action happens when a site changes. It runs against a local Chrome or a Browserbase cloud browser, which adds a model gateway, server-side caching, proxies and session recordings.", "best_for": [ "Developers who already write Playwright-style scripts", "Structured data extraction from logged-in sites", "TypeScript, Python or Go codebases" ], "use_when": "You want to keep control of each browser step in code but let a model handle brittle selectors and data extraction.", "skip_if": "You do not want to write browser code yourself. Caching, the model gateway and cloud browsers require a Browserbase API key, and local runs need Chrome installed.", "install": { "kind": "npm", "label": "Install with pnpm", "snippet": "pnpm add @browserbasehq/stagehand 'zod@~4.4.3'", "source_url": "https://github.com/browserbase/stagehand#readme" }, "platforms": [], "date_added": "2026-10-14", "source_url": "https://github.com/browserbase/stagehand#readme", "picks_rank_weight": 1, "editor_note": null, "category_label": "Browser and computer use", "github_url": "https://github.com/browserbase/stagehand", "page_url": "https://agenttooldrop.com/tools/stagehand", "markdown_url": "https://agenttooldrop.com/tools/stagehand.md", "signals": { "status": "ok", "fetched_at": "2026-10-15T01:19:04.667Z", "full_name": "browserbase/stagehand", "stars": 25671, "forks": 1757, "open_issues": 367, "license_spdx": "MIT", "pushed_at": "2026-10-15T01:09:59Z", "archived": false, "default_branch": "main", "latest_release": { "tag": "@browserbasehq/stagehand@3.7.3", "published_at": "2026-08-28T14:46:59Z" } }, "drop_score": 40, "drop_score_components": { "curation": 0.333, "recency": 0.5, "recency_push": 0.992, "recency_release": 0.009, "traction": 0.379, "basis": "signals" } }, { "slug": "langsmith", "name": "LangSmith", "url": "https://www.langchain.com/langsmith/observability", "github": "langchain-ai/langsmith-sdk", "category": "eval-observability", "tags": [ "tracing", "evals", "monitoring", "langchain", "python", "typescript" ], "pricing": "freemium", "license_note": "MIT (client SDKs); the LangSmith platform is a commercial hosted service", "effect": "Traces, evaluates and monitors LLM apps and agents on LangChain's hosted platform via Python and JS SDKs", "why": "LangSmith is LangChain's platform for debugging, evaluating and monitoring language model apps and agents; the MIT-licensed Python and JavaScript SDKs send traces to it, for example by wrapping the OpenAI client. It works with any LLM application and has a native integration with LangChain Python and LangChain JS. There is a free Developer plan with a monthly trace allowance and paid per-seat plans, with pay-as-you-go usage beyond the included traces.", "best_for": [ "Teams already building with LangChain", "Hosted tracing without running infrastructure", "Python and JS/TS apps" ], "use_when": "You want managed tracing and evals with no servers to run, especially if your app already uses LangChain.", "skip_if": "You want to self-host without an enterprise contract; self-hosted and hybrid deployment are Enterprise plan options. Usage beyond the included traces is billed pay-as-you-go.", "install": { "kind": "pip", "label": "Install the Python SDK", "snippet": "pip install -U langsmith\nexport LANGSMITH_TRACING=true\nexport LANGSMITH_API_KEY=ls_...", "source_url": "https://github.com/langchain-ai/langsmith-sdk#readme" }, "platforms": [], "date_added": "2026-10-14", "source_url": "https://github.com/langchain-ai/langsmith-sdk#readme", "editor_note": "The GitHub repo holds the client SDKs; the LangSmith platform itself is a hosted product.", "picks_rank_weight": 1, "category_label": "Evals and observability", "github_url": "https://github.com/langchain-ai/langsmith-sdk", "page_url": "https://agenttooldrop.com/tools/langsmith", "markdown_url": "https://agenttooldrop.com/tools/langsmith.md", "signals": { "status": "ok", "fetched_at": "2026-10-15T01:19:00.286Z", "full_name": "langchain-ai/langsmith-sdk", "stars": 1079, "forks": 313, "open_issues": 248, "license_spdx": "MIT", "pushed_at": "2026-10-14T17:56:48Z", "archived": false, "default_branch": "main", "latest_release": { "tag": "v0.14.8", "published_at": "2026-10-13T00:42:09Z" } }, "drop_score": 40, "drop_score_components": { "curation": 0.333, "recency": 0.887, "recency_push": 0.962, "recency_release": 0.812, "traction": 0.015, "basis": "signals" } }, { "slug": "repomix", "name": "Repomix", "url": "https://repomix.com", "github": "yamadashy/repomix", "category": "devtool", "tags": [ "cli", "context", "typescript", "mcp", "tree-sitter" ], "pricing": "open-source", "license_note": "MIT", "effect": "Packs a repository into one AI-friendly file in XML, Markdown, JSON or plain text", "why": "Repomix reads a local or remote repository, respects .gitignore and .repomixignore, uses Secretlint to leave out files that match known credential formats, and writes one file with a summary, directory structure and file contents, plus token counts per file. The --compress option uses Tree-sitter to keep key code elements with fewer tokens, and large outputs can be split into parts. It also runs as an MCP server (repomix --mcp) and can generate Claude Agent Skills from a codebase.", "best_for": [ "Feeding a whole codebase to a chat model", "Checking token counts before sending context", "Packing a remote GitHub repo for reference" ], "use_when": "You want to hand an LLM a full or filtered snapshot of a repository in one file, or let an agent pack repos through MCP.", "skip_if": "Your coding agent already reads files in your workspace and you have no need for a single packed file. By default the MCP server can read any path the host user can, so use --sandbox with untrusted clients.", "install": { "kind": "cli", "label": "Run with npx", "snippet": "npx repomix@latest", "source_url": "https://github.com/yamadashy/repomix#readme" }, "platforms": [ "any-mcp-client", "claude-code", "cursor", "vscode", "claude-desktop", "docker", "web" ], "date_added": "2026-10-14", "source_url": "https://github.com/yamadashy/repomix", "picks_rank_weight": 1, "editor_note": null, "category_label": "Devtools", "github_url": "https://github.com/yamadashy/repomix", "page_url": "https://agenttooldrop.com/tools/repomix", "markdown_url": "https://agenttooldrop.com/tools/repomix.md", "signals": { "status": "ok", "fetched_at": "2026-10-15T01:19:03.746Z", "full_name": "yamadashy/repomix", "stars": 28839, "forks": 1592, "open_issues": 153, "license_spdx": "MIT", "pushed_at": "2026-10-11T16:00:19Z", "archived": false, "default_branch": "main", "latest_release": { "tag": "v1.18.1", "published_at": "2026-09-21T08:34:49Z" } }, "drop_score": 39, "drop_score_components": { "curation": 0.333, "recency": 0.402, "recency_push": 0.709, "recency_release": 0.095, "traction": 0.47, "basis": "signals" } }, { "slug": "llm-cli", "name": "LLM", "url": "https://llm.datasette.io", "github": "simonw/llm", "category": "model-api", "tags": [ "cli", "python", "plugins", "sqlite", "embeddings", "local-models" ], "pricing": "open-source", "license_note": "Apache-2.0", "effect": "Runs prompts against remote and local LLMs from the command line or Python, logging calls to SQLite", "why": "LLM is Simon Willison's CLI tool and Python library for OpenAI, Claude, Gemini and dozens of other models, via remote APIs or models installed on your own machine. Plugins such as llm-anthropic, llm-gemini and llm-ollama add providers, and `llm openai endpoint` targets any OpenAI-compatible Chat Completions server without configuration. Prompts and responses are stored in SQLite, and it also generates embeddings, extracts structured content with schemas and lets models execute tools.", "best_for": [ "Scripting LLM calls in the shell", "Comparing models from one CLI", "Keeping a searchable log of prompts" ], "use_when": "You want quick model access from the terminal or a small Python library, with every prompt and response logged locally.", "skip_if": "You need a shared gateway with keys and spend tracking for a team; this is a CLI and Python library. Providers other than OpenAI need a plugin installed.", "install": { "kind": "pip", "label": "Install with pip", "snippet": "pip install llm", "source_url": "https://github.com/simonw/llm#readme" }, "platforms": [], "date_added": "2026-10-14", "source_url": "https://github.com/simonw/llm#readme", "picks_rank_weight": 1, "editor_note": null, "category_label": "Models and APIs", "github_url": "https://github.com/simonw/llm", "page_url": "https://agenttooldrop.com/tools/llm-cli", "markdown_url": "https://agenttooldrop.com/tools/llm-cli.md", "signals": { "status": "ok", "fetched_at": "2026-10-15T01:19:01.498Z", "full_name": "simonw/llm", "stars": 12622, "forks": 1042, "open_issues": 755, "license_spdx": "Apache-2.0", "pushed_at": "2026-10-15T00:38:55Z", "archived": false, "default_branch": "main", "latest_release": { "tag": "0.36", "published_at": "2026-09-22T18:48:37Z" } }, "drop_score": 37, "drop_score_components": { "curation": 0.333, "recency": 0.549, "recency_push": 0.989, "recency_release": 0.109, "traction": 0.242, "basis": "signals" } }, { "slug": "deepeval", "name": "DeepEval", "url": "https://deepeval.com", "github": "confident-ai/deepeval", "category": "eval-observability", "tags": [ "evals", "python", "pytest", "testing", "rag", "agents" ], "pricing": "open-source", "license_note": "Apache-2.0", "effect": "Unit tests LLM apps and agents in a Pytest-style workflow with LLM-as-a-judge and other metrics", "why": "DeepEval works like Pytest for LLM apps: you write test cases and assert them against metrics such as G-Eval, task completion, tool correctness, answer relevancy, faithfulness and hallucination, then run them with `deepeval test run`. Metrics cover agents, RAG, multi-turn chat, MCP use and multimodal output, can be powered by any LLM, and support both end-to-end and component-level evaluation. It also generates synthetic datasets, runs benchmarks like MMLU and GSM8K, and integrates with LangChain, LangGraph, CrewAI, OpenAI Agents, Pydantic AI and others.", "best_for": [ "Python teams that want evals in their test suite", "RAG and agent metrics", "CI checks on LLM output quality" ], "use_when": "You want LLM quality checks written as tests that run locally or in any CI environment.", "skip_if": "You are not working in Python (it requires Python 3.9+). Shared reports, iteration comparisons and production monitoring live in the separate Confident AI platform, which needs an account.", "install": { "kind": "pip", "label": "Install with pip", "snippet": "pip install -U deepeval", "source_url": "https://github.com/confident-ai/deepeval#readme" }, "platforms": [], "date_added": "2026-10-14", "source_url": "https://github.com/confident-ai/deepeval#readme", "picks_rank_weight": 1, "editor_note": null, "category_label": "Evals and observability", "github_url": "https://github.com/confident-ai/deepeval", "page_url": "https://agenttooldrop.com/tools/deepeval", "markdown_url": "https://agenttooldrop.com/tools/deepeval.md", "signals": { "status": "ok", "fetched_at": "2026-10-15T01:18:56.852Z", "full_name": "confident-ai/deepeval", "stars": 18867, "forks": 2071, "open_issues": 728, "license_spdx": "Apache-2.0", "pushed_at": "2026-10-12T19:31:54Z", "archived": false, "default_branch": "main", "latest_release": { "tag": "python-v4.2.4", "published_at": "2026-09-22T16:36:00Z" } }, "drop_score": 36, "drop_score_components": { "curation": 0.333, "recency": 0.452, "recency_push": 0.795, "recency_release": 0.108, "traction": 0.303, "basis": "signals" } }, { "slug": "figma-context-mcp", "name": "Framelink MCP for Figma", "url": "https://www.framelink.ai/", "github": "GLips/Figma-Context-MCP", "category": "mcp-server", "tags": [ "figma", "design-to-code", "frontend", "cursor" ], "pricing": "open-source", "license_note": "MIT", "effect": "Gives a coding agent the layout and styling data behind a Figma file, frame or group link", "why": "Paste a Figma link into your agent's chat and the server fetches that design's metadata from the Figma API, then simplifies it to the layout and styling information before handing it to the model. The author's case is that this reduced, structured context lets the model implement designs more accurately than pasting screenshots. It runs locally through npx with your own Figma access token.", "best_for": [ "Implementing Figma designs in code", "Cursor users", "Front-end work from design handoff" ], "use_when": "You build UI from Figma designs and want the agent to read the actual file data instead of a screenshot.", "skip_if": "You do not use Figma or cannot create a Figma personal access token. The README says it is designed specifically for Cursor, though the same config works in other MCP clients.", "install": { "kind": "mcp-json", "label": "Add to your MCP client config (macOS / Linux)", "snippet": "{\n \"mcpServers\": {\n \"Framelink MCP for Figma\": {\n \"command\": \"npx\",\n \"args\": [\n \"-y\",\n \"figma-developer-mcp\",\n \"--figma-api-key=YOUR-KEY\",\n \"--stdio\"\n ]\n }\n }\n}", "source_url": "https://github.com/GLips/Figma-Context-MCP#readme" }, "platforms": [ "any-mcp-client", "cursor", "macos", "linux", "windows" ], "date_added": "2026-10-14", "source_url": "https://github.com/GLips/Figma-Context-MCP#readme", "picks_rank_weight": 1, "editor_note": null, "category_label": "MCP servers", "github_url": "https://github.com/GLips/Figma-Context-MCP", "page_url": "https://agenttooldrop.com/tools/figma-context-mcp", "markdown_url": "https://agenttooldrop.com/tools/figma-context-mcp.md", "signals": { "status": "ok", "fetched_at": "2026-10-15T01:18:57.710Z", "full_name": "GLips/Figma-Context-MCP", "stars": 15988, "forks": 1265, "open_issues": 27, "license_spdx": "MIT", "pushed_at": "2026-10-14T17:28:49Z", "archived": false, "default_branch": "main", "latest_release": { "tag": "v0.13.2", "published_at": "2026-06-18T16:38:25Z" } }, "drop_score": 36, "drop_score_components": { "curation": 0.333, "recency": 0.48, "recency_push": 0.961, "recency_release": 0, "traction": 0.288, "basis": "signals" } }, { "slug": "instructor", "name": "Instructor", "url": "https://python.useinstructor.com/", "github": "567-labs/instructor", "category": "model-api", "tags": [ "structured-outputs", "pydantic", "python", "validation", "extraction" ], "pricing": "open-source", "license_note": "MIT", "effect": "Returns validated Pydantic objects from LLM calls, retrying automatically when validation fails", "why": "Instructor lets you pass a Pydantic model as `response_model` and get back a typed, validated object instead of parsing JSON or tool calls yourself. `instructor.from_provider` uses the same API for OpenAI, Anthropic, Google, Ollama, Groq and other providers. Failed validations are retried with the error message, partial objects can be streamed as they are generated, and nested models work without extra code. Ports exist for TypeScript, Ruby, Go and Elixir.", "best_for": [ "Structured extraction from text", "Pydantic-based Python apps", "Typed outputs across providers" ], "use_when": "You need reliable structured data from an LLM call and want validation and retries handled for you.", "skip_if": "You need full agent runs, built-in observability or shareable traces; the Instructor README itself points to PydanticAI for that.", "install": { "kind": "pip", "label": "Install with pip", "snippet": "pip install instructor", "source_url": "https://github.com/567-labs/instructor#readme" }, "platforms": [], "date_added": "2026-10-14", "source_url": "https://github.com/567-labs/instructor#readme", "picks_rank_weight": 1, "editor_note": null, "category_label": "Models and APIs", "github_url": "https://github.com/567-labs/instructor", "page_url": "https://agenttooldrop.com/tools/instructor", "markdown_url": "https://agenttooldrop.com/tools/instructor.md", "signals": { "status": "ok", "fetched_at": "2026-10-15T01:18:59.246Z", "full_name": "567-labs/instructor", "stars": 14014, "forks": 1307, "open_issues": 189, "license_spdx": "MIT", "pushed_at": "2026-10-14T22:21:57Z", "archived": false, "default_branch": "main", "latest_release": { "tag": "v1.17.0", "published_at": "2026-09-09T02:25:36Z" } }, "drop_score": 36, "drop_score_components": { "curation": 0.333, "recency": 0.504, "recency_push": 0.98, "recency_release": 0.028, "traction": 0.258, "basis": "signals" } }, { "slug": "smolagents", "name": "smolagents", "url": "https://huggingface.co/docs/smolagents/index", "github": "huggingface/smolagents", "category": "agent-framework", "tags": [ "python", "code-agents", "hugging-face", "model-agnostic", "mcp" ], "pricing": "open-source", "license_note": "Apache-2.0", "effect": "Runs agents that write their actions as Python code, with core agent logic in about 1,000 lines", "why": "smolagents keeps abstractions minimal: the agent logic fits in roughly 1,000 lines, and its CodeAgent writes its actions in code. It is model-agnostic (local transformers or Ollama models, Hugging Face inference providers, or OpenAI, Anthropic and others via LiteLLM) and can use tools from MCP servers, LangChain or Hub Spaces. Agents and tools can be pushed to and pulled from the Hugging Face Hub, and a smolagent CLI runs agents from the terminal.", "best_for": [ "Agents that act by writing code", "Hugging Face Hub users", "Reading and learning a compact agent loop" ], "use_when": "You want a small, readable agent library and agents that act by writing code, with your choice of local or hosted models.", "skip_if": "You cannot run agent code in a sandbox: the README says the built-in LocalPythonExecutor is not a security boundary and recommends E2B, Blaxel, Modal or Docker.", "install": { "kind": "pip", "label": "Install with pip", "snippet": "pip install \"smolagents[toolkit]\"", "source_url": "https://github.com/huggingface/smolagents#readme" }, "platforms": [], "date_added": "2026-10-14", "source_url": "https://github.com/huggingface/smolagents#readme", "picks_rank_weight": 1, "editor_note": null, "category_label": "Agent frameworks", "github_url": "https://github.com/huggingface/smolagents", "page_url": "https://agenttooldrop.com/tools/smolagents", "markdown_url": "https://agenttooldrop.com/tools/smolagents.md", "signals": { "status": "ok", "fetched_at": "2026-10-15T01:19:04.498Z", "full_name": "huggingface/smolagents", "stars": 29849, "forks": 3079, "open_issues": 934, "license_spdx": "Apache-2.0", "pushed_at": "2026-10-06T18:35:07Z", "archived": false, "default_branch": "main", "latest_release": { "tag": "v1.26.0", "published_at": "2026-05-29T05:09:47Z" } }, "drop_score": 34, "drop_score_components": { "curation": 0.333, "recency": 0.219, "recency_push": 0.437, "recency_release": 0, "traction": 0.485, "basis": "signals" } }, { "slug": "sentry-mcp", "name": "Sentry MCP Server", "url": "https://mcp.sentry.dev", "github": "getsentry/toolkit", "category": "mcp-server", "tags": [ "error-tracking", "debugging", "observability", "hosted", "oauth" ], "pricing": "free", "license_note": "FSL-1.1-Apache-2.0 (source-available; each version converts to Apache-2.0 two years after release)", "effect": "Gives coding agents access to Sentry errors, issues, traces and performance data over MCP", "why": "Sentry runs a remote MCP server at mcp.sentry.dev that uses OAuth and acts as middleware to the Sentry API, with tools chosen for debugging workflows in coding assistants rather than full Sentry coverage. You can scope the connection to one organization or project, which sets defaults and hides discovery tools. For self-hosted Sentry there is a stdio transport (npx @sentry/mcp-server) that takes a user auth token; its AI-powered search tools need an OpenAI, Azure OpenAI, Anthropic or OpenRouter key.", "best_for": [ "Teams already on Sentry", "Debugging production errors from the editor", "Self-hosted Sentry via the stdio transport" ], "use_when": "Your app reports to Sentry and you want the coding agent to pull issue details, errors and traces while it works on a fix.", "skip_if": "You do not use Sentry. Sentry says it is not a general-purpose server for all Sentry functionality, and the stdio transport is still a work in progress.", "install": { "kind": "cli", "label": "Add to Claude Code", "snippet": "claude mcp add --transport http sentry https://mcp.sentry.dev/mcp/{organizationSlug}/{projectSlug}", "source_url": "https://mcp.sentry.dev" }, "platforms": [ "any-mcp-client", "claude-code", "cursor", "vscode" ], "date_added": "2026-10-14", "source_url": "https://github.com/getsentry/toolkit", "editor_note": "The Sentry MCP server now lives in getsentry/toolkit alongside the Sentry CLI.", "picks_rank_weight": 1, "category_label": "MCP servers", "github_url": "https://github.com/getsentry/toolkit", "page_url": "https://agenttooldrop.com/tools/sentry-mcp", "markdown_url": "https://agenttooldrop.com/tools/sentry-mcp.md", "signals": { "status": "ok", "fetched_at": "2026-10-15T01:19:04.508Z", "full_name": "getsentry/toolkit", "stars": 928, "forks": 162, "open_issues": 154, "license_spdx": "NOASSERTION", "pushed_at": "2026-10-15T01:09:10Z", "archived": false, "default_branch": "main", "latest_release": { "tag": "cli@0.47.0", "published_at": "2026-10-05T13:38:37Z" } }, "drop_score": 34, "drop_score_components": { "curation": 0.333, "recency": 0.69, "recency_push": 0.992, "recency_release": 0.388, "traction": 0, "basis": "signals" } }, { "slug": "aider", "name": "Aider", "url": "https://aider.chat/", "github": "Aider-AI/aider", "category": "coding-agent", "tags": [ "cli", "terminal", "python", "git", "pair-programming" ], "pricing": "open-source", "license_note": "Apache-2.0", "effect": "Pair programs with an LLM in your terminal, editing files in your git repo and committing each change", "why": "Aider builds a map of your whole codebase so it can work in larger projects, and it commits its changes to git with descriptive messages so you can diff and undo them with normal git tools. It connects to most cloud LLMs and to local models, can lint and test after every edit and fix what fails, and also takes requests from comments you add in your editor. Images, web pages and voice input can be added to the chat.", "best_for": [ "Git-centric terminal workflows", "Bring-your-own-model setups, including local models", "Editing existing codebases" ], "use_when": "You want an agent that records every change as a git commit and lets you choose the model provider, including local models.", "skip_if": "You want an IDE-native agent with visual diffs, or you do not want to manage your own model API keys.", "install": { "kind": "pip", "label": "Install with pip and aider-install", "snippet": "python -m pip install aider-install\naider-install", "source_url": "https://aider.chat/docs/install.html" }, "platforms": [ "macos", "linux", "windows" ], "date_added": "2026-10-14", "source_url": "https://github.com/Aider-AI/aider#readme", "picks_rank_weight": 1, "editor_note": null, "category_label": "Coding agents", "github_url": "https://github.com/Aider-AI/aider", "page_url": "https://agenttooldrop.com/tools/aider", "markdown_url": "https://agenttooldrop.com/tools/aider.md", "signals": { "status": "ok", "fetched_at": "2026-10-15T01:18:53.522Z", "full_name": "Aider-AI/aider", "stars": 49553, "forks": 5059, "open_issues": 1920, "license_spdx": "Apache-2.0", "pushed_at": "2026-05-22T14:02:20Z", "archived": false, "default_branch": "main", "latest_release": { "tag": "v0.86.0", "published_at": "2025-08-09T17:42:19Z" } }, "drop_score": 33, "drop_score_components": { "curation": 0.333, "recency": 0, "recency_push": 0, "recency_release": 0, "traction": 0.652, "basis": "signals" } }, { "slug": "exa-mcp", "name": "Exa MCP Server", "url": "https://exa.ai/docs/get-started/exa-mcp", "github": "exa-labs/exa-mcp-server", "category": "mcp-server", "tags": [ "web-search", "web-fetch", "research", "hosted", "agent-skills" ], "pricing": "open-source", "license_note": "MIT", "effect": "Gives agents Exa web search and page fetching as MCP tools, with optional multi-step research runs", "why": "Exa hosts the server at mcp.exa.ai/mcp and it works without an API key under rate limits, so setup is a URL or a plugin install. The default tools are web_search_exa, which returns clean content for search results, and web_fetch_exa, which reads pages as markdown; advanced search with domain and date filters and Exa Agent runs for multi-step research can be turned on with the tools URL parameter. The repo also ships search and exa-agent skills that load with plugin installs.", "best_for": [ "Coding agents that need current web results", "Multi-step research and list-building", "Improving built-in web search in Claude, ChatGPT or Codex" ], "use_when": "Your agent needs to search the web or read pages as clean markdown and you want a hosted server with nothing to install.", "skip_if": "You need more than light anonymous use: the keyless server is rate-limited, and Exa Agent requires OAuth or an Exa API key.", "install": { "kind": "cli", "label": "Install the Claude Code plugin", "snippet": "claude plugin install exa@claude-plugins-official", "source_url": "https://github.com/exa-labs/exa-mcp-server#readme" }, "platforms": [ "any-mcp-client", "claude-code", "codex", "cursor", "vscode", "gemini-cli", "windsurf", "zed", "claude-desktop" ], "date_added": "2026-10-14", "source_url": "https://github.com/exa-labs/exa-mcp-server", "picks_rank_weight": 1, "editor_note": null, "category_label": "MCP servers", "github_url": "https://github.com/exa-labs/exa-mcp-server", "page_url": "https://agenttooldrop.com/tools/exa-mcp", "markdown_url": "https://agenttooldrop.com/tools/exa-mcp.md", "signals": { "status": "ok", "fetched_at": "2026-10-15T01:18:57.659Z", "full_name": "exa-labs/exa-mcp-server", "stars": 5130, "forks": 403, "open_issues": 18, "license_spdx": "MIT", "pushed_at": "2026-10-07T22:45:09Z", "archived": false, "default_branch": "main", "latest_release": null }, "drop_score": 32, "drop_score_components": { "curation": 0.333, "recency": 0.491, "recency_push": 0.491, "recency_release": 0.491, "traction": 0.121, "basis": "signals" } }, { "slug": "steel-browser", "name": "Steel Browser", "url": "https://steel.dev", "github": "steel-dev/steel-browser", "category": "browser-agent", "tags": [ "browser-api", "self-hosted", "docker", "cdp", "puppeteer" ], "pricing": "open-source", "license_note": "Apache-2.0", "effect": "Runs a self-hostable browser API that manages Chrome sessions agents connect to with Puppeteer or Playwright", "why": "Steel is an open-source browser API server: it manages sessions, pages and Chrome processes so your agent code connects to a ready browser with Puppeteer, Playwright or Selenium. Sessions keep cookies and local storage across requests, and the server includes proxy chain management, custom Chrome extensions, stealth plugins and fingerprint management, and a UI for viewing and debugging sessions. Quick action endpoints return a page's HTML, a screenshot or a PDF in one REST call, and the Node and Python SDKs work against both self-hosted Steel and Steel Cloud.", "best_for": [ "Hosting your own browser infrastructure for agents", "Stateful sessions with proxies and extensions", "Teams already using Puppeteer, Playwright or Selenium" ], "use_when": "Your agent needs remote, stateful browser sessions and you want to run them yourself in Docker or use the managed Steel Cloud.", "skip_if": "You want something that decides what to do on a page: Steel provides the browser infrastructure, not the agent. The README describes the project as in public beta.", "install": { "kind": "cli", "label": "Run with Docker", "snippet": "docker run -p 3000:3000 -p 9223:9223 ghcr.io/steel-dev/steel-browser", "source_url": "https://github.com/steel-dev/steel-browser#readme" }, "platforms": [ "docker" ], "date_added": "2026-10-14", "source_url": "https://github.com/steel-dev/steel-browser#readme", "picks_rank_weight": 1, "editor_note": null, "category_label": "Browser and computer use", "github_url": "https://github.com/steel-dev/steel-browser", "page_url": "https://agenttooldrop.com/tools/steel-browser", "markdown_url": "https://agenttooldrop.com/tools/steel-browser.md", "signals": { "status": "ok", "fetched_at": "2026-10-15T01:19:05.284Z", "full_name": "steel-dev/steel-browser", "stars": 7805, "forks": 996, "open_issues": 74, "license_spdx": "Apache-2.0", "pushed_at": "2026-10-13T15:37:05Z", "archived": false, "default_branch": "main", "latest_release": { "tag": "v0.5.4-beta", "published_at": "2026-08-25T19:41:16Z" } }, "drop_score": 31, "drop_score_components": { "curation": 0.333, "recency": 0.435, "recency_push": 0.863, "recency_release": 0.007, "traction": 0.152, "basis": "signals" } }, { "slug": "autogen", "name": "AutoGen", "url": "https://microsoft.github.io/autogen/", "github": "microsoft/autogen", "category": "agent-framework", "tags": [ "python", "multi-agent", "event-driven", "microsoft", "maintenance-mode" ], "pricing": "open-source", "license_note": "MIT for code (LICENSE-CODE); CC-BY-4.0 for docs and other content", "effect": "Builds multi-agent AI apps that act autonomously or alongside humans; now in maintenance mode", "why": "AutoGen is Microsoft's multi-agent framework, layered into a Core API (message passing, event-driven agents, local and distributed runtimes), an AgentChat API for common patterns such as two-agent chat and group chat, and an Extensions API for model clients and code execution. AutoGen Studio adds a no-code GUI for prototyping multi-agent workflows, though the README says it is not meant to be a production-ready app. Existing users can keep using it, but it no longer receives new features.", "best_for": [ "Maintaining existing AutoGen projects", "Studying multi-agent orchestration patterns" ], "use_when": "You already run AutoGen and need it to keep working while you plan a migration.", "skip_if": "You are starting a new project: AutoGen is in maintenance mode and community managed, and Microsoft recommends its successor, Microsoft Agent Framework, which has a migration guide from AutoGen.", "install": { "kind": "pip", "label": "Install with pip", "snippet": "pip install -U \"autogen-agentchat\" \"autogen-ext[openai]\"", "source_url": "https://github.com/microsoft/autogen#readme" }, "platforms": [], "date_added": "2026-10-14", "source_url": "https://github.com/microsoft/autogen#readme", "editor_note": "The README marks AutoGen as in maintenance mode (no new features, community managed) and names Microsoft Agent Framework as its successor.", "picks_rank_weight": 0, "category_label": "Agent frameworks", "github_url": "https://github.com/microsoft/autogen", "page_url": "https://agenttooldrop.com/tools/autogen", "markdown_url": "https://agenttooldrop.com/tools/autogen.md", "signals": { "status": "ok", "fetched_at": "2026-10-15T01:18:54.516Z", "full_name": "microsoft/autogen", "stars": 61408, "forks": 9284, "open_issues": 1109, "license_spdx": "CC-BY-4.0", "pushed_at": "2026-04-15T11:59:09Z", "archived": false, "default_branch": "main", "latest_release": { "tag": "python-v0.7.5", "published_at": "2025-09-30T06:18:26Z" } }, "drop_score": 22, "drop_score_components": { "curation": 0, "recency": 0, "recency_push": 0, "recency_release": 0, "traction": 0.742, "basis": "signals" } }, { "slug": "container-use", "name": "Container Use", "url": "https://container-use.com", "github": "dagger/container-use", "category": "sandbox", "tags": [ "mcp", "containers", "git", "docker", "parallel-agents", "cli" ], "pricing": "open-source", "license_note": "Apache-2.0", "effect": "Gives each coding agent its own container and git branch so several can work in parallel without conflicts", "why": "Container Use is an MCP server, run from a CLI and built on Dagger, that gives each agent a fresh container on its own git branch, so you can run several agents at once and discard failed attempts. You can see the full command history and logs of what an agent did, drop into its terminal to take over, and review its work with a normal git checkout of its branch. Setup is the same for any MCP-compatible agent, adding container-use stdio as an MCP server, and the docs have guides for Claude Code, Cursor, Windsurf, Zed, Codex, Gemini CLI and others.", "best_for": [ "Running several coding agents in parallel", "Keeping agent changes off your working tree until reviewed", "Claude Code, Cursor and other MCP agents" ], "use_when": "You want coding agents to work in isolated, reviewable environments on your own machine instead of directly in your checkout.", "skip_if": "You do not have Docker and Git installed, which the quickstart requires. The project describes itself as experimental and in early development.", "install": { "kind": "cli", "label": "Install with Homebrew and add to Claude Code", "snippet": "brew install dagger/tap/container-use\ncd /path/to/repository\nclaude mcp add container-use -- container-use stdio", "source_url": "https://container-use.com/quickstart" }, "platforms": [ "claude-code", "cursor", "windsurf", "zed", "codex", "gemini-cli", "vscode", "any-mcp-client" ], "date_added": "2026-10-14", "source_url": "https://github.com/dagger/container-use#readme", "picks_rank_weight": 1, "editor_note": null, "category_label": "Sandboxes", "github_url": "https://github.com/dagger/container-use", "page_url": "https://agenttooldrop.com/tools/container-use", "markdown_url": "https://agenttooldrop.com/tools/container-use.md", "signals": { "status": "ok", "fetched_at": "2026-10-15T01:18:56.147Z", "full_name": "dagger/container-use", "stars": 4069, "forks": 212, "open_issues": 63, "license_spdx": "Apache-2.0", "pushed_at": "2026-09-21T14:05:02Z", "archived": false, "default_branch": "main", "latest_release": { "tag": "v0.4.2", "published_at": "2025-08-19T22:31:40Z" } }, "drop_score": 18, "drop_score_components": { "curation": 0.333, "recency": 0.049, "recency_push": 0.097, "recency_release": 0, "traction": 0.106, "basis": "signals" } } ] }