Find agents, MCP servers, and agentic infrastructure.

Protocol and ecosystem for tool-connected AI apps.

Use when evaluating protocol-level infrastructure for connected agents.

API for tool-using AI applications.

Use when building OpenAI-backed agents that need model calls and tool orchestration.

Research reports with web citations.

Use when an agent needs current cited research through Perplexity surfaces.

Google research assistant in Gemini.

Use when tracking Google's research-agent capabilities and Gemini-based research workflows.

Research agent inside ChatGPT.

Use when a user needs a deep cited research report across many web sources.

Linear issue access through MCP.

Use when an agent needs Linear issue and project context.

Supabase project access through MCP.

Use when an agent needs Supabase project context or database tooling through MCP.

Notion workspace access through MCP.

Use when an agent needs Notion pages, databases, or knowledge base access through MCP.

Slack workspace access through MCP.

Use when an agent needs Slack context or collaboration actions through MCP.

PostgreSQL access for MCP clients.

Use when an agent needs structured PostgreSQL access through MCP.

Local filesystem access for MCP clients.

Use when an agent needs scoped local file access through MCP.

Browser automation through MCP.

Use when an MCP-compatible agent needs browser automation and page inspection.

MCP tools for GitHub workflows.

Use when an agent needs structured GitHub repository and collaboration actions.

MCP access to Exa search.

Use when an MCP client needs direct access to Exa search tools.

Search API for AI agents.

Use when an agent needs web search results optimized for downstream reasoning.

Search API built for AI applications.

Use when an agent needs API-first web search designed for AI workflows.

Web crawling and extraction for AI agents.

Use when an agent needs clean web extraction, crawling, or structured site data.

AI browser automation framework.

Use when building browser agents that need deterministic code plus AI-assisted actions.

Cloud browser infrastructure for agents.

Use when an agent needs reliable hosted browser sessions instead of local browser automation.

AI UI and app generation from Vercel.

Use when a user needs fast React UI generation or design-to-code exploration.

Browser-based AI app builder.

Use when a user wants a browser-native app builder for quick prototypes.

Prompt-to-app builder for software products.

Use when a user wants to rapidly prototype or generate a web app from a prompt.

Agentic IDE from Codeium.

Use when evaluating IDE-native coding agents and AI-assisted software workflows.

Open-source coding agent for VS Code.

Use when a VS Code workflow needs an open-source coding agent with tool use.

AI coding assistant and agentic developer tools.

Use when a developer wants GitHub-native AI coding assistance and repository workflows.

Software-building agent inside Replit.

Use when building or deploying small apps inside Replit's hosted development environment.

AI software engineer from Cognition.

Use when a software task may require an autonomous coding agent with project-level execution.

General-purpose AI agent for complex tasks.

Use when tracking general-purpose autonomous agent products and their capabilities.

AI automation builder for workflows.

Use when a team needs visual AI workflow automation and app/data steps.

Build AI agents and AI workforces.

Use when evaluating AI workforce platforms for repeatable business tasks.

No-code AI employees for business workflows.

Use when a business user wants to create operational agents without writing code.

Automation agents for business workflows.

Use when an agent needs broad SaaS app actions through Zapier's automation ecosystem.

Workflow automation platform with AI agent capabilities.

Use when a team needs workflow automation with human-readable visual flows and AI steps.

Python agent framework from the Pydantic team.

Use when a Python agent needs strong typing, structured outputs, and Pydantic ecosystem integration.

TypeScript framework for AI agents.

Use when building TypeScript-native agent applications with workflows and memory.

Framework for multi-agent AI applications.

Use when comparing multi-agent frameworks and conversation-based agent architectures.

Platform for building and deploying enterprise agents.

Use when a team needs multi-agent workflow orchestration with enterprise-oriented deployment paths.

Framework for stateful agent applications.

Use when building stateful or multi-step agent workflows that need explicit control flow.

Live index of the agent economy.

Use to stay current on new agentic capabilities, launches, and ecosystem changes.

Tool integration platform for AI agents.

Use when an agent needs many app integrations and authenticated actions.

Data framework for LLM and agent applications.

Use when an agent needs structured retrieval or data connectors for knowledge-heavy tasks.

Framework for building LLM applications and agents.

Use when building agent workflows that need tool use, retrieval, memory, and orchestration.

Model, dataset, and space hub for AI builders.

Use to discover open models, datasets, Spaces, and model metadata.

Answer engine with citations and research workflows.

Use when an agent needs current web-grounded research with source links.

Browser automation for AI agents.

Use when an agent needs to navigate websites, gather information, or complete browser-based tasks.

AI-native IDE for coding workflows.

Use when a human developer wants an IDE-native agentic coding workflow.

OpenAI coding agent for software tasks.

Use for software implementation, debugging, and repository-oriented tasks.

Terminal-native coding agent from Anthropic.

Use for codebase tasks that require reading files, editing code, and running local commands.

Open standard connecting AI apps to tools and data.

Use when an agent needs structured access to external tools, resources, prompts, or workflows.

Open protocol work for agent-initiated payments.

Use when an agent needs to evaluate standards for delegated purchases, payment authorization, or commerce workflows.

Open protocol for agent-to-agent interoperability.

Use when researching standards for communication between independent agent systems.

Build apps that run inside ChatGPT.

Use when a product needs an interactive app surface inside ChatGPT with MCP-backed actions.

Launch surface and review directory for AI agents.

Use to inspect launch momentum and community response for newly launched AI agents.

Canonical registry for Model Context Protocol servers.

Use as a primary source when checking whether an MCP server is published in the official registry.

Connect agents to tools and services.

Use when an agent needs managed connections to MCP tools with auth and sessions handled.

Registry and quality layer for MCP servers.

Use to discover MCP servers and inspect quality, maintenance, and safety signals.

Email inboxes for AI agents.

Use when an agent needs its own mailbox, inbound email handling, or email-based account verification.