CrewAI
実運用評価向け: CrewAI is an agent framework for building multi-agent workflows with roles, tasks, tools, memory, and orchestration patterns.
900
AI Agents
32 agents available
Allagent-framework (1)agent-network (1)agent-skills (4)analysis (3)autonomous-agent (1)browser-agent (2)business (1)coding (3)coding-agent (9)marketing (2)open-source-agent (1)personal-agent (2)productivity (1)security (1)
agent-framework
Cline
実運用評価向け: Cline is an open-source autonomous coding agent for VS Code-style workflows, capable of editing files, running terminal commands, using browser tools, and working with MCP.
900
Aider
実運用評価向け: Aider is an open-source AI pair-programming and coding-agent tool that works in the terminal, edits local repositories, and integrates with Git workflows.
1300
Skyvern
実運用評価向け: Skyvern is an AI browser automation platform for automating web workflows with computer vision, browser actions, and agentic task execution.
1300
browser-use
実運用評価向け: browser-use is an open-source browser automation agent framework that lets LLMs operate websites through browser actions for research, QA, and workflow automation.
1300
OpenHands
実運用評価向け: OpenHands is an open-source software-development agent platform for building, running, and customizing coding agents that can edit code, run commands, and browse.
1500
Devin
実運用評価向け: Devin by Cognition is an autonomous AI software engineer designed to take software tasks, work through implementation steps, and return tested changes.
2300
Cursor Background Agents
実運用評価向け: Cursor Background Agents extend Cursor's AI editor workflow into asynchronous repository tasks while keeping developers inside the IDE-centered coding experience.
1200
GitHub Copilot Coding Agent
実運用評価向け: GitHub Copilot coding agent lets developers assign tasks from GitHub issues and pull requests so the agent can work inside GitHub's review and branch workflow.
1200
Google Jules
実運用評価向け: Google Jules is Google's autonomous coding agent for asynchronous software tasks, running work in cloud virtual machines and returning verified changes.
1100
OpenAI Codex
実運用評価向け: OpenAI Codex is OpenAI's software-engineering agent family spanning Codex CLI, IDE, desktop, and cloud task workflows for coding, bug fixing, and code review.
1200
Claude Code
実運用評価向け: Claude Code is Anthropic's terminal-native coding agent for reading repositories, editing files, running commands, using MCP tools, and carrying software tasks through a plan-edit-test loop.
900
ClawSecure OpenClaw Security
実運用評価向け: ClawSecure OpenClaw Security tracks security issues around OpenClaw-style agents, especially skill supply chain risk, exposed instances, prompt injection, and unsafe permissions.
1600
Moltbook
実運用評価向け: Moltbook is an AI-agent social network associated with the OpenClaw ecosystem, notable as an experiment in agent-to-agent communication, identity, and autonomous posting.
1200
Clawdbot / Moltbot
実運用評価向け: Clawdbot and Moltbot are historical names tied to the OpenClaw personal AI assistant ecosystem, useful for users searching older tutorials, repos, and community posts.
1400
Awesome OpenClaw Skills
実運用評価向け: Awesome OpenClaw Skills is a curated GitHub list for discovering OpenClaw-compatible skills across coding, DevOps, browser automation, AI, research, notes, and productivity.
1100
Oh My OpenClaw
実運用評価向け: Oh My OpenClaw is a community resource hub for finding and installing OpenClaw, Moltbot, and Clawdbot skills and workflow extensions.
1200
OpenClaw Skills Directory
実運用評価向け: OpenClaw Skills Directory is a discovery surface for browsing the fast-growing OpenClaw skill ecosystem by category, task type, and install command.
1200
ClawHub
実運用評価向け: ClawHub is the public registry for OpenClaw skills and plugins, letting users publish, version, discover, and install reusable agent capabilities.
1400
Claw Code
実運用評価向け: Claw Code is an open-source AI coding agent framework described as a clean-room Python and Rust rewrite of Claude Code-style agent harness architecture.
1300
Hermes Agent
実運用評価向け: Hermes Agent is Nous Research's open-source autonomous agent focused on persistent memory, local infrastructure, and model-flexible long-running assistance.
1300
OpenClaw
実運用評価向け: OpenClaw is a viral open-source personal AI assistant that runs as a self-hosted, always-on agent across chat apps, local tools, memory, skills, and automation workflows.
800
Agent Safety Reviewer Agent
2026年のAIワークフロー向け実践コンテンツ: Reviews agent workflows for prompt injection, excessive autonomy, secret leakage, and unsafe tool permissions.
1100
Community Signal Analyst Agent
2026年のAIワークフロー向け実践コンテンツ: Synthesizes X, Reddit, GitHub, and Product Hunt signals without over-trusting viral claims.
1300
AI agent selection
AI agents should be judged by execution control, not by chatbot polish.
The agent market is moving from simple chat assistants toward runtimes that can inspect files, call tools, write outputs, preserve state, and resume work. A useful agent directory should help users compare autonomy, guardrails, observability, memory, and deployment fit.
What changed
Clarify capability boundaries before adoption.
Sandboxed workspaces
Agents now need a controlled place to act
OpenAI’s April 15, 2026 Agents SDK update focuses on agents that inspect files, run commands, edit code, and continue long-horizon tasks in controlled sandbox environments. That makes workspace design, file permissions, and output directories first-class evaluation criteria.
Runtime, not demo
Production agents need memory, tools, traces, and recovery
OpenAI’s update describes configurable memory, MCP tool use, skills, AGENTS.md instructions, shell execution, patch-based file edits, snapshotting, and rehydration. Google ADK similarly surfaces tools, sessions, memory, observability, evaluation, and safety as core agent concerns.
Reliability diagnosis
Most agent failures are context failures
LangChain’s docs argue that agents often fail because the right context was not passed to the model, not because the base model is always too weak. For users, this means an agent profile should explain how it chooses tools, stores memory, and filters task context.
Popular starting points
Choose the right AI agent for building, automation, or research
Compare AI agents by what you need them to do: learn the basics, build an AI agent, connect automation in n8n, explore OpenAI Agent and Google AI Agent ecosystems, or evaluate agent builders such as Vertex AI Agent Builder.
ai agent
Broad definition and directory discoveryStart here if you need a clear explanation of AI agents, common agent examples, useful agent tools, and practical business use cases.
how to create an ai agent
Build tutorialBest for users who want a build path: goal definition, tools, memory, approval gates, sandboxing, deployment checks, and evaluation.
n8n ai agent
Workflow automationHelpful for automation users who want to connect AI agents with triggers, workflows, apps, approvals, and repeatable operations.
Explore AI agent paths
Filter by the job to be done.
The page should satisfy both beginners and builders: definition searches, platform searches, workflow automation searches, and course/comparison searches.
Builder intent
These queries need practical architecture: goal, tools, memory, runtime, approval gates, observability, evaluation, and launch readiness.
how to build an ai agenthow to create an ai agentopen ai agent buildervertex ai agent builder
Platform intent
Users are comparing ecosystems. Add neutral language about OpenAI Agent workflows, Google AI Agent tools, Manus AI Agent style products, and coding agents such as Replit AI Agent.
openai agentgoogle ai agentmanus ai agentreplit ai agent
Automation and education intent
These users need examples and learning paths. The copy should connect AI agents with automation workflows, courses, and hands-on implementation.
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Agent types compared
Compare by capability, cost, and risk.
A lightweight assistant and a production agent are different products. The comparison should start with what the agent is allowed to do.
Assistant agent
- Answers and drafts inside a chat flow
- Low integration risk
- Best for writing, Q&A, brainstorming, and summarization
Workflow agent
- Calls tools and moves data between systems
- Needs authentication, retries, and approval checkpoints
- Best for research, support, reporting, and operations
Sandbox agent
- Works inside files, code, terminals, or isolated workspaces
- Needs strict permissions and audit logs
- Best for coding, data rooms, document processing, and long tasks
What to inspect before using an agent
Fit the tool into a real workflow.
Tool permissions: what can it read, write, delete, publish, or send?
Memory model: what persists across sessions, and can users inspect or clear it?
Human approval: which actions pause for review before execution?
Observability: can you see tool calls, intermediate artifacts, failures, and final evidence?
Related AI agent topics
Use these topics to move from general AI agents into builders, automation platforms, open-source agents, free agents, and hands-on courses.
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Agent FAQ
Common questions and selection answers.
What makes an agent production-ready?
It has a clear task boundary, controlled tools, memory rules, human approval for risky actions, logs or traces, failure handling, and measurable output quality. Without those pieces, it is closer to a demo than a reliable worker.
Are multi-agent systems always better?
No. Multi-agent systems help when work can be split into roles or parallel tasks. They add coordination cost, so single-purpose or workflow agents are often better for repetitive business processes.
Why do agent pages need security details?
Because agents can act. Any agent that touches files, APIs, customer data, payments, or publishing systems should disclose permissions, isolation boundaries, approval steps, and logging behavior.