AgentToolDrop

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OpenBot

Runs self-hosted AI coworkers with their own browser, files and shell, each action policy-checked and logged

Run the published Docker image

docker run -p 3001:3001 --env-file .env \
  -e EMBEDDED_POSTGRES=on -v openbot-data:/var/lib/postgresql \
  ghcr.io/copilotkit/openbot:latest

Copied from the project's docs at github.com/CopilotKit/OpenBot

Why you'd want it

OpenBot is a template for an in-house agent platform: Docker Compose brings up the app, PostgreSQL and the Bots, and you supply the model key. Each Bot gets its own container with a Chromium profile, a workspace volume and a shell, and every browser, file, shell, MCP or component action passes through one gateway that evaluates CEL policy rules, writes an audit row and only then acts. Any agent that speaks the AG-UI protocol, built with LangGraph, Mastra, CrewAI, Pydantic AI, Google ADK or by hand, can join as a coworker with its own channel, and you can watch its screen and take over. It ships with General Assistant and Knowledge coworkers plus example roles to copy.

Best for

  • Self-hosted agent coworkers for a team
  • Auditable browser and shell actions
  • Bringing AG-UI agents into one workspace

Use it when

You want to give agents real computers inside your own infrastructure but need allow and deny rules and an audit trail for what they do.

Skip it if

You want a finished product: the README calls it an alpha template to clone and change, with no hosted version. It needs Docker, Bun, a model key and a CopilotKit Intelligence project (a free plan exists, or you can self-host it), and Bots drive a browser with saved logins and run shell commands, so set up sign-in before anyone else can reach it.

Editor's note

The .env comes from the repo's .env.example; the README says the CopilotKit key and a model key are the values you fill in.

Related tools

Same category or shared tags.

  1. n8n

    Framework Standard

    Builds AI agents and automation workflows on a visual canvas that connects to 1500+ integrations

    • 207k stars
    • 77.6 stars/day since creation
    • pushed
    • [email protected] on
    • Sustainable Use License

    Run with Docker

    docker volume create n8n_data
docker run -it --rm --name n8n -p 5678:5678 -v n8n_data:/home/node/.n8n docker.n8n.io/n8nio/n8n
    Use it when, skip it if, stats for n8n

    Why you'd want it

    n8n combines a node-based visual editor with custom JavaScript, Python and npm packages, and its README says workflows can use OpenAI, Anthropic, Google or open-source models. You can self-host it with Docker or npm, or use the paid n8n Cloud. Its docs cover building agents, LangChain components and MCP servers inside workflows.

    Best for

    • Connecting agents to business apps
    • Self-hosted internal automations
    • Teams mixing visual building with code

    Use it when

    You want an agent to act across many SaaS tools through a workflow you can see, edit and self-host.

    Skip it if

    You need an OSI open source license or plan to offer n8n to others as a paid service; the Sustainable Use License limits use to internal business, non-commercial or personal purposes. If you build agents in code, a library will fit better.

    Stats

    Stars
    207,417
    Last push
    Release
    [email protected],
    License
    Sustainable Use License
    Pricing
    Free tier + paid
    Works with
    Docker, Web
    Stars per day, since repo creation
    77.6
    Tier
    Standard, not ranked

    Full stat block for n8n

  2. Agno

    Framework Standard

    Builds Python agents, teams and workflows and serves them as an API with the AgentOS runtime

    • 42.7k stars
    • 26.3 stars/day since creation
    • pushed
    • v3.1.3 on
    • Apache-2.0

    Install with pip

    pip install -U agno openai
    Use it when, skip it if, stats for Agno

    Why you'd want it

    Agno is a Python SDK with three primitives (agents, teams and workflows) plus a runtime, AgentOS, that serves them through a production API with 50+ endpoints over SSE and websockets. Sessions, memory, knowledge and traces are stored in your own database, and the runtime adds human approval, OpenTelemetry tracing, JWT-based RBAC with multi-tenant isolation, cron scheduling, and interfaces for Slack, Telegram, WhatsApp, Discord, AG-UI and A2A. Starter templates deploy it with Docker to Railway, AWS, GCP, Azure, Fly, Render, Modal or Helm.

    Best for

    • Self-hosted agent platforms
    • Serving agents to many users
    • Agents exposed in Slack or messaging apps

    Use it when

    You want to build agents and also run them as a multi-user service on your own infrastructure, with data kept in your own database.

    Skip it if

    You are not on Python. Agno sends a telemetry event per agent run by default (prompts, messages and outputs are not sent); set AGNO_TELEMETRY=false to turn it off.

    Stats

    Stars
    42,736
    Last push
    Release
    v3.1.3,
    License
    Apache-2.0
    Pricing
    Open source
    Stars per day, since repo creation
    26.3
    Tier
    Standard, not ranked

    Full stat block for Agno

  3. Rank 2

    Reef

    Framework

    Turns feedback on an agent's live requests into versioned updates to its model weights or its harness

    • score 74
    • 7.6k stars
    • 168 stars/day since creation
    • pushed
    • v0.1.3 on
    • Apache-2.0

    Install from PyPI with uv

    uv venv && source .venv/bin/activate
uv pip install reef-infra
python3 -c "import reef; print(reef.__version__)"
    Use it when, skip it if, stats for Reef

    Why you'd want it

    Reef sits between an agent and its model as an OpenAI- and Anthropic-compatible inference endpoint, records each interaction and returns a receipt that a later report can score or comment on. Recipes turn eligible feedback into updates, either training model weights with Slime and SGLang on a GPU stack or refining the harness, meaning prompts, rules and skills, through a model API with no local GPUs. Accepted updates go into a version history and are served without restarting Reef. The built-in Reefine recipe refines a coding harness from plain-language asks, such as reproducing a bug with a failing test first.

    Best for

    • Agents that should learn from user feedback
    • Harness optimization without GPUs
    • Continual learning research

    Use it when

    You run an agent with a steady stream of tasks or feedback and want that signal to produce evaluated, versioned improvements instead of manual prompt edits.

    Skip it if

    You want a stable agent today: harness work needs a model endpoint, representative tasks and an evaluator, weight training needs a supported GPU stack, and it is at v0.1.3 with the repo created on 2026-08-31. The harness install pipes a script into bash and the pi adapter runs commands with no sandbox, so keep the install root outside the project the agent works in.

    Stats

    Stars
    7,602
    Last push
    Release
    v0.1.3,
    License
    Apache-2.0
    Pricing
    Open source
    Stars per day, since repo creation
    168
    New and rising score
    74 of 100

    Full stat block for Reef

For agents

The same entry, as files an agent can read without scraping this page. 

# read one entry
curl -s https://agenttooldrop.com/tools/openbot.md

# list the MCP servers in the catalog
curl -s https://agenttooldrop.com/tools.json \
  | jq -r '.tools[] | select(.category=="mcp-server") | .slug'