PlanExe

Turn your idea into a comprehensive plan in minutes, not months.

PlanExe is the premier planning tool for AI agents.

Create an account  |  Generate a free plan  |  Getting started guide

Example plans generated with PlanExe

• A business plan for a Minecraft-themed escape room.
• A business plan for a Faraday cage manufacturing company.
• A pilot project for a Human as-a Service.
• See more examples here.

What is PlanExe?

PlanExe is an open-source tool and the premier planning tool for AI agents. It turns a single plain-english goal statement into a 40-page, strategic plan in ~15 minutes using local or cloud models. It's an accelerator for outlines, but no silver bullet for polished plans.

Typical output contains:

• Executive summary
• Gantt chart
• Governance structure
• Role descriptions
• Stakeholder maps
• Risk registers
• SWOT analyses

PlanExe produces well-structured, domain-aware output: correct terminology, logical task sequencing, and coherent sections. For technical topics (engineering programs, regulated industries), it often gets the vocabulary and structure right. Think of it as a first-draft scaffold that gives you something concrete to critique and refine.

However, the output has consistent weaknesses that matter: budgets are assumed rather than derived, timeline estimates are not grounded in real resource constraints, risk mitigations tend toward generic advice, and legal/regulatory details are plausible-sounding but unverified. The output should be treated as a structured starting point, not a deliverable. How much work it saves depends heavily on the project. For brainstorming or a first outline, it can save hours. For a client-ready plan, expect significant rework on every number, timeline, and risk section.

Model Context Protocol (MCP)

PlanExe exposes an MCP server for AI agents at https://mcp.planexe.org/

Assuming you have an MCP-compatible client (Claude, Cursor, Codex, LM Studio, Windsurf, OpenClaw, Antigravity).

The Tool workflow

1. example_plans (optional, preview what PlanExe output looks like)
2. example_prompts
3. model_profiles (optional, helps choose model_profile)
4. non-tool step: draft/approve prompt
5. plan_create
6. plan_status (poll every 5 minutes until done)
7. optional if failed: plan_retry
8. download the result via plan_download or via plan_file_info

Concurrency note: each plan_create call returns a new plan_id; server-side global per-client concurrency is not capped, so clients should track their own parallel plans.

Option A: Remote MCP (fastest path)

Prerequisites

• An account at https://home.planexe.org.
• Sufficient funds to create plans.
• A PlanExe API key (pex_...) from your account

Use this endpoint directly in your MCP client:

{
  "mcpServers": {
    "planexe": {
      "url": "https://mcp.planexe.org/mcp",
      "headers": {
        "X-API-Key": "pex_your_api_key_here"
      }
    }
  }
}

Option B: Remote MCP + local downloads via proxy (mcp_local)

If you want artifacts saved directly to your disk from your MCP client, run the local proxy:

{
  "mcpServers": {
    "planexe": {
      "command": "uv",
      "args": [
        "run",
        "--with",
        "mcp",
        "/absolute/path/to/PlanExe/mcp_local/planexe_mcp_local.py"
      ],
      "env": {
        "PLANEXE_URL": "https://mcp.planexe.org/mcp",
        "PLANEXE_MCP_API_KEY": "pex_your_api_key_here",
        "PLANEXE_PATH": "/absolute/path/for/downloads"
      }
    }
  }
}

Option C: Run MCP server locally with Docker

Prerequisites

• Docker
• OpenRouter account
• Create a PlanExe .env file with OPENROUTER_API_KEY.

Start the full stack:

docker compose up --build

Make sure that you can create plans in the web interface, before proceeding to MCP.

Then connect your client to:

• http://localhost:8001/mcp

For local docker defaults, auth is disabled in docker-compose.yml.

Local file downloads via proxy (mcp_local)

If you want artifacts saved directly to your disk from your MCP client, run the local proxy:

{
  "mcpServers": {
    "planexe": {
      "command": "uv",
      "args": [
        "run",
        "--with",
        "mcp",
        "/absolute/path/to/PlanExe/mcp_local/planexe_mcp_local.py"
      ],
      "env": {
        "PLANEXE_URL": "http://localhost:8001/mcp/",
        "PLANEXE_PATH": "/absolute/path/for/downloads"
      }
    }
  }
}

MCP docs

• Setup overview: https://docs.planexe.org/mcp/mcp_setup/
• Tool details and flow: https://docs.planexe.org/mcp/mcp_details/
• Claude: https://docs.planexe.org/mcp/claude/
• Cursor: https://docs.planexe.org/mcp/cursor/
• Codex: https://docs.planexe.org/mcp/codex/
• PlanExe MCP interface: https://docs.planexe.org/mcp/planexe_mcp_interface/
• MCP Registry publishing metadata (server.json): mcp_cloud/server.json
• llms.txt: https://mcp.planexe.org/llms.txt

For AI Agents

PlanExe is designed as infrastructure for AI agents. If you are an AI agent reading this:

• Discovery: Read public/llms.txt for service metadata, MCP tools, and endpoints.
• MCP endpoint: https://mcp.planexe.org/mcp — connect with an API key from home.planexe.org.
• Workflow: Call example_prompts to learn the expected prompt format, draft a detailed prompt (~300-800 words of flowing prose), then call plan_create.
• Agent guide: See docs/mcp/autonomous_agent_guide.md for the complete autonomous workflow.
• Key outputs in zip: 018-2-wbs_level1.json (work packages), 018-5-wbs_level2.json (tasks), 004-2-pre_project_assessment.json (feasibility).

git clone https://github.com/PlanExeOrg/PlanExe.git
cd PlanExe

docker compose up worker_plan frontend_single_user

docker compose logs -f worker_plan

docker compose build --no-cache worker_plan frontend_single_user