PlanExeOrg/PlanExe — Gitpedia

<picture> <source media="(prefers-color-scheme: dark)" srcset="docs/hero/planexe-hero-v1-grid-dark.svg"> <img src="docs/hero/planexe-hero-v1-grid-light.svg" alt="The PlanExe icon is the P character and E character" width="100%"> </picture> Turn your idea into a comprehensive plan in minutes, not months. <a href="https://app.mach-ai.com/planexe_early_access"> <img src="https://img.shields.io/badge/%F0%9F%9A%80%20Try%20PlanExe%20in%20your%20browser-Generate%20a%20free%20plan-2ea44f?style=for-the-badge" alt="Try PlanExe in your browser — generate a free plan" height="48"> </a> Describe your idea, hit submit, and PlanExe returns a ~40-page plan in about 15 minutes. <a href="https://home.planexe.org/">Create an account</a>  |  <a href="https://planexe.org/examples/">See example plans</a>  |  <a href="https://docs.planexe.org/getting_started/">Getting started guide</a>

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

example_plans (optional, preview what PlanExe output looks like)
example_prompts
model_profiles (optional, helps choose model_profile)
non-tool step: draft/approve prompt
plan_create
plan_status (poll every 5 minutes until done)
optional if failed: plan_retry
download the result 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:

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

Option B: Run MCP server locally with Docker

Prerequisites

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

Start the full stack:

bash
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.

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

Running the Pipeline via CLI (no frontend)

If you have a local Python environment set up and want to invoke the pipeline
directly — without the Flask UI — use the planexe CLI script at
the repo root.

bash
# Create a plan from a text prompt
./planexe create_plan \
    --plan-text "Small coffee shop in Copenhagen, Denmark" \
    --output-dir ./planexe-outputs/1984-12-31/MyCoffeeShop_v1

# Or: read the plan prompt from a file
./planexe create_plan \
    --plan-file my_plan.txt \
    --output-dir ./planexe-outputs/1984-12-31/MyCoffeeShop_v1

The script creates the run directory if it does not exist and writes:

File	Contents
`start_time.json`	`{"server_iso_utc": "<current UTC ISO timestamp>"}`
`plan.txt`	The plain-text plan prompt

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: wbs_level1.json (work packages), wbs_level2.json (tasks), pre_project_assessment.json (feasibility).

<details> <summary> Run locally with Docker (Click to expand)</summary>

Prerequisite: Docker with Docker Compose installed; you only need basic Docker knowledge. No local Python setup is required because everything runs in containers.

Follow these steps

Clone the repo and enter it:

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

Provide an LLM provider. Copy .env.docker-example to .env and fill in OPENROUTER_API_KEY with your key from OpenRouter. The containers mount .env and llm_config/; pick a model profile there. For host-side Ollama, use the docker-ollama-llama3.1 entry and ensure Ollama is listening on http://host.docker.internal:11434.
Start the stack (first run builds the images):

bash
docker compose up worker_plan frontend_multi_user

The worker listens on http://localhost:8000 and the UI comes up on http://localhost:5001 after the Postgres and worker healthchecks pass.

Open http://localhost:5001 in your browser, create an account (or log in with the admin credentials from .env), enter your idea, and watch progress with:

bash
docker compose logs -f worker_plan

Outputs are written to run/ on the host (mounted into both containers).

Stop with Ctrl+C (or docker compose down). Rebuild after code/dependency changes:

bash
docker compose build --no-cache worker_plan frontend_multi_user

For compose tips, alternate ports, or troubleshooting, see docs/docker.md or docker-compose.md.

Configuration

Config A: Run a model in the cloud using a paid provider. Follow the instructions in OpenRouter.

Config B: Run models locally on a high-end computer. Follow the instructions for either Ollama or LM Studio. When using host-side tools with Docker, point the model URL at the host (for example http://host.docker.internal:11434 for Ollama).

Recommendation: I recommend Config A as it offers the most straightforward path to getting PlanExe working reliably.

</details>

<details> <summary> Help (Click to expand)</summary>

For help or feedback.

Join the PlanExe Discord.

</details>