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\n \n \n $\"The$ \n \n

\n\n

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

\n\n

\n \n $\"Try$ \n \n

\n\n

\n Describe your idea, hit submit, and PlanExe returns a ~40-page plan in about 15 minutes.\n

\n\n

\n Create an account | \n See example plans | \n Getting started guide\n

\n\n---\n\n## Example plans generated with PlanExe\n\n- A business plan for a [Minecraft-themed escape room](https://planexe.org/20251016_minecraft_escape_report.html).\n- A business plan for a [Faraday cage manufacturing company](https://planexe.org/20250720_faraday_enclosure_report.html).\n- A pilot project for a [Human as-a Service](https://planexe.org/20251012_human_as_a_service_protocol_report.html).\n- See more [examples here](https://planexe.org/examples/).\n\n## What is PlanExe?\n\nPlanExe 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.\n\nTypical output contains:\n\n- Executive summary\n- Gantt chart\n- Governance structure\n- Role descriptions\n- Stakeholder maps\n- Risk registers\n- SWOT analyses\n\nPlanExe 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.\n\nHowever, 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.\n\n---\n\n## Model Context Protocol (MCP)\n\nPlanExe exposes an MCP server for AI agents at [https://mcp.planexe.org/](https://mcp.planexe.org/)\n\nAssuming you have an MCP-compatible client ([Claude](https://docs.planexe.org/mcp/claude/), [Cursor](https://docs.planexe.org/mcp/cursor/), [Codex](https://docs.planexe.org/mcp/codex/), [LM Studio](https://docs.planexe.org/mcp/lm_studio/), [Windsurf](https://docs.planexe.org/mcp/windsurf/), OpenClaw, [Antigravity](https://docs.planexe.org/mcp/antigravity/)).\n\nThe Tool workflow\n\n1. `example_plans` (optional, preview what PlanExe output looks like)\n2. `example_prompts`\n3. `model_profiles` (optional, helps choose `model_profile`)\n4. non-tool step: draft/approve prompt\n5. `plan_create`\n6. `plan_status` (poll every 5 minutes until done)\n7. optional if failed: `plan_retry`\n8. download the result via `plan_file_info`\n\nConcurrency 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.\n\n### Option A: Remote MCP (fastest path)\n\n#### Prerequisites\n\n- An account at [https://home.planexe.org](https://home.planexe.org).\n- Sufficient funds to create plans.\n- A PlanExe API key (`pex_...`) from your account\n\nUse this endpoint directly in your MCP client:\n\n```json\n{\n \"mcpServers\": {\n \"planexe\": {\n \"url\": \"https://mcp.planexe.org/mcp\",\n \"headers\": {\n \"X-API-Key\": \"pex_your_api_key_here\"\n }\n }\n }\n}\n```\n\n### Option B: Run MCP server locally with Docker\n\n#### Prerequisites\n\n- Docker\n- OpenRouter account\n- Create a PlanExe `.env` file with `OPENROUTER_API_KEY`.\n\nStart the full stack:\n\n```bash\ndocker compose up --build\n```\n\nMake sure that you can create plans in the web interface, before proceeding to MCP.\n\nThen connect your client to:\n\n- `http://localhost:8001/mcp`\n\nFor local docker defaults, auth is disabled in `docker-compose.yml`.\n\n### MCP docs\n\n- Setup overview: [https://docs.planexe.org/mcp/mcp_setup/](https://docs.planexe.org/mcp/mcp_setup/)\n- Tool details and flow: [https://docs.planexe.org/mcp/mcp_details/](https://docs.planexe.org/mcp/mcp_details/)\n- Claude: [https://docs.planexe.org/mcp/claude/](https://docs.planexe.org/mcp/claude/)\n- Cursor: [https://docs.planexe.org/mcp/cursor/](https://docs.planexe.org/mcp/cursor/)\n- Codex: [https://docs.planexe.org/mcp/codex/](https://docs.planexe.org/mcp/codex/)\n- PlanExe MCP interface: [https://docs.planexe.org/mcp/planexe_mcp_interface/](https://docs.planexe.org/mcp/planexe_mcp_interface/)\n- MCP Registry publishing metadata (`server.json`): `mcp_cloud/server.json`\n- `llms.txt`: [https://mcp.planexe.org/llms.txt](https://mcp.planexe.org/llms.txt)\n\n---\n\n## Running the Pipeline via CLI (no frontend)\n\nIf you have a local Python environment set up and want to invoke the pipeline\ndirectly — without the Flask UI — use the `planexe` CLI script at\nthe repo root.\n\n```bash\n# Create a plan from a text prompt\n./planexe create_plan \\\n --plan-text \"Small coffee shop in Copenhagen, Denmark\" \\\n --output-dir ./planexe-outputs/1984-12-31/MyCoffeeShop_v1\n\n# Or: read the plan prompt from a file\n./planexe create_plan \\\n --plan-file my_plan.txt \\\n --output-dir ./planexe-outputs/1984-12-31/MyCoffeeShop_v1\n```\n\nThe script creates the run directory if it does not exist and writes:\n\n| File | Contents |\n|---|---|\n| `start_time.json` | `{\"server_iso_utc\": \"\"}` |\n| `plan.txt` | The plain-text plan prompt |\n\n---\n\n## For AI Agents\n\nPlanExe is designed as infrastructure for AI agents. If you are an AI agent reading this:\n\n- **Discovery:** Read [`public/llms.txt`](https://github.com/PlanExeOrg/PlanExe/blob/main/public/llms.txt) for service metadata, MCP tools, and endpoints.\n- **MCP endpoint:** `https://mcp.planexe.org/mcp` — connect with an API key from [home.planexe.org](https://home.planexe.org/).\n- **Workflow:** Call `example_prompts` to learn the expected prompt format, draft a detailed prompt (~300-800 words of flowing prose), then call `plan_create`.\n- **Agent guide:** See [`docs/mcp/autonomous_agent_guide.md`](https://github.com/PlanExeOrg/PlanExe/blob/main/docs/mcp/autonomous_agent_guide.md) for the complete autonomous workflow.\n- **Key outputs in zip:** `wbs_level1.json` (work packages), `wbs_level2.json` (tasks), `pre_project_assessment.json` (feasibility).\n\n---\n\n

Run locally with Docker (Click to expand)

\n\n
\n\n**Prerequisite:** Docker with Docker Compose installed; you only need basic [Docker](https://en.wikipedia.org/wiki/Docker_(software)) knowledge. No local Python setup is required because everything runs in containers.\n\n### Follow these steps\n\n1. Clone the repo and enter it:\n\n```bash\ngit clone https://github.com/PlanExeOrg/PlanExe.git\ncd PlanExe\n```\n\n2. Provide an LLM provider. Copy `.env.docker-example` to `.env` and fill in `OPENROUTER_API_KEY` with your key from [OpenRouter](https://openrouter.ai/). 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`.\n\n3. Start the stack (first run builds the images):\n\n```bash\ndocker compose up worker_plan frontend_multi_user\n```\n\n The worker listens on http://localhost:8000 and the UI comes up on http://localhost:5001 after the Postgres and worker healthchecks pass.\n\n4. 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:\n\n```bash\ndocker compose logs -f worker_plan\n```\n\n Outputs are written to `run/` on the host (mounted into both containers).\n\n5. Stop with `Ctrl+C` (or `docker compose down`). Rebuild after code/dependency changes:\n\n```bash\ndocker compose build --no-cache worker_plan frontend_multi_user\n```\n\nFor compose tips, alternate ports, or troubleshooting, see `docs/docker.md` or `docker-compose.md`.\n\n### Configuration\n\n**Config A:** Run a model in the cloud using a paid provider. Follow the instructions in [OpenRouter](https://docs.planexe.org/ai_providers/openrouter/).\n\n**Config B:** Run models locally on a high-end computer. Follow the instructions for either [Ollama](https://docs.planexe.org/ai_providers/ollama/) or [LM Studio](https://docs.planexe.org/ai_providers/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).\n\nRecommendation: I recommend **Config A** as it offers the most straightforward path to getting PlanExe working reliably.\n\n

\n\n---\n\n

Help (Click to expand)

\n\n
\n\nFor help or feedback.\n\nJoin the [PlanExe Discord](https://planexe.org/discord).\n\n

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