plane-agent 1.0.1

Plane MCP Agent

Plane Agent

CLI or API | MCP | Agent

Version: 1.0.1

Documentation — Installation, deployment, usage across the API, CLI, and MCP interfaces, and guidance for provisioning a self-hosted Plane instance are maintained in the official documentation.

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Table of Contents

• Overview
• Key Features
• CLI or API
• MCP
  • Available MCP Tools
  • MCP Configuration Examples
  • Dynamic Tool Selection & Visibility
• Agent
  • Running the Agent CLI
  • Docker Compose Orchestration
• Security & Governance
• Environment Variables
• Installation
• Documentation
• Repository Owners
• Contribute

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Overview

Plane Agent is a production-grade Agent and Model Context Protocol (MCP) server designed to interface directly with Plane MCP Agent.

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Key Features

• Consolidated Action-Routed MCP Tools: Minimizes token overhead and eliminates tool bloat in LLM contexts by grouping methods into optimized, togglable tool modules.
• Enterprise-Grade Security: Comprehensive support for Eunomia policies, OIDC token delegation, and granular execution context tracking.
• Integrated Graph Agent: Built-in Pydantic AI agent supporting the Agent Control Protocol (ACP) and standard Web interfaces (AG-UI).
• Native Telemetry & Tracing: Out-of-the-box OpenTelemetry exports and native Langfuse tracing.

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CLI or API

This agent wraps the Plane MCP Agent API. You can interact with it programmatically or via its integrated execution entrypoints.

Detailed instructions on how to use the underlying API wrappers, extended schema bindings, and developer SDK references are maintained in docs/index.md.

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MCP

This server utilizes dynamic Action-Routed tools to optimize token overhead and maximize IDE compatibility.

Available MCP Tools

This table is auto-generated from the live server — do not edit by hand.

Condensed action-routed tools (default — MCP_TOOL_MODE=condensed)

MCP Tool	Toggle Env Var	Description
plane_cycles	CYCLESTOOL	Manage plane cycles operations.
plane_epics	EPICSTOOL	Manage plane epics operations.
plane_initiatives	INITIATIVESTOOL	Manage plane initiatives operations.
plane_intake	INTAKETOOL	Manage plane intake operations.
plane_labels	LABELSTOOL	Manage plane labels operations.
plane_milestones	MILESTONESTOOL	Manage plane milestones operations.
plane_modules	MODULESTOOL	Manage plane modules operations.
plane_pages	PAGESTOOL	Manage plane pages operations.
plane_projects	PROJECTSTOOL	Manage plane projects operations.
plane_states	STATESTOOL	Manage plane states operations.
plane_users	USERSTOOL	Manage plane users operations.
plane_work_items	WORK_ITEMSTOOL	Manage plane work items operations.
plane_workspaces	WORKSPACESTOOL	Manage plane workspaces operations.

Verbose 1:1 API-mapped tools (MCP_TOOL_MODE=verbose or both)

102 per-operation tools — one per public API method (click to expand)

MCP Tool	Toggle Env Var	Description
plane_add_work_items_to_cycle	APITOOL	Add work items to a cycle.
plane_add_work_items_to_milestone	APITOOL	Add work items to a milestone.
plane_add_work_items_to_module	APITOOL	Add work items to a module.
plane_advanced_search_work_items	APITOOL	Advanced search for work items.
plane_archive_module	APITOOL	Archive a module.
plane_create_cycle	APITOOL	Create a new cycle.
plane_create_epic	APITOOL	Create a new epic (technically a work item with epic type).
plane_create_initiative	APITOOL	Create a new initiative in the workspace.
plane_create_intake_work_item	APITOOL	Create a new intake work item in a project.
plane_create_label	APITOOL	Create a new label.
plane_create_milestone	APITOOL	Create a new milestone.
plane_create_module	APITOOL	Create a new module.
plane_create_project_page	APITOOL	Create a new project page.
plane_create_state	APITOOL	Create a new state.
plane_create_work_item	APITOOL	Create a new work item.
plane_create_work_item_comment	APITOOL	Create a comment for a work item.
plane_create_work_item_link	APITOOL	Create a link for a work item.
plane_create_work_item_property	APITOOL	Create a new work item property.
plane_create_work_item_relation	APITOOL	Create relations for a work item.
plane_create_work_item_type	APITOOL	Create a new work item type.
plane_create_work_log	APITOOL	Create a work log for a work item.
plane_delete_cycle	APITOOL	Delete a cycle by ID.
plane_delete_epic	APITOOL	Delete an epic by ID.
plane_delete_initiative	APITOOL	Delete an initiative by ID.
plane_delete_intake_work_item	APITOOL	Delete an intake work item by work item ID.
plane_delete_milestone	APITOOL	Delete a milestone by ID.
plane_delete_module	APITOOL	Delete a module by ID.
plane_delete_project	APITOOL	Delete a project by ID.
plane_delete_state	APITOOL	Delete a state by ID.
plane_delete_work_item	APITOOL	Delete a work item by ID.
plane_delete_work_item_comment	APITOOL	Delete a comment for a work item.
plane_delete_work_item_link	APITOOL	Delete a link for a work item.
plane_delete_work_item_property	APITOOL	Delete a work item property by ID.
plane_delete_work_item_type	APITOOL	Delete a work item type by ID.
plane_delete_work_log	APITOOL	Delete a work log for a work item.
plane_get_me	APITOOL	Get current user information.
plane_get_project_features	APITOOL	Get features of a project.
plane_get_project_members	APITOOL	Get all members of a project.
plane_get_project_worklog_summary	APITOOL	Get work log summary for a project.
plane_get_workspace	APITOOL	Get current workspace details.
plane_get_workspace_features	APITOOL	Get features of the current workspace.
plane_get_workspace_members	APITOOL	Get all members of the current workspace.
plane_list_archived_cycles	APITOOL	List archived cycles in a project.
plane_list_archived_modules	APITOOL	List archived modules in a project.
plane_list_cycle_work_items	APITOOL	List work items in a cycle.
plane_list_cycles	APITOOL	List all cycles in a project.
plane_list_epics	APITOOL	List all epics in a project.
plane_list_initiatives	APITOOL	List all initiatives in the workspace.
plane_list_intake_work_items	APITOOL	List all intake work items in a project.
plane_list_labels	APITOOL	List all labels in a project.
plane_list_milestone_work_items	APITOOL	List work items in a milestone.
plane_list_milestones	APITOOL	List all milestones in a project.
plane_list_module_work_items	APITOOL	List work items in a module.
plane_list_modules	APITOOL	List all modules in a project.
plane_list_projects	APITOOL	List all projects in the workspace.
plane_list_states	APITOOL	List all states in a project.
plane_list_users	APITOOL	List all users in the workspace.
plane_list_work_item_activities	APITOOL	List activities for a work item.
plane_list_work_item_comments	APITOOL	List comments for a work item.
plane_list_work_item_links	APITOOL	List links for a work item.
plane_list_work_item_properties	APITOOL	List work item properties for a work item type.
plane_list_work_item_relations	APITOOL	List relations for a work item.
plane_list_work_item_types	APITOOL	List work item types in a project.
plane_list_work_items	APITOOL	List work items in a project.
plane_list_work_logs	APITOOL	List work logs for a work item.
plane_remove_work_item_from_cycle	APITOOL	Remove a work item from a cycle.
plane_remove_work_item_from_module	APITOOL	Remove a work item from a module.
plane_remove_work_item_relation	APITOOL	Remove a relation from a work item.
plane_remove_work_items_from_milestone	APITOOL	Remove work items from a milestone.
plane_retrieve_cycle	APITOOL	Retrieve a cycle by ID.
plane_retrieve_epic	APITOOL	Retrieve an epic by ID.
plane_retrieve_initiative	APITOOL	Retrieve an initiative by ID.
plane_retrieve_intake_work_item	APITOOL	Retrieve an intake work item by work item ID.
plane_retrieve_milestone	APITOOL	Retrieve a milestone by ID.
plane_retrieve_module	APITOOL	Retrieve a module by ID.
plane_retrieve_project	APITOOL	Retrieve a project by ID.
plane_retrieve_project_page	APITOOL	Retrieve a project page by ID.
plane_retrieve_state	APITOOL	Retrieve a state by ID.
plane_retrieve_work_item	APITOOL	Retrieve a work item by ID.
plane_retrieve_work_item_activity	APITOOL	Retrieve a specific activity for a work item.
plane_retrieve_work_item_by_identifier	APITOOL	Retrieve a work item by project identifier and issue sequence number.
plane_retrieve_work_item_comment	APITOOL	Retrieve a specific comment for a work item.
plane_retrieve_work_item_link	APITOOL	Retrieve a specific link for a work item.
plane_retrieve_work_item_property	APITOOL	Retrieve a work item property by ID.
plane_search_work_items	APITOOL	Search work items across a workspace.
plane_transfer_cycle_work_items	APITOOL	Transfer work items from one cycle to another.
plane_unarchive_module	APITOOL	Unarchive a module.
plane_update_cycle	APITOOL	Update a cycle by ID.
plane_update_epic	APITOOL	Update an epic by ID.
plane_update_initiative	APITOOL	Update an initiative by ID.
plane_update_intake_work_item	APITOOL	Update an intake work item by work item ID.
plane_update_milestone	APITOOL	Update a milestone by ID.
plane_update_module	APITOOL	Update a module by ID.
plane_update_project_features	APITOOL	Update features of a project.
plane_update_state	APITOOL	Update a state by ID.
plane_update_work_item	APITOOL	Update a work item by ID.
plane_update_work_item_comment	APITOOL	Update a comment for a work item.
plane_update_work_item_link	APITOOL	Update a link for a work item.
plane_update_work_item_property	APITOOL	Update a work item property by ID.
plane_update_work_item_type	APITOOL	Update a work item type by ID.
plane_update_work_log	APITOOL	Update a work log for a work item.
plane_update_workspace_features	APITOOL	Update features of the current workspace.

13 action-routed tool(s) (default) · 102 verbose 1:1 tool(s). Each is enabled unless its <DOMAIN>TOOL toggle is set false; MCP_TOOL_MODE selects the surface (condensed default · verbose 1:1 · both). Auto-generated — do not edit.

Detailed tool schemas, parameter shapes, and validation constraints are preserved in docs/index.md.

Dynamic Tool Selection & Visibility

This MCP server supports dynamic toolset selection and visibility filtering at runtime. This allows you to restrict the set of exposed tools in order to prevent blowing up the LLM's context window.

You can configure tool filtering via multiple input channels:

• CLI Arguments: Pass --tools or --toolsets (or their disabled counterparts --disabled-tools and --disabled-toolsets) during startup.
• Environment Variables: Define standard environment variables:
  • MCP_ENABLED_TOOLS / MCP_DISABLED_TOOLS
  • MCP_ENABLED_TAGS / MCP_DISABLED_TAGS
• HTTP SSE Request Headers: Pass custom headers during transport initialization:
  • x-mcp-enabled-tools / x-mcp-disabled-tools
  • x-mcp-enabled-tags / x-mcp-disabled-tags
• HTTP SSE Request Query Parameters: Append query parameters directly to your transport connection URL:
  • ?tools=tool1,tool2
  • ?tags=tag1

When query strings or parameters are supplied, an LLM-free Knowledge Graph resolution layer (using DynamicToolOrchestrator) matches query intents against known tool tags, names, or descriptions, with safe fallback and automated 24-hour background cache refreshing.

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MCP Configuration Examples

Install the slim [mcp] extra. All examples install plane-agent[mcp] — the MCP-server extra that pulls only the FastMCP / FastAPI tooling (agent-utilities[mcp]). It deliberately excludes the heavy agent runtime (pydantic-ai, the epistemic-graph engine, dspy, llama-index), so uvx / container installs are far smaller. Use the full [agent] extra only when you need the integrated Pydantic AI agent.

stdio Transport (local IDEs — Cursor, Claude Desktop, VS Code)

{
  "mcpServers": {
    "plane-mcp": {
      "command": "uvx",
      "args": [
        "--from",
        "plane-agent[mcp]",
        "plane-mcp"
      ],
      "env": {
        "MCP_TOOL_MODE": "condensed",
        "CYCLESTOOL": "True",
        "EPICSTOOL": "True",
        "INITIATIVESTOOL": "True",
        "INTAKETOOL": "True",
        "LABELSTOOL": "True",
        "MILESTONESTOOL": "True",
        "MODULESTOOL": "True",
        "PAGESTOOL": "True",
        "PLANE_API_KEY": "your_plane_api_key_here",
        "PLANE_BASE_URL": "https://api.plane.so",
        "PLANE_WORKSPACE_SLUG": "",
        "PROJECTSTOOL": "True",
        "STATESTOOL": "True",
        "USERSTOOL": "True",
        "WORKSPACESTOOL": "True",
        "WORK_ITEMSTOOL": "True"
      }
    }
  }
}

Streamable-HTTP Transport (networked / production)

{
  "mcpServers": {
    "plane-mcp": {
      "command": "uvx",
      "args": [
        "--from",
        "plane-agent[mcp]",
        "plane-mcp",
        "--transport",
        "streamable-http",
        "--port",
        "8000"
      ],
      "env": {
        "TRANSPORT": "streamable-http",
        "HOST": "0.0.0.0",
        "PORT": "8000",
        "MCP_TOOL_MODE": "condensed",
        "CYCLESTOOL": "True",
        "EPICSTOOL": "True",
        "INITIATIVESTOOL": "True",
        "INTAKETOOL": "True",
        "LABELSTOOL": "True",
        "MILESTONESTOOL": "True",
        "MODULESTOOL": "True",
        "PAGESTOOL": "True",
        "PLANE_API_KEY": "your_plane_api_key_here",
        "PLANE_BASE_URL": "https://api.plane.so",
        "PLANE_WORKSPACE_SLUG": "",
        "PROJECTSTOOL": "True",
        "STATESTOOL": "True",
        "USERSTOOL": "True",
        "WORKSPACESTOOL": "True",
        "WORK_ITEMSTOOL": "True"
      }
    }
  }
}

Alternatively, connect to a pre-deployed Streamable-HTTP instance by url:

{
  "mcpServers": {
    "plane-mcp": {
      "url": "http://localhost:8000/plane-mcp/mcp"
    }
  }
}

Deploying the Streamable-HTTP server via Docker:

docker run -d \
  --name plane-mcp-mcp \
  -p 8000:8000 \
  -e TRANSPORT=streamable-http \
  -e HOST=0.0.0.0 \
  -e PORT=8000 \
  -e MCP_TOOL_MODE=condensed \
  -e CYCLESTOOL=True \
  -e EPICSTOOL=True \
  -e INITIATIVESTOOL=True \
  -e INTAKETOOL=True \
  -e LABELSTOOL=True \
  -e MILESTONESTOOL=True \
  -e MODULESTOOL=True \
  -e PAGESTOOL=True \
  -e PLANE_API_KEY=your_plane_api_key_here \
  -e PLANE_BASE_URL=https://api.plane.so \
  -e PLANE_WORKSPACE_SLUG="" \
  -e PROJECTSTOOL=True \
  -e STATESTOOL=True \
  -e USERSTOOL=True \
  -e WORKSPACESTOOL=True \
  -e WORK_ITEMSTOOL=True \
  knucklessg1/plane-agent:mcp

Auto-generated from the code-read env surface (MCP_TOOL_MODE + package vars) — do not edit.

Additional Deployment Options

plane-agent can also run as a local container (Docker / Podman / uv) or be consumed from a remote deployment. The Deployment guide has full, copy-paste mcp_config.json for all four transports — stdio, streamable-http, local container / uv, and remote URL:

• Local container / uv — launch the server from mcp_config.json via uvx, docker run, or podman run, or point at a local streamable-http container by url.
• Remote URL — connect to a server deployed behind Caddy at http://plane-mcp.arpa/mcp using the "url" key.

Agent

This repository features a fully integrated Pydantic AI Graph Agent. It communicates over the Agent Control Protocol (ACP) and interacts seamlessly with the Agent Web UI (AG-UI) and Terminal interface.

Running the Agent CLI

To start the interactive command-line agent:

# Set credentials
export PLANE_BASE_URL="your_value"
export PLANE_WORKSPACE_SLUG="your_value"
export DEBUG="your_value"
export PYTHONUNBUFFERED="your_value"
export PLANE_API_KEY="your_value"

# Run the agent server
plane-agent --provider openai --model-id gpt-4o

Docker Compose Orchestration

The following docker/agent.compose.yml configures the Agent, Web UI, and Terminal Interface together:

version: '3.8'

services:
  plane-agent-mcp:
    image: knucklessg1/plane-agent:mcp
    container_name: plane-agent-mcp
    hostname: plane-agent-mcp
    restart: always
    env_file:
      - ../.env
    environment:
      - PYTHONUNBUFFERED=1
      - HOST=0.0.0.0
      - PORT=8000
      - TRANSPORT=streamable-http
    ports:
      - "8000:8000"
    healthcheck:
      test: ["CMD", "python3", "-c", "import urllib.request; urllib.request.urlopen('http://localhost:8000/health')"]
      interval: 30s
      timeout: 10s
      retries: 3
      start_period: 10s
    logging:
      driver: json-file
      options:
        max-size: "10m"
        max-file: "3"

  plane-agent-agent:
    image: knucklessg1/plane-agent:latest
    container_name: plane-agent-agent
    hostname: plane-agent-agent
    restart: always
    depends_on:
      - plane-agent-mcp
    env_file:
      - ../.env
    command: [ "plane-agent" ]
    environment:
      - PYTHONUNBUFFERED=1
      - HOST=0.0.0.0
      - PORT=9004
      - MCP_URL=http://plane-agent-mcp:8000/mcp
      - PROVIDER=${PROVIDER:-openai}
      - MODEL_ID=${MODEL_ID:-gpt-4o}
      - ENABLE_WEB_UI=True
      - ENABLE_OTEL=True
    ports:
      - "9004:9004"
    healthcheck:
      test: ["CMD", "python3", "-c", "import urllib.request; urllib.request.urlopen('http://localhost:9004/health')"]
      interval: 30s
      timeout: 10s
      retries: 3
      start_period: 10s
    logging:
      driver: json-file
      options:
        max-size: "10m"
        max-file: "3"

Detailed graph node architecture explanations, custom skill configurations, and agentic trace guides are available in docs/overview.md and docs/index.md.

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Security & Governance

Built directly upon the enterprise-ready agent-utilities core, standard security parameters are fully supported:

Access Control & Policy Enforcement

• Eunomia Policies: Fine-grained, policy-driven tool authorization. Supports none, local embedded (mcp_policies.json), or centralized remote modes.
• OIDC Token Delegation: Compliant with RFC 8693 token exchange for flowing authenticating user credentials from Web UI / ACP → Agent → MCP.
• Scoped Credentials: Execution context runs restricted to the specific caller identity.

Runtime Security Grid

Feature	Functionality	Enablement
Tool Guard	Sensitivity inspection with human-in-the-loop validation	Enabled by default
Prompt Injection Defense	Input scanning, repetition monitoring, and recursive loop blocks	Enabled by default
Context Safety Guard	Stuck-loop detectors and contextual overflow preemptive alerts	Enabled by default

Environment Variables

Package environment variables

Variable	Example	Description
HOST	0.0.0.0
PORT	8000
TRANSPORT	stdio	options: stdio, streamable-http, sse
ENABLE_OTEL	True
OTEL_EXPORTER_OTLP_ENDPOINT	http://localhost:8080/api/public/otel
OTEL_EXPORTER_OTLP_PUBLIC_KEY	pk-...
OTEL_EXPORTER_OTLP_SECRET_KEY	sk-...
OTEL_EXPORTER_OTLP_PROTOCOL	http/protobuf
EUNOMIA_TYPE	none	options: none, embedded, remote
EUNOMIA_POLICY_FILE	mcp_policies.json
EUNOMIA_REMOTE_URL	http://eunomia-server:8000
PLANE_BASE_URL	https://api.plane.so
PLANE_WORKSPACE_SLUG	—
DEBUG	False
PYTHONUNBUFFERED	1
PLANE_API_KEY	your_plane_api_key_here
PROJECTSTOOL	True
WORK_ITEMSTOOL	True
CYCLESTOOL	True
EPICSTOOL	True
MILESTONESTOOL	True
MODULESTOOL	True
STATESTOOL	True
USERSTOOL	True
WORKSPACESTOOL	True
INITIATIVESTOOL	True
INTAKETOOL	True
LABELSTOOL	True
PAGESTOOL	True
DEFAULT_AGENT_NAME	plane-agent
AGENT_UTILITIES_TESTING	False
GRAPH_BACKEND	networkx
LLM_API_KEY	—
LLM_BASE_URL	—
MCP_URL	http://localhost:8000/mcp
MODEL_ID	gpt-4o

Inherited agent-utilities variables (apply to every connector)

Variable	Example	Description
MCP_TOOL_MODE	condensed	Tool surface: condensed
MCP_ENABLED_TOOLS	—	Comma-separated tool allow-list
MCP_DISABLED_TOOLS	—	Comma-separated tool deny-list
MCP_ENABLED_TAGS	—	Comma-separated tag allow-list
MCP_DISABLED_TAGS	—	Comma-separated tag deny-list
MCP_CLIENT_AUTH	—	Outbound MCP auth (oidc-client-credentials for fleet calls)
OIDC_CLIENT_ID	—	OIDC client id (service-account auth)
OIDC_CLIENT_SECRET	—	OIDC client secret (service-account auth)
PROVIDER	openai	LLM provider for the agent
ENABLE_WEB_UI	True	Serve the AG-UI web interface

36 package + 10 inherited variable(s). Auto-generated from .env.example + the shared agent-utilities set — do not edit.

The Plane Agent supports the following environment variables for configuration and integration:

Variable	Description
PLANE_BASE_URL	The base URL of the Plane instance.
PLANE_WORKSPACE_SLUG	The workspace slug of the Plane workspace.
PLANE_API_KEY	The API key for authentication with Plane.
MCP_URL	The URL of the MCP server.
MODEL_ID	Default LLM model identifier (e.g. gpt-4o).
PROVIDER	The LLM provider (e.g. openai, anthropic).
ENABLE_WEB_UI	Set to True to enable the built-in Web UI.
ENABLE_OTEL	Set to True to enable OpenTelemetry telemetry.
AGENT_UTILITIES_TESTING	Set to True during testing to bypass production setups.
AUTH_TYPE	The authentication type to use (e.g., jwt, none).
DEFAULT_API_KEY	Default API key for fast server fallback authentication.
OTEL_EXPORTER_OTLP_ENDPOINT	The OpenTelemetry OTLP endpoint.
PROJECTSTOOL	Set to True/False to toggle the Projects tool module.
WORK_ITEMSTOOL	Set to True/False to toggle the Work Items tool module.
CYCLESTOOL	Set to True/False to toggle the Cycles tool module.
EPICSTOOL	Set to True/False to toggle the Epics tool module.
MILESTONESTOOL	Set to True/False to toggle the Milestones tool module.
MODULESTOOL	Set to True/False to toggle the Modules tool module.
STATESTOOL	Set to True/False to toggle the States tool module.
USERSTOOL	Set to True/False to toggle the Users tool module.
WORKSPACESTOOL	Set to True/False to toggle the Workspaces tool module.
INITIATIVESTOOL	Set to True/False to toggle the Initiatives tool module.
INTAKETOOL	Set to True/False to toggle the Intake tool module.
LABELSTOOL	Set to True/False to toggle the Labels tool module.
PAGESTOOL	Set to True/False to toggle the Pages tool module.

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Installation

Pick the extra that matches what you want to run:

Extra	Installs	Use when
plane-agent[mcp]	Slim MCP server only (agent-utilities[mcp] — FastMCP/FastAPI)	You only run the MCP server (smallest install / image)
plane-agent[agent]	Full agent runtime (agent-utilities[agent,logfire] — Pydantic AI + the epistemic-graph engine)	You run the integrated agent
plane-agent[all]	Everything (mcp + agent + logfire)	Development / both surfaces

# MCP server only (recommended for tool hosting — slim deps)
uv pip install "plane-agent[mcp]"

# Full agent runtime (Pydantic AI + epistemic-graph engine)
uv pip install "plane-agent[agent]"

# Everything (development)
uv pip install "plane-agent[all]"      # or: python -m pip install "plane-agent[all]"

Container images (:mcp vs :agent)

One multi-stage docker/Dockerfile builds two right-sized images, selected by --target:

Image tag	Build target	Contents	Entrypoint
knucklessg1/plane-agent:mcp	--target mcp	plane-agent[mcp] — slim, no engine/pydantic-ai/dspy/llama-index/tree-sitter	plane-mcp
knucklessg1/plane-agent:latest	--target agent (default)	plane-agent[agent] — full agent runtime + epistemic-graph engine	plane-agent

docker build --target mcp   -t knucklessg1/plane-agent:mcp    docker/   # slim MCP server
docker build --target agent -t knucklessg1/plane-agent:latest docker/   # full agent

docker/mcp.compose.yml runs the slim :mcp server; docker/agent.compose.yml runs the agent (:latest) with a co-located :mcp sidecar.

Knowledge-graph database (epistemic-graph)

The full agent ([agent] / :latest) embeds the epistemic-graph engine (pulled in transitively via agent-utilities[agent]). For production — or to share one knowledge graph across multiple agents — run epistemic-graph as its own database container and point the agent at it instead of embedding it. Deployment recipes (single-node + Raft HA), connection config, and the full database architecture (with diagrams) are documented in the epistemic-graph deployment guide. The slim [mcp] server does not require the database.

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Documentation

The complete documentation is published as the official documentation site and is the recommended reference for installation, deployment, and day-to-day operation.

Page	Contents
Installation	pip, source, uv, prebuilt Docker image
Deployment	run the MCP server and agent, Compose, Caddy + Technitium, env config
Usage	the MCP tools, the Api client, the CLI
Backing Platform	deploy a self-hosted Plane instance with Docker
Overview	ecosystem role, enterprise posture, architecture
Concepts	concept registry (CONCEPT:PLANE-*)

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Repository Owners

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• Format code using ruff format .
• Lint code using ruff check .
• Validate type-safety with mypy .
• Execute test suites using pytest

Deploy with agent-os-genesis

This package can be provisioned for you — skill-guided — by the agent-os-genesis universal skill (its single-package deploy mode): it picks your install method, seeds secrets to OpenBao/Vault (or .env), trusts your enterprise CA, registers the MCP server, and verifies it — the same machinery that stands up the whole Agent OS, narrowed to just this package. Ask your agent to "deploy plane-agent with agent-os-genesis".

Install mode	Command
Bare-metal, prod (PyPI)	uvx plane-mcp · or uv tool install plane-agent
Bare-metal, dev (editable)	uv pip install -e ".[all]" · or pip install -e ".[all]"
Container, prod	deploy knucklessg1/plane-agent:latest via docker-compose / swarm / podman / podman-compose / kubernetes
Container, dev (editable)	deploy docker/compose.dev.yml (source-mounted at /src; edits live on restart)

Secrets are read-existing + seeded via vault_sync — you are only prompted for what's missing.