Meeseeks Docs

Meeseeks Docs

Meeseeks is an AI task agent assistant that breaks a request into small actions, runs the right tools, and replies with a clean summary. This landing page mirrors the README feature highlights so the overview stays consistent. Update both when core positioning changes.

Documentation map

Overview

• README - high-level product overview and feature highlights

Setup and configuration

• Getting started - environment setup, MCP config, and run commands

Repository map

• Components - monorepo layout and core packages

Reference

• API reference - mkdocstrings reference for core modules

Feature highlights (quick view)

• Plan → act → observe loop to keep work grounded in tool results.
• Multiple interfaces (chat UI, REST API, Home Assistant, terminal CLI) backed by one core engine.
• Tool registry for local tools plus optional MCP tools.
• Built-in local file and shell tools (Aider adapters) for edit blocks, read, list, and shell execution.
• Session transcripts with compaction for long runs and context budget awareness.
• Context snapshots built from recent turns plus summaries of prior activity.
• Step-level reflection after tool execution to validate outcomes.
• Permission gate with approval callbacks plus lightweight hooks around tool execution.
• Optional components (Langfuse, Home Assistant) auto-disable when not configured.
• Langfuse tracing is session-scoped when enabled, grouping multi-turn runs.

Repo map (short)

• packages/meeseeks_core/: orchestration loop, schemas, session storage, compaction, tool registry.
• packages/meeseeks_tools/: tool implementations and integrations.
• apps/meeseeks_api/: Flask API that exposes the assistant over HTTP.
• apps/meeseeks_chat/: Streamlit UI for interactive chat.
• apps/meeseeks_cli/: terminal CLI for interactive sessions.
• meeseeks_ha_conversation/: Home Assistant integration that routes voice requests to the API.

Prompts are packaged under packages/meeseeks_core/src/meeseeks_core/prompts/.

Architecture in a glance

• The UI or API sends a user request into the core orchestrator.
• The orchestrator builds a short action plan, runs tools, and replans if needed.
• Tool results and summaries are stored in a session transcript for continuity.

flowchart LR
  User --> CLI
  User --> Chat
  User --> API
  HA --> API
  CLI --> Core
  Chat --> Core
  API --> Core
  Core --> Planner
  Planner --> Tools
  Tools --> LocalTools
  Tools --> MCP
  Tools --> HomeAssistant
  Core --> SessionStore
  Core --> Langfuse

Getting started

See getting-started.md for full setup (env, MCP, configs, and how to run each interface).

CLI quick commands

• /help show commands
• /models pick a model from your API
• /mcp list MCP servers/tools (use /mcp select to filter)
• /mcp init scaffold an MCP config file
• /summarize compact the session
• /new start a fresh session
• /automatic auto-approve tool actions for the session
• /quit exit the CLI

Deployment (Docker)

See getting-started.md for Docker setup and environment requirements.