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A powerful AI agentic system

Project description

Captain Claw

Python License Interface Models

Captain Claw

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An open-source AI agent with multi-agent orchestration, autonomous cognitive systems, and a full management dashboard. Runs locally, supports every major LLM provider, and ships with 44 built-in tools.

What Makes Captain Claw Different

Flight Deck — Multi-Agent Command Center

A full management dashboard for running teams of AI agents. Spawn, monitor, configure, and coordinate agents from a single UI.

captain-claw-fd    # http://0.0.0.0:25080
  • Agent Forge — Describe a business goal in plain text. An LLM designs a specialized team with roles, tools, operating procedures, and a lead coordinator. Review, customize, and spawn the entire team in one click.
  • Agent Council — Structured multi-agent deliberation. Run brainstorms, debates, reviews, or planning sessions with 2-N agents. Each agent self-scores suitability, chooses actions (answer, challenge, refine, broaden), and responds in moderated rounds. A moderator synthesizes conclusions; all agents vote. Export as markdown minutes.
  • Fleet Communication — Agents discover peers automatically. Consult (synchronous ask) or delegate (asynchronous queue) tasks to specialist agents. Shared workspace and file transfer across the fleet.
  • Director Panel — Unified overview of all agents. Broadcast messages fleet-wide. Per-agent token/cost analytics, trace timelines, datastore browser, file browser, config editor.
  • Multi-user Auth — JWT authentication, admin dashboard, rate limiting, and quotas.

Cognitive Architecture

Captain Claw has a five-layer memory system and autonomous cognitive processes that run without user intervention.

Memory Layers:

Layer What it stores How it's used
Working Memory Current conversation in the LLM context window Immediate reasoning
Semantic Memory Hybrid vector + BM25 full-text search over documents and sessions Auto-injected when relevant to the current query
Deep Memory Typesense-backed long-term archive, scales to millions of documents Searched on demand for deep recall
Insights Auto-extracted facts, contacts, decisions, and deadlines (SQLite + FTS5) Cross-session knowledge injected into system prompt
Nervous System Autonomous "intuitions" — patterns, hypotheses, and connections Surfaces non-obvious findings the agent wouldn't otherwise notice

Autonomous Processes:

  • Dreaming — Background dream cycles cross-reference all memory layers to synthesize intuitions. Runs after every N messages and during idle hours. Intuitions have confidence scores that decay over time unless validated.
  • Tension Tracking — Holds unresolved contradictions (like musical dissonance) rather than forcing premature resolution. Tensions persist until evidence resolves them.
  • Maturation Pipeline — New intuitions sit through multiple dream cycles before being surfaced to the agent, reducing noise.
  • Cognitive Tempo — Detects whether the user is in deep contemplative mode or rapid task execution, and adapts processing depth accordingly (adagio / moderato / allegro).
  • Cognitive Modes — Seven tunable behavioral profiles (Ionian through Locrian, inspired by musical scales) that shift the agent between analytical, creative, cautious, and exploratory approaches.
  • Self-Reflection — Periodic self-assessment that reviews conversations, memory, and completed tasks to generate improvement directives injected into the system prompt.
  • Insights Extraction — Automatically identifies durable knowledge from conversations — deduplicates, categorizes, and stores for future context injection.

Visualization:

  • Brain Graph — Interactive 3D force-directed graph of the entire cognitive topology. Insights, intuitions, tasks, contacts, and sessions rendered as typed nodes with provenance edges. Live WebSocket updates.
  • Process of Thoughts — Full lineage tracking across all cognitive subsystems. Every message, insight, intuition, and task is connected via provenance IDs, forming a traversable thought graph.

Orchestrator / DAG Mode

Decompose complex tasks into a dependency graph and execute subtasks in parallel across separate agent sessions.

/orchestrate Research startups in 3 countries, analyze founders, create comparison spreadsheet
  • LLM decomposes the prompt into a task DAG with dependencies
  • Parallel execution with configurable worker count
  • Shared workspace for inter-task data flow
  • Structured output validation (JSON Schema with auto-retry)
  • Real-time trace timeline (Gantt-style visualization)
  • Headless CLI mode for cron/scripts: captain-claw-orchestrate

BotPort — Agent-to-Agent Network

Connect multiple Captain Claw instances through a routing hub. Agents delegate tasks to specialists based on expertise tags, persona matching, or LLM-powered routing.

  • BotPort Swarm — DAG-based multi-agent orchestration across networked instances. Approval gates, retry with fallback, checkpointing, inter-agent file transfer (up to 50 MB), cron scheduling, and a visual dashboard.

MCP Server

Captain Claw runs as a Model Context Protocol server over stdio — Claude Desktop and other MCP clients can browse sessions, read conversation history, and send prompts to the full agent.

captain-claw-mcp    # stdio, configure in claude_desktop_config.json

Safety Guards

Three layers of protection that run before, during, and after agent operations:

  • Input guards — Validate user intent before the LLM sees it
  • Script guards — AST-level analysis of generated code before execution
  • Output guards — Validate tool results for hallucinations and safety

Guards support two modes: stop_suspicious (block automatically) or ask_for_approval (prompt the user).

Multi-Model Support

Mix providers freely — each session independently selects its model.

Provider Models
OpenAI GPT-5.4, GPT-5.4-mini, GPT-5.4-nano, o3, o4-mini, gpt-image-1.5
Anthropic Claude Opus 4.6, Sonnet 4.6, Haiku 4.5 (with prompt caching)
Google Gemini 3.1 Pro/Flash, Gemini 2.5 Pro/Flash
Ollama Any local model
OpenRouter 200+ models via meta-router

Quick Start

pip install captain-claw
export OPENAI_API_KEY="sk-..."          # or ANTHROPIC_API_KEY, GEMINI_API_KEY, etc.
captain-claw-web                         # http://127.0.0.1:23080
captain-claw-web          # Web UI (default)
captain-claw              # Interactive terminal
captain-claw --tui        # Terminal UI
captain-claw-fd           # Flight Deck multi-agent dashboard
captain-claw-mcp          # MCP server for Claude Desktop
botport                   # Agent-to-agent routing hub

First run starts onboarding automatically. For Ollama, no key needed — set provider: ollama in config.yaml.

44 Built-in Tools

Shell, file I/O, web fetch/search, browser automation, PDF/DOCX/XLSX/PPTX extraction, image generation (DALL-E), OCR, vision, TTS, STT, email (SMTP/Mailgun/SendGrid), Google Workspace (Drive, Docs, Sheets, Slides, Gmail, Calendar), desktop automation, screen capture with voice commands, persistent cross-session memory (todos, contacts, scripts, APIs, playbooks), datastore (SQLite tables with protection rules), deep memory (Typesense), personality system, cron scheduling, BotPort fleet discovery, and Termux (Android).

See USAGE.md for the full reference.

Web UI

Chat, Computer (retro-themed research workspace with 14 themes), monitor pane, instruction editor, command palette, persona selector, datastore browser, deep memory dashboard, insights browser, nervous system browser, Brain Graph 3D visualization, reflections dashboard, personality editor, playbook editor, and LLM usage analytics.

Computer — A standalone research workspace at /computer with themed visual generation, exploration trees, folder browser (local + Google Drive), file attachments, PDF export, and public mode with BYOK (Bring Your Own Key).

Docker

docker pull kstevica/captain-claw:latest
docker run -d -p 23080:23080 \
  -v $(pwd)/config.yaml:/app/config.yaml:ro \
  -v $(pwd)/.env:/app/.env:ro \
  -v $(pwd)/docker-data/home-config:/root/.captain-claw \
  -v $(pwd)/docker-data/workspace:/data/workspace \
  kstevica/captain-claw:latest

See README_DETAILED.md for Docker Compose and persistent data setup.

Configuration

YAML-driven with environment variable overrides (CLAW_ prefix).

model:
  provider: gemini
  model: gemini-2.5-flash
  allowed:
    - id: claude-sonnet
      provider: anthropic
      model: claude-sonnet-4-20250514
    - id: gpt-4o
      provider: openai
      model: gpt-4o

web:
  enabled: true
  port: 23080

Load precedence: ./config.yaml > ~/.captain-claw/config.yaml > env vars > .env > defaults.

Full reference: USAGE.md (23 config sections).

Architecture

Component Path
Agent (14-mixin composition) captain_claw/agent.py
LLM providers captain_claw/llm/
44 tools + registry captain_claw/tools/
Flight Deck (FastAPI + React) captain_claw/flight_deck/
DAG orchestrator captain_claw/session_orchestrator.py
Semantic memory (vector + BM25) captain_claw/semantic_memory.py
Deep memory (Typesense) captain_claw/deep_memory.py
Insights (fact extraction) captain_claw/insights.py
Nervous system (dreaming) captain_claw/nervous_system.py
Cognitive tempo captain_claw/cognitive_tempo.py
MCP server captain_claw/mcp_serve.py
BotPort client captain_claw/botport_client.py
Web UI + REST API captain_claw/web/
Prompt templates (~100 files) captain_claw/instructions/
Config (Pydantic) captain_claw/config.py

Documentation

  • USAGE.md — Complete reference for all commands, tools, config, and features
  • README_DETAILED.md — Extended README with feature-by-feature breakdown

License

MIT

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