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Castor

The Universal 245K Agent Microkernel for Local LLMs.

Install / Use

claude mcp add ApatheticMioz -- npx -y github:ApatheticMioz/Castor

If the server publishes to npm under a different name, use that package instead — check the repo README.

About this skill
🔌

MCP Server

Model Context Protocol server

Quality Score

76/100

Supported Platforms

Claude Code
Claude Desktop

Our assessment of Castor

Castor scores 76/100 on our quality scale, 824th of 956 AI & Machine Learning skills we index.

Its MCP Server is 9.0 KB long, well organised into 36 sections with 10 code examples: a thorough specification that gives an agent plenty to work with.

It has 10 GitHub stars, so there is little community track record yet; judge it on its content.

Substance
29/30
Structure
20/20
Description
8/15
Adoption
4/20
Freshness
15/15

Maintenance, license and trust

  • The repository was last updated today, so Castor is actively maintained.
  • It is released under AGPL-3.0, a copyleft license: you can use it, but modified versions you distribute must carry the same license.
  • Its trust signals score 92/100, with 1 caution from licensing, adoption, age or documentation. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.

Castor compared with similar skills

All 4 of these similar skills score higher than Castor; compare them before choosing.

SkillScoreStarsUpdatedFormat
Castor (this skill)by ApatheticMioz7610todayMCP Server
claude-memby thedotmack10099.0ktodayCLAUDE.md
Agent-Reachby Panniantong10094.8k1d agoCLAUDE.md
Understand-Anythingby Egonex-AI10085.8ktodayCLAUDE.md
headroomby headroomlabs-ai10074.8ktodayCLAUDE.md

Frequently asked questions

How do I install Castor?
Run claude mcp add ApatheticMioz -- npx -y github:ApatheticMioz/Castor. The install tabs above show the steps for each supported agent.
Which AI agents does Castor work with?
It is written for Claude Code and Claude Desktop, as a MCP Server file. Other agents that read the same format can often use it too.
Is Castor safe to use?
It is AGPL-3.0-licensed and scores 92/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
Is Castor still maintained?
The repository was last updated today, so Castor is actively maintained.

Castor

Pair cloud orchestrators (Claude, Gemini, Antigravity) with local execution models (Ollama, vLLM, LM Studio) via MCP — offloading AST surgery, file operations, and test loops at $0 token cost.

CI npm Rust License: AGPL-3.0 Test Gate: 327/327


Quickstart

1. Install & Register (1 Command)

Register Castor automatically with all detected MCP clients (Claude Code, Claude Desktop, Antigravity IDE):

# Run directly via npx
npx -y mcp-castor install --client all

# Or install globally
npm install -g mcp-castor
castor install --client all

Or configure manually in your MCP client configuration (claude_desktop_config.json or mcp_config.json):

{
  "mcpServers": {
    "castor": {
      "command": "npx",
      "args": ["-y", "mcp-castor", "mcp"]
    }
  }
}

2. Connect Your Local Model

Castor connects to any OpenAI-compatible local endpoint. By default, it looks for http://127.0.0.1:18020/v1:

# Ollama (port 11434)
export CASTOR_BASE_URL="http://127.0.0.1:11434/v1"
export CASTOR_MODEL="qwen2.5-coder:32b"

# LM Studio (port 1234)
export CASTOR_BASE_URL="http://127.0.0.1:1234/v1"
export CASTOR_MODEL="qwen3.8-27b"

# vLLM (default port 18020)
export CASTOR_BASE_URL="http://127.0.0.1:18020/v1"
export CASTOR_MODEL="Qwen3.8-27B"

Configuration persists in ~/.castor/config.json and respects CASTOR_* environment variable overrides.


System Architecture

Cloud coding agents (Claude Code, Gemini 3.8 Flash, Cursor) excel at system-level reasoning and planning, but reading thousands of lines of repository context or executing repetitive edit-and-test loops inflates cloud token costs and latency.

Castor provides an in-process Rust microkernel as a Model Context Protocol (MCP) server. It splits work between cloud planning and local hands-on execution:

sequenceDiagram
    autonumber
    actor Dev as Developer
    participant Cloud as Cloud Architect (Claude / Gemini)
    participant Castor as Castor Microkernel (Rust)
    participant Local as Local Model (Ollama / vLLM / LM Studio)

    Dev->>Cloud: High-level architectural task
    Cloud->>Castor: castor_coworker(prompt, workspace)
    activate Castor
    loop In-Tree Autonomous Loop ($0 token cost)
        Castor->>Local: Prompt + In-Tree Tool Definitions
        Local-->>Castor: Tool Calls (ast_replace, bash, read_file)
        Castor-->>Local: Tool Outputs + In-Memory AST Validation
    end
    Castor-->>Cloud: Grounded diffs & task deliverable
    deactivate Castor
    Cloud-->>Dev: Verified changes & synthesis

Key Principles

  • Zero Cloud Token Hoarding: The Lead Architect (cloud) directs tasks and reviews diffs without loading thousands of source lines into expensive prompt context.
  • In-Process Microkernel Speed: Filesystem operations and structural AST mutations execute in-process (<0.1 ms dispatch) without subprocess spawning overhead.
  • Zero-Turn Reactive Wait: Long-running background jobs yield an OS-level wait hook (curl :18021/task/<id>/wait). Cloud supervisors block at $0 token cost and resume immediately upon task completion.

Zero-Trust Sandboxed Execution

Castor enforces a 5-layer defense-in-depth boundary ensuring neither coworker models nor external agents can escape the workspace root:

flowchart TD
    Req[Tool Request: fs / shell / ast] --> L1[Layer 1: PathEscape Normalizer]
    L1 -->|Valid Relative Path| L2[Layer 2: Realpath & Symlink Resolution]
    L2 -->|Inside Workspace Root| L3[Layer 3: Root-Overwrite Guard]
    L3 -->|Target Protected| L4[Layer 4: Dangerous Shell AST Filter]
    L4 -->|Safe Command| L5[Layer 5: In-Memory Syntax Gate]
    L5 -->|Valid AST Parse| Disk[Disk Commit & Execution]

    L1 -.->|Escape Traversal| Refuse[Refused: 137 Containment Vectors]
    L2 -.->|Symlink Breakout| Refuse
    L3 -.->|Root Deletion| Refuse
    L4 -.->|Dangerous Pattern| Refuse
    L5 -.->|Syntax Parse Error| Rollback[In-Memory Rollback]
  1. PathEscape Normalizer: Neutralizes directory traversal (../../), drive escapes (C:\), and device namespaces (NUL, CON).
  2. Symlink Realpath Containment: Resolves canonical symlink targets to prevent out-of-tree escapes.
  3. Workspace Root Guard: Blocks accidental or malicious deletion of the workspace root or parent folders.
  4. Dangerous Shell Filter: AST validator blocks destructive commands (rm -rf /, format, fork bombs, dd).
  5. In-Memory Syntax Gate: Validates syntactical integrity (Rust, TypeScript, JavaScript, Python, JSON) before disk commits, rolling back automatically on errors.

Regression-tested against a 137-vector automated containment suite (123 attack vectors blocked, 14 allow vectors).


Consolidated MCP Interface

Castor registers three stdio tools across any MCP client:

| Tool | Purpose | Primary Actions & Parameters | |---|---|---| | castor_coworker | Hands-on execution & peer programming | Dispatches prompt with in-tree tools (read_file, edit_file, ast_search, ast_replace, bash, web_search, paper_lookup). | | castor_task | Background task control & telemetry | Actions: status, cancel, cancel_all, list, stats, extend_lease. | | castor_server | Local inference engine supervisor | Actions: status, start, stop. Manages engine boots, health canaries, and auto-healing. |


CLI Command Reference

# Model Context Protocol stdio server (default)
castor mcp

# Automatically register with MCP clients
castor install --client <all|claude|antigravity>

# Display operational telemetry dashboard
castor stats [-s 24h] [-d] [-j]

# Inspect effective configuration hierarchy
castor config

# Manage local inference engine lifecycle
castor server <status|start|stop>

# Clean stale sessions, leases, and tasks per retention policy
castor clean [--yes]

# Run zero-turn HTTP status and wait endpoint (:18021)
castor status

# Run universal SSE streaming proxy (:18022)
castor proxy

Operational Telemetry (castor stats)

Castor maintains an append-only JSONL event ledger in ~/.castor/sessions/*/events.jsonl. Running castor stats compiles a live terminal dashboard summarizing token efficiency and local execution metrics:

# View dashboard with full terminal formatting
castor stats

# Filter by time horizon (e.g., last 24 hours, 7 days)
castor stats -s 24h

# Output machine-readable JSON for monitoring
castor stats -j

Building from Source

# Prerequisites: Rust >= 1.85 (2024 edition)
git clone https://github.com/ApatheticMioz/Castor.git
cd Castor

# Build binary
cargo build --release

# Run verification suite (327 tests)
cargo test

# Register local release build with clients
./target/release/castor install --client all

Testing & Verification

Castor maintains a strict zero-warning and offline verification gate:

# Fast offline test gate (327 tests, ~5s)
cargo test

# Strict clippy lint gate (zero warnings enforced)
cargo clippy --all-targets -- -D warnings

Repository Structure

Castor/
  Cargo.toml                # Root crate manifest (dual-target: lib + bin)
  package.json              # npm distribution manifest (bin/castor.js)
  bin/castor.js             # Cross-platform Node.js execution shim
  skills/                   # Reusable SKILL.md recipes
  src/
    lib.rs                  # Library crate root (public modules & embeddings)
    main.rs                 # CLI entry point (delegates to castor::cli)
    cli.rs                  # Clap command line parsing & execution routing
    config.rs               # Unified serde config (env > json > defaults)
    platform.rs             # Cross-OS path translation and process trees
    mcp/                    # rmcp stdio transport, tool schemas, and server
    task/                   # Task registry, long-poll wait (:18021), semaphore
    proxy/                  # Stream proxy (:18022), SSE sanitizer, loop breaker
    engine/                 # Provider interface, engine lifecycle, health canaries
    runner/                 # Multi-turn autonomous loop & event ledgers
    tools/                  # Sandboxed FS, AST search/replace, shell, web, papers
    evo/                    # Evolutionary optimizer & lineage DAG

License & Inquiries

Licensed under the GNU Affero General Public License v3 (AGPL-3.0).

  • Open Source: Free software under AGPLv3. Network-deployed modifications must make source code available.
  • Commercial Dual-Licensing: Available for proprietary embedding without copyleft obligations. Inquiries: ApatheticMioz@gmail.com.

Related Skills

View on GitHub
GitHub Stars10
CategoryAI
Updated4h ago
Forks1

Languages

Rust

Trust signals

92/100

From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.

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