⚓ Anchor-Lab-Ai

Autonomous AI Laboratory Manager, Universal Model Context Protocol (MCP) Server & Claude Code Plugin tailored for multi-system LLM training, quantization research labs, and distributed compute.

🚀 Overview

Modern AI research spans fragmented infrastructure: local development workstations, Google Colab notebooks, Kaggle GPU clusters, remote SSH training rigs, and API providers. When working with AI coding agents, agents often:

• Crash the host laptop by attempting heavy training jobs locally.

• Lose track of past test results, leading to forgotten baselines and repeated failures.

• Lack visibility into in-flight background training runs.

• Produce unreadable visualizations that fail accessibility standards or chromatic contrast.

Anchor-Lab-Ai solves this by establishing an autonomous, persistent laboratory anchor that acts as the operating system for your AI research.

Related MCP server: open-monitor

🏛️ System Architecture

flowchart TD
    subgraph HostLaptop ["Host Workstation"]
        Claude["Claude Code / OpenCode CLI"]
        Shield["Local Compute Shield<br/>(Guards localhost from heavy runs)"]
        Digest["Executive Digest<br/>(Injects active 3D context & baselines)"]
        Claude --> Shield
        Claude --> Digest
    end

    subgraph AnchorDaemon ["Persistent Daemon (tmux: anchor-lab-worker)"]
        Poller["Background Poller (watcher.js)"]
        ColabPoll["Colab CLI: colab status/log/download"]
        KagglePoll["Kaggle CLI: kaggle kernels status/output"]
        SSHPoll["SSH Rig: 192.168.1.80 journalctl"]
        
        Poller --> ColabPoll
        Poller --> KagglePoll
        Poller --> SSHPoll
    end

    subgraph ComputeTargets ["Execution Targets"]
        ColabCloud["Google Colab (Colab MCP + Colab CLI)"]
        KaggleCloud["Kaggle Kernels (Kaggle CLI)"]
        DesktopRig["Desktop Lab Rig: 192.168.1.80 (systemd-run)"]
    end

    Shield -. "Routed Execution" .-> ColabCloud
    Shield -. "Routed Execution" .-> KaggleCloud
    Shield -. "Routed Execution" .-> DesktopRig

    ColabCloud --> ColabPoll
    KaggleCloud --> KagglePoll
    DesktopRig --> SSHPoll

    Poller --> Storage["3D Partition Storage<br/>(models/model/activity/experiment/)<br/>• active/ (In-flight live stream)<br/>• completed/ (Verified finished)<br/>• ledger.json"]
    Storage --> UniversalMCP["anchor-lab-ai MCP Server (14 Tools)"]
    UniversalMCP --> Claude

⚡ Core Capabilities

1. Intelligent 6-Type Graph Engine

The engine automatically detects the mathematical shape of your data and generates clean, eye-friendly ASCII terminal charts and vector SVG plots:

• Line Convergence Curves (/anchorgraph loss): Sequential loss decay & step progression.

• Horizontal Ranking Bars (/anchorgraph rank): Perplexity leaderboards & benchmark comparisons.

• Grouped Multi-Metric Bars (/anchorgraph grouped): Side-by-side metric comparisons across runs.

• Pareto Trade-Off Scatters (/anchorgraph pareto): 2D trade-off plots (e.g. Precision Bits vs Perplexity with Pareto frontier).

• Proportional Donut Charts (/anchorgraph trits): Categorical distributions (e.g. Base-3 Trit balance {-1, 0, +1} or sparsity).

• Radar Spider Fingerprints (/anchorgraph radar): Multi-axis model capability profiles.

2. Local Compute Shield

Guards the local workstation against GPU Out-of-Memory (OOM) crashes, swap thrashing, and disk overflow. Heavy training runs are intercepted via hooks and routed to Colab, Kaggle, or the desktop rig.

3. 3D Workspace Partitioning

Strictly organizes experiments across a 3D coordinate space:

models/
  └── <model>/           (e.g., qwen2.5-0.5b)
        └── <activity>/  (training | quantization | evaluation | experiment)
              └── <experiment>/
                    ├── active/          (In-flight runs & live streams)
                    ├── completed/       (Verified finishes & safetensors)
                    └── ledger.json      (Validated benchmark metrics)

4. Historical Archeologist & Synthesizer

Performs whole-project scans across past session transcripts and synthesizes findings into an isolated historical archive:

• HISTORICAL_DOSSIER.md: Architectures explored and validated leaderboards.

• FAILURE_GRAVEYARD.md: Documented hardware, kernel, and dtype traps with actionable mitigations.

• HISTORICAL_LEADERBOARD.json: Ranked empirical results kept isolated so legacy runs never contaminate new benchmarks.

5. Multi-Model Advisory Research Council

Convenes external models (Luna, Nemotron, AGY) for literature review and formulation of smoke test proposals. Council agents are strictly advisory with zero file-modification permissions.

6. Persistent Watcher Daemon

Runs inside a dedicated, self-healing tmux session (anchor-lab-worker) to watch remote runs, parse live step sentinels (__ANCHOR_STEP__), update ledgers, and alert the agent.

🎮 Claude Code Slash Commands

🛠️ Terminal CLI Utilities

# Health, fleet, and hardware audit
anchor-lab-ai doctor

# Active context & in-flight runs
anchor-lab-ai status

# Audit historical scan, spawned subagents & supervisor invariants
anchor-lab-ai check

# Switch harvester worker model
anchor-lab-ai model openai/gpt-5.6-luna

# Background watcher daemon management
anchor-lab-ai start    # Launch in background tmux session
anchor-lab-ai attach   # View live streaming daemon logs
anchor-lab-ai stop     # Terminate background watcher

# Historical scan & retrieval
anchor-lab-ai scan
anchor-lab-ai sweep "learning rate warmup"

# Intelligent visualization
anchor-lab-ai graph pareto
anchor-lab-ai graph trits
anchor-lab-ai graphlive

# Research paper & council
anchor-lab-ai paper
anchor-lab-ai council "Hadamard vs Random Orthogonal rotations"

🔌 Universal MCP Server (15 Tools)

Anchor-Lab-Ai exposes a standard Model Context Protocol (MCP) server:

1. lab_get_state: Inspect in-flight runs, active model & baseline references.

2. lab_set_context: Switch 3D focus (model, activity, experiment).

3. lab_calc_vram: Pre-run VRAM budget estimator (prevents OOM crashes).

4. lab_verify_safetensor: Bit-exact zero-float audit for integer containers.

5. lab_scan_run_health: Silent failure detector (NaN loss, zero gradients, dead loops).

6. lab_remote_poll: Poll live status of Colab, Kaggle, or SSH rig jobs.

7. lab_remote_fetch_logs: Pull unbuffered execution logs or download checkpoints.

8. lab_remote_colab_dispatch: Execute detached Python jobs on Colab VMs.

9. lab_get_logger_template: Unbuffered streaming logger boilerplate.

10. lab_get_container_spec: Lookup integer container packing specifications.

11. lab_get_test_report: Retrieve isolated benchmark tables.

12. lab_reconcile_memory: 3-way delta: recalled assumptions vs actual code vs ledger.

13. lab_deep_sweep: Historical transcript search with 5-hour quota protection.

14. lab_get_morning_handoff: 4-bullet morning resume card after late-night runs.

15. anchor_lab_check: Audit historical scan progress, subagent tasks & supervisor invariants.

📦 Installation & Setup

# 1. Clone the repository
git clone https://github.com/CodeMasterCody3D/anchor-lab-ai.git
cd anchor-lab-ai

# 2. Run the automated installer
chmod +x install.sh
./install.sh

# 3. Verify installation
anchor-lab-ai doctor

📄 License

MIT License. Copyright (c) 2026 Cody Dixon.