Claw AI Lab
code GitHub
Claw AI Lab
Academic Simulation Engine V1.0

Claw AI Lab: Autonomous Multi-Agent Research Team

One dashboard. An entire research team.

Instant Onboarding

One Script.
Fully Staffed.

Clone, install, and launch. Your PI Dashboard opens immediately, waking up your virtual VLM experts, coding specialists, and peer reviewers.

pi-terminal ~ bash

pi@local:$ git clone https://github.com/Claw-AI-Lab/Claw-AI-Lab.git

pi@local:$ cd Claw-AI-Lab && ./start.sh

> Booting Claw AI Lab Core...

> Initializing 5 Research Agents [OK]

> Connecting to local GPU sandbox [OK]

✓ PI Dashboard ready → http://localhost:5903

Waiting for Principal Investigator input...

Lab Organization

5-Layer Hierarchical Pipeline

Multiple lobster agents work concurrently across every layer. Artifacts flow downward through FIFO queues while L4 execution results feed back to L1 for iterative refinement.

L1 · THE READING GROUP

Literature Review → Synthesis → Hypothesis Generation

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L2 · METHODS COMMITTEE

Experiment Design, Baselines, and Ablation Strategies

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PI GATE
L3 · THE ENGINEERING PIT

Code Search → Generation → Environment Setup

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L4 · GPU SANDBOX

Execution → Log Analysis → Empirical Refinement

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L5 · THE WRITING DESK

Drafting → Auto-Figures → Peer Review

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replay L4 → L1 FEEDBACK LOOP arrow_upward

terminal Claw-Code Harness

A built-in execution harness for code tasks: spin up isolated runs, capture logs, and feed verified results back into the agent loop for fast iteration.

person Human-in-the-Loop

Quality gates with rollback at key pipeline stages. Live feedback panel targets any layer at any time. LLM auto-classifies input intent.

groups Lab Member System

Build your own research team — N parallel agents per angle. e.g., an Embodied AI lab with VLM, World Model, VLA specialists running concurrently — or any custom research domain.

VLM Expert World Model VLA Custom...

auto_stories Intelligent Manuscript Drafting

Assists in drafting structured papers with publication-ready figures. Automatically renders framework diagrams and experiment charts featuring mandatory error bars and robust anti-fabrication verification.

Feature Spotlight

The Power of Consensus

Multi-Agent Discussion transforms isolated findings into unified research consensus.

Round 1 · Present
A
VLM Expert

Video world models + MPC planning is the most deployable path. VLMPC grounding enables online re-planning with safety constraints. Human video → robot action transfer creates data moat opportunities.

Round 1 · Present
B
World Model

Disagree on priority. UVA paradigm — train with video, infer with action — is more practical. ECoT-style explicit reasoning enables human diagnostics in production.

Round 2 · Critique
C
VLA Specialist

Both overlook the easiest landing: step understanding + anomaly detection — no robot control needed. Action anticipation is a practical middle ground.

Round 3 · Consensus
hub
Unified Roadmap

Resolved as sequential dependency: step understanding → video-action policy → world-model planning. 3 contradictions resolved, 5 testable hypotheses, 9 research directions.

Impact Metrics
0
HYPOTHESES (BEFORE)
5
HYPOTHESES (AFTER)
3
CONFLICTS
9
DIRECTIONS
Round 1 · Present
A
VLM Expert

Video world models + MPC planning is the most deployable path. VLMPC grounding enables online re-planning with safety constraints. Human video → robot action transfer creates data moat opportunities.

Round 1 · Present
B
World Model

Disagree on priority. UVA paradigm — train with video, infer with action — is more practical. ECoT-style explicit reasoning enables human diagnostics in production.

Round 2 · Critique
C
VLA Specialist

Both overlook the easiest landing: step understanding + anomaly detection — no robot control needed. Action anticipation is a practical middle ground.

Round 3 · Consensus
hub
Unified Roadmap

Resolved as sequential dependency: step understanding → video-action policy → world-model planning. 3 contradictions resolved, 5 testable hypotheses, 9 research directions.

Impact Metrics
0
HYPOTHESES (BEFORE)
5
HYPOTHESES (AFTER)
3
CONFLICTS
9
DIRECTIONS

Capabilities

Core Features

dynamic_feed

Multi-Task Parallel

Multiple research topics run simultaneously. Each task gets its own agent swarm and resource allocation across the full pipeline.

forum

Multi-Agent Discussion

3-round debate with cross-vendor LLMs. Cross-project pairing for interdisciplinary insights.

groups

Custom Lab Members

Define custom expert personas for any research domain — e.g., an Embodied AI team with VLM / World Model / VLA specialists, or any other angles you need.

memory

GPU Orchestration

Multi-GPU allocation per project. Auto-release on completion. Queue gating when resources are scarce.

person

Human-in-the-Loop

Quality gates with rollback. Live chat feedback panel targets any layer at any time.

brush

Auto Figures & Charts

Generates publication-ready figures and experiment comparison charts with error bars automatically.

monitoring

Real-Time Dashboard

Live agent status visualization. CPU/GPU/memory monitoring. FIFO queue counters and log streaming.

touch_app

User-Friendly UI

No complex operations needed. Submit a topic, monitor progress, and download papers — all from one clean web interface.