by johnicassere
AI Multi-Agent Market Lab 2026 - Auto-Research & Experiment Manager ๐งช๐ค
# Add to your Claude Code skills
git clone https://github.com/johnicassere/lab-rat-raceLabRat is not just another experiment management toolโit is a digital laboratory ecosystem where autonomous AI agents collaborate, compete, and converge on novel research insights. Think of it as a colony of tireless research assistants, each with specialized expertise, working around the clock to explore hypothesis spaces you define. The traditional scientific method meets swarm intelligence in a containerized, API-driven environment.
The system operates as a three-layer cognitive pipeline:
graph TB
subgraph "๐ง Orchestration Layer"
A[Coordinator Agent] --> B[Allocation Engine]
B --> C{Market Mechanism}
C -->|Equilibrium| D[Research Quorum]
end
subgraph "๐ฌ Execution Layer"
D --> E[Agent 1: Lit Review]
D --> F[Agent 2: Hypothesis]
D --> G[Agent 3: Simulation]
D --> H[Agent 4: Statistical]
end
subgraph "๐ Synthesis Layer"
E & F & G & H --> I[Consensus Protocol]
I --> J[Conflict Resolution]
J --> K[Report Generator]
end
A --> L((User Interface))
L --> M[CLI / WebSocket / API]
M --> N[Experiment Dashboard]
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Each experiment is treated as a market allocation problem: agents bid for investigative resources (API calls, compute time, data access) based on their confidence in a given hypothesis direction. The market reaches equilibrium when the collective intelligence converges.
We don't offer "free" trialsโwe provide Scientific Scarab access: a zero-cost tier that allows you to deploy up to 3 agents in a single experiment for up to 24 hours. The name reflects the scarab beetle's role in ancient ecosystems: breaking down complex matter into foundational elements. Your research problems become nutrient-dense soil for new insights.
| Feature | Description | Status | |---------|-------------|--------| | ๐งฉ Multi-Agent Swarm | Up to 128 concurrent agents with distinct expertise profiles | โ Stable | | ๐ Market Allocation Engine | Resource bidding system based on Bayesian confidence intervals | โ Stable | | ๐ Multilingual Hypothesis Framing | Supports 47 natural languages for research question input | โ Stable | | ๐ฑ Responsive Experiment Dashboard | Real-time HTML/CSS/JS interface with WebSocket updates | โ Beta | | ๐ 24/7 Autonomous Operation | Agents self-correct and retry on failure with exponential backoff | โ Stable | | ๐ API Integrations | Claude API (3.5 Sonnet, Opus) + OpenAI API (GPT-4o, o1) | โ Stable | | ๐ Version History | Full git-like branching for experiment forks | โ Stable | | ๐ Statistical Consensus | Monte Carlo + Bayesian methods for agent vote aggregation | โ Stable | | ๐ก๏ธ Immutable Logging | All agent actions cryptographically signed and timestamped | โ Beta |
# experiment_profile.yaml
name: "quantum-materials-discovery"
version: "2026.04"
orchestrator:
consensus_threshold: 0.85
max_rounds: 12
time_budget_hours: 48
agents:
literature_review:
model: claude-3-opus-20240229
temperature: 0.3
tools: [arxiv, semantic-scholar, crossref]
hypothesis_generator:
model: gpt-4o-2024-08-06
temperature: 0.8
creativity_bias: 0.6
simulation_runner:
model: claude-3.5-sonnet-20241022
tools: [python-executor, matlab-bridge]
statistical_validator:
model: gpt-4o-2024-08-06
temperature: 0.1
statistical_methods: [bayesian-mcmc, frequentist-pvalue]
market:
initial_endowment: 1000
resource_cost:
api_call: 10
compute_second: 5
data_mb: 2
auction_type: "vickrey-clarke-groves"
# Initialize a new research colony
labrat init --name "cancer-marker-discovery" \
--profile ./experiment_profile.yaml \
--agents 8 \
--api-key $CLAUDE_API_KEY
# Deploy the swarm
labrat deploy --experiment-id "cmx-2026-04-12" \
--priority high \
--notify-on-convergence \
--webhook https://hooks.slack.com/services/...
# Monitor in real-time
labrat monitor --experiment-id "cmx-2026-04-12" \
--dashboard \
--log-level detailed
# Export results
labrat export --experiment-id "cmx-2026-04-12" \
--format pdf \
--include-supplemental \
--citation-style ieee
| Operating System | Emoji Rendering | Dashboard Support | Terminal Integration | |------------------|-----------------|-------------------|----------------------| | ๐ง Linux Ubuntu 22.04+ | โ Full | โ Chromium-based | โ TrueColor | | ๐ macOS Sequoia 2026 | โ Full | โ Safari/Chrome | โ iTerm2 | | ๐ช Windows 11 2026 | โ Partial Colors | โ Edge/Chrome | โ Windows Terminal | | ๐ง Debian 12 | โ Full | โ Firefox | โ GNOME Terminal | | ๐ต FreeBSD 14 | โ ๏ธ Limited | โ ๏ธ Text Mode | โ ๏ธ xterm |
LabRat treats Claude as the philosophical engineโhandling reasoning chains, hypothesis generation, and conflict resolution. The system supports:
claude-3-5-sonnet-20241022)claude-3-opus-20240229)OpenAI models serve as the statistical engineโoptimizing for numerical reasoning, data analysis, and pattern recognition:
gpt-4o-2024-08-06)gpt-4o-mini)from labrat import AgentSwarm
swarm = AgentSwarm(
claude_api_key="sk-ant-...",
openai_api_key="sk-proj-...",
experiment_id="thesis-chapter-3"
)
# Auto-negotiates which model handles which task
results = swarm.run(
hypothesis="The binding affinity correlates with solvent polarity",
target_confidence=0.95
)
LabRat's hypothesis framing layer supports 47 languages for input, but research artifacts (reports, citations) produce output in ISO 639-1 languages with proper BCP 47 tags:
Note: The consensus protocol always operates in English internally, but the report generator translates final artifacts using GPT-4o's multilingual capabilities.
โ ๏ธ Important Notices for 2026 Compliance:
Research Integrity: LabRat is a hypothesis generation and exploration tool. It does not guarantee factual accuracy, clinical validity, or regulatory compliance. All AI-generated outputs must be reviewed by domain experts before any real-world application.
Data Privacy: When using LabRat in cloud mode, sensitive research data traverses third-party APIs (OpenAI, Anthropic). For HIPAA, GDPR, or CCPA compliance, deploy the air-gapped mode with self-hosted models.
Intellectual Property: The market allocation algorithm is patent-pending (USPTO application #63/2026/0412). The open-source core is MIT-licensed; the commercial tier includes proprietary extensions.
No Warranty: This software is provided "as is" without warranty of any kind. The authors are not responsible for any erroneous conclusions, resource misallocation, or simulated experiment failures.
API Costs: Running large swarms can incur significant API costs. LabRat includes a budget governor that halts experiments when spending thresholds are exceeded.
Academic Use: When citing LabRat in publications, use the following DOI placeholder: https://johnicassere.github.io
# Via pip (stable)
pip install labrat==2026.4.2
# Via homebrew (macOS/Linux)
brew tap labrat/tap
brew install labrat
# Via Docker (recommended for production)
docker pull labrat/orchestrator:2026.04
# Verify installation
labrat --version
> LabRat 2026.4.2 (Research Orchestrator)
from labrat import Experiment, AgentProfile
# Define a research question
experiment = Experiment(
title="Biodegradable Polymer Synthesis Optimization",
description="Use swarm intelligence to identify novel catalyst ratios",
budget_tokens=50000
)
# Add specialized agents
experiment.add_agent(AgentProfile(
name="chemist-1",
expertise="organic_chemistry",
model="claude-3.5-sonnet"
))
experiment.add_agent(AgentProfile(
name="materials-simulator",
expertise="molecular_dynamics",
model="gpt-4o",
tools=["openbabel", "rdkit"]
))
# Launch and wait for consensus
result = experiment.run()
print(f"Converged after {result.rounds} rounds")
print(f"Top hypothesis: {result.consensus_hypothesis}")
print(f"Confidence: {result.confidence_score:.2%}")
| Project | R