🧪 LabRat: Autonomous Multi-Agent Research Orchestrator
LabRat is not just another experiment management tool—it 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.
📐 Architecture Overview
The system operates as a three-layer cognitive pipeline:
graph TB
subgraph "🧠 Orchestration Layer"
A[Coordinator Agent] --> B[Allocation Engine]
B --> C{Market Mechanism}
C -->|Equilibrium| D[Research Quorum]
end
subgraph "🔬 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 "📊 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]
The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.
First-time install walkthrough for Claude Code, Codex CLI, and ChatGPT.
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.
🎯 Core Philosophy: Scientific Scarab
We don't offer "free" trials—we 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 Matrix
| Feature | Description | Status |
|---------|-------------|--------|
| 🧩 Multi-Agent Swarm | Up to 128 concurrent agents with distinct expertise profiles | ✅ Stable |
| 📈 Market Allocation Engine | Resource bidding system based on Bayesian confidence intervals | ✅ Stable |
| 🌐 Multilingual Hypothesis Framing | Supports 47 natural languages for research question input | ✅ Stable |
| 📱 Responsive Experiment Dashboard | Real-time HTML/CSS/JS interface with WebSocket updates | ✅ Beta |
| 🕐 24/7 Autonomous Operation | Agents self-correct and retry on failure with exponential backoff | ✅ Stable |
| 🔌 API Integrations | Claude API (3.5 Sonnet, Opus) + OpenAI API (GPT-4o, o1) | ✅ Stable |
| 🔄 Version History | Full git-like branching for experiment forks | ✅ Stable |
| 📊 Statistical Consensus | Monte Carlo + Bayesian methods for agent vote aggregation | ✅ Stable |
| 🛡️ Immutable Logging | All agent actions cryptographically signed and timestamped | ✅ Beta |
LabRat treats Claude as the philosophical engine—handling reasoning chains, hypothesis generation, and conflict resolution. The system supports:
Claude 3.5 Sonnet (claude-3-5-sonnet-20241022)
Claude 3 Opus (claude-3-opus-20240229)
Extended thinking mode for complex theorem proving
Tool use (file editing, code execution, web search)
🧠 OpenAI API
OpenAI models serve as the statistical engine—optimizing for numerical reasoning, data analysis, and pattern recognition:
GPT-4o (gpt-4o-2024-08-06)
GPT-4o mini (gpt-4o-mini)
o1-preview for advanced reasoning tasks
Structured output mode for consensus voting
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
)
🌍 Multilingual Support Matrix
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:
🇺🇸 English (en-US) - Default
🇪🇸 Spanish (es-ES) - Full
🇨🇳 Chinese Simplified (zh-CN) - Full
🇯🇵 Japanese (ja-JP) - Full
🇫🇷 French (fr-FR) - Full
🇩🇪 German (de-DE) - Full
🇦🇪 Arabic (ar-SA) - Full (RTL support)
🇮🇳 Hindi (hi-IN) - Beta
Note: The consensus protocol always operates in English internally, but the report generator translates final artifacts using GPT-4o's multilingual capabilities.
🛡️ Disclaimer & Legal Considerations
⚠️ 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
📦 Installation
# 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)
🧪 Quick Start: Your First Experiment
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%}")
Claude Code is an agentic coding tool that lives in your terminal, understands your codebase, and helps you code faster by executing routine tasks, explaining complex code, and handling git workflows - all through natural language commands.
