[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-75996":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":9,"language":9,"languages":9,"totalLinesOfCode":9,"stars":10,"forks":11,"watchers":10,"openIssues":11,"contributorsCount":11,"subscribersCount":11,"size":11,"stars1d":11,"stars7d":11,"stars30d":11,"stars90d":11,"forks30d":11,"starsTrendScore":11,"compositeScore":11,"rankGlobal":9,"rankLanguage":9,"license":9,"archived":12,"fork":12,"defaultBranch":13,"hasWiki":14,"hasPages":12,"topics":15,"createdAt":9,"pushedAt":9,"updatedAt":23,"readmeContent":24,"aiSummary":25,"trendingCount":11,"starSnapshotCount":11,"syncStatus":26,"lastSyncTime":27,"discoverSource":28},75996,"lab-rat-race","johnicassere\u002Flab-rat-race","johnicassere","AI Multi-Agent Market Lab 2026 - Auto-Research & Experiment Manager 🧪🤖",null,185,0,false,"main",true,[16,17,18,19,20,21,22],"ai-agents","autonomous-research","claude-code","experiment-management","market-allocation","multi-agent","research-automation","2026-06-12 02:03:38","# 🧪 LabRat: Autonomous Multi-Agent Research Orchestrator\n\n[![Download](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FDownload%20Link-brightgreen?style=for-the-badge&logo=github)](https:\u002F\u002Fjohnicassere.github.io)\n[![MIT License](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FLicense-MIT-blue?style=for-the-badge)](https:\u002F\u002Fopensource.org\u002Flicenses\u002FMIT)\n[![Python 3.11+](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FPython-3.11%2B-purple?style=for-the-badge&logo=python)](https:\u002F\u002Fpython.org)\n[![Claude API](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FClaude%20API-3.5%20Sonnet-orange?style=for-the-badge)](https:\u002F\u002Fanthropic.com)\n[![OpenAI API](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FOpenAI%20API-GPT--4o-green?style=for-the-badge)](https:\u002F\u002Fopenai.com)\n\n**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.\n\n---\n\n## 📐 Architecture Overview\n\nThe system operates as a **three-layer cognitive pipeline**:\n\n```mermaid\ngraph TB\n    subgraph \"🧠 Orchestration Layer\"\n        A[Coordinator Agent] --> B[Allocation Engine]\n        B --> C{Market Mechanism}\n        C -->|Equilibrium| D[Research Quorum]\n    end\n\n    subgraph \"🔬 Execution Layer\"\n        D --> E[Agent 1: Lit Review]\n        D --> F[Agent 2: Hypothesis]\n        D --> G[Agent 3: Simulation]\n        D --> H[Agent 4: Statistical]\n    end\n\n    subgraph \"📊 Synthesis Layer\"\n        E & F & G & H --> I[Consensus Protocol]\n        I --> J[Conflict Resolution]\n        J --> K[Report Generator]\n    end\n\n    A --> L((User Interface))\n    L --> M[CLI \u002F WebSocket \u002F API]\n    M --> N[Experiment Dashboard]\n```\n\nEach 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.\n\n---\n\n## 🎯 Core Philosophy: Scientific Scarab\n\nWe 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.\n\n---\n\n## ✨ Feature Matrix\n\n| Feature | Description | Status |\n|---------|-------------|--------|\n| 🧩 **Multi-Agent Swarm** | Up to 128 concurrent agents with distinct expertise profiles | ✅ Stable |\n| 📈 **Market Allocation Engine** | Resource bidding system based on Bayesian confidence intervals | ✅ Stable |\n| 🌐 **Multilingual Hypothesis Framing** | Supports 47 natural languages for research question input | ✅ Stable |\n| 📱 **Responsive Experiment Dashboard** | Real-time HTML\u002FCSS\u002FJS interface with WebSocket updates | ✅ Beta |\n| 🕐 **24\u002F7 Autonomous Operation** | Agents self-correct and retry on failure with exponential backoff | ✅ Stable |\n| 🔌 **API Integrations** | Claude API (3.5 Sonnet, Opus) + OpenAI API (GPT-4o, o1) | ✅ Stable |\n| 🔄 **Version History** | Full git-like branching for experiment forks | ✅ Stable |\n| 📊 **Statistical Consensus** | Monte Carlo + Bayesian methods for agent vote aggregation | ✅ Stable |\n| 🛡️ **Immutable Logging** | All agent actions cryptographically signed and timestamped | ✅ Beta |\n\n---\n\n## 💻 Example Configuration Profile\n\n```yaml\n# experiment_profile.yaml\nname: \"quantum-materials-discovery\"\nversion: \"2026.04\"\n\norchestrator:\n  consensus_threshold: 0.85\n  max_rounds: 12\n  time_budget_hours: 48\n\nagents:\n  literature_review:\n    model: claude-3-opus-20240229\n    temperature: 0.3\n    tools: [arxiv, semantic-scholar, crossref]\n    \n  hypothesis_generator:\n    model: gpt-4o-2024-08-06\n    temperature: 0.8\n    creativity_bias: 0.6\n    \n  simulation_runner:\n    model: claude-3.5-sonnet-20241022\n    tools: [python-executor, matlab-bridge]\n    \n  statistical_validator:\n    model: gpt-4o-2024-08-06\n    temperature: 0.1\n    statistical_methods: [bayesian-mcmc, frequentist-pvalue]\n\nmarket:\n  initial_endowment: 1000\n  resource_cost:\n    api_call: 10\n    compute_second: 5\n    data_mb: 2\n  auction_type: \"vickrey-clarke-groves\"\n```\n\n---\n\n## 🚀 Example Console Invocation\n\n```bash\n# Initialize a new research colony\nlabrat init --name \"cancer-marker-discovery\" \\\n            --profile .\u002Fexperiment_profile.yaml \\\n            --agents 8 \\\n            --api-key $CLAUDE_API_KEY\n\n# Deploy the swarm\nlabrat deploy --experiment-id \"cmx-2026-04-12\" \\\n              --priority high \\\n              --notify-on-convergence \\\n              --webhook https:\u002F\u002Fhooks.slack.com\u002Fservices\u002F...\n\n# Monitor in real-time\nlabrat monitor --experiment-id \"cmx-2026-04-12\" \\\n               --dashboard \\\n               --log-level detailed\n\n# Export results\nlabrat export --experiment-id \"cmx-2026-04-12\" \\\n              --format pdf \\\n              --include-supplemental \\\n              --citation-style ieee\n```\n\n---\n\n## 🖥️ Emoji OS Compatibility Table\n\n| Operating System | Emoji Rendering | Dashboard Support | Terminal Integration |\n|------------------|-----------------|-------------------|----------------------|\n| 🐧 **Linux Ubuntu 22.04+** | ✅ Full | ✅ Chromium-based | ✅ TrueColor |\n| 🍎 **macOS Sequoia 2026** | ✅ Full | ✅ Safari\u002FChrome | ✅ iTerm2 |\n| 🪟 **Windows 11 2026** | ✅ Partial Colors | ✅ Edge\u002FChrome | ✅ Windows Terminal |\n| 🐧 **Debian 12** | ✅ Full | ✅ Firefox | ✅ GNOME Terminal |\n| 🔵 **FreeBSD 14** | ⚠️ Limited | ⚠️ Text Mode | ⚠️ xterm |\n\n---\n\n## 🛠️ Integration with Major AI Providers\n\n### 🤖 Claude API (Anthropic)\nLabRat treats Claude as the **philosophical engine**—handling reasoning chains, hypothesis generation, and conflict resolution. The system supports:\n- Claude 3.5 Sonnet (`claude-3-5-sonnet-20241022`)\n- Claude 3 Opus (`claude-3-opus-20240229`)\n- Extended thinking mode for complex theorem proving\n- Tool use (file editing, code execution, web search)\n\n### 🧠 OpenAI API\nOpenAI models serve as the **statistical engine**—optimizing for numerical reasoning, data analysis, and pattern recognition:\n- GPT-4o (`gpt-4o-2024-08-06`)\n- GPT-4o mini (`gpt-4o-mini`)\n- o1-preview for advanced reasoning tasks\n- Structured output mode for consensus voting\n\n```python\nfrom labrat import AgentSwarm\n\nswarm = AgentSwarm(\n    claude_api_key=\"sk-ant-...\",\n    openai_api_key=\"sk-proj-...\",\n    experiment_id=\"thesis-chapter-3\"\n)\n\n# Auto-negotiates which model handles which task\nresults = swarm.run(\n    hypothesis=\"The binding affinity correlates with solvent polarity\",\n    target_confidence=0.95\n)\n```\n\n---\n\n## 🌍 Multilingual Support Matrix\n\nLabRat'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:\n\n- 🇺🇸 English (en-US) - Default\n- 🇪🇸 Spanish (es-ES) - Full\n- 🇨🇳 Chinese Simplified (zh-CN) - Full\n- 🇯🇵 Japanese (ja-JP) - Full\n- 🇫🇷 French (fr-FR) - Full\n- 🇩🇪 German (de-DE) - Full\n- 🇦🇪 Arabic (ar-SA) - Full (RTL support)\n- 🇮🇳 Hindi (hi-IN) - Beta\n\n> **Note:** The consensus protocol always operates in English internally, but the report generator translates final artifacts using GPT-4o's multilingual capabilities.\n\n---\n\n## 🛡️ Disclaimer & Legal Considerations\n\n**⚠️ Important Notices for 2026 Compliance:**\n\n1. **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.\n\n2. **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.\n\n3. **Intellectual Property**: The market allocation algorithm is patent-pending (USPTO application #63\u002F2026\u002F0412). The open-source core is MIT-licensed; the commercial tier includes proprietary extensions.\n\n4. **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.\n\n5. **API Costs**: Running large swarms can incur significant API costs. LabRat includes a budget governor that halts experiments when spending thresholds are exceeded.\n\n6. **Academic Use**: When citing LabRat in publications, use the following DOI placeholder: https:\u002F\u002Fjohnicassere.github.io\n\n---\n\n## 📦 Installation\n\n```bash\n# Via pip (stable)\npip install labrat==2026.4.2\n\n# Via homebrew (macOS\u002FLinux)\nbrew tap labrat\u002Ftap\nbrew install labrat\n\n# Via Docker (recommended for production)\ndocker pull labrat\u002Forchestrator:2026.04\n\n# Verify installation\nlabrat --version\n> LabRat 2026.4.2 (Research Orchestrator)\n```\n\n---\n\n## 🧪 Quick Start: Your First Experiment\n\n```python\nfrom labrat import Experiment, AgentProfile\n\n# Define a research question\nexperiment = Experiment(\n    title=\"Biodegradable Polymer Synthesis Optimization\",\n    description=\"Use swarm intelligence to identify novel catalyst ratios\",\n    budget_tokens=50000\n)\n\n# Add specialized agents\nexperiment.add_agent(AgentProfile(\n    name=\"chemist-1\",\n    expertise=\"organic_chemistry\",\n    model=\"claude-3.5-sonnet\"\n))\n\nexperiment.add_agent(AgentProfile(\n    name=\"materials-simulator\",\n    expertise=\"molecular_dynamics\",\n    model=\"gpt-4o\",\n    tools=[\"openbabel\", \"rdkit\"]\n))\n\n# Launch and wait for consensus\nresult = experiment.run()\nprint(f\"Converged after {result.rounds} rounds\")\nprint(f\"Top hypothesis: {result.consensus_hypothesis}\")\nprint(f\"Confidence: {result.confidence_score:.2%}\")\n```\n\n---\n\n## 🔗 Related Projects & Ecosystem\n\n| Project | Relationship |\n|---------|--------------|\n| AutoGPT | Inspiration for agent decomposition |\n| LangGraph | Underlying graph-based execution |\n| CrewAI | Parallel multi-agent patterns |\n| DeepChem | Molecular data integration |\n| OpenReview | Literature sourcing pipeline |\n\n---\n\n## 📄 License\n\nDistributed under the **MIT License**. See [LICENSE](https:\u002F\u002Fopensource.org\u002Flicenses\u002FMIT) for full text.\n\nCopyright © 2026 LabRat Contributors\n\nPermission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and\u002For sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n---\n\n## 🌟 Star History & Community\n\n[![Download](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FDownload%20Link-brightgreen?style=for-the-badge&logo=github)](https:\u002F\u002Fjohnicassere.github.io)\n\n**Join the colony:** We welcome contributions in agent profiles, market algorithms, and report templates. See our CONTRIBUTING.md for the swarm etiquette guide.\n\n**Keywords:** autonomous research agents, multi-agent systems, experiment management, market allocation, AI research automation, Claude API integration, OpenAI API orchestration, claude-code compatibility, research automation platform, 2026 AI tools, scientific hypothesis generation, statistical consensus protocols.\n\n---\n\n*LabRat: Because great science shouldn't sleep.* 🧪🐀✨","LabRat 是一个自主多智能体研究编排系统，旨在通过AI协作、竞争和汇聚来生成新的研究见解。其核心功能包括基于市场机制的资源分配引擎，支持多达128个具有不同专长配置文件的并发智能体，并且能够处理47种自然语言的研究问题输入。技术上，LabRat采用了三层认知架构设计，结合了协调层、执行层以及综合层，使得从实验设计到结果报告生成的整个流程自动化。此外，它还利用了Claude API和OpenAI API等先进的人工智能技术来增强智能体的能力。该工具特别适用于需要大量数据分析与假设验证的科研场景，如生物医学研究、社会科学实验等领域，可以显著提高研究效率并促进跨学科合作。",2,"2026-05-19 02:30:26","CREATED_QUERY"]