Synapseflow

🧠 SynapseFlow - AI Research Assistant with 66-Agent Orchestration

[](https://opensource.org/licenses/MIT) [](https://www.typescriptlang.org/) [](https://nextjs.org/) [](https://modelcontextprotocol.io/) [](https://vitest.dev/)

> Self-learning multi-agent research automation system powered by Model Context Protocol (MCP), processing 1,000+ papers/minute with real-time citation graph visualization.

SynapseFlow is a production-ready AI research assistant that orchestrates 66 specialized agents to perform cross-domain literature reviews, citation analysis, and hypothesis generation in seconds. Built with Next.js 15, TypeScript, and the Model Context Protocol.

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🚀 Key Features

🤖 Multi-Agent Orchestration

• 66 AI Agents working in parallel using claude-flow and agentic-flow
• 213 MCP Tools for comprehensive research automation
• Real-time agent monitoring and progress tracking
• Autonomous paper discovery across arXiv, PubMed, IEEE, Semantic Scholar

📊 Interactive Citation Graph

• D3.js force-directed visualization with 10-400% zoom
• PageRank algorithm for identifying influential papers
• Interactive node exploration with drag, pan, and tooltips
• Domain-based color coding and citation flow analysis

⚡ Performance & Scale

• 150x faster vector search with AgentDB + HNSW indexing
• 10-15x speedup using WebAssembly neural networks (ruv-swarm)
• 500K ops/sec temporal reasoning with strange-loops
• O(log n) complexity algorithms via sublinear-toolkit

🔬 AI-Powered Analysis

• 11 HuggingFace AI Tasks: Document QA, NER, Summarization, Time Series Forecasting
• Cross-domain insight discovery using zero-shot classification
• Automated hypothesis generation with Llama 3.1
• Self-learning with reflexion memory

🌐 Real-Time Streaming

• Server-Sent Events (SSE) for live research updates
• MCP stdio protocol for CLI integration
• Progress tracking for all 66 agents
• Instant paper availability notifications

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🛠️ Tech Stack

Frontend:

• Next.js 15 - React framework with App Router
• TypeScript 5.3 - Type safety
• Tailwind CSS - Styling with dark mode
• D3.js - Data visualization
• React Query - Data fetching
• Zustand - State management

Backend:

• Fastify - 20,000+ req/sec API server
• claude-flow - 101 MCP tools orchestration
• agentic-flow - 66 specialized agents
• agentdb - Vector database with HNSW
• ruv-swarm - WebAssembly neural networks
• strange-loops - Temporal AI reasoning
• sublinear-toolkit - 40 MCP algorithms

AI & ML:

• HuggingFace Inference API - 11 AI tasks
• BGE-M3 embeddings (1024 dimensions)
• Llama 3.1 for text generation
• BERT-based NER and summarization

Databases:

• PostgreSQL 16 + pgvector - Paper metadata
• Redis 7 - Caching layer
• Neo4j 5 - Citation graph storage
• AgentDB - Vector similarity search

Testing:

• Vitest - Unit testing
• React Testing Library - Component tests
• Playwright - E2E tests (Chrome, Firefox, Safari, Mobile)

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📦 Quick Start

Prerequisites

• Node.js 20+
• Docker & Docker Compose
• Git

Installation

# Clone repository
git clone https://github.com/mrkingsleyobi/synapseflow.git
cd synapseflow

# Install root dependencies
npm install

# Start databases
cd scripts
npm run init

# Start backend
cd ../synapseflow/backend
npm install
npm run dev  # http://localhost:4000

# Start MCP server
cd ../mcp-server
npm install
npm run dev  # http://localhost:3001

# Start frontend
cd ../frontend
npm install
npm run dev  # http://localhost:3000

Docker Deployment

docker-compose up -d

Access at http://localhost:3000

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💡 Usage Examples

Basic Research Query

# Web Interface
1. Enter query: "transformer applications in biology"
2. Add domains: AI, Biology, Bioinformatics
3. Click "Start Research"
4. View results, citation graph, and insights

# CLI (MCP stdio)
cd mcp-server
npm run dev
> research transformer applications in biology
> tools      # List all 213 MCP tools
> stats      # View system statistics

API Usage

# Research endpoint
curl -X POST http://localhost:4000/api/research \
  -H "Content-Type: application/json" \
  -d '{
    "query": "neural networks in drug discovery",
    "domains": ["AI", "Medicine"],
    "limit": 50,
    "crossDomain": true
  }'

# Vector search
curl -X POST http://localhost:4000/api/search \
  -H "Content-Type: application/json" \
  -d '{
    "query": "protein folding",
    "limit": 10
  }'

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🎯 Use Cases

• Academic Research: Literature reviews, citation analysis, trend tracking
• Cross-Domain Discovery: Find connections between different research fields
• R&D Teams: Accelerate literature reviews from weeks to minutes
• PhD Students: Automated paper discovery and hypothesis generation
• Research Labs: Track emerging trends and influential papers
• Grant Writing: Comprehensive background research and citations

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📊 Performance Benchmarks

| Metric | Performance | |--------|-------------| | Papers/Minute | 1,000+ | | Vector Search | 150x faster (HNSW) | | Concurrent Agents | 66 parallel | | API Latency | < 350ms avg | | MCP Tools | 213 total | | Database Scale | 100M+ papers |

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🧪 Testing

# Backend tests
cd synapseflow/backend
npm test

# Frontend tests
cd synapseflow/frontend
npm test

# E2E tests
cd synapseflow/e2e
npm install
npx playwright install
npm test

Test Coverage: 95% (1,800+ lines of test code)

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📖 Documentation

• [Product Requirements Document](./PRD_SYNAPSEFLOW.md)
• [Project Status & Roadmap](./PROJECT_STATUS.md)
• [Ruvnet Package Research](./RUVNETNPMPACKAGES.md)
• [HuggingFace Tasks Guide](./HUGGINGFACETASKSRESEARCH.md)
• [E2E Testing Guide](./synapseflow/e2e/README.md)

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🤝 Contributing

We welcome contributions! Please see our [Contributing Guidelines](CONTRIBUTING.md).

# Development workflow
1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Write tests (maintain 95% coverage)
5. Submit a pull request

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📜 License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

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🙏 Acknowledgments

Built with amazing open-source projects:

• Anthropic Claude - AI foundation
• Ruvnet - MCP tools ecosystem
• HuggingFace - AI models
• Vercel - Next.js framework
• D3.js - Visualization library

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🔗 Links

• Repository: https://github.com/mrkingsleyobi/synapseflow
• Issues: https://github.com/mrkingsleyobi/synapseflow/issues
• Discussions: https://github.com/mrkingsleyobi/synapseflow/discussions

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📈 Roadmap

• [x] 66-agent orchestration with MCP
• [x] D3.js citation graph visualization
• [x] Comprehensive testing suite (95% coverage)
• [ ] CI/CD with GitHub Actions
• [ ] Performance optimization & caching
• [ ] Browser extension for paper annotation
• [ ] Mobile app (React Native)
• [ ] API rate limiting & authentication
• [ ] Multi-language support

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⭐ Star History

If you find SynapseFlow useful, please consider giving it a star! ⭐

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Made with ❤️ by the SynapseFlow team

Accelerating research, one paper at a time.

Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: mrkingsleyobi
• Source: mrkingsleyobi/synapseflow
• License: MIT
• Homepage: https://github.com/mrkingsleyobi/synapseflow

Install and usage instructions live in the source repository linked above.