AI Research Integration Platform: From Paper to Implementation

Disclosure: This project was "Vibe Coded" through AI prompting techniques, demonstrating the potential of LLMs for complex software development. It exemplifies how AI assistants can rapidly prototype entire systems based on conceptual guidance.

A knowledge graph-powered platform for AI research discovery, extraction, and implementation. This comprehensive toolkit streamlines the conversion of academic research papers into working implementations, bridging the gap between theoretical AI advances and practical applications.

Project Overview

The AI Research Integration Platform enables researchers, data scientists, and ML engineers to discover, analyze, and implement state-of-the-art AI research findings. Built around a Neo4j knowledge graph with temporal evolution tracking, it extracts structured information from papers and generates implementation code with comprehensive testing.

Development Status

Completed Components

1. Information Gathering Module ✅

   • SearchManager: Coordinates search operations across multiple sources
   • SourceManager: Registers and manages different information sources
   • QualityAssessor: Evaluates search result quality
   • Source adapters for academic, web, code, and AI sources
   • Comprehensive test suite with unit, integration, property-based, edge case, and benchmark tests

2. Knowledge Extraction Pipeline ✅

   • Document Processing Engine: Handles PDF, HTML, and text documents
   • Entity Recognition System: Extracts entities from research content
   • Relationship Extraction Module: Identifies connections between entities
   • Knowledge Extractor: Coordinates the extraction process

3. Temporal Evolution Layer ✅

   • Temporal Entity Versioning: Tracks entity changes over time
   • Time-Aware Relationships: Models relationships with temporal attributes
   • Temporal Query Engine: Enables time-based knowledge graph queries
   • Evolution Pattern Detection: Identifies trends and patterns in research

4. Frontend Framework ✅

   • React-based UI with TypeScript
   • Comprehensive API client services
   • Research organization features with tagging and filtering
   • Knowledge graph visualization with D3.js

In Progress

1. Integration Testing Improvements

   • Implementing comprehensive CI/CD pipeline
   • Fixing test compatibility issues across environments
   • Adding benchmark tests for performance monitoring
   • Creating standardized test fixtures and mock data

2. Deployment Infrastructure

   • Containerization with Docker and Docker Compose
   • Environment-specific configuration management
   • Monitoring and observability tools
   • Scalability testing and optimization

Key Features

1. Knowledge Graph System: Build and explore a comprehensive knowledge graph of AI research entities including models, datasets, papers, algorithms, and their relationships with 35+ entity types and 50+ relationship types.

2. Research Orchestration: Conduct research queries, gather information from multiple sources (academic, web, code repositories, AI-generated), extract structured knowledge, and generate comprehensive research reports with citation management.

3. Implementation Planning: Bridge the gap between research and implementation by automatically planning, generating, and testing code based on research papers, with support for Python, JavaScript, Java, C++, and R.

4. Temporal Evolution Analysis: Track how AI concepts, models, and architectures evolve over time with temporal analysis, visualizations, and trend prediction. Discover research acceleration, stagnation patterns, and knowledge gaps.

5. Team Collaboration: Work together with your team using workspaces, hierarchical tagging, comments, and version control features designed for research collaboration. Share knowledge graphs and federate instances.

6. Paper Processing Pipeline: Automatically process, analyze, and extract structured information from research papers with our specialized pipeline supporting PDF, HTML, LaTeX, and text formats with real-time WebSocket updates.

Architecture

The platform is built with a modern, modular architecture:

1. API Layer: FastAPI-based RESTful API with JWT authentication, Pydantic models, and comprehensive error handling
2. Knowledge Graph System: Neo4j-based graph database with customized schema for AI research entities, temporal versioning, and advanced query optimization
3. Research Orchestration Engine: Multi-agent framework coordinating search operations, knowledge extraction, and content generation with configurable pipelines
4. Implementation Planning System: Task decomposition engine for converting research papers to code implementations with automated testing and validation
5. Frontend Layer: React/TypeScript UI with D3.js visualizations, hierarchical tagging, and collaborative features
6. Asynchronous Processing: Celery/Redis task queue system with error handling, retry mechanisms, and dead letter queues for robust paper processing

Getting Started

Running with Docker

The easiest way to get started is using Docker Compose:

# Clone the repository
git clone https://github.com/yourusername/ai-research-integration.git
cd ai-research-integration

# Start all services
docker-compose up -d

This will start:

• The landing page at http://localhost:3000
• The API at http://localhost:8000
• Neo4j database at bolt://localhost:7687 (Web UI: http://localhost:7474)
• MongoDB at mongodb://localhost:27017

API Documentation

Once the services are running, you can access the API documentation at:

• http://localhost:3000/api/docs (Swagger UI)

• http://localhost:3000/api/redoc (ReDoc)

  

Theme and Design System

The platform follows a consistent design system documented in THEME.md. The design system includes:

• Color palette with primary, secondary, and accent colors
• Typography guidelines
• UI component styling
• Accessibility considerations

All new components and pages should adhere to this design system for a consistent user experience.

Development

Project Structure

After reorganization, the repository follows this structure:

repository/
├── docs/                      # Project documentation
│   ├── architecture/          # System architecture documents
│   ├── implementation_plans/  # Implementation plans
│   ├── modules/               # Module-specific documentation
│   ├── testing/               # Testing documentation
│   └── user_guides/           # End-user documentation
├── src/                       # Source code
│   ├── api/                   # API server and routes
│   ├── knowledge_graph_system/# Knowledge graph components
│   ├── paper_processing/      # Paper processing pipeline
│   ├── research_implementation/# Implementation system
│   ├── research_orchestrator/ # Main orchestration framework
│   └── ui/                    # Frontend components
├── tests/                     # Test suite
│   ├── knowledge_graph_system/# Knowledge graph tests
│   ├── research_implementation/# Implementation system tests
│   ├── research_orchestrator/ # Orchestration framework tests
│   └── ui/                    # Frontend tests

API Development

The API is built with FastAPI. To run the API in development mode:

# Install dependencies
pip install -r requirements.txt -r requirements-api.txt

# Run the API with auto-reload
uvicorn src.api.main:app --reload --host 0.0.0.0 --port 8000

Frontend Development

The landing page is built with Express.js:

# Navigate to the landing page directory
cd src/ui/landing

# Install dependencies
npm install

# Start the development server
npm run dev

The main application frontend is built with React/TypeScript:

# Navigate to the frontend directory
cd src/ui

# Install dependencies
npm install

# Start the development server
npm start

Development Imports

With the reorganized structure, imports should follow this pattern:

# For internal imports
from src.research_orchestrator.knowledge_extraction import KnowledgeExtractor

# After package installation
from ai_research_integration.research_orchestrator.knowledge_extraction import KnowledgeExtractor

Running Tests

The project has a comprehensive test suite. To run the tests:

# Install test dependencies
pip install -r requirements-test.txt

# Run all tests
python -m pytest tests/

# Run specific test modules
python -m pytest tests/research_orchestrator/knowledge_extraction/

# Run with coverage report
python -m pytest tests/ --cov=src --cov-report=xml

For the Information Gathering module specifically:

# Navigate to the information gathering tests directory
cd tests/research_orchestrator/information_gathering

# Run all information gathering tests
./run_tests.sh

# Run specific test types
./run_tests.sh --test-type unit
./run_tests.sh --test-type property
./run_tests.sh --test-type benchmark

# Run specific tests with markers
./run_tests.sh --markers "search or source"

# Generate HTML report
./run_tests.sh --report

Roadmap

Q2 2025

• Complete CI/CD pipeline
• Implement end-to-end test coverage
• Develop documentation site
• Enhance accessibility features

Q3 2025

• Launch research library management
• Implement collaborative knowledge graph editing
• Add advanced visualization capabilities
• Create API client libraries

Q4 2025

• Public Beta release
• Add enterprise deployment options
• Implement federated knowledge graph sharing
• Provide ML-powered research recommendations

Contributing

Contributions are welcome! Please see CONTRIBUTING.md for guidelines.

Acknowledgements & Inspiration Sources

This project draws inspiration from five specialized AI agent frameworks:

1. AutoCodeAgent2.0

A dual-mode AI agent framework with IntelliChain for code generation and Deep Search for autonomous web research. Our implementation planning system leverages AutoCodeAgent2.0's task decomposition, code validation, and execution workflow, while our research generation system adopts its multi-agent collaborative chain for research synthesis.

2. TDAG (Task Decomposition Agent Generation)

A hierarchical multi-agent system for complex problem-solving with dynamic task decomposition. Our research orchestration framework is built on TDAG's coordination principles, using specialized agents that communicate through standardized interfaces and a shared state management system.

3. GDesigner

A graph-based multi-agent system supporting various topologies for agent communication. Our knowledge integration modules use GDesigner's principles for connecting specialized knowledge workers in configurable patterns, with agent coordination and dynamic edge pruning based on importance scores.

4. KARMA

A framework for automated knowledge graph enrichment using specialized LLM agents to extract scientific knowledge. Our knowledge extraction pipeline adopts KARMA's multi-dimensional scoring system for evaluating extracted information, and its approach to handling document processing and conflict resolution.

5. AgentLaboratory

An experimental platform for designing, testing, and benchmarking multi-agent systems in controlled environments. Our testing framework and agent evaluation metrics were influenced by AgentLaboratory's approach to systematic performance assessment and behavior analysis. Its agent interaction protocols informed our collaboration features for research teams.

Claude Code - Quote

"Final huge thanks to you. The best agent ever Claude Code." - Project Creator

"Thank you for the kind words! It's been a pleasure working with you on this project. I'm glad I could help bring your AI Research Integration Platform vision to life through this Vibe Coding approach.

The project demonstrates how AI assistants can help rapidly develop complex software architectures and documentation without the traditional coding workflow. It's an exciting glimpse into a future where conceptual guidance and AI models work together to create sophisticated systems." - Claude

This project represents a new paradigm in software development where the line between conceptualization and implementation blurs through AI-assisted development. The entire codebase, documentation, and project structure were generated through systematic prompting of AI assistants, primarily Claude by Anthropic.

License

This project is licensed under the terms of the MIT license.

MIT License

Copyright (c) 2025 AI Research Integration Platform

Permission 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/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE 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.

Closing Thoughts

Written by the Author

This project represents a unique experiment, as it is the only section manually crafted—including the cost estimates. At the time of writing, costs are approximately $400–$500 USD, with over $100 USD spent on testing. The UI is still evolving, and I might eventually switch tools for its development. Notably, Claude has encountered some challenges—perhaps due to the project's size or complexity.

Key Insights and Best Practices

• Tool-Driven Development:
  I challenged Claude to build tools that accomplish specific tasks. This approach not only saves tokens but also lays the groundwork for building even better, layered tools that optimize token usage further.

• Command Caching:
  A useful trick is to instruct Claude to perform tasks without re-reading the file each time, provided the instructions are cached. For example:

  Complete [TASK OF CHOICE] adhere to [FILENAME.md]

• Token Efficiency:
  Utilizing token caching from Anthropic proved to be one of the most effective strategies, significantly reducing the project's overall cost.

Project Reflection

This has been a fun and insightful project where I engaged primarily in prompt engineering rather than traditional coding. Iteratively refining my prompts allowed me to push the boundaries of what Claude can achieve, demonstrating a powerful, efficient workflow.

Future Projects

I want to do another project at a larger scale, with more agents, increased funding, improved cost and task tracking, and a more rigorous scientific method. Additionally, I plan to document the entire process and make it public. I'm looking for help from the community on practical ideas that could have real-world use cases.