🎯 What I Built
ResearchSwarm is a system that rely on MINIMAX AI via OpenRouter to reimagines how academic research is discovered and connected. It uses the unique capabilities of Agentic Postgres to let several specialized processes work in parallel each on its own isolated database fork to uncover hidden relationships between research papers up to four times faster than traditional methods.
The Inspiration
As a researcher, I’ve often struggled with manually tracking citations, finding cross-disciplinary links, and identifying trends buried in thousands of papers. Most research tools only handle basic keyword searches or limited semantic matching. I wanted to build something that could explore data from different angles at once bringing together multiple analyses to reveal insights that would otherwise take weeks to find.
Core Concept
At the heart of ResearchSwarm is how it uses “Agentic Postgres.”
Each process runs independently on its own database fork, avoiding conflicts and boosting speed:
Citation Analyzer – Maps and explores citation relationships
Topic Discovery Unit – Groups related papers into themes
Connection Finder – Detects links between different domains
Trend Tracker – Monitors how topics evolve over time
Together, these components complete analysis in about 5 seconds compared to 15 seconds or more using sequential execution.
Repository
GitHub:
https://github.com/thaywo/agentic_challenge_backend
https://github.com/thaywo/frontend_researchswarm
Key Features
- Hybrid Search
Combines BM25 keyword ranking with semantic vector search.
POST /api/search/hybrid
{
"query": "quantum machine learning",
"keywordWeight": 0.5,
"vectorWeight": 0.5
}
- Parallel Discovery
Runs all four processes simultaneously.
POST /api/agents/discover
Response:
{
"total_duration": 2845,
"agents": 4,
"successful": 4,
"results": [...]
}
- Citation Network
Displays an interactive graph of how papers reference one another.
GET /api/papers/:id/network?depth=2
- Cross-Domain Connections
Reveals research that bridges different academic areas.
GET /api/analytics/connections/cross-domain
Screenshots
- Hybrid Search Interface
Combines keyword and semantic results for better discovery.
- Parallel Execution Dashboard
All four processes running at once on separate database forks.
- Citation Network Visualization
Interactive graph showing citation relationships.
- Cross-Domain Links
Papers connecting quantum computing and machine learning.
🚀 How Agentic Postgres Was Used
- Fast Database Forks (Zero-Copy)
Challenge: Running multiple tasks at once without conflicts.
Solution: Each process creates its own zero-copy database fork using Tiger Cloud.
async createFork(name) {
const forkName = ${name}-${Date.now()};
const command = tiger service fork create --name ${forkName};
const { stdout } = await execAsync(command);
const info = JSON.parse(stdout);
return {
forkId: info.service_id,
connectionString: info.connection_string
};
}
Impact:
Forks created in under 500 ms
No extra storage until data diverges
Automatic cleanup after completion
4× faster execution
- Hybrid Search (pg_textsearch + pgvector)
Challenge: Academic search needs both keyword precision and contextual meaning.
Solution: Combine BM25 and vector similarity in a single query.
CREATE FUNCTION hybrid_search(...) RETURNS TABLE (...) AS $$
BEGIN
WITH keyword_search AS (...),
vector_search AS (...)
SELECT COALESCE(k.id, v.id),
(k.keyword_score * keyword_weight + v.vector_score * vector_weight) AS combined_score
FROM keyword_search k
FULL OUTER JOIN vector_search v ON k.id = v.id
ORDER BY combined_score DESC;
END;
$$ LANGUAGE plpgsql;
Impact:
Balances precision and context
Handles synonyms and related concepts
Responds in under 200 ms for 10K+ papers
- Tiger MCP Integration
Challenge: Each component needed awareness of the database schema.
Solution: Used Tiger MCP (Model Context Protocol) to provide schema context.
const mcpContext = await tigerMCP.getContext({
schema: 'research_discovery',
tables: ['papers', 'citations', 'topics'],
include_docs: true
});
Impact:
Automatically generates correct SQL queries
Reduces setup time by half
Ensures consistent structure across components
- Tiger CLI for DevOps
Challenge: Simplifying database infrastructure management.
Solution: Used Tiger CLI for automation.
tiger service create --name research-swarm --addons time-series,ai
tiger service fork create --name citation-analyzer-123
tiger db connection-string --service-id ywwb0507h1
Impact:
Easy setup and monitoring
Ready for CI/CD pipelines
Clean and intuitive workflow
- TimescaleDB Hypertables
Challenge: Efficient trend tracking over time.
Solution: Used TimescaleDB hypertables for time-series optimization.
CREATE TABLE trends (
topic_id INTEGER,
time_period DATE,
paper_count INTEGER,
citation_velocity FLOAT,
growth_rate FLOAT,
is_emerging BOOLEAN
);
SELECT create_hypertable('trends', 'time_period', chunk_time_interval => INTERVAL '1 month');
Impact:
10× faster time-based queries
Automatic compression
Smooth long-term data analysis
💡 Experience Summary
What Worked Well
Instant Forks: Creating large database forks in seconds.
Hybrid Search: Strong combination of text and vector search.
Developer Tools: Excellent CLI and dashboard experience.
Challenges and Fixes
Fork Management: Solved with automatic cleanup in the orchestrator.
Search Weighting: Added configurable keyword/vector balance.
Result Merging: Used JSONB columns for flexible data storage.
Key Takeaways
Agentic Postgres changes the way databases are used.
It’s not just a place to store data it’s an active partner in data analysis:
Databases can fork and parallelize their own workloads
Searches understand meaning, not just text
Infrastructure adjusts automatically based on need
This marks a major shift in how large-scale data exploration can be done.
What’s Next
Live arXiv Integration – Automatically fetch new papers daily.
More Specialized Components – For summarization and collaboration suggestions.
Advanced Visuals – 3D citation graphs, timeline views, and heatmaps.
Suggestions for the Tiger Team
Fork Management UI: Add a visual fork tree and analytics.
pg_textsearch Upgrades: Include built-in hybrid functions and multilingual support.
MCP Documentation: Provide more examples and integration tips.
📊 Performance Metrics
Metric Value
Parallel Execution 2.8 s (vs 12 s sequential)
Fork Creation Time < 500 ms
Hybrid Search Latency < 200 ms (10 K papers)
Citation Network Query < 300 ms (depth = 2)
Database Size ~ 50 MB
Fork Storage Overhead 0 bytes (until divergence)
🛠️ Tech Stack
Backend: Node.js, Express
Database: PostgreSQL 16 (Tiger Cloud)
Extensions: pgvector, timescaledb, pg_textsearch
CLI: Tiger CLI v0.15.0
Hosting: Tiger Cloud
Deployment: Tiger Cloud Service (ywwb0507h1)
🎓 Why It Stands Out
True Parallel Architecture – Independent forks for full isolation
Production-Ready Implementation – Structured APIs, logging, and monitoring
Proven Results – Real, measurable performance gains
Meaningful Use Case – Solves an everyday research challenge
🙏 Acknowledgments
Special thanks to:
Tiger Data Team – For creating Agentic Postgres
TimescaleDB – For the solid foundation
DEV Community – For hosting this challenge
Researchers worldwide – For inspiring this project
🔗 Final Links
Live Demo: https://agentic-researchswarm.vercel.app/
Built with passion for the Agentic Postgres Challenge.
ResearchSwarm – where isolated database forks work together to uncover what traditional systems overlook.
Top comments (1)
Hii there!!
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