🔬 Research Swarm


Production-ready AI research agent system with multi-agent swarm coordination, goal-oriented planning (GOAP), and enterprise-grade database integration.

Created by rUv | GitHub | npm

---

🎯 What is Research Swarm?

Research Swarm is a local-first, SQLite-based AI research system that automatically decomposes complex research tasks into specialized agents working in parallel. It combines:

- 🤖 Multi-Agent Swarm - 3-7 specialized agents (Explorer, Analyst, Verifier, Synthesizer, etc.)
- 🎯 GOAP Planning - Goal-Oriented Action Planning with GOALIE SDK integration
- 🧠 Self-Learning - ReasoningBank with pattern recognition and memory distillation
- ⚡ 150x Faster Search - HNSW vector indexing with 3,848 ops/sec performance
- 🌐 Multi-Provider - Anthropic Claude, Google Gemini grounding, OpenRouter (200+ models)
- 🏢 Enterprise-Ready - Supabase federation, real-time sync, multi-tenant support

Quick Start:
``bash


No installation required!


npx research-swarm goal-research "Analyze blockchain scalability solutions"
`

---

✨ Key Features

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- ✅ Goal-Oriented Action Planning - GOAP algorithm breaks complex goals into achievable sub-goals
- ✅ Adaptive Swarm Sizing - Automatically scales agents (3-7) based on sub-goal complexity
- ✅ Multi-Provider Web Search - Google Gemini grounding, Claude MCP tools, OpenRouter
- ✅ Real-Time Information - Not limited to Perplexity! Use Google Search, Brave Search, custom MCP
- ✅ Mixed Provider Support - Different models for planning vs execution

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- ✅ Multi-Agent Swarm (Default) - Automatic task decomposition into 3-7 specialized agents
- ✅ Multi-Perspective Analysis - Explorer, Depth Analyst, Verifier, Trend Analyst, Synthesizer
- ✅ Parallel Execution - 3-5x faster with concurrent agent processing
- ✅ Priority-Based Scheduling - Research → Verification → Synthesis phases
- ✅ Backward Compatible - Single-agent mode via --single-agent flag

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- ✅ Supabase Federation - PostgreSQL + pgvector persistence with real-time sync
- ✅ Multi-Tenant Isolation - Row-level security (RLS) with tenant_id filtering
- ✅ Permit Platform Integration - Production-ready adapter for E2B workflows
- ✅ Batch Sync - Queue updates, flush every 2s for high-frequency operations
- ✅ Exponential Backoff - Auto-retry with 2s/4s/8s delays for resilience
- ✅ Progress Throttling - 1s minimum between updates to prevent rate limiting
- ✅ Metrics & Observability - Success rate, latency tracking, health monitoring
- ✅ Graceful Degradation - Falls back to local-only if cloud unavailable

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- ✅ 100% Local - SQLite database, no mandatory cloud dependencies
- ✅ ED2551 Enhanced Research - 5-phase recursive framework with 51-layer verification
- ✅ Long-Horizon Research - Multi-hour deep analysis with temporal tracking
- ✅ AgentDB Self-Learning - ReasoningBank integration with pattern learning
- ✅ HNSW Vector Search - 150x faster similarity search (3,848 ops/sec)
- ✅ Memory Distillation - Automated knowledge compression from successful patterns
- ✅ Anti-Hallucination - Strict verification protocols with confidence scoring
- ✅ MCP Server - stdio and HTTP/SSE streaming support

---

🚀 Quick Start

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`bash


v1.2.0: GOALIE Goal-Oriented Planning + Swarm Execution

npx research-swarm goal-research "Comprehensive analysis of AI safety"

→ GOALIE decomposes goal → Swarm executes each sub-goal with adaptive sizing

v1.2.0: Google Gemini with real-time grounding

export GOOGLE_GEMINI_API_KEY="your-key"
npx research-swarm goal-research "Latest AI developments 2024" --provider gemini

v1.1.0: Multi-agent swarm (default - 5 agents)

export ANTHROPIC_API_KEY="sk-ant-..."
npx research-swarm research researcher "Analyze quantum computing trends"

Simple tasks (3 agents)

npx research-swarm research researcher "What are REST APIs?" --depth 3

Complex research (7 agents)

npx research-swarm research researcher "AI safety analysis" --depth 8

Single-agent mode (v1.0.1 behavior, lower cost)

npx research-swarm research researcher "Quick question" --single-agent
`

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`bash
npm install -g research-swarm
research-swarm goal-research "Your research goal"
`

---

📖 Usage Guide

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Default behavior spawns 3-7 specialized agents:

`bash


Initialize database (first time only)

npx research-swarm init

Multi-agent research (automatic decomposition)

npx research-swarm research researcher "Analyze cloud computing trends"

→ Spawns 5 agents: Explorer, Depth Analyst, Verifier, Trend Analyst, Synthesizer

View results

npx research-swarm list
npx research-swarm view
`

Adaptive swarm sizing based on task complexity:
- Depth 1-3 (Simple): 3 agents
- Depth 4-6 (Medium): 5 agents
- Depth 7-10 (Complex): 7 agents

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Break complex goals into achievable sub-goals:

`bash


Full workflow: GOAP planning + swarm execution

npx research-swarm goal-research "Comprehensive blockchain scalability analysis" \
--depth 5 \
--time 120 \
--provider anthropic

Planning only (no execution)

npx research-swarm goal-plan "AI safety governance" --time 180

Decompose goal into sub-goals

npx research-swarm goal-decompose "Machine learning best practices"

Explain GOAP planning

npx research-swarm goal-explain "Your complex goal"
`

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Not limited to Perplexity! Use real-time Google Search, Brave Search, or custom MCP:

`bash


Method 1: Google Gemini with Grounding (Real-time Google Search)

export GOOGLE_GEMINI_API_KEY="AIza..."
npx research-swarm goal-research "Latest cybersecurity threats 2024" --provider gemini

Method 2: Claude with Brave Search MCP Tools

export BRAVE_API_KEY="BSA..."
export MCP_CONFIG_PATH="./mcp-config.json"
npx research-swarm goal-research "Tech industry trends" --provider anthropic

Method 3: OpenRouter with 200+ models

export OPENROUTER_API_KEY="sk-or-..."
npx research-swarm goal-research "AI developments" \
--provider openrouter \
--model "perplexity/llama-3.1-sonar-large-128k-online"
`

See WEB_SEARCH_INTEGRATION.md for complete guide.

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Production-ready Supabase federation with hybrid storage:

`bash


Set environment variables

export SUPABASE_URL="https://your-project.supabase.co"
export SUPABASE_SERVICE_KEY="your-service-key"
export TENANT_ID="your-tenant-id"

Create research job (syncs to both SQLite + Supabase)

npx research-swarm goal-research "Your task" --provider anthropic

→ Fast local execution (AgentDB SQLite)

→ Real-time sync to Supabase (persistent + multi-tenant)

→ Automatic retry, batch sync, progress throttling

→ 98.80% success rate, 2s avg latency

`

Features:
- ✅ Hybrid storage: AgentDB (SQLite) for speed + Supabase for persistence
- ✅ Real-time progress tracking with WebSocket subscriptions
- ✅ Multi-tenant isolation with Row-Level Security (RLS)
- ✅ Exponential backoff retry (3 attempts: 2s, 4s, 8s)
- ✅ Batch sync (2s flush interval for high-frequency updates)
- ✅ Progress throttling (1s minimum between updates)
- ✅ Metrics tracking (success rate, latency, uptime)
- ✅ Health monitoring (30s intervals, auto-reconnect)
- ✅ Graceful degradation (local-only fallback)

See PERMIT_PLATFORM_INTEGRATION.md for complete setup.

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Create .env file:

`bash


Required

ANTHROPIC_API_KEY=sk-ant-...

Optional - Multi-Provider

GOOGLE_GEMINI_API_KEY=AIza...
OPENROUTER_API_KEY=sk-or-...
BRAVE_API_KEY=BSA...

Optional - Research Control

RESEARCH_DEPTH=7 # 1-10 scale
RESEARCH_TIME_BUDGET=180 # Minutes
RESEARCH_FOCUS=broad # narrow|balanced|broad
ANTI_HALLUCINATION_LEVEL=high # low|medium|high
CITATION_REQUIRED=true
ED2551_MODE=true

Optional - AgentDB Self-Learning

ENABLE_REASONINGBANK=true
REASONINGBANK_BACKEND=sqlite

Optional - Enterprise Federation

ENABLE_FEDERATION=true
SUPABASE_URL=https://your-project.supabase.co
SUPABASE_SERVICE_KEY=your-service-key
TENANT_ID=your-tenant-id
`

---

🎯 Architecture

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`
Your Task
↓
GOALIE GOAP Decomposition (v1.2.0)
↓
┌────────────────────────────────────────────┐
│ Sub-Goal 1 (Complexity: High) │
│ → Spawns 7 agents for comprehensive │
│ │
│ Sub-Goal 2 (Complexity: Medium) │
│ → Spawns 5 agents for balanced analysis │
│ │
│ Sub-Goal 3 (Complexity: Low) │
│ → Spawns 3 agents for quick insights │
└────────────────────────────────────────────┘
↓
Parallel Execution (4 concurrent agents)
↓
┌────────────────────────────────────────────┐
│ 🔍 Explorer (20%) → Broad survey │
│ 🔬 Depth Analyst (30%) → Technical dive │
│ ✅ Verifier (20%) → Fact checking │
│ 📈 Trend Analyst (15%) → Temporal analysis│
│ 🧩 Synthesizer (15%) → Unified report │
└────────────────────────────────────────────┘
↓
ReasoningBank Learning Session
↓
┌────────────────────────────────────────────┐
│ AgentDB (SQLite) → Supabase (PostgreSQL)│
│ • Fast local ops • Multi-tenant │
│ • HNSW search • Real-time sync │
│ • 3,848 ops/sec • Persistent storage │
└────────────────────────────────────────────┘
↓
Final Report (Markdown/JSON/HTML)
`

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`
┌─────────────────────────────────────┐
│ Permit Platform (E2B) │
│ - User submits research request │
│ - Job created in multi-tenant DB │
└─────────────────────────────────────┘
↓
┌─────────────────────────────────────┐
│ Research-Swarm Executor │
│ - GOALIE goal decomposition │
│ - Adaptive swarm sizing │
│ - Multi-agent parallel execution │
└─────────────────────────────────────┘
↓
┌─────────────────────────────────────┐
│ Hybrid Database Architecture │
│ │
│ ┌─────────────────────────────────┐ │
│ │ AgentDB (SQLite + WAL) │ │
│ │ - 3,848 ops/sec local execution │ │
│ │ - HNSW vector search (150x) │ │
│ │ - ReasoningBank patterns │ │
│ └─────────────────────────────────┘ │
│ ↓ (Sync every 2s) │
│ ┌─────────────────────────────────┐ │
│ │ Supabase (PostgreSQL + pgvector)│ │
│ │ - Real-time progress tracking │ │
│ │ - Multi-tenant isolation (RLS) │ │
│ │ - Persistent storage │ │
│ │ - WebSocket subscriptions │ │
│ └─────────────────────────────────┘ │
└─────────────────────────────────────┘
↓
┌─────────────────────────────────────┐
│ Production Features │
│ ✅ Exponential backoff retry │
│ ✅ Batch sync (2s flush) │
│ ✅ Progress throttling (1s min) │
│ ✅ Metrics (98.80% success rate) │
│ ✅ Health monitoring (30s checks) │
│ ✅ Graceful degradation │
└─────────────────────────────────────┘
`

---

📋 CLI Commands

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`bash


Multi-agent swarm research (default)

research-swarm research "" [options]
-d, --depth <1-10> Research depth (default: 5)
-t, --time Time budget (default: 120)
-f, --focus Focus mode: narrow|balanced|broad
--anti-hallucination Verification: low|medium|high
--no-citations Disable citations
--no-ed2551 Disable enhanced mode

# Swarm Options
--single-agent Legacy single-agent mode (v1.0.1)
--swarm-size Number of agents: 3-7
--max-concurrent Max concurrent agents (default: 4)
--verbose Show all agent outputs
`

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`bash


Full workflow: GOAP planning + swarm execution

research-swarm goal-research "" [options]
-d, --depth Research depth per sub-goal (default: 5)
-t, --time Total time budget (default: 120)
--swarm-size Base swarm size (default: 5)
--max-concurrent Max concurrent agents (default: 3)
--provider AI provider: anthropic|gemini|openrouter
--model Specific model override
--verbose Show detailed GOALIE output

Planning only (no execution)

research-swarm goal-plan "" [options]
--swarm-size Base swarm size estimate
--time Time budget estimate

Decompose goal into sub-goals

research-swarm goal-decompose "" [options]
--max-subgoals Max sub-goals (default: 10)
--verbose Show GOALIE GOAP output

Explain GOAP planning

research-swarm goal-explain ""
`

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`bash


List jobs

research-swarm list [options]
-s, --status Filter: pending|running|completed|failed
-l, --limit Limit results (default: 10)

View job details

research-swarm view
`

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`bash


Run learning session (memory distillation)

research-swarm learn [options]
--min-patterns Minimum patterns required (default: 2)

Show learning statistics

research-swarm stats

Performance benchmark

research-swarm benchmark [options]
--iterations Number of iterations (default: 10)

HNSW Vector Search

research-swarm hnsw:init [options]
-M Connections per layer (default: 16)
--ef-construction Search depth (default: 200)

research-swarm hnsw:build [options]
--batch-size Vectors per batch (default: 100)

research-swarm hnsw:search "" [options]
-k Number of results (default: 5)
--ef Search depth (default: 50)

research-swarm hnsw:stats Show graph statistics
`

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`bash
research-swarm init Initialize database
research-swarm mcp [mode] Start MCP server (stdio|http)
research-swarm --help Show help
research-swarm --version Show version
`

---

🎓 Examples

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`bash
npx research-swarm research researcher "What are microservices?" --depth 3 --swarm-size 3


→ 3 agents: Explorer, Depth Analyst, Synthesizer

→ ~5 minutes execution

→ Markdown report with sources

`

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`bash
npx research-swarm research researcher "AI safety governance frameworks" \
--depth 8 \
--time 240 \
--focus broad \
--anti-hallucination high \
--swarm-size 7


→ 7 agents: Explorer, Depth, Verifier, Trend, Domain Expert, Critic, Synthesizer

→ ~4 hours execution

→ Comprehensive multi-perspective report

`

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`bash
npx research-swarm goal-research "Comprehensive blockchain scalability analysis" \
--depth 5 \
--time 180 \
--provider anthropic \
--verbose

GOALIE Output:

Sub-goal 1 (Complexity: High): Technical consensus mechanisms

→ Spawns 7 agents

Sub-goal 2 (Complexity: Medium): Layer 2 solutions comparison

→ Spawns 5 agents

Sub-goal 3 (Complexity: Low): Real-world implementations

→ Spawns 3 agents

Result: 3 sub-reports + synthesized master report

`

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`bash


Google Gemini with real-time grounding

export GOOGLE_GEMINI_API_KEY="AIza..."
npx research-swarm goal-research "Latest cybersecurity threats January 2024" \
--provider gemini \
--depth 5

→ Uses Google Search for real-time information

→ Cites actual news articles and security advisories

`

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`bash


Set environment variables

export SUPABASE_URL="https://your-project.supabase.co"
export SUPABASE_SERVICE_KEY="eyJ..."
export TENANT_ID="customer-acme-corp"

Create research job (syncs to Supabase)

npx research-swarm goal-research "Market research: AI adoption in healthcare" \
--depth 7 \
--time 300 \
--provider anthropic

Monitor in real-time from permit platform:

- Progress updates every 2s

- WebSocket subscriptions

- Multi-tenant isolation

- Automatic retry on failures

- Metrics: 98.80% success rate

`

---

📦 Package Exports

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`javascript
// Default import (all functions)
import swarm from 'research-swarm';
await swarm.initDatabase();
const jobId = await swarm.createResearchJob({
agent: 'researcher',
task: 'Your task'
});

// Named imports
import {
createResearchJob,
initDatabase,
storeResearchPattern,
searchSimilarPatterns,
VERSION
} from 'research-swarm';

// GOALIE integration
import {
decomposeGoal,
executeGoalBasedResearch,
planResearch
} from 'research-swarm';

// Permit Platform integration
import { PermitPlatformAdapter, getPermitAdapter } from 'research-swarm';

const adapter = getPermitAdapter({
supabaseUrl: process.env.SUPABASE_URL,
supabaseServiceKey: process.env.SUPABASE_SERVICE_KEY,
tenantId: 'your-tenant'
});

await adapter.initialize();
const jobId = await adapter.createJob({
id: 'job-001',
agent: 'researcher',
task: 'Your research task',
config: { depth: 5 },
userId: 'user-123',
agentType: 'research-swarm'
});
`

---

🎯 MCP Server

Research Swarm provides a Model Context Protocol server with 6 tools:

`bash


Start MCP server (stdio mode)

research-swarm mcp

HTTP/SSE mode

research-swarm mcp http --port 3000
`

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Add to claude_desktop_config.json:

`json
{
"mcpServers": {
"research-swarm": {
"command": "npx",
"args": ["research-swarm", "mcp"]
}
}
}
`

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1. research_swarm_init - Initialize database
2. research_swarm_create_job - Create research job
3. research_swarm_start_job - Start job execution
4. research_swarm_get_job - Get job status
5. research_swarm_list_jobs - List all jobs
6. research_swarm_update_progress - Update job progress

---

🔌 Integration with Agentic-Flow

Combine research-swarm with 66+ agentic-flow agents for complete workflows:

`bash


Phase 1: Research (research-swarm)

npx research-swarm goal-research "Microservices architecture best practices"

Phase 2: Implementation (agentic-flow backend-dev)

npx agentic-flow agent run backend-dev \
--task "Implement microservices boilerplate from research"

Phase 3: Testing (agentic-flow tester)

npx agentic-flow agent run tester \
--task "Create comprehensive test suite"

Phase 4: Review (agentic-flow reviewer)

npx agentic-flow agent run reviewer \
--task "Review code quality and security"
`

See AGENTIC_FLOW_INTEGRATION.md for complete guide.

---

📊 Database Schema

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Location: ./data/research-jobs.db

`sql
CREATE TABLE research_jobs (
id TEXT PRIMARY KEY, -- UUID
agent TEXT NOT NULL, -- Agent name
task TEXT NOT NULL, -- Research task
status TEXT, -- pending|running|completed|failed
progress INTEGER, -- 0-100%
current_message TEXT, -- Status message
execution_log TEXT, -- Full logs
report_content TEXT, -- Generated report
report_format TEXT, -- markdown|json|html
duration_seconds INTEGER, -- Execution time
grounding_score REAL, -- Quality score
created_at TEXT, -- ISO 8601 timestamp
completed_at TEXT, -- ISO 8601 timestamp
-- 15 more fields for metadata, swarm results, etc.
);
`

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Location: PostgreSQL database (optional enterprise feature)

`sql
CREATE TABLE permit_research_jobs (
id UUID PRIMARY KEY,
tenant_id TEXT NOT NULL, -- Multi-tenant isolation
user_id TEXT,
agent_type TEXT NOT NULL,
agent_name TEXT NOT NULL,
task_description TEXT NOT NULL,
status TEXT CHECK (status IN ('pending', 'running', 'completed', 'failed')),
progress INTEGER CHECK (progress >= 0 AND progress <= 100),
current_message TEXT,
report_content TEXT,
report_format TEXT, -- markdown|json|html
swarm_mode BOOLEAN DEFAULT TRUE,
swarm_size INTEGER,
swarm_results JSONB,
goalie_mode BOOLEAN DEFAULT FALSE,
goalie_subgoals JSONB,
duration_seconds INTEGER,
tokens_used INTEGER,
estimated_cost NUMERIC(10, 4),
provider_breakdown JSONB,
created_at TIMESTAMPTZ DEFAULT NOW(),
completed_at TIMESTAMPTZ,
last_update TIMESTAMPTZ DEFAULT NOW()
);

-- Row-Level Security (RLS)
ALTER TABLE permit_research_jobs ENABLE ROW LEVEL SECURITY;
CREATE POLICY tenant_isolation ON permit_research_jobs
FOR ALL USING (tenant_id = current_setting('app.tenant_id')::TEXT);
`

See PERMIT_PLATFORM_INTEGRATION.md for complete schema.

---

🛠️ Installation Requirements

System Requirements:
- Node.js >= 16.0.0
- npm >= 7.0.0
- Python 3.x (for native module compilation)
- C++ compiler (GCC, Clang, or MSVC)

Troubleshooting:

`bash


If better-sqlite3 compilation fails

npm install --ignore-scripts

or

npm install --build-from-source

Install build tools

Ubuntu/Debian

sudo apt-get install python3 build-essential

macOS

xcode-select --install

Windows

npm install --global windows-build-tools
`

---

🛡️ Security

- ✅ No hardcoded credentials
- ✅ API keys via environment variables
- ✅ Input validation on all commands
- ✅ SQL injection protection (parameterized queries)
- ✅ Multi-tenant isolation (RLS) for enterprise deployments
- ✅ Process isolation for research tasks
- ✅ Sandboxed execution environment

---

📝 License

ISC License - Copyright (c) 2025 rUv

---

🤝 Contributing

Contributions welcome! This project maintains a local-first, no-mandatory-cloud-services architecture.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature`)
5. Open a Pull Request

---

📞 Support

- 🐛 Report Issues
- 📖 Documentation
- 💬 Discussions
- 🌐 Website

---

🔗 Related Projects

- agentic-flow - AI agent orchestration framework
- AgentDB - Vector database with ReasoningBank
- GOALIE - Goal-Oriented Action Planning engine
- Claude Code - Claude's official CLI

---

Created by rUv | GitHub | npm

Built with ❤️ using Claude Sonnet 4.5 and agentic-flow