ResearchGravity
Dicoangelo4 yıldız
Metaventions AI Research Framework — Multi-tier signal capture for frontier intelligence. Architected Intelligence.
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Metaventions AI Research Framework — Multi-tier signal capture for frontier intelligence. Architected Intelligence.
Production-hardened API with enterprise security.
# Get JWT token
curl -X POST http://localhost:3847/api/auth/token \
-H "Content-Type: application/json" \
-d '{"client_id": "my-app", "scope": "write"}'
# Use token
curl -H "Authorization: Bearer <token>" http://localhost:3847/api/auth/me
# Or use API key
curl -H "X-API-Key: <your-api-key>" http://localhost:3847/api/v2/stats
export RG_SECRET_KEY=$(python -c "import secrets; print(secrets.token_hex(32))")
export RG_API_KEY="your-service-api-key"
export RG_LOG_LEVEL="INFO" # DEBUG, INFO, WARNING, ERROR
export RG_LOG_JSON="true" # JSON format for production
From manual workflow to intelligent auto-capture. 3x faster research sessions with real-time URL capture.
python3 scripts/session/repl.py
# Commands:
rg> start "multi-agent orchestration" # Initialize session
rg> url https://arxiv.org/... # Log URL (auto-classify)
rg> finding "Key insight about..." # Capture finding
rg> predict # Session quality prediction
rg> search "consensus algorithms" # Semantic search past sessions
rg> archive # Finalize session
python3 scripts/session/auto_capture_v2.py scan # Scan last 24 hours
python3 scripts/session/auto_capture_v2.py scan --hours 48
python3 scripts/session/auto_capture_v2.py status # Show capture stats
python3 scripts/prediction/intelligence.py predict "task" # Session quality prediction
python3 scripts/prediction/intelligence.py optimal-time # Best hour for deep work
python3 scripts/prediction/intelligence.py errors "context" # Likely errors + prevention
python3 scripts/prediction/intelligence.py patterns # Session patterns
python3 scripts/session/watcher.py daemon # Start as background daemon
python3 scripts/session/watcher.py status # Check daemon status
python3 scripts/session/watcher.py stop # Stop daemon
Priority: Qdrant → sqlite-vec → FTS fallback
- Qdrant: Full semantic search (requires server)
- sqlite-vec: Single-file vectors (offline capable)
- FTS: Full-text search fallback (always available)
Priority: Cohere v4 → Cohere v3 → SBERT offline
Cohere embed-v4.0 (default):
- Multimodal (text + images)
- 128k context window
- Matryoshka dimensions: 256, 512, 1024, 1536
Dimension Options:
- 1536d: Maximum quality
- 1024d: Balanced (default)
- 512d: 50% storage savings
- 256d: 83% storage savings
Fallback Chain:
- Cohere v4 → Cohere v3 → SBERT (all-MiniLM-L6-v2)
Auto-switches on API failure. No manual configuration needed.
The AI Second Brain is now complete. Full infrastructure for sovereign knowledge management.
┌──────────────────────────────────────────────────────────────────────────────┐
│ CHIEF OF STAFF INFRASTRUCTURE │
├──────────────────────────────────────────────────────────────────────────────┤
│ │
│ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │
│ │ CAPTURE │───▶│ STORAGE │───▶│ INTELLIGENCE│───▶│ RETRIEVAL │ │
│ │ │ │ TRIAD │ │ │ │ API │ │
│ │ Sessions │ │ │ │ Writer │ │ │ │
│ │ URLs │ │ SQLite │ │ Critic │ │ REST /api/* │ │
│ │ Findings │ │ Qdrant │ │ Oracle │ │ Graph /v2 │ │
│ │ Transcripts │ │ Graph │ │ Evidence │ │ SDK │ │
│ └─────────────┘ └─────────────┘ └─────────────┘ └─────────────┘ │
│ │
│ ┌────────────────────────────────────────────────────────────────────────┐ │
│ │ GRAPH INTELLIGENCE │ │
│ │ │ │
│ │ Sessions ──contains──▶ Findings ──cites──▶ Papers │ │
│ │ │ │ │ │ │
│ │ └──────enables─────────┴────derives_from───┘ │ │
│ │ │ │
│ │ 11,579 Nodes • 13,744 Edges • Concept Clusters • Lineage │ │
│ └────────────────────────────────────────────────────────────────────────┘ │
│ │
└──────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────┐
│ COGNITIVE PRECISION BRIDGE (CPB) │
├─────────────────────────────────────────────────────────────────────────┤
│ │
│ Query → [Complexity Analysis] → Path Selection → Execution → DQ Score │
│ │
│ ┌──────────┬──────────┬──────────┬──────────┬──────────┐ │
│ │ DIRECT │ RLM │ ACE │ HYBRID │ CASCADE │ │
│ │ <0.2 │ 0.2-0.5 │ 0.5-0.7 │ >0.7+ │ >0.7 │ │
│ │ Simple │ Context │ Consensus│ Combined │ Full │ │
│ │ ~1s │ ~5s │ ~5s │ ~10s │ ~15s │ │
│ └──────────┴──────────┴──────────┴──────────┴──────────┘ │
│ │
│ 5-Agent ACE Ensemble: │
│ 🔬 Analyst | 🤔 Skeptic | 🔄 Synthesizer | 🛠️ Pragmatist | 🔭 Visionary │
│ │
└─────────────────────────────────────────────────────────────────────────┘
Research-grounded answers with 95%+ quality target. Combines tiered search, grounded generation, and cutting-edge convergence research.
┌─────────────────────────────────────────────────────────────────────────┐
│ PRECISION MODE v2 PIPELINE │
├─────────────────────────────────────────────────────────────────────────┤
│ │
│ Query │
│ │ │
│ ▼ PHASE 1: TIERED SEARCH (ResearchGravity methodology) │
│ │ ├── Tier 1: arXiv, Labs, Industry News │
│ │ ├── Tier 2: GitHub, Benchmarks, Social │
│ │ └── Tier 3: Internal learnings (Qdrant) │
│ │ │
│ ▼ PHASE 2: CONTEXT GROUNDING │
│ │ └── Build citation-ready context (agents cite ONLY these) │
│ │ │
│ ▼ PHASE 3: GROUNDED CASCADE (7 agents) │
│ │ └── 🔬🤔🔄🛠️🔭📚💡 with citation enforcement │
│ │ │
│ ▼ PHASE 4: MAR CONSENSUS (Multi-Agent Reflexion) │
│ │ └── ValidityCritic + EvidenceCritic + ActionabilityCritic │
│ │ │
│ ▼ PHASE 5: TARGETED REFINEMENT (IMPROVE pattern) │
│ │ └── Fix weakest DQ dimension per retry │
│ │ │
│ ▼ PHASE 6: EDITORIAL FRAME │
│ │ └── Extract thesis / gap / innovation direction │
│ │ │
│ ▼ Result (DQ score + verifiable citations) │
│ │
└─────────────────────────────────────────────────────────────────────────┘
Usage:
python3 -m cpb precision "your research question" --verbose
Traditional research workflows fail at the frontier:
ResearchGravity solves this with:
ResearchGravity/
│
├── api/ # REST API Server (v5.0+)
│ ├── server.py # FastAPI on port 3847 — 25 endpoints
│ └── routes/ # API route modules
│
├── capture/ # Event capture & normalization
├── chrome-extension/ # Browser extension for URL capture
├── cli/ # CLI Package (v6.0) — REPL commands & UI
│
├── coherence_engine/ # Cross-platform coherence detection
├── cpb/ # Cognitive Precision Bridge (v4.0)
├── critic/ # Writer-Critic validation system (v5.0)
├── dashboard/ # Web dashboard UI
├── delegation/ # Intelligent delegation (arXiv:2602.11865)
│
├── docs/ # All documentation
│ ├── context-packs/ # Context pack design & implementation docs
│ ├── meta-learning/ # Meta-learning architecture docs
│ ├── phases/ # Phase completion records
│ ├── prds/ # Product requirement documents
│ ├── routing/ # Routing workflow docs
│ └── ucw/ # UCW whitepaper & cognitive profile
│
├── graph/ # Graph Intelligence (v5.0) — 11K nodes
├── mcp_raw/ # MCP protocol & embeddings layer
├── methods/ # Research methodology definitions
├── notebooklm_mcp/ # NotebookLM MCP server (37 tools)
│
├── scripts/ # All utility scripts
│ ├── backfill/ # Backfill & migration (9 scripts)
│ ├── coherence/ # Coherence analysis pipeline
│ ├── context-packs/ # Context pack build/select/metrics
│ ├── evidence/ # Evidence extraction & validation
│ ├── importers/ # Platform importers (ChatGPT, Grok, CLI)
│ ├── prediction/ # Intelligence & prediction engine
│ ├── proof/ # Demo proof & interactive deck
│ ├── routing/ # Routing metrics & research sync
│ ├── session/ # Session management (status, init, REPL)
│ └── visual/ # Visual generation scripts
│
├── storage/ # Storage Engine — SQLite + Qdrant + sqlite-vec
├── tests/ # All test files
├── ucw/ # Universal Cognitive Wallet
├── webhook/ # Webhook event receiver
│
├── mcp_server.py # MCP server entry point
├── setup.sh # Bootstrap script
├── requirements.txt # Python dependencies
└── ruff.toml # Linter config
The Cognitive Precision Bridge provides precision-aware AI orchestration.
from cpb import cpb, analyze, score_response
# Analyze query complexity
result = analyze("Design a distributed cache system")
print(f"Complexity: {result['complexity_score']:.2f}")
print(f"Path: {result['selected_path']}")
# Build ACE consensus prompts (5 agents)
prompts = cpb.build_ace_prompts("What's the best auth strategy?")
for p in prompts:
print(f"[{p['agent']}] {p['system_prompt'][:50]}...")
# Score response quality
dq = score_response(query, response)
print(f"DQ: {dq.overall:.2f} (V:{dq.validity:.2f} S:{dq.specificity:.2f} C:{dq.correctness:.2f})")
# Analyze query complexity
python3 -m cpb.cli analyze "Your query here"
# Score a response
python3 -m cpb.cli score --query "Q" --response "R"
# View DQ statistics
python3 -m cpb.cli stats --days 30
# Check CPB status
python3 -m cpb.cli status
# Via routing-metrics
python3 scripts/routing/routing-metrics.py cpb analyze "Your query"
python3 scripts/routing/routing-metrics.py cpb status
# Clone the repository
git clone https://github.com/Dicoangelo/ResearchGravity.git
cd ResearchGravity
# Create virtual environment
python3 -m venv .venv
source .venv/bin/activate
# Install dependencies
pip install -r requirements.txt
Create ~/.agent-core/config.json for API keys:
{
"cohere": {
"api_key": "your-cohere-api-key"
}
}
Or use environment variables:
export COHERE_API_KEY="your-cohere-api-key"
Optional (for API server):
export RG_SECRET_KEY=$(python -c "import secrets; print(secrets.token_hex(32))")
export RG_API_KEY="your-service-api-key"
python3 scripts/session/status.py
python3 scripts/session/status.py
# Basic session
python3 scripts/session/init_session.py "your research topic"
# Pre-link to implementation project (v3.1)
python3 scripts/session/init_session.py "multi-agent consensus" --impl-project os-app
# Log a Tier 1 research paper
python3 scripts/session/log_url.py https://arxiv.org/abs/2601.05918 \
--tier 1 --category research --relevance 5 --used
# Log industry news
python3 scripts/session/log_url.py https://techcrunch.com/... \
--tier 1 --category industry --relevance 4 --used
python3 scripts/session/archive_session.py
python3 scripts/session/session_tracker.py status
# Auto-detect from current directory
python3 scripts/session/project_context.py
# List all projects
python3 scripts/session/project_context.py --list
# View unified index
python3 scripts/session/project_context.py --index
1. Scan Tier 1 sources for last 24-48 hours
2. Log ALL URLs (used or not) via log_url.py
3. Tag each with: tier, category, relevance (1-5)
1. Group findings by theme (not source)
2. Identify the GAP — what's missing?
3. Draft thesis: "X is happening because Y, which means Z"
1. Write 1-paragraph summary with thesis
2. Each finding: [Name](URL) + signal + rationale
3. End with: "Innovation opportunity: ..."
Before archiving a session, verify:
When invoking ResearchGravity, always run scripts/session/status.py first:
==================================================
ResearchGravity — Metaventions AI
==================================================
📍 ACTIVE SESSION
Topic: [current topic]
URLs logged: X | Findings: Y | Thesis: Yes/No
📚 RECENT SESSIONS
1. [topic] — [date]
2. [topic] — [date]
--------------------------------------------------
OPTIONS:
→ Continue active session
→ Resume archived session
→ Start fresh
--------------------------------------------------
All research sessions are now automatically tracked. No more lost research.
Link research sessions to implementation projects:
# Pre-link at session start
python3 scripts/session/init_session.py "multi-agent DQ" --impl-project os-app
# Manual link after research
python3 scripts/session/session_tracker.py link [session-id] [project]
Recover research from old Claude sessions:
# Scan recent history
python3 scripts/session/auto_capture.py scan --hours 48
# Backfill specific session
python3 scripts/session/auto_capture.py backfill ~/.claude/projects/.../session.jsonl --topic "..."
Memory injection for Claude sessions. Automatically load relevant learnings, project memory, and research papers at session start.
# Auto-detect project from current directory
python3 scripts/session/prefetch.py
# Specific project with papers
python3 scripts/session/prefetch.py --project os-app --papers
# Filter by topic
python3 scripts/session/prefetch.py --topic multi-agent --days 30
# Copy to clipboard
python3 scripts/session/prefetch.py --project os-app --clipboard
# Inject into ~/CLAUDE.md
python3 scripts/session/prefetch.py --project os-app --inject
After sourcing ~/.claude/scripts/auto-context.sh:
prefetch # Auto-detect project, last 14 days
prefetch os-app 7 # Specific project, last 7 days
prefetch-clip # Copy context to clipboard
prefetch-inject # Inject into ~/CLAUDE.md
prefetch-topic "consensus" # Filter by topic across all sessions
backfill-learnings # Regenerate learnings.md from all sessions
Extract learnings from all archived sessions:
# Process all sessions
python3 scripts/backfill/backfill_learnings.py
# Last 7 days only
python3 scripts/backfill/backfill_learnings.py --since 7
# Specific session
python3 scripts/backfill/backfill_learnings.py --session <session-id>
# Preview without writing
python3 scripts/backfill/backfill_learnings.py --dry-run
ResearchGravity integrates with the Antigravity ecosystem:
Start the Chief of Staff API:
python api/server.py
# Running on http://127.0.0.1:3847
# Get graph stats
curl http://localhost:3847/api/v2/graph/stats | jq
# Get session subgraph
curl "http://localhost:3847/api/v2/graph/session/my-session-id?depth=2" | jq
# Find concept clusters
curl "http://localhost:3847/api/v2/graph/clusters?min_size=5" | jq
# Predict session outcome (Meta-Learning Engine)
curl -X POST http://localhost:3847/api/v2/predict/session \
-H "Content-Type: application/json" \
-d '{"intent": "implement authentication system", "track_prediction": false}' | jq
# Predict potential errors
curl -X POST http://localhost:3847/api/v2/predict/errors \
-H "Content-Type: application/json" \
-d '{"intent": "git commit and push", "include_preventable_only": true}' | jq
# Get optimal work time
curl -X POST http://localhost:3847/api/v2/predict/optimal-time \
-H "Content-Type: application/json" \
-d '{"intent": "deep architecture work"}' | jq
High-stakes outputs are validated by dual-agent critic system:
from critic import ArchiveCritic, EvidenceCritic
# Validate an archive
critic = ArchiveCritic()
result = await critic.validate("session-id")
print(f"Valid: {result.valid}, Confidence: {result.confidence:.2%}")
Query the knowledge graph:
from graph import ConceptGraph, get_research_lineage
# Get session subgraph
graph = ConceptGraph()
await graph.load()
subgraph = await graph.get_session_graph("session-id", depth=2)
d3_data = subgraph.to_d3_format() # For visualization
# Get research lineage
lineage = await get_research_lineage("session-id")
print(f"Ancestors: {len(lineage['ancestors'])}")
print(f"Descendants: {len(lineage['descendants'])}")
# Find concept clusters
clusters = await graph.get_concept_clusters(min_size=5)
MIT License — See LICENSE
Metaventions AI Dico Angelo [email protected]
Metaventions AI Research Framework — Multi-tier signal capture for frontier intelligence. Architected Intelligence.
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SSS
Bu sayfadan Cursor, Claude Desktop, Claude Code veya VS Code için stdio yapılandırmasını kopyalayın. PlugMCP proxy'sini değil, yayıncının kurulumunu kullanır.
Bu kayıtta gerekli bir ortam değişkeni listelenmiyor.
ResearchGravity yerel olarak stdio üzerinden çalışır.
Son kontrol: 2026-10-28. Metaventions AI Research Framework — Multi-tier signal capture for frontier intelligence. Architected Intelligence.