ResearchGravity

Frontier intelligence for meta-invention. Research that compounds.

"Let the invention be hidden in your vision"

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Why • What's New • Architecture • Quick Start • Auto-Capture • Sources • Contact

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Proof Deck — See It Work

9-slide interactive proof: real DB stats, EvidencedFinding schema, 3-stream oracle critique, and a live pipeline demo that writes real findings to antigravity.db.

Open Interactive Deck

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What's New in v6.1 — Security & Reliability (January 2026)

Production-hardened API with enterprise security.

| Feature | Description | |---------|-------------| | 🔐 JWT Authentication | Token-based auth with /api/auth/token endpoint | | ⏱️ Rate Limiting | slowapi integration (10/min search, 30/min write) | | 🛡️ Input Validation | Path traversal prevention, session ID sanitization | | 📝 Structured Logging | JSON/console formats with request context | | 🔄 Dead-Letter Queue | Failed writes queued for retry with exponential backoff | | ⚡ Async Cohere | Non-blocking embedding calls via asyncio.to_thread | | 🔒 Connection Pool | Semaphore-guarded SQLite pool (race condition fix) |

Authentication

# 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 " http://localhost:3847/api/auth/me

# Or use API key
curl -H "X-API-Key: " http://localhost:3847/api/v2/stats

Environment Variables

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

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What's New in v6.0 — Interactive Research Platform (January 2026)

From manual workflow to intelligent auto-capture. 3x faster research sessions with real-time URL capture.

| Feature | Description | |---------|-------------| | 🎮 Interactive REPL | Real-time research CLI with Rich terminal UI | | 🔄 Auto-Capture V2 | Automatic URL/finding extraction from Claude sessions (+70% capture rate) | | 🧠 Intelligence Layer | CLI + API + REPL access to meta-learning predictions | | 💾 sqlite-vec Storage | Local vector storage with FTS fallback (no external dependencies) | | 👁️ File Watcher | Implicit session creation from Claude activity | | 📊 Dual-Write Engine | Qdrant + sqlite-vec with automatic failover |

Interactive REPL

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

Auto-Capture V2

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

Intelligence CLI

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

Intelligence API

| Endpoint | Method | Description | |----------|--------|-------------| | /api/v2/intelligence/status | GET | System capabilities | | /api/v2/intelligence/predict | POST | Unified prediction | | /api/v2/intelligence/patterns | GET | Session patterns | | /api/v2/intelligence/errors | POST | Likely errors | | /api/v2/intelligence/feedback | POST | Outcome feedback |

File Watcher

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

Storage Modes

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)

Embedding Providers (SOTA 2026)

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.

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What's New in v5.0 — Chief of Staff (January 2026)

The AI Second Brain is now complete. Full infrastructure for sovereign knowledge management.

| Feature | Description | |---------|-------------| | 🔮 Meta-Learning Engine | Predictive session intelligence from 666+ outcomes, 1,014 cognitive states | | 🏛️ Storage Triad | SQLite (WAL mode, FTS5) + Qdrant (semantic search) | | ⚖️ Writer-Critic System | 3 critics validate archives, evidence, and context packs | | 🕸️ Graph Intelligence | 11,579 nodes, 13,744 edges — concept relationships & lineage | | 🔌 REST API | 22 endpoints on port 3847 for cross-app integration | | 📊 Oracle Consensus | Multi-stream validation for high-stakes outputs | | 🎯 Evidence Layer | Citations, confidence scoring, source validation |

Chief of Staff Architecture

┌──────────────────────────────────────────────────────────────────────────────┐
│                         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       │  │
│  └────────────────────────────────────────────────────────────────────────┘  │
│                                                                               │
└──────────────────────────────────────────────────────────────────────────────┘

v4.0 Features (Still Available)

| Feature | Description | |---------|-------------| | 🧠 CPB Module | Cognitive Precision Bridge — 5-path AI orchestration | | 🎯 ELITE TIER | 5-agent ACE consensus, Opus-first routing, 0.75 DQ bar | | 📊 DQ Scoring | Validity (40%) + Specificity (30%) + Correctness (30%) | | 🔀 Smart Routing | Auto-select path based on query complexity |

CPB Execution Paths

┌─────────────────────────────────────────────────────────────────────────┐
│                    COGNITIVE PRECISION BRIDGE (CPB)                     │
├─────────────────────────────────────────────────────────────────────────┤
│                                                                         │
│  Query → [Complexity Analysis] → Path Selection → Execution → DQ Score  │
│                                                                         │
│  ┌──────────┬──────────┬──────────┬──────────┬──────────┐              │
│  │  DIRECT  │   RLM    │   ACE    │  HYBRID  │ CASCADE  │              │
│  │  0.7+   │  >0.7    │              │
│  │  Simple  │ Context  │ Consensus│ Combined │ Full     │              │
│  │  ~1s     │  ~5s     │   ~5s    │  ~10s    │  ~15s    │              │
│  └──────────┴──────────┴──────────┴──────────┴──────────┘              │
│                                                                         │
│  5-Agent ACE Ensemble:                                                  │
│  🔬 Analyst | 🤔 Skeptic | 🔄 Synthesizer | 🛠️ Pragmatist | 🔭 Visionary │
│                                                                         │
└─────────────────────────────────────────────────────────────────────────┘

🆕 CPB Precision Mode v2.0

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)                          │
│                                                                         │
└─────────────────────────────────────────────────────────────────────────┘

| Feature | Description | |---------|-------------| | Tiered Search | arXiv API + GitHub API + Internal Qdrant | | Time-Decay Scoring | Research: 23-day half-life, News: 2-day | | Signal Quantification | Stars, citations, dates extracted | | Grounded Generation | Agents can ONLY cite retrieved sources | | MAR Consensus | 3 persona critics → synthesis (arXiv:2512.20845) | | Targeted Refinement | IMPROVE pattern (arXiv:2502.18530) |

Usage:

python3 -m cpb precision "your research question" --verbose

v3.5 Changelog

| Feature | Description | |---------|-------------| | Precision Bridge Research | Tesla US20260017019A1 → RLM synthesis methodology | | Cognitive Wallet Tracking | 114 sessions, 2,530 findings, 8,935 URLs, 27M tokens | | Deep Dive Workflow | Multi-paper synthesis with implementation output | | Framework Extraction | COMPRESS → EXPLORE → RECONSTRUCT pattern identified |

Notable Research Sessions

| Session | Papers | Output | |---------|--------|--------| | Chief of Staff Architecture | 374 | Storage Triad, Graph Intelligence, Writer-Critic | | Tesla Mixed-Precision RoPE | 15 arXiv | recursiveLanguageModel.ts implementation | | Multi-Agent Orchestration | 12 arXiv | ACE/DQ Scoring in OS-App | | CPB Integration | 8 arXiv | cpb/ Python module | | 160+ Papers Meta-Synthesis | 160+ | Unified research index |

What's New in v3.4

| Feature | Description | |---------|-------------| | Context Prefetcher | scripts/session/prefetch.py — Inject relevant learnings into Claude sessions | | Learnings Backfill | scripts/backfill/backfill_learnings.py — Extract learnings from all archived sessions | | Memory Injection | Auto-load project context, papers, and lineage at session start | | Shell Integration | prefetch, prefetch-clip, prefetch-inject shell commands |

v3.3 Changelog

| Feature | Description | |---------|-------------| | YouTube Research | scripts/importers/youtube_channel.py — Channel analysis and transcript extraction | | Enhanced Backfill | Improved session recovery with better transcript parsing | | Ecosystem Sync | Deeper integration with Agent Core orchestration |

v3.2 Changelog

| Feature | Description | |---------|-------------| | Auto-Capture | Sessions automatically tracked — URLs, findings, full transcripts extracted | | Lineage Tracking | Link research sessions to implementation projects | | Project Registry | 4 registered projects with cross-referenced research | | Context Loader | Auto-load project context from any directory | | Unified Index | Cross-reference by paper, topic, or session | | Backfill | Recover research from historical Claude sessions |

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Why ResearchGravity?

Traditional research workflows fail at the frontier:

| Problem | Impact | |---------|--------| | Single-source blindspots | Missing critical signals | | No synthesis | Raw links ≠ research | | No session continuity | Context lost between sessions | | No quality standard | Inconsistent output |

ResearchGravity solves this with:

• Multi-tier source hierarchy — Tier 1 (primary), Tier 2 (amplifiers), Tier 3 (context)
• Cold Start Protocol — Never lose session context
• Synthesis workflow — Thesis → Gap → Innovation Direction
• Quality checklist — Consistent Metaventions-grade output

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Architecture

View light mode architecture

Directory Structure

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-Cri

…

## Source & license

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

- **Author:** [Dicoangelo](https://github.com/Dicoangelo)
- **Source:** [Dicoangelo/ResearchGravity](https://github.com/Dicoangelo/ResearchGravity)
- **License:** MIT
- **Homepage:** https://metaventionsai.com

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