ResearchGravity

Metaventions AI Research Framework — Multi-tier signal capture for frontier intelligence. Architected Intelligence.

Pasang Jalankan di PlugMCPKlaim server ini

1# ResearchGravity

README.mdServer MCP

Transport: stdio
Status: Lokal

Power your agent

Tambahkan server ini

Instal dari penerbit. Bukan endpoint PlugMCP.

Jalankan di PlugMCP

Host a copy with our keys and logs.

Belum ada rilis paket di registry. Ikuti README repositori.

RepositoriInstal dari penerbitLangkah demi langkah: tambahkan di Claude

URL remote atau perintah di mesin Anda? Perbedaan kedua cara instal

Jalankan di PlugMCP

Host a copy with our keys and logs.

A new server you create. It is not this listing's official endpoint. Use the publisher install when you want their URL and their credentials.

Buka

Info server

Lokal

Transport: stdio
Lisensi: MIT
Terakhir diperiksa: 2026-10-28

Bintang: 4
Tool: —
Uptime: —

Tool

Belum ada daftar tool. Server remote mendapatkannya setelah pemeriksaan kesehatan pertama.

FAQ

Pertanyaan

Bagaimana cara menginstal ResearchGravity?

Salin konfigurasi stdio untuk Cursor, Claude Desktop, Claude Code, atau VS Code di halaman ini. Konfigurasi ini memakai instalasi dari penerbit, bukan proxy PlugMCP.

Apakah ResearchGravity memerlukan kunci API?

Tidak ada variabel lingkungan wajib yang tercantum di catatan ini.

Transport apa yang digunakan ResearchGravity?

ResearchGravity berjalan secara lokal melalui stdio.

Kapan ResearchGravity terakhir diperiksa?

Terakhir diperiksa 2026-10-28. Metaventions AI Research Framework — Multi-tier signal capture for frontier intelligence. Architected Intelligence.

Dari penerbit

Dokumentasi

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

Proof Deck — See It Work

New local pilot: BASIX × ResearchGravity compares a bounded image-upscale workload, records source-backed decisions, and exports a selected handoff. Includes CLI, loopback UI, MCP tools and isolated SQLite indexing. See its validation and limits.

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 <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

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

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.

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  │              │
│  │  <0.2    │ 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 │
│                                                                         │
└─────────────────────────────────────────────────────────────────────────┘

🆕 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

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

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-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

CPB Module (v4.0)

The Cognitive Precision Bridge provides precision-aware AI orchestration.

Quick Start

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})")

CLI Commands

# 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

ELITE TIER Configuration

Setting	Value	Description
Complexity Thresholds	0.2 / 0.5	Lower = more orchestration
ACE Agent Count	5	Full ensemble
DQ Quality Bar	0.75	Higher standard
Default Path	cascade	Full pipeline
RLM Iterations	25	Deeper decomposition
Model Routing	Opus-first	Maximum quality

5-Agent ACE Ensemble

Agent	Role	Focus
🔬 Analyst	Evidence evaluator	Data, logic, consistency
🤔 Skeptic	Challenge assumptions	Failure modes, risks
🔄 Synthesizer	Pattern finder	Connections, frameworks
🛠️ Pragmatist	Feasibility checker	Implementation, constraints
🔭 Visionary	Strategic thinker	Long-term, second-order effects

Research Foundation

arXiv:2512.24601 (RLM) - Recursive context externalization
arXiv:2511.15755 (DQ) - Decisional quality measurement (since withdrawn by its author; V+S+C formula adapted)
arXiv:2508.17536 - Voting vs Debate consensus strategies

Installation

Prerequisites

Python 3.12 (what CI builds and tests against)
pip or pipenv

Setup

# 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

Configuration

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"

Verify Installation

python3 scripts/session/status.py

Quick Start

1. Check Session State

python3 scripts/session/status.py

2. Initialize New Session

# 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

3. Research & Log URLs

# 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

4. Archive When Complete

python3 scripts/session/archive_session.py

5. Check Tracker Status (v3.1)

python3 scripts/session/session_tracker.py status

6. Load Project Context (v3.2)

# 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

Research Workflow

Phase 1: Signal Capture (30 min)

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)

Phase 2: Synthesis (20 min)

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"

Phase 3: Editorial Frame (10 min)

1. Write 1-paragraph summary with thesis
2. Each finding: [Name](URL) + signal + rationale
3. End with: "Innovation opportunity: ..."

Source Hierarchy

Tier 1: Primary Sources (Check Daily)

Category	Sources
Research	arXiv (cs.AI, cs.SE, cs.LG), HuggingFace Papers
Labs	OpenAI, Anthropic, Google AI, Meta AI, DeepMind
Industry	TechCrunch, The Verge, Ars Technica, Wired

Tier 2: Signal Amplifiers

Category	Sources
GitHub	Trending, Topics, Releases
Benchmarks	METR, ARC Prize, LMSYS, PapersWithCode
Social	X/Twitter key accounts, HN, Reddit ML

Tier 3: Deep Context

Category	Sources
Newsletters	Import AI, The Batch, Latent Space
Forums	LessWrong, Alignment Forum

Quality Checklist

Before archiving a session, verify:

Scanned all Tier 1 sources for timeframe
Logged 10+ URLs minimum
Identified at least one GAP
Wrote thesis statement
Each finding has: link + signal + rationale
Innovation direction is concrete, not vague

Cold Start Protocol

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
--------------------------------------------------

Auto-Capture & Lineage (v3.1)

All research sessions are now automatically tracked. No more lost research.

What Gets Captured

Artifact	Storage
Full transcript	`~/.agent-core/sessions/[id]/full_transcript.txt`
All URLs	`urls_captured.json`
Key findings	`findings_captured.json`
Cross-project links	`lineage.json`

Lineage Tracking

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]

Backfill Historical Sessions

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 "..."

Context Prefetcher (v3.4)

Memory injection for Claude sessions. Automatically load relevant learnings, project memory, and research papers at session start.

Basic Usage

# 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

Shell Commands

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

CLI Options

Flag	Description
`--project, -p`	Project ID to load context for
`--topic, -t`	Filter by topic keywords
`--days, -d`	Limit to last N days (default: 14)
`--limit, -l`	Max learning entries (default: 5)
`--papers`	Include relevant arXiv papers
`--clipboard, -c`	Copy to clipboard (macOS)
`--inject, -i`	Inject into ~/CLAUDE.md
`--json`	Output as JSON
`--quiet, -q`	Suppress info output

Backfill Learnings

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

What Gets Injected

Component	Source
Project info	`projects.json` — name, focus, tech stack, status
Project memory	`memory/projects/[project].md`
Recent learnings	`memory/learnings.md` — filtered by project/topic/days
Research papers	`paper_index` in projects.json
Lineage	Research sessions → features implemented

Integration

ResearchGravity integrates with the Antigravity ecosystem:

Environment	Use Case
CLI (Claude Code)	Planning, parallel sessions, synthesis
Antigravity (VSCode)	Coding, preview, browser research
Web (claude.ai)	Handoff, visual review

API Server (v5.0)

Start the Chief of Staff API:

python api/server.py
# Running on http://127.0.0.1:3847

Endpoints

Endpoint	Method	Description
`/api/v1/sessions`	GET	List all sessions
`/api/v1/sessions/{id}`	GET	Get session details
`/api/v1/findings`	GET	Search findings
`/api/v1/urls`	GET	Search URLs
`/api/v2/graph/stats`	GET	Graph statistics
`/api/v2/graph/session/{id}`	GET	Session subgraph (D3 format)
`/api/v2/graph/related/{id}`	GET	Related sessions
`/api/v2/graph/lineage/{id}`	GET	Research lineage chain
`/api/v2/graph/clusters`	GET	Concept clusters
`/api/v2/graph/timeline`	GET	Research timeline
`/api/v2/graph/network/{id}`	GET	Concept network
`/api/v2/predict/session`	POST	Predict session outcome, quality, optimal time
`/api/v2/predict/errors`	POST	Predict potential errors with solutions
`/api/v2/predict/optimal-time`	POST	Suggest best time to work on task

Example Queries

# 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

Writer-Critic System (v5.0)

High-stakes outputs are validated by dual-agent critic system:

Critic	Target	Confidence
ArchiveCritic	Archive completeness (files, metadata, findings)	96.3%
EvidenceCritic	Citation accuracy, source validation	Threshold: 0.7
PackCritic	Context pack relevance, token efficiency	Threshold: 0.7

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%}")

Graph Intelligence (v5.0)

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)

Roadmap

Completed ✅

Future

OS-App SDK integration
Real-time WebSocket updates
Browser extension for URL capture
Team collaboration features

License

MIT License — See LICENSE

Contact

Metaventions AI Dico Angelo [email protected]

ResearchGravity

Dicoangelo4 bintangKlaim server ini

Metaventions AI Research Framework — Multi-tier signal capture for frontier intelligence. Architected Intelligence.

Knowledge Memory

Pasang Jalankan di PlugMCPKlaim server ini

GitHub Situs web

Jalankan di PlugMCP

1# ResearchGravity

README.mdServer MCP

Transport: stdio
Status: Lokal

Power your agent

Tambahkan server ini

Instal dari penerbit. Bukan endpoint PlugMCP.

Jalankan di PlugMCP

Host a copy with our keys and logs.

Belum ada rilis paket di registry. Ikuti README repositori.

RepositoriInstal dari penerbitLangkah demi langkah: tambahkan di Claude

URL remote atau perintah di mesin Anda? Perbedaan kedua cara instal

Jalankan di PlugMCP

Host a copy with our keys and logs.

A new server you create. It is not this listing's official endpoint. Use the publisher install when you want their URL and their credentials.

Buka

Info server

Lokal

Transport: stdio
Lisensi: MIT
Terakhir diperiksa: 2026-10-28

Bintang: 4
Tool: —
Uptime: —

Tool

Belum ada daftar tool. Server remote mendapatkannya setelah pemeriksaan kesehatan pertama.

FAQ

Pertanyaan

Bagaimana cara menginstal ResearchGravity?

Salin konfigurasi stdio untuk Cursor, Claude Desktop, Claude Code, atau VS Code di halaman ini. Konfigurasi ini memakai instalasi dari penerbit, bukan proxy PlugMCP.

Apakah ResearchGravity memerlukan kunci API?

Tidak ada variabel lingkungan wajib yang tercantum di catatan ini.

Transport apa yang digunakan ResearchGravity?

ResearchGravity berjalan secara lokal melalui stdio.

Kapan ResearchGravity terakhir diperiksa?

Terakhir diperiksa 2026-10-28. Metaventions AI Research Framework — Multi-tier signal capture for frontier intelligence. Architected Intelligence.

Dari penerbit

Dokumentasi

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

Proof Deck — See It Work

New local pilot: BASIX × ResearchGravity compares a bounded image-upscale workload, records source-backed decisions, and exports a selected handoff. Includes CLI, loopback UI, MCP tools and isolated SQLite indexing. See its validation and limits.

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 <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

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

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.

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  │              │
│  │  <0.2    │ 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 │
│                                                                         │
└─────────────────────────────────────────────────────────────────────────┘

🆕 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

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

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-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

CPB Module (v4.0)

The Cognitive Precision Bridge provides precision-aware AI orchestration.

Quick Start

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})")

CLI Commands

# 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

ELITE TIER Configuration

Setting	Value	Description
Complexity Thresholds	0.2 / 0.5	Lower = more orchestration
ACE Agent Count	5	Full ensemble
DQ Quality Bar	0.75	Higher standard
Default Path	cascade	Full pipeline
RLM Iterations	25	Deeper decomposition
Model Routing	Opus-first	Maximum quality

5-Agent ACE Ensemble

Agent	Role	Focus
🔬 Analyst	Evidence evaluator	Data, logic, consistency
🤔 Skeptic	Challenge assumptions	Failure modes, risks
🔄 Synthesizer	Pattern finder	Connections, frameworks
🛠️ Pragmatist	Feasibility checker	Implementation, constraints
🔭 Visionary	Strategic thinker	Long-term, second-order effects

Research Foundation

arXiv:2512.24601 (RLM) - Recursive context externalization
arXiv:2511.15755 (DQ) - Decisional quality measurement (since withdrawn by its author; V+S+C formula adapted)
arXiv:2508.17536 - Voting vs Debate consensus strategies

Installation

Prerequisites

Python 3.12 (what CI builds and tests against)
pip or pipenv

Setup

# 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

Configuration

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"

Verify Installation

python3 scripts/session/status.py

Quick Start

1. Check Session State

python3 scripts/session/status.py

2. Initialize New Session

# 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

3. Research & Log URLs

# 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

4. Archive When Complete

python3 scripts/session/archive_session.py

5. Check Tracker Status (v3.1)

python3 scripts/session/session_tracker.py status

6. Load Project Context (v3.2)

# 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

Research Workflow

Phase 1: Signal Capture (30 min)

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)

Phase 2: Synthesis (20 min)

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"

Phase 3: Editorial Frame (10 min)

1. Write 1-paragraph summary with thesis
2. Each finding: [Name](URL) + signal + rationale
3. End with: "Innovation opportunity: ..."

Source Hierarchy

Tier 1: Primary Sources (Check Daily)

Category	Sources
Research	arXiv (cs.AI, cs.SE, cs.LG), HuggingFace Papers
Labs	OpenAI, Anthropic, Google AI, Meta AI, DeepMind
Industry	TechCrunch, The Verge, Ars Technica, Wired

Tier 2: Signal Amplifiers

Category	Sources
GitHub	Trending, Topics, Releases
Benchmarks	METR, ARC Prize, LMSYS, PapersWithCode
Social	X/Twitter key accounts, HN, Reddit ML

Tier 3: Deep Context

Category	Sources
Newsletters	Import AI, The Batch, Latent Space
Forums	LessWrong, Alignment Forum

Quality Checklist

Before archiving a session, verify:

Scanned all Tier 1 sources for timeframe
Logged 10+ URLs minimum
Identified at least one GAP
Wrote thesis statement
Each finding has: link + signal + rationale
Innovation direction is concrete, not vague

Cold Start Protocol

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
--------------------------------------------------

Auto-Capture & Lineage (v3.1)

All research sessions are now automatically tracked. No more lost research.

What Gets Captured

Artifact	Storage
Full transcript	`~/.agent-core/sessions/[id]/full_transcript.txt`
All URLs	`urls_captured.json`
Key findings	`findings_captured.json`
Cross-project links	`lineage.json`

Lineage Tracking

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]

Backfill Historical Sessions

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 "..."

Context Prefetcher (v3.4)

Memory injection for Claude sessions. Automatically load relevant learnings, project memory, and research papers at session start.

Basic Usage

# 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

Shell Commands

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

CLI Options

Flag	Description
`--project, -p`	Project ID to load context for
`--topic, -t`	Filter by topic keywords
`--days, -d`	Limit to last N days (default: 14)
`--limit, -l`	Max learning entries (default: 5)
`--papers`	Include relevant arXiv papers
`--clipboard, -c`	Copy to clipboard (macOS)
`--inject, -i`	Inject into ~/CLAUDE.md
`--json`	Output as JSON
`--quiet, -q`	Suppress info output

Backfill Learnings

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

What Gets Injected

Component	Source
Project info	`projects.json` — name, focus, tech stack, status
Project memory	`memory/projects/[project].md`
Recent learnings	`memory/learnings.md` — filtered by project/topic/days
Research papers	`paper_index` in projects.json
Lineage	Research sessions → features implemented

Integration

ResearchGravity integrates with the Antigravity ecosystem:

Environment	Use Case
CLI (Claude Code)	Planning, parallel sessions, synthesis
Antigravity (VSCode)	Coding, preview, browser research
Web (claude.ai)	Handoff, visual review

API Server (v5.0)

Start the Chief of Staff API:

python api/server.py
# Running on http://127.0.0.1:3847

Endpoints

Endpoint	Method	Description
`/api/v1/sessions`	GET	List all sessions
`/api/v1/sessions/{id}`	GET	Get session details
`/api/v1/findings`	GET	Search findings
`/api/v1/urls`	GET	Search URLs
`/api/v2/graph/stats`	GET	Graph statistics
`/api/v2/graph/session/{id}`	GET	Session subgraph (D3 format)
`/api/v2/graph/related/{id}`	GET	Related sessions
`/api/v2/graph/lineage/{id}`	GET	Research lineage chain
`/api/v2/graph/clusters`	GET	Concept clusters
`/api/v2/graph/timeline`	GET	Research timeline
`/api/v2/graph/network/{id}`	GET	Concept network
`/api/v2/predict/session`	POST	Predict session outcome, quality, optimal time
`/api/v2/predict/errors`	POST	Predict potential errors with solutions
`/api/v2/predict/optimal-time`	POST	Suggest best time to work on task

Example Queries

# 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

Writer-Critic System (v5.0)

High-stakes outputs are validated by dual-agent critic system:

Critic	Target	Confidence
ArchiveCritic	Archive completeness (files, metadata, findings)	96.3%
EvidenceCritic	Citation accuracy, source validation	Threshold: 0.7
PackCritic	Context pack relevance, token efficiency	Threshold: 0.7

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%}")

Graph Intelligence (v5.0)

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)