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Instalación del publicador. No es un endpoint de PlugMCP.
Host a copy with our keys and logs.
Aún no hay una versión empaquetada en el registro. Sigue el README del repositorio.
¿URL remota o un comando en tu máquina? En qué se diferencian las dos instalaciones
Ejecutar en PlugMCP
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.
AbrirDel publicador
Local-first knowledge graph for your browsing.
Weft turns browser tabs into a searchable, clustered knowledge graph using Louvain community detection and PageRank. No cloud. No accounts. Everything runs on your machine.
Think: Obsidian graph view, but for the web you already have open.
Local-first knowledge graph for browser tabs. Louvain clustering, PageRank, MCP server for AI assistants, Chrome extension, CLI. 100% private.
You have 80 tabs open. Some are related. Some are duplicates. You can't remember what you were researching yesterday.
Weft fixes this:
Live knowledge graph that tracks your browsing in real-time.
#keyword, and @domain filters with search historyj/k/Enter/o navigation, vim-stylechrome://extensionsextension/ folderBatch processing, terminal UI, API server, and MCP integration.
pip install weft-graph
Or from source:
git clone https://github.com/Avi-141/weft.git
cd weft
pip install -e .
# Build knowledge graph from browser tabs
weft weave
# Explore in terminal UI
weft explore
# Start API + MCP server
weft serve
weft weaveExtracts tabs from browsers and builds a knowledge graph.
# Default: all browsers, Louvain clustering
weft weave
# Chrome only, fast mode (no crawling)
weft weave --browser chrome --no-crawl
# With LLM summaries (requires Ollama)
weft weave --summarize
# Legacy clustering (Union-Find + mutual KNN)
weft weave --no-louvain
# Tighter clusters
weft weave --louvain-resolution 1.5
# Looser clusters
weft weave --louvain-resolution 0.7
weft exploreInteractive TUI for browsing your knowledge graph.
weft explore
weft explore my_graph.json
weft insightsPrint a markdown report of your browsing: top topics, key themes, domain breakdown.
weft insights
weft serveRun the API and MCP server for real-time extension sync and Claude Desktop integration.
weft serve
# Runs at http://localhost:8000
weft install-mcp (macOS)Configure Claude Desktop to talk to Weft via MCP.
weft install-mcp
Then ask Claude: "What was I researching about distributed systems?"
weft export-obsidianExport knowledge clusters as linked markdown notes to your Obsidian vault.
weft export-obsidian "/path/to/vault"
┌─────────────────────────────────────────────────────┐
│ DATA SOURCES │
│ Chrome (AppleScript) Firefox (sessionstore) │
│ Chrome Extension (live capture via chrome.tabs) │
└──────────────────────┬──────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────┐
│ EXTRACT + ANALYZE │
│ HTTP crawl (requests + trafilatura) │
│ Content script DOM extraction (extension) │
│ Keyword extraction (TF) + SimHash fingerprinting │
│ Optional: LLM summaries + embeddings (Ollama) │
└──────────────────────┬──────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────┐
│ DEDUPLICATE │
│ Canonical URL normalization (strip tracking, www) │
│ SimHash near-duplicate detection (same domain) │
│ Union-Find to merge duplicate sets │
└──────────────────────┬──────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────┐
│ CLUSTER (LOUVAIN + PAGERANK) │
│ Similarity matrix (Jaccard or cosine + domain) │
│ Louvain community detection (modularity Q) │
│ PageRank for node importance scoring │
│ PageRank-weighted TF-IDF group labeling │
└──────────────────────┬──────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────┐
│ GRAPH OUTPUT │
│ { tabs, groups, edges, stats } │
│ schema_version: 1 │
│ modularity score + clustering method in stats │
└──────┬───────────┬────────────┬─────────────────────┘
│ │ │
▼ ▼ ▼
CLI / TUI Extension Server / MCP
(Textual) (Cytoscape) (FastAPI)
Weft uses the Louvain method for clustering tabs. Instead of forcing tabs together by domain or arbitrary thresholds, Louvain optimizes modularity — finding groups where internal similarity is denser than expected by chance.
How it works:
Resolution parameter: Controls cluster granularity. Default is 1.0. Values >1.0 produce smaller, tighter clusters. Values <1.0 produce larger, looser ones.
Modularity Q: A score from -0.5 to 1.0 measuring cluster quality. Values above 0.3 indicate meaningful community structure. Weft reports this in graph stats and the extension UI.
Each tab gets a PageRank score based on its position in the similarity graph. Tabs that are highly connected to other important tabs rank higher.
Used for:
All scores are clamped to [0, 1].
Two-stage deduplication using Union-Find:
Weft exposes your browsing context to AI assistants via the Model Context Protocol.
Resources:
browsing://insights — browsing memory report with top topics, themes, sourcesbrowsing://groups — all clusters with labels, keywords, domains, sizesbrowsing://stats — graph metrics: tab count, modularity, clustering methodTools:
search_knowledge(query) — ranked search across titles, summaries, keywords, URLs. Scores by keyword overlap + PageRank.get_group_details(group_id) — cluster deep-dive: synthesized summary, tabs sorted by PageRank, top keywords and domainsget_tab_neighbors(tab_id) — graph neighborhood: all tabs connected by similarity or navigation edges, with edge weightsfind_recent_tabs(hours) — temporal query: tabs active within the last N hoursfind_stale_tabs(stale_hours) — cleanup candidates: tabs not accessed in N+ hours, sorted by stalenessfind_related_to_topic(topic) — topic expansion: finds direct keyword matches, then follows graph edges to discover related content you wouldn't find by search alone# Install MCP for Claude Desktop
weft install-mcp
# Start the server
weft serve
Example prompts for Claude:
# LLM summaries (embedding-based clustering)
brew install ollama
ollama pull llama3.1:8b
ollama pull nomic-embed-text
weft weave --summarize
# Firefox tab export
pip install lz4
# JS-heavy page rendering
pip install playwright
Weft is fully local:
MIT
Local-first knowledge graph for browser tabs. Louvain clustering, PageRank, MCP server for AI assistants, Chrome extension, CLI. 100% private.
Herramientas
Aún no hay lista de herramientas. Los servidores remotos la reciben tras la primera comprobación de estado.
Preguntas frecuentes
Copia en esta página la configuración stdio para Cursor, Claude Desktop, Claude Code o VS Code. Usa la instalación del publicador, no un proxy de PlugMCP.
Este registro no indica ninguna variable de entorno obligatoria.
Weft se ejecuta localmente por stdio.
Última comprobación: 2026-10-28. Local-first knowledge graph for browser tabs. Louvain clustering, PageRank, MCP server for AI assistants, Chrome extension, CLI. 100% private.
