project-tessera 0.7.6

Tessera — Personal Knowledge Layer for AI. Own your memory across every AI tool.

Tessera

Personal Knowledge Layer for AI. Own your memory across every AI tool.

You use Claude, ChatGPT, Gemini, Copilot. Each conversation generates knowledge that disappears when the session ends. Tessera captures that knowledge, stores it locally, and serves it back to any AI. Your memory, your machine, your data.

What makes Tessera different

• Auto-learning -- Tessera records every interaction and extracts decisions, preferences, and facts automatically. No manual "remember this."
• Interface-agnostic core -- One knowledge engine, multiple interfaces. MCP today, HTTP API for ChatGPT/Gemini/extensions coming next.
• Cross-session memory -- AI remembers your decisions and context between conversations.
• 100% local -- No cloud, no API keys, no data leaving your machine. LanceDB + fastembed/ONNX.
• Hybrid search -- Semantic + keyword search with reranking. Not just vector similarity.

Architecture

                    +-----------------+
                    |    src/core.py  |   Business logic (35 functions)
                    |                 |   Search, memory, knowledge graph,
                    |                 |   auto-extract, interaction log
                    +-----------------+
                     /        |        \
    +-------------+  +----------------+  +----------+
    | mcp_server  |  | http_server.py |  | cli.py   |
    | (stdio/MCP) |  | (REST API)     |  | (CLI)    |
    | Claude      |  | ChatGPT,       |  |          |
    | Desktop     |  | Gemini, etc.   |  |          |
    +-------------+  +----------------+  +----------+
                          (planned)

    Core engine:
    +--------------------------------------------------+
    | LanceDB (vectors) | SQLite (metadata, analytics) |
    | fastembed/ONNX (local embeddings, no API keys)   |
    | Auto-extract (pattern-based fact detection)       |
    | Interaction log (every tool call recorded)        |
    +--------------------------------------------------+

One core, multiple interfaces. The same knowledge base works regardless of which AI tool you use.

Get started

1. Install

pip install project-tessera

Or with uv:

uvx --from project-tessera tessera setup

2. Setup

tessera setup

This does everything:

• Creates a workspace config
• Downloads the embedding model (~220MB, first time only)
• Configures Claude Desktop automatically

3. Restart Claude Desktop

Ask Claude about your documents. It searches automatically.

Supported file types (40+)

Category	Extensions	Install
Documents	.md .txt .rst .csv	included
Office	.xlsx .docx .pdf	pip install project-tessera[xlsx,docx,pdf]
Code	.py .js .ts .tsx .jsx .java .go .rs .rb .php .c .cpp .h .swift .kt .sh .sql .cs .dart .r .lua .scala	included
Config	.json .yaml .yml .toml .xml .ini .cfg .env	included
Web	.html .htm .css .scss .less .svg	included
Images	.png .jpg .jpeg .webp .gif .bmp .tiff	pip install project-tessera[ocr] (for text extraction)

Tools (39)

Search

Tool	What it does
search_documents	Semantic + keyword hybrid search across all docs
unified_search	Search documents AND memories in one call
view_file_full	Full file view (CSV as table, XLSX per sheet, etc.)
read_file	Read any file's full content
list_sources	See what's indexed

Memory

Tool	What it does
remember	Save knowledge that persists across sessions
recall	Search past memories from previous conversations
learn	Save and immediately index new knowledge
digest_conversation	Auto-extract decisions/facts from the current session
list_memories	Browse saved memories
forget_memory	Delete a specific memory
export_memories	Batch export all memories as JSON
import_memories	Batch import memories from JSON
memory_tags	List all unique tags with counts
search_by_tag	Filter memories by specific tag
memory_categories	List auto-detected categories (decision/preference/fact)
search_by_category	Filter memories by category

Knowledge graph

Tool	What it does
find_similar	Find documents similar to a given file
knowledge_graph	Build a Mermaid diagram of document relationships
explore_connections	Show connections around a specific topic

Auto-learn

Tool	What it does
digest_conversation	Extract and save knowledge from the current session
toggle_auto_learn	Turn auto-learning on/off or check status
review_learned	Review recently auto-learned memories
session_interactions	View tool calls from current/past sessions
recent_sessions	Session history with interaction counts

Workspace

Tool	What it does
ingest_documents	Index documents (first-time or full rebuild)
sync_documents	Incremental sync (only changed files)
project_status	Recent changes per project
extract_decisions	Find past decisions from logs
audit_prd	Check PRD quality (13-section structure)
organize_files	Move, rename, archive files
suggest_cleanup	Detect backup files, empty dirs, misplaced files
tessera_status	Server health: tracked files, sync history, cache
health_check	Comprehensive workspace diagnostics
search_analytics	Search usage patterns, top queries, response times
check_document_freshness	Detect stale documents older than N days

CLI

tessera setup          # One-command setup
tessera init           # Interactive setup
tessera ingest         # Index all sources
tessera sync           # Re-index changed files
tessera check          # Workspace health
tessera status         # Project status
tessera install-mcp    # Configure Claude Desktop
tessera version        # Show version

How it works

Documents (Markdown, CSV, XLSX, DOCX, PDF)
    |
    v
Parse & chunk --> Embed locally (fastembed/ONNX) --> LanceDB (local vector DB)
    |
    v
src/core.py (search, memory, knowledge graph, auto-extract)
    |
    v
MCP server (Claude Desktop) / HTTP API (ChatGPT, Gemini, extensions)

Everything runs on your machine. No external API calls for search or embedding.

Claude Desktop config

With uvx (recommended):

{
  "mcpServers": {
    "tessera": {
      "command": "uvx",
      "args": ["--from", "project-tessera", "tessera-mcp"]
    }
  }
}

With pip:

{
  "mcpServers": {
    "tessera": {
      "command": "tessera-mcp"
    }
  }
}

Config location:

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
• Windows: %APPDATA%\Claude\claude_desktop_config.json

Configuration

tessera setup creates workspace.yaml. All parameters are tunable:

workspace:
  root: /Users/you/Documents
  name: my-workspace

sources:
  - path: .
    type: document

search:
  reranker_weight: 0.7     # Semantic vs keyword balance
  max_top_k: 50            # Max results per search

ingestion:
  chunk_size: 1024         # Text chunk size
  chunk_overlap: 100       # Overlap between chunks

watcher:
  poll_interval: 30.0      # Seconds between scans
  debounce: 5.0            # Wait before syncing

Or skip config entirely -- Tessera auto-detects your workspace. Set TESSERA_WORKSPACE=/path/to/docs to specify a folder.

Roadmap

See ROADMAP.md for the full plan from v0.6 to v1.0.

Phase	Version	What changes
Sponge	v0.7	Manual memory becomes automatic learning
Radar	v0.8	Reactive search becomes proactive intelligence
Gateway	v0.9	MCP-only becomes multi-interface (HTTP API)
Cortex	v1.0	Search tool becomes Claude's persistent brain

License

AGPL-3.0 -- see LICENSE.

Commercial licensing: [e-mail]