Tessera

Every AI conversation produces knowledge. When the session ends, it's gone. Tessera keeps it.

One knowledge base for Claude Desktop, with an HTTP API for scripts and automation. Runs locally. No API keys, no Docker, no data leaving your machine.

pip install project-tessera
tessera setup
# Done. Claude Desktop now has persistent memory + document search.

---

Why Tessera over alternatives

	Tessera	Mem0	Basic Memory	mcp-memory-service
Works without API keys	Yes	No (needs OpenAI)	Yes	Partial
Works without Docker	Yes	No	Yes	No
Document search (40+ types)	Yes	No	Markdown only	No
ChatGPT integration (via tunnel)	Yes	No	No	No
Contradiction detection	Yes	No	No	No
Memory confidence scoring	Yes	No	No	No
Encrypted vault (AES-256)	Yes	No	No	No
HTTP API for non-MCP tools	58 endpoints	Yes	No	Yes
Auto-learning from conversations	Yes	Yes	No	No
MCP tools	58	~10	~15	24

The short version

Most memory tools store text and search it. Tessera does that, plus:

• HTTP API: 58 REST endpoints let scripts, ChatGPT (via tunnel + Custom GPT Actions), and local LLMs read and write the same knowledge base.
• Self-maintaining: finds contradictions between old and new memories, scores confidence by reinforcement frequency, flags stale knowledge, auto-merges near-duplicates.
• Zero infrastructure: pip install and go. LanceDB and fastembed are embedded -- no Docker, no database server, no API keys.
• Encrypted: set TESSERA_VAULT_KEY and all memories are AES-256-CBC encrypted at rest.

---

Architecture

How search works (query path)

    User asks: "What did we decide about the database?"
                            |
                            v
                +-----------------------+
                |    Query Processing   |
                |  Multi-angle decomp   |    "database decision"
                |  (2-4 perspectives)   |    "database", "decision"
                +-----------------------+    "decision about database"
                            |
              +-------------+-------------+
              |                           |
              v                           v
    +------------------+        +------------------+
    |  Vector Search   |        |  Keyword Search  |
    |  (LanceDB)       |        |  (FTS index)     |
    |  384-dim MiniLM  |        |  BM25 scoring    |
    +------------------+        +------------------+
              |                           |
              +-------------+-------------+
                            |
                            v
                +-----------------------+
                |      Reranking        |
                |  70% semantic weight  |    LinearCombinationReranker
                |  30% keyword weight   |    + version-aware scoring
                +-----------------------+
                            |
                            v
                +-----------------------+
                |   Result Assembly     |
                |  Dedup (content hash) |    2-pass deduplication
                |  Verdict labels       |    found / weak / none
                |  Cache (60s TTL)      |
                +-----------------------+
                            |
                            v
                    Top-K results with
                    confidence scores

How ingestion works (ingest path)

    Documents: .md .pdf .docx .xlsx .py .ts .go ...  (40+ types)
                            |
                            v
                +-----------------------+
                |   File Type Router    |
                |  Markdown, CSV, XLSX  |    Type-specific parsers
                |  Code, PDF, Images    |    with metadata extraction
                +-----------------------+
                            |
                            v
                +-----------------------+
                |   Chunking Engine     |
                |  1024 tokens/chunk    |    Sentence-boundary aware
                |  100 token overlap    |    Heading-preserving
                +-----------------------+
                            |
                            v
                +-----------------------+
                |   Local Embedding     |
                |  fastembed/ONNX       |    paraphrase-multilingual
                |  384 dimensions       |    MiniLM-L12-v2
                |  No API calls         |    101 languages
                +-----------------------+
                            |
              +-------------+-------------+
              |                           |
              v                           v
    +------------------+        +------------------+
    |    LanceDB       |        |     SQLite       |
    |  Vector storage  |        |  File metadata   |
    |  Columnar format |        |  Search analytics|
    |  Zero-config     |        |  Interaction log |
    +------------------+        +------------------+

System overview

                    +--------------------------------------------+
                    |              src/core.py                    |
                    |         58 orchestration functions          |
                    |   69 specialized modules, 31k LOC           |
                    +--------------------------------------------+
                     /                |                \
    +---------------+  +-------------------+  +--------------+
    | MCP Server    |  | HTTP API Server   |  | CLI          |
    | Claude Desktop|  | FastAPI + Swagger |  | 11 commands  |
    | 58 tools      |  | 58 endpoints      |  | setup, sync  |
    | stdio         |  | port 8394         |  | ingest, api  |
    +---------------+  +-------------------+  +--------------+
           |                    |                     |
           v                    v                     v
    +------------------------------------------------------------+
    |                    Storage Layer                            |
    |  LanceDB         SQLite           Filesystem               |
    |  (vectors)       (metadata,       (memories as .md,        |
    |                   analytics,       encrypted with           |
    |                   interactions)    AES-256-CBC)             |
    |                                                            |
    |  fastembed/ONNX: local embedding, no API keys              |
    |  101 languages, 384-dim vectors, ~220MB model              |
    +------------------------------------------------------------+

---

Get started

1. Install

pip install project-tessera

Or with uv:

uvx --from project-tessera tessera setup

2. Setup

tessera setup

Creates workspace config, downloads embedding model (~220MB, first time only), configures Claude Desktop.

3. Restart Claude Desktop

Ask Claude about your documents. It searches automatically.

Use with ChatGPT (Custom GPT Actions)

tessera api                     # Start REST API on localhost:8394
ngrok http 8394                 # Expose to the internet
# Then create a Custom GPT with the Actions spec from /chatgpt-actions/openapi.json

Full setup guide at http://127.0.0.1:8394/chatgpt-actions/setup. Swagger docs at http://127.0.0.1:8394/docs.

---

How it works

Hybrid search with reranking

Every search goes through four stages:

1. Query decomposition -- the query is split into 2-4 search angles (core keywords, individual terms, reversed emphasis)
2. Hybrid retrieval -- vector similarity (LanceDB) and keyword matching (FTS/BM25) run in parallel
3. Reranking -- a LinearCombinationReranker merges the two result sets (70% semantic, 30% keyword weight)
4. Verdict scoring -- each result gets a label: confident match (>= 45%), possible match (25-45%), or low relevance (< 25%)

When multiple versions of the same document exist, Tessera prefers the latest.

Cross-session memory

# Via MCP (Claude)
"Remember that we chose PostgreSQL for the production database"

# Via HTTP API (scripts, local LLMs, ChatGPT via tunnel)
curl -X POST http://127.0.0.1:8394/remember \
  -H "Content-Type: application/json" \
  -d '{"content": "Use PostgreSQL for production", "tags": ["db", "architecture"]}'

Each memory gets a category (decision, preference, or fact), is checked for duplicates against existing memories (cosine similarity, 0.92 threshold), and receives a confidence score -- weighted by repetition (35%), recency (25%), source diversity (20%), and category (20%). Set TESSERA_VAULT_KEY to encrypt all memories with AES-256-CBC.

Auto-learning

Tessera picks up decisions, preferences, and facts from your conversations without being asked. toggle_auto_learn turns it on or off; review_learned shows what it caught.

Contradiction detection

Memories contradict each other over time. Tessera finds them:

CONTRADICTION (HIGH severity):
  "We decided to use PostgreSQL" (2026-03-01)
  vs
  "Switched to MongoDB for the main database" (2026-03-10)

  The newer memory (2026-03-10) likely reflects the current state.

Works with both English and Korean negation patterns.

ChatGPT integration (requires tunnel)

ChatGPT can talk to Tessera through Custom GPT Actions, but since ChatGPT's servers need to reach your machine, you need a tunnel (ngrok, Cloudflare Tunnel, etc.) to expose your local API.

Requirements: Your computer must be on, the API server running, and the tunnel active. When any of these stop, ChatGPT loses access.

# 1. Start Tessera API + tunnel
tessera api
ngrok http 8394   # or: cloudflared tunnel --url http://localhost:8394

# 2. Get the OpenAPI spec for your Custom GPT
curl https://your-tunnel-url/chatgpt-actions/openapi.json?server_url=https://your-tunnel-url

# 3. Get the GPT instruction template
curl https://your-tunnel-url/chatgpt-actions/instructions

Create a Custom GPT, paste the instructions, import the OpenAPI spec as an Action.

You can also import past ChatGPT conversations to extract knowledge from them:

curl -X POST http://127.0.0.1:8394/import-conversations \
  -H "Content-Type: application/json" \
  -d '{"data": "<ChatGPT export JSON>", "source": "chatgpt"}'

Export as Obsidian vault (wikilinks), Markdown, CSV, or JSON:

curl http://127.0.0.1:8394/export?format=obsidian

Memory health

Each memory is healthy, stale (90+ days without reinforcement), or orphaned (no metadata, no category). The health report tells you what to clean up and tracks growth over time.

Plugin hooks

Run your own scripts when things happen:

# workspace.yaml
hooks:
  on_memory_created:
    - script: ./notify-slack.sh
  on_contradiction_found:
    - script: ./alert.py

7 event types: on_memory_created, on_memory_deleted, on_search, on_session_start, on_session_end, on_ingest_complete, on_contradiction_found.

---

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]

---

MCP tools (58)

Search (5)

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)
read_file	Read any file's full content
list_sources	See what's indexed

Memory (13)

Tool	What it does
remember	Save knowledge that persists across sessions
recall	Search past memories with date/category filters
learn	Save and immediately index new knowledge
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
find_similar	Find documents similar to a given file
knowledge_graph	Build a Mermaid diagram of document relationships

Auto-learn (5)

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

Intelligence (7)

Tool	What it does
decision_timeline	How your decisions changed over time, by topic
context_window	Pack the best context into a token budget
smart_suggest	Query suggestions based on your past searches
topic_map	Cluster memories by topic with Mermaid mindmap
knowledge_stats	Aggregate statistics (categories, tags, growth)
user_profile	Auto-built profile (language, preferences, expertise)
explore_connections	Show connections around a specific topic

Insight (6)

Tool	What it does
deep_search	Breaks a query into 2-4 angles, searches each, merges best results
deep_recall	Multi-angle memory recall with verdict labels
detect_contradictions	Find conflicting memories with severity rating
memory_confidence	How reliable is each memory (repetition, recency, source diversity)
memory_health	Which memories are healthy, stale, or orphaned
list_plugin_hooks	See what hooks are registered

Import/Export (4)

Tool	What it does
export_for_ai	Export memories in portable format
import_from_ai	Import memories from external sources
import_conversations	Extract knowledge from ChatGPT/Claude conversation exports
export_knowledge	Export as Obsidian (wikilinks), Markdown, CSV, or JSON

ChatGPT can connect via Custom GPT Actions (requires tunnel). See /chatgpt-actions/setup.

Security and data (2)

Tool	What it does
vault_status	Check AES-256 encryption status
migrate_data	Upgrade data from older schema versions

Workspace (11)

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	Full workspace diagnostics
search_analytics	Search usage patterns, top queries, response times
check_document_freshness	Detect stale documents older than N days

---

HTTP API (58 endpoints)

pip install project-tessera[api]
tessera api  # http://127.0.0.1:8394

Swagger UI at http://127.0.0.1:8394/docs. Optional auth via TESSERA_API_KEY env var.

All endpoints

Method	Path	What it does
GET	/health	Health check
GET	/version	Version info
POST	/search	Semantic + keyword search
POST	/unified-search	Search docs + memories
POST	/remember	Save a memory
POST	/recall	Search memories with filters
POST	/learn	Save and index knowledge
GET	/memories	List memories
DELETE	/memories/{id}	Delete a memory
GET	/memories/categories	List categories
GET	/memories/search-by-category	Filter by category
GET	/memories/tags	List tags
GET	/memories/search-by-tag	Filter by tag
POST	/context-window	Build token-budgeted context
GET	/decision-timeline	Decision evolution
GET	/smart-suggest	Query suggestions
GET	/topic-map	Topic clusters
GET	/knowledge-stats	Stats dashboard
POST	/batch	Multiple operations in one call
GET	/export	Export as Obsidian/MD/CSV/JSON
GET	/export-for-ai	Export memories in portable format
POST	/import-from-ai	Import memories from external sources
POST	/import-conversations	Import past conversations
POST	/migrate	Run data migration
GET	/vault-status	Encryption status
GET	/user-profile	User profile
GET	/status	Server status
GET	/health-check	Workspace diagnostics
POST	/deep-search	Multi-angle document search
POST	/deep-recall	Multi-angle memory recall
GET	/contradictions	Detect conflicting memories
GET	/memory-confidence	Memory reliability scores
GET	/memory-health	Memory health analytics
GET	/hooks	List plugin hooks
GET	/entity-search	Search entity knowledge graph
POST	/entity-graph	Mermaid diagram from entities
GET	/consolidation-candidates	Find similar memory clusters
POST	/consolidate	Merge similar memories
GET	/dashboard	Web dashboard (dark theme, entity graph, stats)
POST	/sleep-consolidate	Auto-merge near-duplicate memories
POST	/retention-policy	Flag old or low-quality memories
GET	/retention-summary	Age distribution and at-risk counts
GET	/adapters/{framework}	Setup code for LangChain, CrewAI, AutoGen
POST	/auto-curate	Classify, tag, deduplicate, and clean up memories
GET	/auto-insights	Trending topics, decision patterns, hidden connections
GET	/chatgpt-actions/openapi.json	OpenAPI spec for Custom GPT Actions
GET	/chatgpt-actions/instructions	GPT instruction template
GET	/chatgpt-actions/setup	ChatGPT integration setup guide

Quick examples

# Search documents
curl -X POST http://127.0.0.1:8394/search \
  -H "Content-Type: application/json" \
  -d '{"query": "database architecture", "top_k": 5}'

# Save a memory
curl -X POST http://127.0.0.1:8394/remember \
  -H "Content-Type: application/json" \
  -d '{"content": "Use PostgreSQL for production", "tags": ["db"]}'

# Export memories
curl http://127.0.0.1:8394/export-for-ai?target=chatgpt

# Batch (multiple operations, single request)
curl -X POST http://127.0.0.1:8394/batch \
  -H "Content-Type: application/json" \
  -d '{"operations": [{"method": "search", "params": {"query": "test"}}, {"method": "knowledge_stats"}]}'

---

CLI (11 commands)

tessera setup          # One-command setup (config + model download + Claude Desktop)
tessera init           # Interactive setup
tessera ingest         # Index all document sources
tessera sync           # Re-index changed files only
tessera serve          # Start MCP server (stdio)
tessera api            # Start HTTP API server (port 8394)
tessera migrate        # Upgrade data schema
tessera check          # Workspace health diagnostics
tessera status         # Project status summary
tessera install-mcp    # Configure Claude Desktop
tessera version        # Show version

---

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:

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

sources:
  - path: .
    type: document

search:
  reranker_weight: 0.7     # Semantic vs keyword balance (0.0 = keyword only, 1.0 = vector only)
  max_top_k: 50            # Max results per search

ingestion:
  chunk_size: 1024         # Tokens per chunk
  chunk_overlap: 100       # Overlap between chunks

hooks:                      # Optional plugin hooks
  on_memory_created:
    - script: ./my-hook.sh

Or set TESSERA_WORKSPACE=/path/to/docs to skip config file entirely.

Environment variables:

• TESSERA_API_KEY -- enable API authentication
• TESSERA_VAULT_KEY -- enable AES-256 encryption for memories

---

Technical details

Component	Technology	Why
Vector store	LanceDB	Embedded columnar store. No server process, handles vector + metadata queries natively
Embeddings	fastembed/ONNX	Local inference, no API keys. paraphrase-multilingual-MiniLM-L12-v2 (384-dim, 101 languages)
Metadata	SQLite	File tracking, search analytics, interaction logging. Thread-safe with reentrant locks
Memory storage	Filesystem (.md)	Human-readable, git-friendly, encryptable. YAML frontmatter for metadata
Encryption	Pure Python AES-256-CBC	No OpenSSL dependency. PKCS7 padding, random IV per memory
HTTP API	FastAPI	Swagger docs, Pydantic validation, async-capable
MCP	FastMCP (stdio)	Standard MCP protocol for Claude Desktop

Numbers

Metric	Count
MCP tools	58
HTTP endpoints	58
CLI commands	11
Core modules	69
Lines of code	31,000+
Tests	1102
File types	40+

---

License

AGPL-3.0 -- see LICENSE.

Commercial licensing: bessl.framework@gmail.com