project-tessera

Tessera

  ![Tests]()   

<a href="https://glama.ai/mcp/servers/@besslframework-stack/project-tessera"> <img width="380" height="200" src="https://glama.ai/mcp/servers/@besslframework-stack/project-tessera/badge" /> </a>

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)

<details> <summary><strong>Search (5)</strong></summary>

| 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 |

</details>

<details> <summary><strong>Memory (13)</strong></summary>

| 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 |

</details>

<details> <summary><strong>Auto-learn (5)</strong></summary>

| 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 |

</details>

<details> <summary><strong>Intelligence (7)</strong></summary>

| 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 |

</details>

<details> <summary><strong>Insight (6)</strong></summary>

| 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 |

</details>

<details> <summary><strong>Import/Export (4)</strong></summary>

| 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.

</details>

<details> <summary><strong>Security and data (2)</strong></summary>

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

</details>

<details> <summary><strong>Workspace (11)</strong></summary>

| 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 |

</details>

---

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.

<details> <summary><strong>All endpoints</strong></summary>

| 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 |

</details>

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