VectorLane

Searchable local memory for AI agents.

VectorLane is a local vector memory store designed for AI agents. It provides fast, offline-capable semantic search over text, documents, and data using embeddings. No cloud APIs required — everything runs on your machine.

Features

• Fully local — No external API calls, no data leaves your machine
• Offline embeddings — Uses local-hash (256-dim) by default, with OpenAI/HuggingFace options
• Multiple backends — JSONL (default), SQLite, in-memory
• MCP integration — Native Model Context Protocol support for AI assistants
• REST API — Full HTTP API on port 3090 (configurable)
• CLI — Complete command-line interface for all operations
• SDK — JavaScript/TypeScript and Python client libraries
• Citation tracking — Automatic source attribution for search results
• Multi-source ingestion — Text, URLs, files, and bulk imports

Quick Start

Install

# npm
npm install -g @talocode/vectorlane

# pip
pip install talocode-vectorlane

Initialize and Use

# Start the server
vectorlane serve

# Initialize a project
vectorlane init

# Ingest some text
vectorlane ingest-text "The quick brown fox jumps over the lazy dog."

# Search
vectorlane search "fox"

Or use the SDK

import { VectorLane } from '@talocode/vectorlane';

const vl = new VectorLane();
await vl.init();
await vl.ingestText('The quick brown fox jumps over the lazy dog.');
const results = await vl.search('fox');
console.log(results);

from vectorlane import VectorLane

vl = VectorLane()
vl.init()
vl.ingest_text("The quick brown fox jumps over the lazy dog.")
results = vl.search("fox")
print(results)

Architecture

+-----------+     +-----------+     +-----------+
|  CLI /    |---->|  REST API |---->|  Vector   |
|  SDK      |     |  :3090    |     |  Store    |
+-----------+     +-----------+     +-----------+
                         |                |
                  +------+------+  +------+------+
                  |  Embedding  |  |  Backend    |
                  |  Engine     |  |  (JSONL/   |
                  |  (local/    |  |   SQLite)  |
                  |   openai)   |  +-------------+
                  +-------------+

Installation

See docs/INSTALL.md for detailed installation instructions.

Requirements

• Node.js 18+ (for npm package)
• Python 3.9+ (for pip package)
• No external dependencies required for basic usage

Quick Install

# npm
npm install -g @talocode/vectorlane

# pip (Python SDK)
pip install talocode-vectorlane

CLI Reference

See docs/CLI.md for the full CLI reference.

Core Commands

| Command | Description | |---------|-------------| | vectorlane init | Initialize a new VectorLane project | | vectorlane serve | Start the API server | | vectorlane search <query> | Search the vector store | | vectorlane ingest <file> | Ingest a file into the store | | vectorlane ingest-text <text> | Ingest raw text | | vectorlane ingest-url <url> | Ingest content from a URL | | vectorlane doctor | Run diagnostics | | vectorlane demo | Run a demo session |

Collection Commands

| Command | Description | |---------|-------------| | vectorlane collection create <name> | Create a new collection | | vectorlane collection list | List all collections | | vectorlane collection show <name> | Show collection details | | vectorlane collection stats <name> | Show collection statistics | | vectorlane collection delete <name> | Delete a collection |

Configuration Commands

| Command | Description | |---------|-------------| | vectorlane config get <key> | Get a config value | | vectorlane config set <key> <value> | Set a config value | | vectorlane config list | List all config values |

Import Commands

| Command | Description | |---------|-------------| | vectorlane import-memorylane | Import from MemoryLane | | vectorlane import-contextlane | Import from ContextLane | | vectorlane sync memorylane | Sync with MemoryLane | | vectorlane sync contextlane | Sync with ContextLane |

SDK Reference

See docs/SDK.md for the full SDK reference.

JavaScript/TypeScript

import { VectorLane } from '@talocode/vectorlane';

const vl = new VectorLane({ port: 3090 });

// Initialize
await vl.init();

// Create a collection
await vl.collection.create('docs');

// Ingest text
await vl.ingestText('Your text content here', { collection: 'docs' });

// Ingest a URL
await vl.ingestUrl('https://example.com/article', { collection: 'docs' });

// Search
const results = await vl.search('your query', { collection: 'docs', limit: 5 });

// Get stats
const stats = await vl.collection.stats('docs');

Python

from vectorlane import VectorLane

vl = VectorLane(port=3090)

# Initialize
vl.init()

# Create a collection
vl.collection.create("docs")

# Ingest text
vl.ingest_text("Your text content here", collection="docs")

# Ingest a URL
vl.ingest_url("https://example.com/article", collection="docs")

# Search
results = vl.search("your query", collection="docs", limit=5)

# Get stats
stats = vl.collection.stats("docs")

REST API

See docs/API.md for the full API reference.

Quick Reference

| Method | Endpoint | Description | |--------|----------|-------------| | GET | /health | Health check | | POST | /v1/vectorlane/init | Initialize project | | POST | /v1/vectorlane/collections | Create collection | | GET | /v1/vectorlane/collections | List collections | | GET | /v1/vectorlane/collections/:name | Get collection | | DELETE | /v1/vectorlane/collections/:name | Delete collection | | GET | /v1/vectorlane/collections/:name/stats | Collection stats | | POST | /v1/vectorlane/ingest | Ingest file | | POST | /v1/vectorlane/ingest-text | Ingest text | | POST | /v1/vectorlane/ingest-url | Ingest URL | | POST | /v1/vectorlane/search | Search vectors | | POST | /v1/vectorlane/import-memorylane | Import MemoryLane | | POST | /v1/vectorlane/import-contextlane | Import ContextLane | | POST | /v1/vectorlane/sync-memorylane | Sync MemoryLane | | POST | /v1/vectorlane/sync-contextlane | Sync ContextLane | | POST | /v1/vectorlane/demo | Run demo |

MCP Integration

See docs/MCP.md for MCP configuration.

VectorLane provides native MCP (Model Context Protocol) support. Add to your MCP config:

{
  "mcpServers": {
    "vectorlane": {
      "command": "vectorlane",
      "args": ["mcp"]
    }
  }
}

Available MCP Tools

| Tool | Description | |------|-------------| | vectorlane_init | Initialize VectorLane | | vectorlane_collection_create | Create a collection | | vectorlane_collection_list | List collections | | vectorlane_collection_stats | Get collection stats | | vectorlane_ingest | Ingest a file | | vectorlane_ingest_text | Ingest text content | | vectorlane_search | Search the vector store | | vectorlane_import_memorylane | Import from MemoryLane | | vectorlane_import_contextlane | Import from ContextLane | | vectorlane_doctor | Run diagnostics | | vectorlane_demo | Run a demo | | vectorlane_clear_collection | Clear a collection |

Vector Store

See docs/VECTOR_STORE.md for backend details.

| Backend | Description | Use Case | |---------|-------------|----------| | jsonl | JSON Lines file storage (default) | Small to medium datasets | | sqlite | SQLite database | Larger datasets, concurrent access | | memory | In-memory only | Testing, ephemeral data |

Embeddings

See docs/EMBEDDINGS.md for embedding options.

| Model | Dimensions | Description | |-------|------------|-------------| | local-hash | 256 | Default, fully offline, fast | | openai | 1536 | Requires API key, highest quality | | huggingface | 384 | Local, requires model download |

Search

See docs/SEARCH.md for search capabilities.

vectorlane search "machine learning"
vectorlane search "API docs" --collection docs --limit 10
vectorlane search "error handling" --threshold 0.7

Chunking

See docs/CHUNKING.md for chunking strategies.

• Fixed-size — Default, 512 tokens per chunk with 50 token overlap
• Sentence — Splits on sentence boundaries
• Paragraph — Splits on paragraph boundaries
• Recursive — Hierarchical splitting with fallbacks

Citations

See docs/CITATIONS.md for citation tracking.

Every search result includes source attribution:

{
  "id": "abc123",
  "text": "The quick brown fox...",
  "score": 0.95,
  "citation": {
    "source": "document.txt",
    "page": 1,
    "offset": 0,
    "timestamp": "2026-07-15T10:30:00Z"
  }
}

Integrations

See docs/INTEGRATIONS.md for integration guides.

• MemoryLane — Import and sync conversation history
• ContextLane — Import and sync context documents
• MCP-compatible tools — Works with Claude, Cursor, and other MCP clients
• LangChain — VectorLane retriever integration
• LlamaIndex — VectorLane vector store integration

Configuration

Configuration is stored in ~/.vectorlane/config.json:

{
  "port": 3090,
  "backend": "jsonl",
  "embedding": "local-hash",
  "storage_path": "~/.vectorlane/data",
  "default_collection": "default",
  "chunk_size": 512,
  "chunk_overlap": 50
}

vectorlane config get port
vectorlane config set port 3091
vectorlane config list

Storage

All data is stored locally in ~/.vectorlane/:

~/.vectorlane/
  config.json          # Configuration
  data/                # Vector store data
    collections/       # Collection data
    embeddings/        # Cached embeddings
  logs/                # Application logs

Troubleshooting

See docs/TROUBLESHOOTING.md for common issues.

Quick Fixes

Server won't start

vectorlane doctor

Port in use

vectorlane config set port 3091
vectorlane serve

Reset everything

vectorlane clear
vectorlane init

Roadmap

See docs/ROADMAP.md for the development roadmap.

v0.1.0 (Current)

• Core vector store with JSONL backend
• local-hash embedding model
• CLI with all core commands
• REST API
• MCP integration
• Python and JavaScript SDKs

v0.2.0

• SQLite backend
• OpenAI embedding support
• HuggingFace embedding support
• LangChain integration

v0.3.0

• Hybrid search (keyword + semantic)
• Multi-modal embeddings (images)
• Distributed mode
• Web UI dashboard

Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

MIT License - see LICENSE for details.

Support

• Documentation: docs/
• Issues: GitHub Issues
• Discord: Join our Discord