lance-context

A storage engine for AI data — agent memory and RL training data — built on Lance.

Modern AI systems produce two kinds of data that are awkward to store well:

1. Agent memory — the running history of a chat or agent: text, images, tool calls, and their embeddings, which you later search over to recall context.
2. RL training data — the trajectories, rewards, and logprobs produced when you train models with reinforcement learning (GRPO, RLVR, PPO, ...).

lance-context stores both in one place. It gives you a durable, columnar, versioned table you can append to, search, filter, and time-travel through — without standing up a database server (though a server is available if you want one).

Why use it

• Two use cases, one engine. A Context store for agent memory and a RolloutStore (a purpose-built RolloutDB) for RL rollouts. Same storage format, same versioning, same cloud backends.
• Multimodal. Store text, images, and binary blobs next to their embeddings and typed metadata — the raw bytes are kept, not just a pointer.
• Search built in. Run vector search, full-text search, or hybrid retrieval directly on the table. No separate vector database.
• Versioned. Every write creates a new immutable snapshot. Roll back, branch, or reproduce an exact state with checkout(version).
• Runs anywhere. Local files, or S3 / GCS / Azure via a simple storage_options dict. Embedded in your process, or behind an HTTP server.

Install

pip install lance-context

Then head to the Quickstart.

Project layout

crates/lance-context-core    # Rust engine: Context + Rollout stores (no Python deps)
crates/lance-context-api     # Shared request/response types (DTOs)
crates/lance-context-server  # HTTP server for remote access
crates/lance-context-client  # HTTP client for the server
crates/lance-context-master  # Control plane, scheduler, and admin UI
crates/lance-context         # Re-export crate for downstream clients
python/                      # Python bindings (PyO3) + tests
deploy/kubernetes/           # Kubernetes master examples
examples/                    # Runnable example projects

Examples

The examples/ directory has self-contained, runnable projects:

• pypi-basic — 5-minute quickstart
• multi-session — concurrent multi-bot writes
• eval-quality — measuring retrieval quality
• mcp-claude-code — serving a store over MCP

License

Licensed under the Apache License, Version 2.0. See LICENSE for details.