Vectorize

Vectorize provides an open-source agent memory system designed to help AI agents retain and utilize user-specific context over time. Its primary function is to enable agents to remember user preferences, history, and decisions, ensuring that each user’s context is persistent and remains separate from others. The system is model-agnostic, allowing integration with any large language model (LLM) without loss of learned information when switching models.

A notable feature of Vectorize is its cross-session persistence, which allows agents to pick up interactions where they left off, even after weeks. Fast memory recall is supported, with parallel search enabling retrieval of the most relevant memories in under 100 milliseconds. The platform not only stores and retrieves information but also allows agents to learn from mistakes—when a tool call fails or a user corrects the agent, this experience is incorporated so the agent can avoid repeating errors. The reflection layer synthesizes individual facts into consolidated knowledge, automatically detecting patterns and building judgment over time.

Vectorize supports shared context across multiple agents, enabling one agent to learn a user preference that another agent can apply. The memory system is designed to improve itself, consolidating raw observations into reusable knowledge. It installs with a single command for agents that are compatible with the MCP protocol, registering tools for remembering, recalling, and reflecting. The system is suitable for teams and developers building advanced AI agents that require persistent, evolving memory capabilities.

The platform is open source and distributed under the MIT license. Implementation services, team training, and architecture consulting are offered for organizations seeking assistance with integrating Vectorize into their systems. Benchmark results indicate a LongMemEval score of 94.6%. Vectorize is developed by Vectorize, Inc.