Per-user memory
Hindsight keeps separate memory for each user so preferences, history, and decisions do not mix across accounts.
Open-source agent memory for persistent context and cross-session recall
Vectorize’s Hindsight is open source agent memory for applications that need persistent context instead of stateless chat. It is built to help agents remember users, retain context across sessions, and improve their responses as they encounter more conversations and tool outcomes.
The product is available in two main deployment modes: self-hosted Hindsight, which runs on your own infrastructure with a Docker command and an MIT license, and Hindsight Cloud, which is a managed service with usage-based billing. The site positions the system as model-agnostic and works with MCP-capable agents, so teams can connect it to existing workflows without tying memory to a specific LLM.
Hindsight keeps separate memory for each user so preferences, history, and decisions do not mix across accounts.
Memory persists across session boundaries, letting an agent pick up context weeks later instead of starting from scratch.
The product runs dense vector search, sparse keyword search, graph traversal, and temporal search in parallel to retrieve relevant context from multiple angles.
Results are merged with token budgets rather than top-K counts, so the prompt size stays predictable while still drawing from multiple retrieval methods.
The reflection layer synthesizes raw observations into consolidated knowledge, turning repeated experiences into reusable patterns.
Hindsight exposes remember, recall, and reflect through MCP, and also offers a Python SDK and REST API for application integration.
Add memory to an agent that supports returning users, so preferences and prior decisions follow the person across sessions.
Build workflows where a failed tool call or corrected answer becomes future learning instead of a repeated mistake.
Support agents that need to reason over changing facts, related entities, and earlier events without a manual tagging system.
Ship a managed production setup when you want scaling, backups, analytics, and collaboration features without running the infrastructure yourself.
Integrate memory into an existing agent stack through MCP, a Python SDK, or a REST API, without committing to one LLM.
Yes. The self-hosted version is MIT licensed, can be run with a single Docker command, and the pricing page says it has no usage limits, no telemetry, and no restrictions.
Self-hosted Hindsight runs on your own infrastructure, while Hindsight Cloud is a managed service. The cloud plan includes infrastructure management, scaling, backups, analytics, and collaboration features.
The product exposes remember, recall, and reflect capabilities through an MCP server, plus a Python SDK and REST API. The product page also shows an example client using retain and recall calls.
The pricing page says Hindsight Cloud uses pay-as-you-go token billing with no fixed monthly fee and no per-seat pricing. It charges separately for operations such as Retain, Recall, Reflect, Iris Extract, and Mental Model actions.
The site says it works with any MCP-capable agent and is model-agnostic, so you can swap LLMs without losing what the agent learned.
Traffic data is for reference only.
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