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AI Memory & Search🔴Developer
V

Vectorize Hindsight

Open-source agent memory that learns from mistakes — scored 94.6% on LongMemEval — with a managed cloud and an MCP server.

Starting at$0
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In Plain English

Open-source agent memory that learns from mistakes — scored 94.6% on LongMemEval — with a managed cloud and an MCP server.

OverviewFeaturesPricingUse CasesIntegrationsFAQ

Overview

Vectorize's Hindsight is an open-source memory system built on a contrarian idea: agents shouldn't just remember facts, they should remember mistakes. When a tool call fails or a user corrects an agent mid-flow, Hindsight captures that as an "experience." Its reflection layer then synthesizes raw observations into consolidated mental models, so the next run of a similar task starts smarter instead of repeating the same dead ends. The team publishes a peer-reviewed LongMemEval score of 94.6%, compared to 85.2% for Supermemory, 71.2% for Zep, and 60.2% for GPT-4o on the same benchmark — that gap matters most when your agent is expected to function across weeks of user history, not a single chat window.

The architecture has four memory networks (Retain, Recall, Reflect, plus structured extraction via Iris) sitting on top of an embedded PostgreSQL instance. Parallel recall returns relevant memories in under 100ms, the memory layer is model-agnostic so you can swap GPT-4o, Claude, or a local Llama without losing what the agent learned, and per-user isolation is built in so a multi-tenant SaaS doesn't have to bolt that on later. There's a Python SDK and a REST API, an official MCP server that registers remember, recall, and reflect tools in any MCP-capable client (Claude Desktop, Cursor, Goose), and an npx add-skill vectorize-io/hindsight installer that lets a coding agent set up its own memory layer end-to-end.

Pricing is unusually transparent: the entire system is MIT-licensed and runs free on your own infrastructure with a single Docker command. Hindsight Cloud is pay-as-you-go on token usage with no monthly minimum and no seat pricing — for a small project, that often works out to a few dollars a month. Storage and retention are not metered separately. Enterprise adds BYOC/on-prem, dedicated infrastructure, up to 99.95% uptime with a 30-minute Sev 1 SLA, and SSO/RBAC.

The best fit is teams who need agents that get better at a specific user's job over time — coding assistants that learn an engineer's style and project conventions, support bots that remember a customer's account history, research agents that build a personal knowledge base. The biggest caution is the same as any memory system: persistent memory adds privacy, consent, deletion, and retention obligations to your product. Hindsight gives you the APIs to manage that (delete, export, scope by user) but you still have to design the policy.

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Key Features

Feature information is available on the official website.

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Pricing Plans

Self-hosted

$0

    Hindsight Cloud

    Pay-as-you-go (free credits to start)

      Enterprise

      Custom

        See Full Pricing →Free vs Paid →Is it worth it? →

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        Best Use Cases

        🎯

        Coding agents that learn an engineer's style and project conventions over time

        ⚡

        Customer support bots that remember account history and prior resolutions

        🔧

        Research and analyst agents that accumulate a long-term personal knowledge base

        🚀

        Multi-agent systems where one agent's learning needs to propagate to peers

        💡

        Personal productivity assistants that adapt to recurring constraints and preferences

        Integration Ecosystem

        5 integrations

        Vectorize Hindsight works with these platforms and services:

        🧠 LLM Providers
        OpenAIAnthropicGoogleOllama
        📊 Vector Databases
        postgresql (embedded)
        View full Integration Matrix →

        Pros & Cons

        ✓ Pros

        • ✓Highest published LongMemEval score (94.6%) — measurable advantage on long-horizon tasks
        • ✓True open source under MIT, including the four-network learning system
        • ✓Pay-as-you-go cloud with no monthly minimum or seat pricing — cheap to start, predictable to scale
        • ✓First-class MCP server makes integration with Claude Desktop, Cursor, and Goose a one-line config
        • ✓Reflection layer that learns from failures, not just stores facts — genuinely different from RAG-style memory

        ✗ Cons

        • ✗Newer project — smaller community than incumbent memory tools like Zep or Mem0
        • ✗Token-based pricing is hard to forecast for high-volume agents until you measure
        • ✗Self-hosted PostgreSQL backend is fine for many teams but limits scaling levers vs purpose-built vector DBs
        • ✗Memory governance (consent, deletion, retention policy) is still your responsibility to design
        • ✗Enterprise features like SSO/RBAC and BYOC live only behind a sales conversation

        Frequently Asked Questions

        How much does Vectorize Hindsight cost?+

        Vectorize Hindsight pricing starts at $0. They offer 3 pricing tiers.
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        Quick Info

        Category

        AI Memory & Search

        Website

        vectorize.io
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