Vectorize
Open-source agent memory that learns from mistakes and improves over time.
Hindsight is the strongest open-source agent memory for teams that want agents to learn from mistakes autonomously. The 94.6% LongMemEval score and MIT license are compelling, but cloud token costs can mount. If your agents need to improve without manual tuning, this is the best starting point.
Verified 11d ago · liveness 78/100 · cite: rightaichoice.com/tools/vectorize
- Developers building AI agents that need persistent learning from mistakes
- Teams using MCP-capable agents like Claude Code or Cursor
- Projects requiring per-user memory across sessions and agents
- Agent systems that need to improve autonomously via reflection
- Simple chatbot use cases that don't require learning or reflection
- Non-MCP-compatible agent frameworks without custom integration effort
- Teams needing a completely free, unlimited cloud-hosted solution
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Skip Hindsight if you're building a simple chatbot that doesn't need learning or reflection, or if you lack DevOps skills for self-hosting and want to avoid unpredictable cloud token costs.
Cloud pricing is usage-based: Retain costs $10 per million tokens, Iris Extract $7.50 per million tokens, and Reflect $0.05 per call, so costs scale with your agent's activity.
Hindsight pricing fits indie developers and startups who can self-host for free with MIT licensing, and scale to Hindsight Cloud's pay-as-you-go model as they grow. Compared to managed alternatives like Zep or Supermemory, Hindsight's self-host option is cheaper, but cloud token costs can rival or exceed those peers at high volume.
In short
Vectorize — Open-source agent memory that learns from mistakes and improves over time. Best for Developers building AI agents that need persistent learning from mistakes, Teams using MCP-capable agents like Claude Code or Cursor, Projects requiring per-user memory across sessions and agents. Free to use.
What's new in Vectorize
Checked 11 days agoAcross the latest 1 update: 1 news mention.
Viability Score
How well maintained and how widely used is Vectorize? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: September 2026
How we score →Key Features
- Learns from agent mistakes and tool call failures
- Automatic pattern detection via reflection layer
- Builds judgment over time with curated mental models
- Per-user persistent memory across sessions and agents
- Cross-session context persistence
- Fast memory recall under 100ms
- Model-agnostic memory layer
- Single-command MCP skill install
- Four memory networks: Retain, Recall, Reflect, Mental Models
- Self-host via single Docker command (MIT license)
- Pay-as-you-go cloud with managed infrastructure
- Daily backups and dashboard analytics
- MCP server built in
- Compound memory across multiple agents sharing context
- LongMemEval benchmark score 94.6%
About Vectorize
Vectorize offers Hindsight, an open-source (MIT) agent memory layer that goes beyond simple storage. It captures agent mistakes and tool call failures, reflects on patterns automatically, and builds reusable judgment over time. Built for developers creating production-grade AI agents with MCP-capable tools like Claude Code, Cursor, or Google ADK, Hindsight provides per-user persistent memory across sessions and agents. The core of Hindsight is four memory networks: Retain (store memories), Recall (retrieve them), Reflect (synthesize patterns), and Mental Models (guide future behavior). It's model-agnostic, so you can swap LLMs without losing what your agent has learned. Setting up is a one-command process: `npx add-skill vectorize-io/hindsight --skill hindsight-docs`, which installs the memory tools automatically for any MCP-capable agent. On the independent LongMemEval benchmark, Hindsight scores 94.6%, outperforming Supermemory (85.2%), Zep (71.2%), and GPT-4o (60.2%). This makes it a strong choice for teams that need agents to actually improve from their mistakes without manual tuning. You can self-host for free via Docker (MIT license, no restrictions), or opt for Hindsight Cloud, a managed pay-as-you-go service with no fixed monthly fee. Growth in 2026 has focused on practical use cases like customer support agents, as highlighted in a recent demo with Google's ADK.
Behind the Verdict
Hindsight stands out because it doesn't just store memories—it actually learns. Most agent memory tools are retrieval-only: they store facts and pull them back when relevant. Hindsight goes further with a reflection layer that synthesizes individual experiences into reusable patterns, and mental models that guide future behavior. That's a meaningful difference for teams building agents that operate in complex, evolving environments. Strengths: - Benchmark leadership: 94.6% on LongMemEval, beating Supermemory (85.2%), Zep (71.2%), and GPT-4o (60.2%). This is a peer-reviewed benchmark, independently reproduced, so the numbers carry weight. - MIT license: Self-hosting is free, with no usage limits, telemetry, or restrictions. You can run it in production on your own infrastructure at zero cost. - Easy installation: A single command (`npx add-skill`) installs memory tools into any MCP-capable agent, like Claude Code or Cursor. The agent reads the docs and sets things up itself. - Model-agnostic: You can swap LLMs without losing learned behavior. That flexibility is rare. - Compound memory: Agents share context automatically, so what one agent learns benefits others. This is critical for multi-agent systems. Weaknesses: - Cloud pricing can be unpredictable: Pay-as-you-go on token usage means costs scale with activity. High-volume operations like Retain ($10/M tokens) and Iris Extract ($7.50/M tokens) can add up. - Self-hosting requires DevOps skills: If you don't want to manage infrastructure, you'll need to accept cloud costs. - Integration with non-MCP agents requires manual work: If you're not using an MCP-compatible framework, expect extra effort. Where it fits: Developers building production agents that need to learn from mistakes, teams using MCP-capable tools, and anyone wanting a free, open-source memory layer with fast recall. Where it doesn't: Simple chatbots that don't need learning or reflection, teams without DevOps for self-hosting, or those needing a fully free unlimited cloud solution.
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Real-world workflow fit
Concrete scenarios for the personas Vectorize actually fits — and what changes day-one when you adopt it.
You want your assistant to remember your preferences and past conversations without managing a database.
Outcome: With Hindsight self-hosted, you run `npx add-skill` to install memory tools into Claude Code. Your assistant now remembers your coffee order and project notes across sessions, and even learns from your corrections.
Your support bot keeps failing on the same edge cases, frustrating customers.
Outcome: Hindsight Cloud records each tool failure as an experience, the reflection layer spots the pattern, and the agent learns to handle those cases correctly next time. Your support KPIs improve without manual prompt tuning.
You have multiple agents handling different tasks, and they need to share context about users.
Outcome: With Hindsight's compound memory, when Agent A learns a user preference, Agent B applies it automatically. You deploy via Docker on your own infrastructure or use Enterprise cloud for dedicated SLAs.
Use Cases
- Building a personal AI assistant that remembers user preferences across sessions
- Creating multi-agent systems where agents share context automatically
- Developing a customer support bot that learns from past mistakes to improve responses
- Integrating memory into existing LLM applications via MCP or SDK
- Using Hindsight Cloud for production workloads without managing infrastructure
Limitations
- Vectorize is model-agnostic, designed to work with any LLM, and no specific underlying model is named in the provided evidence.
- Self-hosting requires Docker, and cloud pricing is pay-as-you-go based on token usage, which may be variable.
- The service integrates via MCP server, Python SDK, and REST API, with MCP capability required for auto-install skills.
as of 2026-08-29
Verification history
We have re-verified Vectorize 18 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 18 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Vectorize tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Self-hosted
$0/mo
Ideal for
Solo developers and startups with Docker expertise who want zero-cost, unrestricted memory for their agents.
What this tier adds
Free and MIT licensed; run everything yourself with a single Docker command, including all four memory networks and MCP server.
Hindsight Cloud
Pay as you go
Ideal for
Teams that want managed infrastructure, automatic scaling, and daily backups without DevOps overhead.
What this tier adds
Adds usage-based billing on tokens, managed infrastructure, 99.9% uptime SLA, and dashboard analytics; no fixed monthly fee.
Enterprise
Custom
Ideal for
Large organizations needing dedicated infrastructure, compliance, and custom SLAs for production workloads.
What this tier adds
Adds bring-your-own-cloud, on-premises deployment, SSO/RBAC, up to 99.95% uptime SLA, and 24×7 support with 30-minute response.
Where the pricing makes sense
The company stage and team size where Vectorize's pricing actually pencils out — and where peers do it cheaper.
Hindsight pricing fits indie developers and startups who can self-host for free with MIT licensing, and scale to Hindsight Cloud's pay-as-you-go model as they grow. Compared to managed alternatives like Zep or Supermemory, Hindsight's self-host option is cheaper, but cloud token costs can rival or exceed those peers at high volume.
Setup time & first value
How long it actually takes to get something useful out of Vectorize — broken out by persona, not the marketing-page minute.
Developers using MCP-capable agents (Claude Code, Cursor) can install Hindsight in minutes with one command. Self-hosting via Docker takes about 10-15 minutes including setup. For custom integration with non-MCP agents, expect a few hours of development work.
Switching to or from Vectorize
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From Zep or Supermemory: Contact Vectorize for migration assistance; the data models are similar, so most memories can be exported and re-imported.
- ↗To another memory system: Export your memories via the REST API and import them into the new system; the data is JSON-structured.
Integrations
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “Vectorize”, and we withheld 6: 6 could not be judged, because “Vectorize” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Vectorize.
Official links
Frequently Asked Questions
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