Vectorize is live · free plan; usage-based paid tier · last verified 25 Oct 2026 · viability healthy
Vectorize
Agent memory that learns from failed tool calls and user corrections, not just stores facts
Hindsight is the most defensible open-source answer to agents that keep repeating avoidable mistakes. The numbers are specific and comparable, and the MIT license means you can verify the learning loop yourself with one Docker command. Start self-hosted. If you'd rather not run Postgres, Hindsight Cloud bills per operation with no fixed monthly fee, but Retain at $10.00 per million tokens is the line item to model before you commit.
Verdict generatedVerified 5d ago · liveness 81/100 · cite: rightaichoice.com/tools/vectorize
- Developers building AI agents that must learn from failed tool calls, not just retrieve facts
- Teams running MCP-capable coding agents (Claude Code, Codex CLI, Cursor CLI, opencode, Copilot CLI)
- Support teams building agents on Hindsight and Google's ADK that improve from real interactions
- Multi-agent systems where one agent's discovery should benefit the others
- Simple chatbots with no need for learning, reflection or cross-session memory
- Agent frameworks with no MCP path and no budget for Python SDK or REST integration work
- Teams unwilling to curate and maintain mental models for their recurring situations
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Skip Hindsight if you only need fact lookup for a single-session chatbot — a plain vector store with a memory API costs less and asks nothing of you in curation.
Retain is billed at $10.00 per million tokens, so agents that store a lot of raw experience run up the largest line on your Cloud bill.
Self-hosting at $0 with the MIT license and no usage limits is the cheapest serious option in this category — nothing to forecast. Hindsight Cloud trades that certainty for no fixed monthly fee and a free-credit start; teams with heavy Retain volume should model $10.00 per million tokens against a flat-rate competitor before committing. Enterprise is a contracted, invoiced tier with volume discounts available on request.
In short
Vectorize — Agent memory that learns from failed tool calls and user corrections, not just stores facts. Best for Developers building AI agents that must learn from failed tool calls, not just retrieve facts, Teams running MCP-capable coding agents (Claude Code, Codex CLI, Cursor CLI, opencode, Copilot CLI), Support teams building agents on Hindsight and Google's ADK that improve from real interactions. Free to start; it also has paid plans whose price we could not confirm — see the pricing table.
What is Vectorize?
Vectorize — Agent memory that learns from failed tool calls and user corrections, not just stores facts. Best for Developers building AI agents that must learn from failed tool calls, not just retrieve facts, Teams running MCP-capable coding agents (Claude Code, Codex CLI, Cursor CLI, opencode, Copilot CLI), Support teams building agents on Hindsight and Google's ADK that improve from real interactions. Free to start; it also has paid plans whose price we could not confirm — see the pricing table.
Vectorize's Hindsight is an agent memory layer built around a blunt premise: retrieval-only memory is not enough. When a tool call fails or a user corrects the agent, Hindsight records that as experience and runs it through a reflection layer that turns raw observations into reusable knowledge. The result is an agent that stops making the same mistake twice. It's aimed at developers and platform teams running agents that work across sessions, users, and tools — especially those using MCP-capable coding agents or building support and assistant workflows on top of their own apps. The engine runs four memory networks: Retain, Recall, Reflect and Mental Models. Per-user persistent context keeps preferences, history, and decisions separate, and cross-session persistence means context survives weeks between conversations. Recall is parallelized and returns relevant memories in under 100ms, and the layer is model-agnostic — swapping LLMs doesn't wipe what your agent learned. Memory compounds across agents too: Agent A learns a user preference, Agent B applies it. On the coding side, one command (npx @vectorize-io/hindsight-coding-agents install all) wires 20+ harnesses — Claude Code, Codex CLI, Cursor CLI, opencode, Copilot CLI — to a per-repo memory bank built from git history and past sessions. Vectorize published benchmarks including 94.6% on LongMemEval-S against 74.0% for the next-best system, 92.0% on LoComo, 86.6% on PersonaMem, 71.5% on LifeBench, and 64.1% on BEAM at 10M tokens. Personal agents Hermes and OpenClaw plug Hindsight in as their memory provider, and your own apps get retain, recall and reflect methods from Python, TypeScript or Go over MCP or plain HTTP. Deployment is flexible: self-host Hindsight 1.0 under the MIT license with a single Docker command and embedded PostgreSQL, use Hindsight Cloud pay-as-you-go on token usage with no fixed monthly fee, or run Enterprise in a private cloud, BYOC on AWS, Google Cloud or Azure, or on-premises. Compared
Key features
- Learns from failed tool calls and user corrections instead of only storing facts
- Four memory networks: Retain, Recall, Reflect and Mental Models
- Reflection layer synthesizes individual facts into consolidated, reusable knowledge
- Curated mental models that guide recurring user situations
- Per-user persistent memory keeping preferences, history and decisions separate
- Cross-session persistence that survives weeks between conversations
- Parallel memory recall returning relevant memories in under 100ms
- Model-agnostic memory layer that survives swapping LLMs
- Compound memory across agents: one agent's learning applies to another
- One-command install wires 20+ coding harnesses to a per-repo memory bank
- Hermes and OpenClaw install Hindsight as their memory provider
- Retain, recall and reflect methods from Python, TypeScript or Go SDKs
- MCP server and plain HTTP API for custom integrations
- Self-host under the MIT license with Docker and embedded PostgreSQL
- Hindsight Cloud managed infrastructure with dashboard, usage analytics and daily backups
Behind the verdict
Pick Hindsight when your agent's problem is judgment, not recall. If users keep correcting the same behavior or tool calls fail in ways the agent should remember, a vector store will not fix that — Hindsight's reflection layer and curated mental models address the consolidation step that plain RAG skips. We'd reach for it first in multi-agent setups where one agent's discovery should benefit the others, and in coding teams already running Claude Code, Codex CLI, Cursor CLI, opencode, or Copilot CLI; the one-command per-repo memory bank is the fastest path to seeing whether the learning loop pays off. Pass if all you need is fact retrieval. A conventional vector database is cheaper and simpler to operate, and Hindsight adds moving parts — four memory networks, a reflection layer, and mental models that need curation for recurring situations. Teams unwilling to maintain those models will get less out of it than the benchmarks suggest. Cost is the other watch-out. Self-hosting is free with no usage limits, but Hindsight Cloud's Retain runs $10.00 per million tokens, with Recall at $0.75 per million tokens, Reflect at $0.05 per call, and storage at $0.25 per million tokens per month after the first 30 days free. High-retain-volume workloads should model that before committing; free credits let you test first. Enterprise adds single-tenant isolation, private networking, SSO and enforced MFA, audit logs, SIEM, and SLAs up to 99.95% — relevant if a security team will review the deployment. If your agents run inside Google's ADK, Vectorize's recent support-agent walkthrough shows the recall-before-answer, retain-after pattern working in a real ticketing flow.
Limitations
- Self-hosting means running Hindsight yourself with Docker and embedded PostgreSQL; Hindsight Cloud is the managed alternative and Enterprise can run single-tenant in your own AWS, Google Cloud or Azure account.
- Auto-install through the npx command requires an MCP-capable agent — other frameworks need the Python, TypeScript or Go SDK, or plain HTTP.
- Memory quality depends on the mental models you curate; the reflection layer consolidates patterns but recurring situations still need someone to shape them.
- Cloud costs scale with token volume, and Retain at $10.00 per million tokens is the largest of them, so model your volumes before you commit.
- The layer is model-agnostic and no specific underlying LLM is named in the vendor's materials.
as of 2026-10-20
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Is Vectorize still active?
Vectorize is live · free plan; usage-based paid tier · last verified 25 Oct 2026 · viability healthy.
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: October 2026
How we score →What's new in Vectorize
Checked 9 days agoAcross the latest 1 update: 1 news mention.
What do users say about Vectorize?
What people actually say about Vectorize — is it worth it?
We scanned public community sources for Vectorize on Oct 11, 2026 and could not establish that the discussion we found is about this tool rather than something else sharing its name. Only 1 of the posts we fetched could be positively tied to Vectorize. Rather than publish a sentiment score built on the wrong subject, we publish nothing here and re-run the scan.
How we verified this
Every listed fact about Vectorize— pricing, features, links, status — is re-read from the vendor's own pages by our verification pipeline and each change is logged with a date; the last pass ran on 25 Oct 2026. Pages mix model-assisted synthesis with human review; see how we verify.
Verification history
We have re-verified Vectorize 22 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.
- — changed who should skip it, description, summary, our verdict, features, who it suits, pricing tiers
- — changed pricing page
- — changed who should skip it, summary, switching in, our verdict, setup time, features, pricing fit, hidden costs, limitations, switching out, pricing tiers, who it suits, use cases, description
- — changed who it suits, summary, description, our verdict, integrations, pricing tiers, who should skip it, features
- — changed pricing page
- — changed description, our verdict, who it suits, features, who should skip it, summary, pricing tiers
Showing the 6 most recent of 22 verification passes.
Free to cite with attribution — this page re-verifies continuously.
How much does Vectorize cost in 2026?
Vectorize: Free to start; it also has paid plans whose price we could not confirm — see the pricing table. Pricing last verified 25 Oct 2026.
Plans compared
Every published Vectorize tier, its price as the vendor publishes it, who it fits, and what it adds over the tier below. The 12-month cost projection follows.
| Plan | Price | Billing | Who it's for |
|---|---|---|---|
| Self-hosted | Free | — | Developers and small teams who can run Docker and want learning memory without usage limits or telemetry on their own infrastructure.Adds: Free entry point: MIT license, all four memory networks, Retain/Recall/Reflect APIs, MCP server and embedded PostgreSQL, no usage limits. |
| Hindsight Cloud | Usage-basedVendor: “Pay as you go” | usage | Teams shipping agent memory to production who don't want to operate Postgres, scaling and backups themselves.Adds: Adds managed infrastructure, automatic scaling, daily backups, dashboard and usage analytics, and team collaboration on usage-based token billing with free credits. |
| Enterprise | Contact sales | quote | Organizations whose security team requires SSO, private networking, audit logs and a contracted SLA.Adds: Adds single-tenant deployment, private cloud/BYOC/on-prem, Hindsight Memlake, SSO and enforced MFA, Memory Defense, audit logs and SIEM, invoiced billing and SLAs up to 99.95%. |
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.
Where the pricing makes sense
The company stage and team size where Vectorize's pricing actually pencils out — and where peers do it cheaper.
Self-hosting at $0 with the MIT license and no usage limits is the cheapest serious option in this category — nothing to forecast. Hindsight Cloud trades that certainty for no fixed monthly fee and a free-credit start; teams with heavy Retain volume should model $10.00 per million tokens against a flat-rate competitor before committing. Enterprise is a contracted, invoiced tier with volume discounts available on request.
Has Vectorize's pricing changed?
No plan price for Vectorize has moved since we began logging in Aug 2026; the pricing page is re-read on every verification pass.
Who should pick Vectorize?
Pick Vectorize if you are Developers building AI agents that must learn from failed tool calls, not just retrieve facts, Teams running MCP-capable coding agents (Claude Code, Codex CLI, Cursor CLI, opencode, Copilot CLI), Support teams building agents on Hindsight and Google's ADK that improve from real interactions, Multi-agent systems where one agent's discovery should benefit the others.
Look elsewhere if you are Simple chatbots with no need for learning, reflection or cross-session memory, Agent frameworks with no MCP path and no budget for Python SDK or REST integration work, Teams unwilling to curate and maintain mental models for their recurring situations, High-retain-volume workloads that can't absorb Cloud token cost at $10.00 per million tokens.
Skip it if Skip Hindsight if you only need fact lookup for a single-session chatbot — a plain vector store with a memory API costs less and asks nothing of you in curation.
GeneratedReal-world workflow fit
Concrete scenarios for the personas Vectorize actually fits — and what changes day-one when you adopt it.
Run npx @vectorize-io/hindsight-coding-agents install all; Hindsight wires every detected coding agent to a per-repo memory bank built from git history and past sessions.
Outcome: On AMB's sdebench, Claude Code needed 0.36 corrections per task instead of 0.85, at lower cost.
Wire Hindsight into an agent built on Google's ADK so every resolved or corrected ticket is retained, reflected on, and turned into a mental model for the next one.
Outcome: The agent stops resetting between sessions and applies what it learned from prior corrections to new tickets.
Call retain, recall and reflect from the Python or TypeScript SDK over MCP or plain HTTP; extraction, entity linking, four-way retrieval and reranking happen behind those three methods.
Outcome: Cross-session per-user context ships without building a memory pipeline in-house.
Use cases
- Building a personal AI assistant that remembers user preferences across sessions
- Running multi-agent systems where one agent's learning is applied by another
- Building a customer support agent on Hindsight and Google's ADK that resolves tickets using persistent memory
- Adding memory to an existing LLM application through MCP, the Python SDK or REST API
- Giving Claude Code, Codex CLI, Cursor CLI, opencode or Copilot CLI a per-repo memory bank from git history
- Running production memory workloads on managed Cloud infrastructure instead of operating it yourself
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.
Self-hosted: one Docker command and you have Hindsight 1.0 on embedded PostgreSQL — the fastest path to a working memory engine. Coding-agent install: one npx command wires every detected harness. Your own app: three methods over MCP or HTTP, so most teams wire a first retain/recall loop in an afternoon. Enterprise: single-tenant deployment, SSO and private networking follow a sales and
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 a plain vector store: point your retrieval calls at Hindsight's recall API and let the Reflect layer consolidate what the vector store only stored.
- →From self-hosted to Hindsight Cloud: Vectorize's team assists with migrating memory data from your self-hosted instance.
- →From OpenAI Agents SDK style memory: call retain, recall and reflect from the Python or TypeScript SDK over MCP or plain HTTP.
- →From Hermes or OpenClaw's built-in memory: set Hindsight as the memory provider in Settings — the Hermes desktop app does it without a terminal.
- ↗To a plain vector database: export retained memories and re-embed them, accepting that reflection and mental-model consolidation is lost.
- ↗To a flat-rate managed memory competitor: budget for manual re-ingestion of stored memories, and note Hindsight memories are never deleted on Cloud.
- ↗To self-hosting: move off Cloud onto your own Docker deployment and import the memory data with Vectorize's migration assistance.
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
What are the best Vectorize alternatives?
The closest alternatives to Vectorize we list are Mem0, Zep Memory and Hebbrix. Ranked by direct product-type match, not category overlap.
| Alternative | Pricing | Starts at | Last verified |
|---|---|---|---|
| Mem0 | Freemium | Pricing not published | — |
| Zep Memory | Freemium | Pricing not published | — |
| Hebbrix | Freemium | Pricing not published | — |
Alternatives to Vectorize
View all
Mem0
Mem0 is a memory layer that gives AI agents and apps persistent context across sessions and users
Zep Memory
Build governed AI agent memory with Zep's temporal context graph and dated, traceable facts
Tools that pair well with Vectorize
Common stack mates teams adopt alongside Vectorize, with the specific reason each pairing earns its keep.
Mem0
Mem0 is a memory layer that gives AI agents and apps persistent context across sessions and users
Zep Memory
Build governed AI agent memory with Zep's temporal context graph and dated, traceable facts
Hebbrix
Hebbrix is outcome-weighted persistent memory for AI agents — it recalls what actually worked, not what merely sounds related.
Vectorize vs its closest rival
Vectorize's closest head-to-head is with Voicemem — These aren't really substitutes — they overlap only at the abstract level of 'agent memory.' Pick Vectorize if you're building text or MCP-based agents (Claude Code, Cursor, Google ADK) and want memory that turns failures into reusable judgment, with a free MIT self-host path and a managed cloud option when you'd rather not run infrastructure. Pick VoiceMem only if you're building a real-time voice agent that needs sub-150ms streaming memory with persona and emotion — and you're comfortable with a v0.0.2 research project, self-hosting, and benchmark claims that come from the vendor's own report. Verdict generated
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