Versuno AI
Open-source Postgres memory engine for AI agents that sends memory conflicts to a human instead of overwriting them.
Versuno's human conflict review is the reason to care — silent overwrites are how agent memory quietly rots, and memloom simply refuses to do them. It's an Apache-2.0 engine, not a chat wrapper, so the buy-in is real: API, CLI, and MCP, with Postgres already on your side. If your team lives in code, the free tier and $19/month Pro are cheap enough to test this week.
Verified 5d ago · liveness 76/100 · cite: rightaichoice.com/tools/versuno-ai
- Developers building MCP-compatible AI agents that need durable, inspectable memory
- Engineering teams that want contradictions reviewed by a human instead of newest-wins overwrites
- Postgres shops that would rather not run a second vector database next to their primary one
- Projects needing local-first, self-hostable memory under Apache-2.0
- Non-technical teams looking for a point-and-click memory dashboard
- Buyers who want memory bundled inside an agent platform with zero setup
- Teams with no capacity to review memory conflicts — the review step is the point
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Skip Versuno if your team wants memory managed through a point-and-click dashboard rather than an API, CLI, and MCP client — and if nobody can own a manual review queue for conflicting memories.
Free caps out at 2,000 public brain queries and 50 chat messages a month, so an agent in daily use will hit the ceiling well before the month ends.
Versuno pitches below bundled enterprise memory add-ons: Free covers evaluation, $19/month Pro suits a solo builder running agents daily, and $89/month Team covers a 5-seat workspace with RBAC and a shared prompt library. Yearly billing saves two months against those monthly rates. Solo hobbyist volumes fit Free; if you only need memory inside a platform you already pay for, that bundled option may cost less in staff time than the $19/month does in dollars.
In short
Versuno AI — Open-source Postgres memory engine for AI agents that sends memory conflicts to a human instead of overwriting them. Best for Developers building MCP-compatible AI agents that need durable, inspectable memory, Engineering teams that want contradictions reviewed by a human instead of newest-wins overwrites, Postgres shops that would rather not run a second vector database next to their primary one. Free to start; paid plans from $5.
What's new in Versuno AI
Checked 5 days agoAcross the latest 5 updates: 2 feature updates, 2 launches and 1 changelog entry.
Introducing memloom: a local-first memory engine for AI agents
Versuno launched memloom, an open-source local-first memory engine for AI agents. It is free to use and the first project built under Versuno.
Agents self-signup for Versuno
AI agents can now create Versuno accounts autonomously with no human in the loop, and receive a working API key on behalf of the user.
Public Brains: clean, up-to-date context for coding agents
Public Brains deliver up-to-date context to coding agents at runtime via API and MCP, aimed at reducing hallucinations from stale conventions.
v0.6: RBAC & new onboarding
Versuno v0.6 added role-based access control and a new onboarding flow.
Versuno v0.5
Versuno v0.5 release, listed on the public changelog between the v0.2 and v0.6 entries.
What people actually say about Versuno AI — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
23 mentions across 1 source (YouTube) · researched Aug 17, 2026.
Average across the 1 source that answered — each source counts once, not each post.
- +Open-source engine under Apache-2.0 — transparent and auditable.
- +Runs fully locally via PGLite for offline development and testing.
- +Unified relational, vector, keyword, and graph retrieval in one DB.
- +Conflict resolution with human-in-the-loop review prevents silent overwrites.
- +Public Brains deliver fresh context to coding agents, cutting hallucinations.
- −Zero community feedback or user reviews available anywhere.
- −No evidence of reliability or performance in real-world use.
- −Human-in-the-loop review may disrupt fast autonomous workflows.
- −Requires Postgres—a hurdle for teams not already using it.
- −Support quality unverified due to lack of reported feedback.
- • Credit packs that never expire could encourage overspending if not tracked.
- • Over-usage charges on API calls may accumulate quickly for production agents.
- • Postgres hosting and maintenance costs are separate and not included.
Viability Score
How well maintained and how widely used is Versuno AI? 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 →Key Features
- Open-source memory engine memloom, Apache-2.0 and written in TypeScript
- Runs on Postgres, including fully local via PGLite with byte-identical code
- Relational, vector, keyword, and graph retrieval in one Postgres database
- Memory conflict detection that routes contradictions to human review
- Revertible memory changes with a full conflict trail
- Public Brains deliver runtime context to coding agents via API and MCP
- Private Brains keep team context behind access controls
- Agent self-signup with autonomous API key generation (no human in the loop)
- Public API v0 for programmatic memory and context access
- MCP v0 support for Model Context Protocol clients
- CLI v0 for scripting and automation workflows
- Role-based access control shipped in v0.6 (May 5, 2026)
- Structured context store for decisions, skills, prompts, and assets
- Rich editor with reusable context blocks
- Credit packs that never expire for usage top-ups
About Versuno AI
Versuno builds memloom, an open-source memory engine for AI agents that runs on Postgres, including a fully local PGLite setup. It is Apache-2.0, written in TypeScript, and Versuno says what runs on your laptop is byte-identical to what runs in the cloud. The behaviour that separates it from other agent memory layers: when a new memory contradicts an existing one, memloom surfaces the conflict for human review rather than resolving it newest-wins, and every change can be reverted. Retrieval runs inside one Postgres database across relational, vector, keyword, and graph modes, so you are not bolting on a second store. Above the engine sit Public Brains, which push current context to coding agents at runtime via API and MCP, and Private Brains, which keep team context behind access controls. Agents can sign themselves up (shipped June 23, 2026) and mint their own API key with no human in the loop. Structured context — decisions, skills, prompts, assets — lives in a rich editor with reusable blocks, and v0.6 (May 5, 2026) added role-based access control plus a new onboarding flow. It targets developers shipping MCP-compatible coding agents and teams that want a shared, inspectable, self-hostable context layer.
Behind the Verdict
Memory is the part of an agent stack that looks fine in a demo and falls apart in month three. Versuno's answer is to stop letting the newest write win: when a new memory contradicts an old one, memloom routes it to a human, and any change can be reverted. That single design decision is what I'd evaluate before anything else on this page. The infrastructure story is just as deliberate. Everything runs inside one Postgres database — relational, vector, keyword, and graph retrieval together — so there's no second vector store to operate, and you can run the whole thing locally through PGLite with the same bytes you'd get in the cloud. It's Apache-2.0 and TypeScript, so you can read the code before you trust it. Pick Versuno when you're building MCP-compatible agents that need durable memory, when a Postgres shop wants to avoid standing up another database, or when the contradiction-audit trail is a requirement rather than a feature. Public Brains are the quick win if stale conventions are causing hallucinations in your coding agents; Private Brains matter once several people share the same layer. Pass when nobody on the team will touch an API or CLI. There's no point-and-click memory dashboard here, and if you want memory shipped inside an agent platform with zero setup, this is more work than you asked for. Teams with no capacity to review conflicts should also think hard — the review step is the product, not a nuisance. Usage-metering suits this shape of tool: Brain Queries and chat messages are pooled on Team, and credit packs start at $5 and don't expire, so a quiet month doesn't burn anything. Pair the free tier with a small credit pack and you can feel the conflict workflow for under ten dollars. Against the closest alternatives, the split is philosophical.
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Real-world workflow fit
Concrete scenarios for the personas Versuno AI actually fits — and what changes day-one when you adopt it.
You point your agent at a Public Brain for current framework conventions via MCP, let it save its own learnings with agent self-signup and a self-minted API key, and leave the Free tier running until query volume outgrows 2,000 a month.
Outcome: The agent stops repeating stale API patterns between sessions, and when it contradicts a saved memory you get a review item instead of a silent overwrite you would never have caught.
You move the team's architecture decisions, prompts, and assets into a shared Team brain, enable RBAC across the 5 included seats, and pool the 60,000 monthly query allowance across all agents.
Outcome: New agents inherit accumulated team context on day one, and every memory change is revertible, so a bad write is a review item rather than a permanent fact.
You self-host memloom under Apache-2.0 on your own Postgres, or run it fully local over PGLite, and keep the same engine shape you would get in the hosted product.
Outcome: Memory stays inside your infrastructure, the local and cloud behaviour match byte for byte, and you are not locked into a vendor's hosted memory stack.
Use Cases
- Feed your coding agent current framework conventions from Public Brains so it stops inventing outdated APIs.
- Give an engineering team one shared brain for decisions, architecture notes, and prompts that survives session restarts.
- Let an agent autonomously sign up, mint an API key, and save and recall its own learnings between runs.
- Run the same memory engine locally over PGLite and in the cloud, with no code differences between the two.
- Put team knowledge behind access controls with Private Brains and RBAC on the Team plan's 5 seats.
Limitations
- memloom's human-in-the-loop conflict review is a deliberate design choice but a recurring manual cost: someone must reconcile contradictions, and at high write volume that queue does not clear itself.
- The free tier is capped at 2,000 public brain queries and 50 chat messages per month, enough to evaluate rather than scale.
- Hosted memloom cloud is listed as coming soon, so today the hosted surface is the Versuno platform at app.versuno.ai, while memloom stays Apache-2.0 and fully local-capable.
- The public sources do not name any underlying AI model.
as of 2026-10-03
Verification history
We have re-verified Versuno AI 9 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 9 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 Versuno AI tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Free
$0/mo
Ideal for
Solo developer evaluating memloom or running one low-volume coding agent before committing budget.
What this tier adds
Starting tier: 2,000 public brain queries and 50 chat messages a month, 10 private assets, 10 projects, 1 seat, community support.
Pro
$19/mo
Ideal for
Solo builder or small project running an agent daily and hitting the Free query ceiling within a week or two.
What this tier adds
Raises usage to 12,000 public brain queries and 500 chat interactions a month, makes private assets and projects unlimited, and adds priority support — still 1 team member.
Team
$89/mo
Ideal for
Engineering teams of up to 5 sharing one context layer across several agents and needing access control.
What this tier adds
Adds team prompt and asset library, role-based access, 5 team members, and pooled allowances of 60,000 public brain queries and 2,500 chat interactions a month.
Starter Credit Pack
$5
Ideal for
Anyone who ran out of monthly usage and needs a small, one-off top-up rather than a plan upgrade.
What this tier adds
Adds 5,000 brain queries and 500 chat interactions for $5, and the credits never expire.
Growth Credit Pack
$12
Ideal for
Projects with steady overage that want better per-query economics than the Starter pack without moving tiers.
What this tier adds
Adds 15,000 brain queries and 1,200 chat interactions for $12, with credits that never expire.
Developer Credit Pack
$25
Ideal for
High-volume agent workloads that blow through monthly allowances and would rather buy usage in bulk.
What this tier adds
Adds 35,000 brain queries and 3,000 chat interactions for $25, the largest and cheapest per-query pack, with credits that never expire.
Where the pricing makes sense
The company stage and team size where Versuno AI's pricing actually pencils out — and where peers do it cheaper.
Versuno pitches below bundled enterprise memory add-ons: Free covers evaluation, $19/month Pro suits a solo builder running agents daily, and $89/month Team covers a 5-seat workspace with RBAC and a shared prompt library. Yearly billing saves two months against those monthly rates. Solo hobbyist volumes fit Free; if you only need memory inside a platform you already pay for, that bundled option may cost less in staff time than the $19/month does in dollars.
Setup time & first value
How long it actually takes to get something useful out of Versuno AI — broken out by persona, not the marketing-page minute.
A solo developer already running Postgres can point memloom at a database or run it locally over PGLite and have an agent writing memories the same afternoon, since the local and cloud paths are stated to be byte-identical. A 5-person team should budget an extra pass for RBAC setup, seeding a shared prompt and asset library, and deciding who owns the conflict review queue — that last decision
Switching to or from Versuno AI
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From a bundled agent-platform memory: export what you can, load it into memloom on Postgres, and keep the bundled store running in parallel until behaviour matches.
- →From a separate vector database: point memloom at the same Postgres instance and consolidate vector retrieval with relational, keyword, and graph queries in one database.
- →From manual notes and prompt files: use Versuno's Import tooling for spreadsheets, files, and AI chat apps to load assets, then organise them in Agent Skills.
- ↗To self-hosted memloom: the engine is Apache-2.0, so you can run your own Postgres-backed instance rather than depend on a hosted service.
- ↗To a bundled agent-platform memory: copy decisions and prompts out of the rich editor and rebuild them inside the target platform's memory store.
- ↗To another vector store: because memloom keeps a standard Postgres database, your relational data is already portable and only the embeddings need re-indexing elsewhere.
Integrations
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “Versuno AI”, and we withheld 6: 6 could not be judged, because “Versuno AI” 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 Versuno AI.
Official links
Featured Head-to-Head Comparisons
Versuno Ai vs Spider Cloud
Choose Versuno AI if your agents need persistent, structured memory and context to reduce hallucinations and avoid re-teaching. Choose Spider Cloud if your primary need is fast, reliable web data extraction for RAG pipelines. They solve different problems—Versuno retains internal knowledge, Spider Cloud fetches external data.
Versuno Ai vs Temporal Ai
Choose Versuno AI if your primary need is persistent, structured memory for AI agents to reduce hallucinations and retain context across sessions. Choose Temporal AI if you need reliable execution of complex, long-running workflows with automatic retries and crash recovery. They solve different problems: memory versus durability.
Versuno Ai vs Presto Voice
Versuno AI and Presto Voice serve completely different markets: one is a developer tool for AI agent memory, the other is a drive-thru voice AI for QSR chains. Choose Versuno if you're building AI agents that need shared, persistent context. Choose Presto if you operate a multi-location drive-thru chain and want upselling automation. There is no direct competition.
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