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OpenMemory VS VATES.jp

Compare OpenMemory VS VATES.jp and see what are their differences

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Contents:
  1. ยป Base Details
  2. ยป Reviews
  3. ยป Alternatives

OpenMemory logo OpenMemory

Give AI agents long-term memory.

VATES.jp logo VATES.jp

Embeddable B2B AI infrastructure. Knowledge is held as structured plain text and interpreted at observation time โ€” no vector database, no embedding pipeline, no re-ranking. Integrate by API like Stripe or Twilio, or run it as a self-serve widget.
Not present
  • VATES.jp Knowledge as inspectable plain text โ€” no vector database
    Knowledge as inspectable plain text โ€” no vector database //
    2026-07-24
  • VATES.jp Answers grounded in your own knowledge base
    Answers grounded in your own knowledge base //
    2026-07-24
  • VATES.jp Embedded widget and standalone chat
    Embedded widget and standalone chat //
    2026-07-24
  • VATES.jp Security overview โ€” audit log, sessions, threats
    Security overview โ€” audit log, sessions, threats //
    2026-07-24

What it is

Embeddable B2B AI infrastructure. Developers integrate conversational AI into their own product through an API, in the same role as Stripe or Twilio. It also runs as a self-serve SaaS โ€” an embedded widget added with one line of JavaScript, or a standalone URL. Both share one foundation.

How it differs from RAG

Knowledge is stored as structured plain text and interpreted at query time. No vector database, no embedding step, no re-ranking.

  • Updates take effect the moment a line is written โ€” no re-embedding, no re-indexing.
  • The knowledge base stays inspectable plain text, so its contents can be read and audited directly.
  • Editing or deleting a line removes it, verifiable by inspection.
  • No dedicated vector database, no GPU, no per-use embedding API.

Three roles

  • bard โ€” turns natural language, images and PDFs into structured entries.
  • druid โ€” management UI: full-scan search, log analysis, reference tracing.
  • vates โ€” the conversational interface; replies in the reader's language.

MCP

A remote MCP server is included, so external AI agents can query a knowledge base directly โ€” by OAuth as a custom connector, or with a bearer API key. One tool is exposed, vates_ask, and it is read-only: it cannot change settings, read conversation logs, or reach other instances. Access is revocable from the console.

Operations

Setup, instance settings, the knowledge base, billing and security are all operable from a phone. Hash-chained tamper-evident audit logs, passkeys, 2FA, per-customer IP allow/denylists, per-instance rate and consumption limits, encrypted daily backups, and an automated data-retention lifecycle.

Pricing

Deposit-based. No subscription, no monthly fee, no minimum spend. Usage is deducted from a prepaid balance; minimum top-up is USD 5.00.

OpenMemory

Website
github.com
Pricing URL
-
$ Details
-
Platforms
-
Release Date
-
Edit details

VATES.jp

Website
vates.standout.jp
Pricing URL
Official VATES.jp Pricing
$ Details
paid $5.0 (Minimum prepaid deposit; usage deducted, no monthly fee)
Platforms
Web REST API
Release Date
2026 June
Startup details
Country
Japan
State
Okayama
City
Okayama
Founder(s)
Shuji Yamashita, Takuya Aoki
Employees
1 - 9
Edit details

OpenMemory features and specs

  • Open Source
    OpenMemory is an open-source project, allowing developers to freely use, modify, and distribute the software according to their needs.
  • Community Support
    Being hosted on GitHub, OpenMemory benefits from a community of contributors who can provide support, improvements, and bug fixes.
  • Free Access
    The project is available for free, lowering the barrier to entry for individuals and organizations looking to incorporate memory management solutions.
  • Transparency
    The open-source nature ensures transparency in how memory is managed, which can help in security reviews and performance optimization.
  • Customizability
    Users and developers can tailor the system to better fit their specific requirements due to the customizable nature of open-source software.

Possible disadvantages of OpenMemory

  • Lack of Official Support
    As an open-source project, there may be no official customer support, making it potentially challenging for users to resolve issues without community help.
  • Variable Quality
    Contributions from multiple sources can lead to inconsistencies in code quality and documentation, which might affect reliability.
  • Potential Security Risks
    Open-source projects can be subject to security vulnerabilities if not regularly monitored and updated by the community.
  • Complexity
    The system might require a level of technical expertise to implement, customize, and maintain, which can be a barrier for less-experienced users.
  • Limited Documentation
    Open source projects sometimes suffer from sparse or outdated documentation, which can hinder user understanding and implementation.

VATES.jp features and specs

  • Retrieval
    Direct observation of structured plain text โ€” no embedding or re-ranking step
  • Knowledge updates
    Effective the moment a line is written; no re-indexing
  • Knowledge visibility
    Plain text, readable and auditable directly
  • Deployment
    Embedded widget (one line of JS) or standalone URL
  • API
    REST with idempotency keys and per-tenant rate limits
  • MCP server
    Remote, OAuth or API key; read-only vates_ask tool
  • Input formats
    Text, logical notation, images and PDFs
  • Reply language
    Answers in the reader's own language
  • Audit Log
    Hash-chained, tamper-evident, CSV export
  • Billing
    Prepaid deposit, no subscription, no monthly fee
  • Authentication
    Passkeys (WebAuthn/FIDO2), 2FA, JWT with session ledger
  • Access Restrictions
    Per-customer IP allowlist and denylist, CIDR ranges

Analysis of OpenMemory

Overall verdict

  • OpenMemory is a solid open-source memory layer for AI applications, offering a self-hostable, privacy-focused way to give LLMs persistent, portable memory across sessions and tools.

Why this product is good

  • Open-source and self-hostable, giving you full control over your data and avoiding vendor lock-in
  • Provides persistent, portable memory that can be shared across different AI apps and LLM clients
  • Privacy-focused design keeps sensitive memory data local rather than sending it to third-party services
  • Integrates with popular protocols like MCP (Model Context Protocol), making it compatible with many AI tools
  • Active community and transparent development typical of open-source projects allow for customization and contributions

Recommended for

  • Developers building AI applications that need long-term or cross-session memory
  • Privacy-conscious users who want to keep AI memory data on their own infrastructure
  • Teams wanting a vendor-neutral, portable memory layer shared across multiple LLM clients
  • Hobbyists and tinkerers comfortable with self-hosting and open-source tooling
  • Projects using MCP-compatible AI assistants that require persistent context

Category Popularity

0-100% (relative to OpenMemory and VATES.jp)

OpenMemory

VATES.jp

AI
100 100%
AI
0% 0
Knowledge Management
0 0%
Knowledge Management
100% 100
Productivity
100 100%
Productivity
0% 0
AI Tools
67 67%
AI Tools
33% 33

Questions & Answers

As answered by people managing OpenMemory and VATES.jp.

What makes your product unique?

VATES.jp's answer:

Knowledge is held as structured plain text and interpreted at the moment it is queried. There is no vector database, no embedding step, and no re-ranking. A line written into the knowledge base takes effect immediately, with no re-indexing, and the contents stay readable โ€” you can see exactly what the system holds, and delete a line and verify it is gone.

Why should a person choose your product over its competitors?

VATES.jp's answer:

Fewer moving parts. No vector database to run, no GPU, no per-use embedding API โ€” which lowers both cost and the number of things that can break. Nothing is hidden behind numeric vectors, so answers can be traced to the material behind them. Billing is a prepaid deposit with no subscription and no monthly fee, so it can be tried at small scale and grown without renegotiating a contract.

How would you describe the primary audience of your product?

VATES.jp's answer:

Two groups. Developers who want to embed conversational AI into their own product through an API, in the same role as Stripe or Twilio. And small to mid-sized businesses that want to run it out of the box โ€” as a widget on their site or a standalone chat โ€” without engineering work. Both are served by the same foundation.

Which are the primary technologies used for building your product?

VATES.jp's answer:

Python and FastAPI on the backend, React with Vite on the front end, SQLite for storage, on Ubuntu with Nginx. Payments through Stripe. Deployed on AWS EC2 behind Cloudflare. Notably absent: there is no vector database and no embedding pipeline in the stack.

What's the story behind your product?

VATES.jp's answer:

VATES is built by STANDOUT Inc. in Okayama, Japan, on a theory its developer worked out independently rather than assembling from existing frameworks. The premise is that meaning is not fixed in a symbol but arises when something is observed โ€” so knowledge is kept as structure and interpreted at read time rather than compressed into coordinates in advance.

A concise formulation has been published as a preprint, IIIS: An Inter-Intelligence Intermediary Syntax for Shared Observation Across Perceptions (https://doi.org/10.5281/zenodo.21374493). The full theory is being prepared as a book.

The three working roles are named after the Celtic oral tradition: bard receives, druid keeps, vates speaks across.

User comments

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What are some alternatives?

When comparing OpenMemory and VATES.jp, you can also consider the following products

Supermemory - ai second brain for all your saved stuff

CustomGPT.ai - Turn Data into Dialogue with AI-Driven Precision.

Mem - Capture and access information from anywhere

Dialogflow - Conversational UX Platform. (ex API.ai)

Byterover - Memory layer for smarter AI coding agents

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