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MemoryOps
Pricing
- Model
- Free
Summary
Most AI assistants treat memory as a vector store you append to and occasionally query — until a user invokes their deletion rights, a tenant's data bleeds into another's context, or a compliance audit asks you to prove what the model remembered and when. MemoryOps AI exists because those three problems have no clean answer in a bare vector database.
The project implements a full memory lifecycle — typed capture, policy evaluation before storage or retrieval, hybrid retrieval combining vector and structured lookup, deletion guarantees, and per-tenant isolation — as a self-hosted governance layer sitting between your AI assistant and whatever storage backends you run. Policy evaluation runs before memory is written or surfaced, which means you can enforce rules about what gets stored rather than cleaning up after the fact. The audit trail is first-class, not a log file bolted on. Where it breaks: this is a governance layer, not a memory provider with managed infrastructure, so your team owns the deployment, the backend wiring, and the operational burden. Early community adoption is thin — the repo reports five stars at the time of writing — which means when you hit an undocumented edge case, you are reading source code, not finding a Stack Overflow answer.
Bottom line: Pick this when your AI assistant needs demonstrable deletion guarantees and tenant isolation and you have the engineering capacity to self-host it; skip it when you need a managed service with a support contract and an established community behind it.
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Pros
Sign in to edit- Policy evaluation runs before a memory is written or retrieved, so governance rules are enforced at the architecture level rather than scattered across application code — which means a policy change doesn't require auditing every place your team remembered to check.
- Deletion guarantees are a first-class feature rather than a soft delete flag, so when a user exercises a right-to-erasure request you have a structural mechanism and an audit record to show for it instead of a manual database cleanup.
- Per-tenant memory isolation is built into the data model, so a multi-tenant assistant deployment doesn't need custom sharding logic to prevent one tenant's context from appearing in another's retrieval results.
- Hybrid retrieval combines vector and structured lookup, so queries that require both semantic similarity and exact metadata filtering don't force you to run two separate retrieval paths and merge the results yourself.
- Self-hosted deployment with Docker Compose and Railway configuration means the entire memory pipeline runs inside your own infrastructure boundary, so sensitive memory contents never leave your environment to reach a third-party managed service.
Cons
Sign in to edit- There is no managed hosting path described in the source material — you provision, operate, and scale every component yourself. For a team that has already burned a sprint on infrastructure work that shipped zero product features, this is the condition under which they switch to a managed memory provider like Zep or a vector database with built-in access controls.
- Community adoption is early-stage — the repository shows five stars and no open issues at the time of scraping, which means undocumented behavior and edge cases in the policy evaluation or deletion pipeline have no community-sourced answers. Your debugging path is reading source code.
- The multi-component architecture (services, SDK, infra, evals) imposes an integration surface area that grows with your stack. Teams running a single-service assistant prototype will spend more time wiring MemoryOps into their deployment than they spend on the feature that needed governance in the first place.
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About
- API Available
- No
- Self-Hosted
- Yes
- Last Updated
- 2026-06-22T10:28:45.680Z
Best For
Who it's for
- Enterprise AI memory governance
- Applications requiring audit and deletion controls
- Hybrid retrieval with policy evaluation
What it does well
- Governed memory for AI assistants
- Policy-enforced storage and retrieval
- Auditable memory lifecycle management
- Tenant-isolated memory systems
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Frequently Asked Questions
- Is MemoryOps free?
- Yes — MemoryOps is fully free to use. There is no paid tier.
- Is MemoryOps open source?
- Yes. MemoryOps is open source.
- Can I self-host MemoryOps?
- Yes. MemoryOps supports self-hosting on your own infrastructure.
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Curated lists that include this category
MemoryOps AI is a self-hosted memory governance layer designed for AI assistants that have to meet enterprise invariants: typed memory capture, policy evaluation at write and read time, hybrid retrieval, controlled deletion, provenance tracking, tenant isolation, and an audit trail. The architecture is described in the README as ‘loop-engineered’ — meaning the memory lifecycle runs as a structured pipeline rather than a fire-and-forget append to a vector index. An SDK package is included alongside separate services, infrastructure definitions, and evaluation tooling, suggesting the intended deployment is a multi-component stack rather than a drop-in library.
The differentiating feature is policy evaluation at the boundary. Memory is not written or retrieved without passing through a policy check, which means governance rules — what a tenant can store, what can be surfaced in a given context, what must be deleted — are enforced structurally rather than through application-layer code your team writes and maintains separately. The audit and provenance layer gives you a record of what was captured, under which policy, and what was deleted, which is the artifact a compliance review actually asks for.
Where it fits: applications where a user’s right to be forgotten is a contractual or legal requirement, multi-tenant AI platforms where cross-tenant memory leakage is a production risk, and assistant deployments where you need to demonstrate to an auditor that the system’s memory operated within defined rules. Where it breaks: teams expecting a managed service, a hosted API, or an active support community will find precious little here. The project is open-source with no commercial offering described in the source page, self-deployment is the only path, and operational maturity signals — issue volume, contributor count, release cadence — are early-stage. A team that needs SLA-backed uptime or enterprise support contracts will need to look at alternatives.
The repository structure includes a Docker Compose file and Railway configuration, indicating the intended deployment targets are containerized environments. A dedicated SDK package under the packages directory suggests the vendor intends programmatic integration rather than a GUI-first workflow. Evaluation tooling in the evals directory implies the project includes facilities for testing retrieval quality, though the depth of that tooling is not described in the available source material.