Tenure
Summary
Memory drift is invisible until your AI code reviewer starts surfacing stale architectural decisions from three sprints ago — or worse, leaking Customer A's context into Customer B's session. Tenure is a self-hosted state layer that replaces probabilistic vector recall with versioned, scoped, auditable beliefs.
Where most memory systems rely on similarity search with soft boundaries, Tenure enforces hard scope isolation at the structural level: engineering beliefs stay in engineering sessions, Project A never bleeds into Project B. The vendor's benchmark claims a drift score of 0.00 against competing memory systems that score above 0.80. Retrieval latency is documented at 15ms with 1.0 precision. The self-hosted Helm install takes roughly 30 seconds and exposes an OpenAI-compatible endpoint, so existing clients require no code changes. The ceiling appears when your team needs managed infrastructure or enterprise support — neither is documented on the vendor site.
Bottom line: Tenure is the right call when a single contaminated memory state would violate a compliance boundary or break a code review workflow — but if your team needs a managed cloud option or a memory layer that works without Kubernetes, you will hit the edges fast.
Pricing Plans
FreeFree (Self-Hosted)
Full Tenure platform via Helm chart or Docker with MIT license, OIDC, SCIM, audit trails, and no call-home telemetry.
- Persistent beliefs with versioning
- Scope isolation and hard boundaries
- Audit trails and provenance logging
- OIDC/SCIM governance
- VS Code, VSCodium, and OpenAI-compatible client integration
- MIT licensed, fully local
View full pricing on tenureai.dev →
Pricing may have changed since last verified. Check the official site for current plans.
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Pros
Sign in to edit- Hard structural scope isolation between projects and teams, so Customer A's session beliefs cannot surface in Customer B's responses — the failure mode that probabilistic filters cannot fully prevent.
- Belief versioning with supersession, which means retired decisions are archived rather than deleted, giving you a full decision history for compliance audits without polluting active retrieval.
- OpenAI-compatible `/v1` endpoint, so VS Code, Open WebUI, and other OpenAI-client tools connect without code changes — reducing the integration cost that typically blocks memory layer adoption.
- No call-home telemetry and a self-hosted deployment model, which means memory data never transits a third-party API — a hard requirement for teams under data residency or regulatory constraints.
- Real-time audit trail recording identity, timestamp, and the triggering query at write time rather than reconstructed post-hoc, so the record holds up under compliance review.
Cons
Sign in to edit- The Helm chart deployment requires a running Kubernetes cluster; teams without that infrastructure hit a dead end before they can evaluate the memory layer itself, and the vendor documents no alternative managed hosting path.
- Scope isolation is a structural guarantee only within Tenure's own belief store — if your agent pipeline mixes Tenure with a separate vector store or retrieval layer, cross-contamination risk migrates to the boundary between systems rather than disappearing.
- There is no documented managed cloud tier, which means teams that need to move fast without owning infrastructure operations will reach for a competitor like Mem0 or a hosted vector memory service, accepting the drift trade-off in exchange for operational simplicity.
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About
- Platforms
- VS Code, VSCodium, OpenAI-compatible clients, Open WebUI, Kubernetes, Docker, Linux/macOS/Windows
- API Available
- Yes
- Self-Hosted
- Yes
- Last Updated
- 2026-06-11T00:24:51.492Z
Best For
Who it's for
- Engineering teams using AI for PR reviews and code decisions
- Organizations subject to data governance or regulatory compliance
- Agentic systems that require deterministic rather than probabilistic state
- Teams unwilling to send memory data to third-party APIs
- Deployments where memory drift or cross-project contamination is unacceptable
What it does well
- Code review workflows requiring persistent context without cross-project interference
- Multi-team AI governance where audit trails and scope isolation are required
- Agent systems that cannot tolerate memory drift or stale belief contamination
- EU AI Act compliance scenarios demanding auditability and traceability
- Mobile and embedded AI scenarios where context capture must be scoped and versioned
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Frequently Asked Questions
- Is Tenure free?
- Tenure is a paid tool. No permanent free tier is offered.
- Is Tenure open source?
- No — Tenure is a closed-source tool. Source code is not publicly available.
- Does Tenure have an API?
- Yes. Tenure exposes a developer API. See the official documentation at https://tenureai.dev for details.
- Can I self-host Tenure?
- Yes. Tenure supports self-hosting on your own infrastructure.
- What platforms does Tenure support?
- Tenure is available on: VS Code, VSCodium, OpenAI-compatible clients, Open WebUI, Kubernetes, Docker, Linux/macOS/Windows.
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Curated lists that include this category
Most AI memory systems let context accumulate and rely on similarity search to surface what is relevant — which means the model never knows what it does not know, and stale decisions keep competing with current ones. Tenure replaces that with structured state: every belief carries an origin, a scope, a version, and a full history. When a decision changes, the old belief is retired and flagged so it is never suggested again, but the record remains for audit. Deployment is a single Helm chart or a one-line bash install that binds to port 5757 and presents an OpenAI-compatible `/v1` endpoint — no code changes required in VS Code, VSCodium, Open WebUI, or OpenClaw.
The differentiating feature is hard scope isolation. Probabilistic filters let context bleed across sessions when similarity scores drift close; Tenure uses structural boundaries so Project A’s memory is physically separated from Project B’s. The vendor pairs this with OIDC authentication, SCIM deprovisioning, and a real-time audit trail — every retrieval is logged with identity, timestamp, and the exact query that triggered it, recorded as it happens rather than reconstructed afterward. This architecture is specifically built for EU AI Act traceability requirements and multi-team governance scenarios where a shared vector store would be a compliance liability.
Tenure fits engineering teams running AI-assisted PR reviews who cannot tolerate a reviewer that has internalized last quarter’s abandoned design patterns, and for organizations where memory data cannot leave the building. The MIT license and no-call-home telemetry policy mean the codebase is auditable and the data stays on your infrastructure. The gaps show up when your deployment environment is not Kubernetes-ready, when you need a managed cloud tier, or when your team does not have the capacity to own the operational burden of a self-hosted state service — none of those paths are documented on the vendor site.