Tenure
Give AI memory, control what it uses, and trust what it says
34 followers
Give AI memory, control what it uses, and trust what it says
34 followers
No one would keep an employee who couldn’t explain what they remembered. Tenure gives solo devs continuity, teams alignment, and everyone control over what AI remembers and why. Instead of catching mistakes after a PR opens, Tenure helps prevent them at generation time by giving AI the right context upfront. Works across VS Code, Cline, Continue, OpenClaw, Open WebUI, and more; with controlled injection, source-backed provenance, 1.0 retrieval precision, <15ms latency, and 0.00 drift.
Products used by Tenure
Explore the tech stack and tools that power Tenure. See what products Tenure uses for development, design, marketing, analytics, and more.
Engineering & Development 1
Engineering & Development 1
MongoDBThe database for modern applications.
5.0 (63 reviews)
Atlas Search gave us full-text search with fuzzy matching, prefix guards, and scoring in the same database as the belief store. No separate search infrastructure, no sync lag, no operational overhead. For a local-first tool that ships as a single Docker Compose file, adding a separate Elasticsearch or Typesense container would have undermined the zero-configuration promise. MongoDB let us keep the entire stack: persistence, encryption, and search all in one place.
Client-Side Field Level Encryption was the other deciding factor. Belief content is sensitive, it's a structured model of how someone thinks and works. CSFLE let us encrypt belief content at the field level before it reaches the database, which means even if someone gets access to the raw database files they can't read the beliefs. That's a hard requirement for a privacy-first local tool and MongoDB was the most straightforward path to it without building custom encryption middleware.
LLMs 1
LLMs 1
Claude by AnthropicA family of foundational AI models
5.0 (878 reviews)
Structured output reliability. Belief extraction requires the model to write back a precise JSON sidecar on every turn without fail. Smaller or less capable models produce inconsistent structured output at scale; one malformed extraction corrupts the belief store. Claude's instruction-following at the structured output level was simply more reliable in testing than the alternatives we evaluated.