grpcer/ownmem
Open-source, Git-native memory for AI coding agents — deterministic local recall for Claude Code, Codex, Cursor, Gemini CLI, and compatible tools.
What it solves
OwnMem provides a way for AI coding agents (like Claude Code, Cursor, and Gemini CLI) to have a persistent, project-specific memory that is owned and controlled by the developers rather than the AI vendor. It prevents "bad memory" from silently influencing agent actions by making memory entries reviewable, deterministic, and subject to evidence-based verification.
How it works
Memory is stored as readable Markdown files within a .ownmem/ directory in the project repository, ensuring it is version-controlled alongside the code. The system uses a "compile-then-recall" architecture where Markdown and trust receipts are turned into immutable snapshots.
When an agent needs information, OwnMem uses five deterministic local retrieval lanes (exact, BM25F, n-gram, fuzzy, and graph) to find relevant memories without making external network or LLM calls. The results pass through four delivery gates (relevance, epistemic validity, task applicability, and action risk) and are delivered in three tiers: a direct quote, a set of pointers for the agent to investigate, or an abstention if no reliable memory is found.
Who it’s for
It is designed for developers and teams who want their project knowledge to be reviewed and migrated with their code, use multiple different coding agents across one repository, and require local, reproducible recall without retrieval API costs.
Highlights
- Git-native: Memory lives in the repo, meaning it is cloned, reviewed, and rolled back just like code.
- Deterministic Local Recall: No model or network calls are required for retrieval, ensuring consistent results and zero latency costs.
- Governance-first: No unattended writes; every memory change must be a commit made by a human.
- Evidence-based Trust: Content cannot declare itself trusted; independent receipts and live evidence checks determine if a memory is delivered as a fact or a lead.
- Net-zero Growth: Implements a hard entry count limit to prevent memory bloat, requiring old entries to be retired when new ones are added to a full corpus.
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