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Mneme is an open-source architectural governance layer for AI-assisted development.
Instead of relying on static prompts or probabilistic memory retrieval, Mneme injects structured project decisions into AI coding workflows and validates architectural constraints before generation.
Built for deterministic governance, auditability, and long-running agent workflows.
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Mneme HQ
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Mneme HQ's answer
Mneme governs AI coding agents at the pre-generation stage. Rules files document standards and memory tools recall context, but Mneme compiles your architectural decisions into deterministic constraints that are enforced before the agent generates code, so violations are blocked at the source rather than caught in review.
Mneme HQ's answer
Most tooling in this space is post-generation: it detects architectural violations after the agent has already acted. Mneme works one layer earlier, preventing the violation from being proposed in the first place. The enforcement is deterministic, so the same standards apply on every call and every session with no probabilistic gaps.
Mneme HQ's answer
Engineering teams that use AI coding agents such as Claude Code, Cursor, Copilot, and agent frameworks, and need their architectural decisions to hold as the volume of generated code grows. It is aimed at teams who care about architectural consistency and auditability, not just raw generation speed.
Mneme HQ's answer
Mneme started from a recurring failure in AI-assisted development: coding agents do not retain a team's architectural decisions, so the same violations resurface every session and drift is only caught in review. It was built to enforce those decisions at the moment of generation instead of documenting them and hoping they are followed.
Mneme HQ's answer
Mneme is built in Python and is deterministic by design, with no vector store and no ML in the retrieval path. Project decisions are stored as structured data in the repo and compiled into enforceable constraints, and it integrates through MCP, CI/CD, and coding-agent hooks.
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