Gives AI coding agents persistent local memory across sessions for decisions and rules.
This tool is described as a local persistent memory layer for AI coding agents, with no declared secrets or remote endpoints, and no obvious high-risk data exfiltration indicators. The main concerns are local execution and persistent read/write of memory data, plus supply-chain uncertainty due to third-party registry origin, low adoption, and unclear license/maintenance.
The material explicitly states that no keys or environment variables are required. No API tokens, account credentials, or other sensitive authentication requirements are disclosed, so credential exposure and abuse risk appears low.
The material lists no remote endpoints, and the description emphasizes a 'local memory layer.' There is no factual indication that user data is sent to external services, so network egress risk appears low based on the provided material.
The system checks explicitly indicate that this tool can execute code ('executes-code'). This means it runs within local processes, which is a standard sensitive capability for MCP tools; however, the material does not show abnormal system privileges beyond its stated function, so this is rated as caution rather than high risk.
It is described as a 'persistent, local memory layer' with recall/remember/search, so it likely needs to persist and read historical memory data locally. Such local data access is functionally expected, but users should still verify storage location, retention period, and whether sensitive development information may be recorded.
A positive factor is that the source is open and auditable. However, it comes from a third-party registry, the repository has 0 stars, no declared license, and unknown maintenance status, so community validation and governance signals are weak. It is better suited for controlled review before use.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "mnemo" yet — see the docs or source repo.
Remember these project conventions for future coding sessions: use TypeScript strict mode on the frontend; run unit tests before commits; API errors must return {code, message}.The tool stores project rules so the agent can recall and follow them in later sessions.
Recall past issues related to authentication session failures in this repo, including causes and fixes, and summarize troubleshooting advice.
Returns previous bug records, related fix decisions, and a reusable troubleshooting checklist.
Search for historical discussions and decision records about why we did not adopt a microservices architecture, then summarize them briefly.
Outputs relevant memory entries and decision context to help the team understand past technical choices quickly.
Provide local-first, auditable, consent-gated memory across AI tools via MCP.
Give AI agents persistent semantic memory with search, decay, and deduplication.
Give AI agents local-first memory, retrieval, and spaced learning workflows.
Manage persistent AI memories with tagging, search, retrieval, and trigger-based recall.
Give coding agents auditable local-first long-term memory for better continuity.
Give coding agents persistent cross-session memory for project context and decisions.