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Manage persistent AI memories with tagging, search, retrieval, and trigger-based recall.
This MCP tool appears to provide persistent memory management for AI assistants. It does not declare any required secrets or remote endpoints and is MIT-licensed open source, which lowers overall risk. Because it is flagged as executing code and implies persistent storage, local execution and data access warrant normal caution, but the provided materials show no concrete high-risk red flags.
The materials explicitly state that no keys or environment variables are required, and no API tokens, account credentials, or third-party authorizations are mentioned, so credential leakage and abuse exposure appears low.
No remote endpoints or network dependencies are declared. Based on the provided materials, there is no explicit path for user data egress. Note that the brief description cannot absolutely rule out other network behavior in code, but the materials themselves provide no such indication.
The system checks flag this tool as capable of executing code, meaning the MCP implementation runs locally. Such local execution is a normal MCP capability, and the current materials do not show requests for unusual system privileges beyond its memory-management purpose.
The description includes 'persistent memory management' and the creation, search, and retrieval of memories, so it is reasonable to infer local persistence, reading, and retrieval of memory data. This access is consistent with the stated function, but the storage location, scope, and isolation model are not described and should be verified before installation.
Positive factors include an auditable open-source repository and an MIT license. However, the source is a third-party registry, the README is absent, community adoption is 0 stars, and maintenance status is unknown, so supply-chain maturity and verifiability are only moderate and warrant manual review of the source and dependencies.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "Mnemonic" yet — see the docs or source repo.
Save this as long-term memory: Client Acme prefers weekly reports in English with numbers rounded to two decimals. Tag it with "client-preferences" and "Acme".
A tagged long-term memory is created and can be retrieved later by client name or tags.
Search past memories related to "product roadmap" and "Q3 planning", then summarize key decisions, owners, and open items.
Relevant memories are returned and summarized into a reusable project context brief.
Create a trigger-based memory: whenever "release retrospective" or "postmortem" appears, remind me to include timeline, impact, root cause, and action items.
A memory with trigger conditions is saved and automatically recalled when the related topic appears.
Give AI agents persistent memory and semantic recall across platforms and workflows.
Gives AI coding agents persistent local memory across sessions for decisions and rules.
Gives AI assistants human-like memory decay and reinforcement for better long-term interactions.
Provide local-first, auditable, consent-gated memory across AI tools via MCP.
Give AI agents persistent semantic memory with search, decay, and deduplication.
Give coding agents auditable local-first long-term memory for better continuity.