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Give AI agents local-first memory to learn, consult, and retrieve related context.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "ai.zimac/mnema" yet — see the docs or source repo.
Use Mnema to consult previously stored project conventions and summarize the points most relevant to the current task.
A summary of relevant past memories or key conventions for the current task.
Store this debugging conclusion in Mnema: the issue was caused by a missing environment variable, and the fix is to add the API_URL configuration.
The new insight is stored in memory for future tasks.
Find memories in Mnema related to 'user login failure' and list the closest matching entries.
A list of related memories to quickly surface similar issues or context.
Developers or product managers can have an AI agent store project rules, naming conventions, or decisions and consult them in later tasks. This reduces repeated explanations and keeps outputs consistent.
When an agent needs to accumulate information across multiple rounds of work, it can learn new content, list stored memories, and retrieve related entries. This fits AI workflows that need long-term context.
This is a memory tool for AI agents exposed over MCP, with a local-first and formally verified design. It supports memory operations such as consult, learn, related, and list.
The provided description only states that it is local-first. Whether it requires internet access or depends on cloud services is not specified here; see the source repository.
No README or installation excerpt is provided, so the exact setup steps, runtime requirements, or key requirements cannot be confirmed here. See the source repository for details.
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