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Add evidence-backed memory, self-inspection, and audit history to AI agents.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "Nahuali" yet — see the docs or source repo.
Developers building long-running agents can use it to keep traceable historical memory and attach evidence during recall, reducing unsupported recollection.
Research or product teams can review self-inspection results and audit history to understand the basis and origin of an agent’s output.
It is an MCP tool for agent memory that emphasizes evidence-backed recall, self-inspection, and audit history. It fits AI agent workflows that need stronger traceability.
Based on the description, it does more than store and retrieve memory: it emphasizes evidence during recall, self-inspection, and audit records. That makes it better suited for reviewable and accountable use cases.
The provided material does not include installation steps, runtime details, or key requirements. See the source repository for integration details.
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