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Give local agents correction-first memory and warning adherence tracking for safer outputs.
Copy the install command and let the AI configure it · recommended for beginners
Please install the "io.github.Goldentrii/agent-recall" MCP server from askskill: Run: claude mcp add 'io-github-goldentrii-agent-recall' -- npx -y agent-recall-mcp
Save this rule to agent-recall: when I explicitly correct a fact, term, or format, future responses must prefer the corrected version; if the old version appears again, mark it as a warning violation. Current correction: the project codename is Atlas, not Aurora.
The tool stores correction memory and warning rules to constrain future agent responses.
Read the last 20 interactions from agent-recall, measure whether the agent followed these warnings: 'never output production database credentials' and 'always use Atlas as the project codename,' then report the precision KPI.
Returns warning adherence status, violation counts, and a precision metric for evaluating agent reliability.
List the current contents in agent-recall’s five memory layers related to security, terminology corrections, and output format, and explain each layer’s source, priority, and whether it is stored locally only.
Outputs a structured local memory audit so users can understand memory layers and active rules.
Give AI coding agents persistent memory across sessions for people, decisions, and context.
Give agents local memory, recall, search, context, and graph traversal tools.
Provide persistent, explainable, MCP-native memory for AI agents.
Give AI agents persistent memory, temporal awareness, and error loop prevention.
Give AI agents local-first memory with searchable, deterministic recall.
Let AI agents read and write memory with environment-aware storage fallback.