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.
Add evidence-backed memory, self-inspection, and audit history to AI agents.
Query all memory stores at once and get a ranked, token-budgeted briefing.
Provide persistent, explainable, MCP-native memory for AI agents.
Give AI agents persistent memory, temporal awareness, and error loop prevention.
Let AI agents read and write memory with environment-aware storage fallback.