Retrieve past work, decisions, and lessons from agentmemory using keyword, meaning, and relationship searches. It expands selected records, groups findings by session, and prioritizes important observations so earlier context can inform the current task.
Search agentmemory for past observations, sessions, and learnings about a topic using hybrid BM25 plus vector plus graph search. Use when the user says "recall", "what did we do about", "did we ever", "have we seen", or needs context from past sessions.
Review the instructions the agent will receive before you install.
The user wants to recall past context about: $ARGUMENTS
memory_smart_search { "query": "jwt refresh token rotation", "limit": 10 }
Expected output:
2 results across 2 sessions.
[importance 8] decision · "Rotate refresh tokens on every use" (session 7f3a9c21)
[importance 5] code · "limit.ts counts per-IP" (session b21d004e)
Only surface what the tool returned. Never fabricate an observation, a session id, or an importance score. If nothing comes back, say so.
memory_smart_search with the user's text as query and limit: 10.
Pass project when the user scopes to a specific repo and agentId when
they request one agent's history.
Use targetLayer: "memory" for explicitly saved knowledge, or
targetLayer: "observation" for captured activity. Omit it for mixed search.memory_smart_search with the selected IDs as a
comma-separated expandIds string and the same project, agent, and layer
filters. If expansion fails or the record is no longer available, report
that limitation; a title alone is not the full fact.memory_recall, inspect matched_count, excluded_by_budget, and
content_truncated before treating an empty or shortened response as absent
knowledge. Recover full content with memory_smart_search using expandIds
and the same project, agent, and layer filters. minimum_budget reports the
estimated budget needed for a minimal preview when the budget is too small.WRONG: results are empty, so you write "We probably discussed token expiry last week" from assumption.
RIGHT: "No memories matched that query. Try refresh token, session expiry,
or auth rotation."
remember: the write side; recall retrieves what it stores.recap, handoff, session-history: session-scoped views of the same data.memory-discipline: when to run this search unprompted.See TROUBLESHOOTING.md if memory_smart_search is not available.
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Connect agentmemory to coding agents such as Claude Code and Codex while preserving existing server settings. It guides configuration, host restarts, and checks that memory tools are available and can save and retrieve information.
Explain how agentmemory stores and retrieves coding agents’ memories, including its engine, search methods, memory lifecycle, ports, and web viewer. Useful for understanding the system or planning extensions to its functions.
Set up and troubleshoot a local agentmemory server by checking MCP connections, authentication, ports, and environment settings. Understand which optional memory features require additional configuration, model downloads, or token usage before enabling them.
Understand how agentmemory automatically records tool activity, prompts, and session boundaries through hooks, actions triggered by session events. Use it to check integration setup, investigate missing records, and distinguish capture from optional summaries and context injection.
Helps agents choose the right agentmemory tool through MCP, a protocol for connecting agents to tools. It explains tool roles and points to parameter references for saving, retrieving, organizing, and maintaining stored knowledge.
Guides HTTP integration with the agentmemory server for saving and searching knowledge. It explains authentication, request conventions, and endpoint references, including how to connect systems that do not support MCP, the agent tool connection protocol.