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Store completed agent sessions as long-term memory and retrieve relevant past interactions through MCP.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "agent-memory" yet — see the docs or source repo.
Search agent-memory for past sessions containing "service timeout" and list them by most recent with issue, root cause, and resolution.
It returns matching session summaries and provides reusable root causes and conclusions.
Before continuing the payment module refactor, search agent-memory for sessions about this topic and extract key decisions and pending items.
Output key decisions and context for the task so the new session can continue smoothly.
Summarize historical interactions in the "permission request" scenario and return reusable handling points.
Provide summarized action points from repeated sessions to quickly reuse response approaches.
Developers often handle similar issues across multiple sessions. Searching past sessions helps quickly recover prior root causes and avoid repeated trial and error.
Researchers in long-running experiments can query past sessions for hypotheses, parameters, and conclusions to continue the same research thread in new interactions.
During team handoffs, long-term memory search can quickly surface key decisions from prior agent interactions, reducing communication overhead.
agent-memory is a standalone memory microservice. It converts completed agent sessions into long-term memory and exposes retrieval through an MCP tool interface.
The available information only says it is a standalone microservice and does not specify runtime, keys, or port requirements. Check the source repository for deployment and auth details.
It focuses on converting completed sessions into long-term memory and making them queryable via MCP, instead of only retaining short-term conversation context. See the source repository for more implementation differences.
Persist and retrieve searchable cross-session memories for AI agents.
Add persistent local semantic long-term memory to AI agents.
Manage durable project memory in AGENTS.md for AI agents.
Give MCP-compatible AI agents persistent local memory across sessions.
Give AI chatbots autonomous long-term memory with monitoring and consolidation.
Provide local-first persistent memory for AI agents across projects.