ReMe

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When to use

• You want a lightweight self-hosted memory service that’s easy to spin up.
• You care about summarizing whole conversation trajectories rather than individual messages.
• You can express logical workspaces by userId (one workspace per user).

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Add the dependency

<dependency>
    <groupId>io.agentscope</groupId>
    <artifactId>agentscope-extensions-reme</artifactId>
    <version>${agentscope.version}</version>
</dependency>

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Quickstart

import io.agentscope.core.memory.reme.ReMeLongTermMemory;

ReMeLongTermMemory memory = ReMeLongTermMemory.builder()
    .userId("task_workspace")            // Maps to ReMe's workspace_id
    .apiBaseUrl("http://localhost:8002") // Your ReMe server
    .build();

ReActAgent agent = ReActAgent.builder()
    .name("Assistant")
    .model(model)
    .longTermMemory(memory)
    .longTermMemoryMode(LongTermMemoryMode.BOTH)
    .build();

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How it works

• Write (record): filtered messages are joined into a single ReMeTrajectory and posted to ReMe’s add endpoint; the server then runs LLM extraction over the trajectory to produce searchable memory snippets.
• Retrieve: the current message is used as the query against ReMe’s search. The server-aggregated answer field is returned when present, otherwise multiple memory snippets are joined.

• Only USER and ASSISTANT messages are kept.
• Assistant messages containing ToolUseBlock (tool-call requests) are skipped.
• Messages containing the <compressed_history> marker are skipped.

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Builder reference

ReMe does not yet expose finer-grained metadata filtering. If you need tag-based segmentation, encode it inside userId (e.g. tenant-a:project-1).