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ReMe is a local-first, self-evolving personal knowledge base for AI agents. It turns conversations and resources into readable, editable, searchable, and interconnected Markdown memory, then keeps that memory organized for future recall. The files remain under the user’s control and can be shared across agents such as QwenPaw, OpenClaw, Hermes, Claude Code, and Codex. The following diagram summarizes ReMe’s file-native approach to shared, portable memory: ReMe design philosophy

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Core Ideas

  • Memory as File, File as Memory: memory is a user-owned file, not hidden database state; users can inspect, edit, move, and back it up directly.
  • Memory from Experience: long-term memory is refined and corrected, not an endless context dump.
  • Human-Agent Shared Memory: people and agents work on the same visible, portable Markdown.
  • Connected and Traceable: Auto Link writes relationships and sources back into the files, providing context and tracing durable conclusions to evidence.
Read the core concepts for the full philosophy and trade-offs.

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What ReMe Solves

Agent context usually disappears when a session ends. Even with a vector database, users may not know what was remembered, where a conclusion came from, or how to correct and move that memory. ReMe provides a complete memory loop in a local workspace without hiding the source data:
  • Capture: raw conversations and resources remain available as traceable sources.
  • Refine: useful facts, preferences, procedures, and knowledge evolve into Markdown memory.
  • Retrieve: BM25 and bounded wikilink expansion work by default; embeddings add optional semantic recall and reciprocal rank fusion.
  • Integrate: Python, AgentScope, MCP, CLI, HTTP, ReMe Studio, plugins, and Skills expose the same file-native memory model; keep one runtime owner per workspace.
Memory becomes shared knowledge owned by the user instead of opaque agent state.

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Where It Fits

  • Personal assistants that retain preferences, background, plans, and important events.
  • Coding agents that carry project decisions, conventions, and debugging experience across sessions.
  • Personal knowledge bases built from conversations, notes, and linked resources.
  • Experience-driven agents that preserve reusable procedures and lessons learned.
ReMe may need another system alongside it when you need strict transactions, complex authorization, or very large-scale online retrieval. It is a memory layer—not a complete agent product—and deliberately avoids invisible, unbounded collection.

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File-Native Memory System

ReMe separates raw evidence, intermediate refinement, durable memory, and derived system state. The diagram shows how people and agents work with the same files while rebuildable indexes support retrieval. ReMe memory-as-file model showing shared files, memory refinement, and derived indexes The workspace maps the flow to these top-level directories: Auto Memory turns conversations into topic-named daily cards, Auto Resource interprets files placed at the root of resource/ or under a dated subdirectory, and Auto Dream consolidates changed daily cards into durable knowledge. By default, Auto Dream examines the latest two-day window and extracts at most five reusable units before creating, corroborating, refining, or correcting digest nodes. The default live watcher indexes Markdown under daily/ and digest/; a full reme reindex also scans resource/ and JSONL. Search returns matching chunks with paths and line ranges, followed by bounded incoming and outgoing wikilink metadata so agents can expand context only when needed.
proactive only reads topics produced by Auto Dream. It does not browse the web, send notifications, or rewrite memory on its own; the host agent decides whether and how to act.

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Offline Use

File operations, keyword search, wikilink traversal, and reading proactive topics do not require an LLM. Conversation refinement, resource interpretation, and long-term consolidation need a compatible LLM. Semantic search additionally needs an embedding service.

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Choose an Integration

Python SDK

Embed ReMe directly or use AgentScope’s first-party ReMeMiddleware.

Agent Integrations

Connect service-based agents such as Claude Code, Hermes, OpenClaw, or Codex.

Integration

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Published Benchmark Results

Repository reference runs report 89.4% over 500 LongMemEval cleaned-s questions, 66.1% on the 100K BEAM setting, 65.0% on the 1M BEAM setting, and a 0.580 PROC score across five personas in π-Bench. See the ReMe benchmark directories for model, prompt, dataset, and judging details; compare results only under matching evaluation settings.

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Research

ReMe’s experience-driven memory direction is described in Remember Me, Refine Me: A Dynamic Procedural Memory Framework for Experience-Driven Agent Evolution, accepted to Findings of ACL 2026.

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Reference

Workspace, configuration, and self-describing interfaces.

FAQ

Common questions and troubleshooting.

Contribution

Development setup, tests, and contribution flow.