AI ERP with agents inside the systems you run
AI phone and voice agents for real calls
LLM.coPrivate, self-hosted LLM deployments
Legal AI infrastructure for firms
AI RFP discovery and response drafting
Automatic.coBusiness process automation
CyberAttack.aiThreat detection and defense
Secure AI virtual data roomsReMe
ReMe is a Python-based memory management system for AI agents that stores conversations and resources as editable Markdown files with automatic indexing and semantic search. It enables agents to build and maintain long-term knowledge bases that improve reasoning across sessions.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Repository | agentscope-ai/ReMe |
| Owner | agentscope-ai |
| Primary language | Python |
| License | Apache-2.0 — OSI-approved |
| Stars | 3.2k |
| Forks | 269 |
| Open issues | 13 |
| Latest release | v0.4.0.9 (2026-07-08) |
| Last updated | 2026-07-08 |
| Source | https://github.com/agentscope-ai/ReMe |
What ReMe is
ReMe implements a file-based memory layer (Python 3.11+) with auto_memory, auto_resource, and auto_dream pipelines that process raw sessions into daily and digest-tier Markdown nodes. It provides hybrid retrieval via wikilinks, BM25, and embedding-based semantic search, with CLI and REST service interfaces.
Get the ReMe source
Clone the repository and explore it locally.
git clone https://github.com/agentscope-ai/ReMe.gitcd ReMe# follow the project's README for install & configurationNeed it deployed, integrated, or customized instead? DEV.co ships production installs.
Best use cases
Implementation considerations
- Requires Python 3.11+; pip install with optional [core] extras for base functionality.
- LLM API credentials (EMBEDDING_API_KEY, LLM_API_KEY) mandatory for semantic retrieval and auto-evolution features; BM25 search and wikilinks work offline.
- Default service runs on 127.0.0.1:2333; port binding, firewall, and authentication setup needed for production deployments.
- Memory consolidation (auto_dream) runs as background jobs; monitor job completion and LLM API costs.
- Markdown frontmatter parsing and wikilink resolution are core; validate against custom YAML schemas and link integrity.
When to avoid it — and what to weigh
- Real-time multi-user collaboration required — ReMe is file-based and local-first; distributed concurrent writes and conflict resolution are not addressed in the data.
- Enterprise-grade security/compliance — No mention of encryption at rest, audit logging, role-based access, or compliance certifications; requires independent security review.
- Minimal deployment overhead — Requires Python 3.11+, environment setup, LLM API credentials for evolution features, and service management (port 2333 default).
- Zero operational dependencies — auto_memory, auto_resource, and auto_dream require external LLM API calls; embedding retrieval needs a configured embedding service.
License & commercial use
Apache License 2.0 (Apache-2.0). Permissive OSI-approved license allowing commercial use, modification, and distribution with attribution and liability disclaimer.
Apache-2.0 permits unrestricted commercial use. However, using external LLM APIs (Alibaba DashScope shown in examples) incurs third-party costs and terms; review those vendor agreements independently. No warranty claimed by ReMe authors.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Adequate |
| License clarity | Clear |
| Deployment complexity | Moderate |
| DEV.co fit | Good |
| Assessment confidence | High |
File-based memory stored in workspace_dir; no encryption at rest mentioned. LLM API keys stored in .env—ensure proper secrets management. No authentication/authorization layer visible for service API (port 2333). REST endpoint does not show authentication mechanism. Embedding and LLM calls expose data to external APIs; review vendor privacy policies (Alibaba DashScope example). Recommend security review before production use with sensitive data.
Alternatives to consider
LangChain memory abstractions (ConversationBufferMemory, etc.)
Lighter-weight in-memory or vector-store backed; tightly integrated with LangChain workflows but less file-first and editable.
Obsidian + API layer + custom indexing
Existing note-taking app with rich markdown support and community plugins; requires building agent hooks and search layer separately.
VectorStores (Pinecone, Weaviate, Milvus) + RAG pipelines
Pure vector-based retrieval scales well for unstructured embedding search; less human-editable and no wikilink/knowledge graph features by default.
Build on ReMe with DEV.co software developers
Start with pip install reme-ai[core], run the quickstart demo, and integrate via REST API or CLI. Review security and API costs before production use.
Talk to DEV.coRelated on DEV.co
Explore the category and the services that help you build with it.
ReMe FAQ
Can ReMe work offline?
How do I integrate ReMe with my agent?
Is ReMe production-ready?
What are the cost implications?
From evaluation to production software
DEV.co pairs senior engineers with teams adopting tools like ReMe. Our software development services span rag frameworks architecture, implementation, and the AI development services that increasingly sit alongside them.
Ready to add persistent memory to your agents?
Start with pip install reme-ai[core], run the quickstart demo, and integrate via REST API or CLI. Review security and API costs before production use.