RAG Frameworks · agentscope-ai

ReMe

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.

Talk to DEV.co Discuss Implementation

Source: GitHub — github.com/agentscope-ai/ReMe

3.2k

GitHub stars

269

Forks

Python

Primary language

Apache-2.0

License (OSI-approved)

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.

Quickstart

Get the ReMe source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/agentscope-ai/ReMe.git2cd ReMe3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Multi-session AI agent memory

Preserve agent context, learned patterns, and procedural knowledge across conversation sessions without vendor lock-in.

Developer-augmented coding agents

Store project style, architecture decisions, and workflow patterns that Claude Code or similar agents can reference and maintain.

User-editable knowledge base

Build searchable Markdown wikis where both agents and humans can read, write, and refine shared facts and procedures.

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

Security considerations

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.

Software development agency

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.co

Related open-source tools

Surfaced by semantic similarity across the DEV.co open-source index.

SimpleMem LightMem MemoryOS

Related on DEV.co

Explore the category and the services that help you build with it.

RAG Frameworks AI Development API Development Talk to DEV.co

ReMe FAQ

Can ReMe work offline?

Partially. File operations, BM25 search, wikilink traversal, and proactive topic reading work without LLM/embedding credentials. auto_memory, auto_resource, auto_dream, and semantic embedding retrieval require external API calls.

How do I integrate ReMe with my agent?

Use the REST API (curl shown in quickstart), CLI commands (reme write/search/read), or agent hooks mentioned for QwenPaw/OpenClaw/Claude Code. Custom integration code required for new agent types.

Is ReMe production-ready?

v0.4.0.9 with 3,162 stars and active development suggests stability, but conduct independent security review (authentication, encryption, secret management) before deploying with sensitive data.

What are the cost implications?

ReMe itself is free (Apache-2.0), but auto pipelines call external LLM and embedding APIs (shown examples use Alibaba DashScope); estimate API costs based on conversation volume and frequency of auto_dream runs.

Custom software development services

DEV.co is a software development agency delivering custom software development services to companies building on open source. Our software developers and web developers design, integrate, and ship production systems — spanning web development, APIs, AI, data, and cloud. If ReMe is part of your rag frameworks roadmap, our team can implement, customize, migrate, and maintain it.

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.

Talk to DEV.co Schedule a Consultation