reme-ai
Remember Me, Refine Me.
Decision gist · record as of 2026-08-14
Yes. ReMe is actively maintained, has no known vulnerabilities, uses a permissive license, and solves a concrete problem for agent developers: persistent, editable, searchable memory under user control. The low install friction and modular design (core features work without LLM keys) make it accessible. Install if you're building agents that need to retain and evolve knowledge across sessions, or if you want a local-first alternative to external memory services.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.11+.
- LLM-powered features (auto_memory, auto_resource, auto_dream) need LLM_API_KEY and LLM_BASE_URL environment variables; basic file operations and BM25 search work without them.
- Low install friction with a pure-Python wheel; active maintenance (last commit 2026-08-13, 3311 stars).
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license allows commercial and private use with minimal restrictions, making it suitable for both open-source and proprietary agent integrations.
last release 2026-08-13 (1 days) · last repo commit 2026-08-13 · 3,311 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 188,191 downloads/mo, #9,949 on PyPI
Alternatives
Verify before relying
pip install "reme-ai[core]"
reme start
reme write path=digest/wiki/demo name="Demo" content="# Demo\n\nReMe stores memory as Markdown."
reme search query="memory markdown" limit=5- Performance characteristics and scalability limits for large knowledge bases (file count, search latency).
- Embedding model requirements and resource overhead when semantic retrieval is enabled.
- Compatibility and integration depth with specific agent frameworks beyond the mentioned examples.
- Data persistence and backup guarantees under concurrent access from multiple agents.
What it is and what it does
ReMe is a persistent memory system for AI agents that stores knowledge as ordinary Markdown files with frontmatter and wikilinks, keeping all data under user control in the local filesystem. It combines keyword search (BM25), optional semantic embeddings, and relationship traversal to retrieve relevant context without loading entire knowledge bases into memory. The system includes automated workflows—Auto Memory distills conversations into daily notes, Auto Resource imports external documents, Auto Dream consolidates notes into long-term knowledge, and Auto Link writes relationships back into files.
The package integrates with agent frameworks through CLI commands, HTTP endpoints, Model Context Protocol (MCP), and embedded Python APIs. It runs as a local service (default port 2333) and includes an optional web UI (ReMe Studio) for browsing and editing. Core file operations, BM25 search, and wikilink traversal work without LLM credentials; AI-powered memory evolution requires OpenAI-compatible API keys. The 18 runtime dependencies include fastapi for the service layer, pydantic for configuration, and loguru for logging.
Use it for
- Give personal assistant agents (QwenPaw, OpenClaw, Hermes) a user-editable long-term memory layer that persists across sessions.
- Preserve coding style, project decisions, and workflow experience for coding agents like Claude Code across multiple development sessions.
- Build a searchable, traceable Markdown wiki from conversations and resources that both users and agents can maintain and query.
- Enable agents to learn from experience by recording successful procedures, failed attempts, and periodic reflections as indexed memory nodes.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
ReMe is actively maintained, has no known vulnerabilities, uses a permissive license, and solves a concrete problem for agent developers: persistent, editable, searchable memory under user control. The low install friction and modular design (core features work without LLM keys) make it accessible. Install if you're building agents that need to retain and evolve knowledge across sessions, or if you want a local-first alternative to external memory services.
Install
reme-ai on PyPI
Before you install
Low install friction with a pure-Python wheel; active maintenance (last commit 2026-08-13, 3311 stars). Requires Python 3.11+ and 18 runtime dependencies including fastapi, openai, and numpy—a substantial but standard stack for agent-memory systems.
Requires Python 3.11+. LLM-powered features (auto_memory, auto_resource, auto_dream) need LLM_API_KEY and LLM_BASE_URL environment variables; basic file operations and BM25 search work without them.
License in practice
Apache-2.0 permissive license allows commercial and private use with minimal restrictions, making it suitable for both open-source and proprietary agent integrations.
Quickstart
pip install "reme-ai[core]"
reme start
reme write path=digest/wiki/demo name="Demo" content="# Demo\n\nReMe stores memory as Markdown."
reme search query="memory markdown" limit=5Verify before relying
- Performance characteristics and scalability limits for large knowledge bases (file count, search latency).
- Embedding model requirements and resource overhead when semantic retrieval is enabled.
- Compatibility and integration depth with specific agent frameworks beyond the mentioned examples.
- Data persistence and backup guarantees under concurrent access from multiple agents.
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.11 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 18 packagesaiofilescroniterfastapifastmcphttpxlogurumistletoenumpyopenaipsutilpydanticpython-frontmatterpyyamlrichuvicornwatchfileszstandardpypdf |
| Maintenance | Actively maintained 1 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 188,191 / month, #9,949 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: DevelopersIntended Audience :: Science/ResearchOperating System :: OS IndependentProgramming Language :: Python :: 3Programming Language :: Python :: 3.11Topic :: Scientific/Engineering :: Artificial Intelligence |
Evidence: reme_ai-0.4.1.7-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “markdown knowledge base”
- reme-aiReMe is a local-first knowledge base that converts conversations and…
- memsearchMemsearch builds a searchable semantic memory index from markdown…
- atlas-doc-parserConverts Atlassian Document Format (ADF) JSON from Confluence and…
Give your agent the search over MCP, or paste the wish link into any chat.
More Artificial Intelligence packages
LiteLLM provides a unified Python interface to call 100+ LLM providers (OpenAI, Anthropic, Gemini, Bedrock, Azure, and others) using OpenAI-compatible API format, available as both a Python SDK and a self-hosted AI Gateway proxy server.
Install it if you need to work with multiple LLM providers or want to centralize LLM routing in your organization.
Client library and CLI tool for downloading, uploading, and managing models, datasets, and repositories on the Hugging Face Hub platform.
Install it if you work with Hugging Face Hub models or datasets.
LangChain provides a framework for building agents and LLM-powered applications by composing language models, tools, and memory through a unified API that abstracts over multiple model providers.
hf-xet provides chunk-based deduplication and efficient file transfer for the Hugging Face Hub, enabling faster uploads and downloads of large files with local disk caching.
Tokenizers converts raw text into token sequences for NLP models, with support for training custom vocabularies and using pre-built tokenizers (BPE, WordPiece) optimized for speed via Rust.
Transformers provides a unified framework for loading, fine-tuning, and running state-of-the-art pretrained models across text, vision, audio, video, and multimodal tasks using PyTorch, JAX, or TensorFlow.
Install it if you need to run or train any transformer-based model for NLP, vision, audio, or multimodal tasks.
See also cognee · memsearch · langmem · stashai · memori · agentscope · mindroom · phidata · agent-framework-azure-ai · zep-python