reme_memory
Set up and use ReMe as a file-native long-term memory system through the reme CLI. Use when an Agent needs to detect whether ReMe is installed or running, install and configure ReMe, start or verify its local service, retrieve prior context, or write and consolidate durable memory.
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- Последна проверка на записа
- 22.09.2026 г.
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ReMe Memory
Use ReMe as the persistent memory layer for this Agent. ReMe stores filtered conversation source records, daily notes,
resources, and long-term digest memories in a user-owned local workspace. auto_memory omits recalled tool results and
base64 data when it persists a source record so retrieved or binary content does not become conversation source
material.
Bootstrap ReMe
Run this workflow before first use and whenever a ReMe command cannot reach the service. Distinguish a missing CLI from an installed but stopped service.
1. Check whether ReMe is installed
Run:
command -v reme
If this prints an executable path, treat ReMe as installed and continue to service discovery. Do not reinstall or upgrade an existing installation unless the user requests it.
If the command is missing, check Python before installing:
python3 -c 'import sys; print(sys.version); raise SystemExit(0 if sys.version_info >= (3, 11) else 1)'
ReMe requires Python 3.11 or newer. If the user has requested setup or installation, install the recommended package in the active Python environment:
python3 -m pip install "reme-ai[core]"
When working from a ReMe source checkout and the user explicitly wants an editable source installation, run this from the repository root instead:
python3 -m pip install -e ".[core]"
Do not silently install into or modify a Python environment when the user only asked to use memory. Explain that ReMe is
missing and ask before installing. After installation, run command -v reme again. If it is still missing, check that
the active environment's executable directory is on PATH; do not repeatedly reinstall.
2. Configure optional model credentials
Basic file operations, BM25 search, wikilink traversal, and reading existing proactive topics work without model
credentials. auto_memory, auto_resource, auto_dream, and proactive refresh require an LLM configuration.
When those model-powered jobs are needed, have the user provide valid values through the environment or a .env file:
LLM_API_KEY=sk-xxx
LLM_BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1
The default LLM backend is OpenAI-compatible and the default model is qwen3.7-plus. Override them when the endpoint
requires different values:
LLM_BACKEND=openai
LLM_MODEL_NAME=qwen3.7-plus