Quick Start

This page uses ReMe’s model-free capabilities to complete a verifiable write → index → search → read loop.

1. Install

ReMe requires Python 3.11 or newer:

pip install "reme-ai[core]"

For source development:

git clone https://github.com/agentscope-ai/ReMe.git
cd ReMe
pip install -e ".[core]"

2. Start the Service

In one terminal, run:

reme start

The default workspace is .reme/ under the launch directory, and the service listens on 127.0.0.1:2333. In another terminal, check it:

reme health_check

Use another port when necessary:

reme start service.port=8181

3. Write a Memory

Use reme write to create a Markdown memory in the durable digest/ area:

reme write \
  path=digest/wiki/quick-start-demo \
  name="Quick Start Demo" \
  description="My first ReMe memory" \
  content="# Quick Start Demo

ReMe stores agent memory as user-readable Markdown."

ReMe adds the .md suffix and writes frontmatter plus content to .reme/digest/wiki/quick-start-demo.md.

4. Search and Read

The background index normally discovers the file automatically. If it does not appear immediately, wait briefly or run reme reindex once.

reme search query="agent memory Markdown" limit=5
reme read path=digest/wiki/quick-start-demo.md

Open the file in the workspace too. Matching CLI output and disk content confirm that the minimum loop works. After completing the loop, the workspace contains these top-level areas:

.reme/
├── metadata/    derived indexes, graph, and runtime state
├── session/     raw sessions
├── resource/    source resources
├── daily/       daily refinement
└── digest/      durable memory

Conversation refinement, resource interpretation, and consolidation require an LLM. Semantic search also requires embeddings. Add a .env in the launch directory when you need them:

LLM_API_KEY=sk-xxx
LLM_BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1

EMBEDDING_API_KEY=sk-xxx
EMBEDDING_BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1

Model names and embedding-store activation are configuration choices. The authoritative defaults are in reme/config/default.yaml. Never commit secrets from .env.

Compare the available integration paths, then use Python integration or connect a service-based agent.
Find workspace, configuration, and interface details in the ReMe reference.
Review the ReMe FAQ for connection or indexing problems.

Get Started

Usage

Resources

1. Install

2. Start the Service

3. Write a Memory

4. Search and Read

5. Enable Memory Refinement When Needed

Next

​1. Install

​2. Start the Service

​3. Write a Memory

​4. Search and Read

​5. Enable Memory Refinement When Needed

​Next

1. Install

2. Start the Service

3. Write a Memory

4. Search and Read

5. Enable Memory Refinement When Needed

Next