> ## Documentation Index
> Fetch the complete documentation index at: https://docs.agentscope.io/llms.txt
> Use this file to discover all available pages before exploring further.

# Quick Start

> Install ReMe and complete a verifiable write, index, search, and read loop.

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:

```bash theme={null}
pip install "reme-ai[core]"
```

For source development:

```bash theme={null}
git clone https://github.com/agentscope-ai/ReMe.git
cd ReMe
pip install -e ".[core]"
```

## 2. Start the Service

In one terminal, run:

```bash theme={null}
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:

```bash theme={null}
reme health_check
```

Use another port when necessary:

```bash theme={null}
reme start service.port=8181
```

## 3. Write a Memory

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

```bash theme={null}
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.

```bash theme={null}
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:

```text theme={null}
.reme/
├── metadata/    derived indexes, graph, and runtime state
├── session/     raw sessions
├── resource/    source resources
├── daily/       daily refinement
└── digest/      durable memory
```

## 5. Enable Memory Refinement When Needed

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:

```bash theme={null}
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`.

## Next

* Compare the available [integration paths](/reme/latest/en/integration/overview), then use [Python integration](/reme/latest/en/integration/python) or connect a [service-based agent](/reme/latest/en/integration/agents).
* Find workspace, configuration, and interface details in the [ReMe reference](/reme/latest/en/reference).
* Review the [ReMe FAQ](/reme/latest/en/faq) for connection or indexing problems.