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refactor: update memory skill loader, plugin config, and add privacy docs (#4905)

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Kartik
2026-04-22 17:15:19 +05:30
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openclaw/README.md
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@@ -2,7 +2,7 @@
Long-term memory for [OpenClaw](https://github.com/openclaw/openclaw) agents, powered by [Mem0](https://mem0.ai).
Your agent forgets everything between sessions. This plugin fixes that — it watches conversations, extracts what matters, and brings it back when relevant. Automatically.
Your agent forgets everything between sessions. This plugin fixes that — it stores conversations, extracts what matters, and brings it back when relevant. Enable `autoRecall` and `autoCapture` in config to run this automatically, or use agent tools for explicit control.
## Requirements
@@ -99,9 +99,78 @@ That's it. No API key, no config file editing, no environment variables. The plu
### Open-Source (Self-hosted)
No Mem0 key needed. Requires `OPENAI_API_KEY` for default embeddings and LLM. Vectors are stored locally in SQLite at `~/.mem0/vector_store.db` — no external database required.
No Mem0 key needed. Vectors are stored locally in SQLite at `~/.mem0/vector_store.db` — no external database required.
Defaults: `text-embedding-3-small` for embeddings, `gpt-5.4` for fact extraction.
Defaults: `text-embedding-3-small` (OpenAI) for embeddings, `gpt-5-mini` (OpenAI) for fact extraction — requires `OPENAI_API_KEY`. For a fully local setup, use Ollama for both LLM and embeddings.
#### Interactive Setup (Recommended)
Run the guided 4-step wizard:
```bash
openclaw mem0 init --mode open-source
```
The wizard walks you through:
1. **LLM provider** — OpenAI (`gpt-5-mini`), Ollama (`llama3.1:8b`, local), or Anthropic (`claude-sonnet-4-5-20250514`)
2. **Embedding provider** — OpenAI (`text-embedding-3-small`) or Ollama (`nomic-embed-text`, local)
3. **Vector store** — Qdrant (`http://localhost:6333`) or PGVector (PostgreSQL)
4. **User ID** — your memory namespace identifier
Each step tests connectivity (Ollama, Qdrant, PGVector) before proceeding.
#### Non-Interactive Setup
For CI/CD, scripts, or agent-driven setup — pass all options as flags:
```bash
# Fully local with Ollama + Qdrant
openclaw mem0 init --mode open-source \
--oss-llm ollama --oss-embedder ollama --oss-vector qdrant
# OpenAI + Qdrant
openclaw mem0 init --mode open-source \
--oss-llm openai --oss-llm-key <key> \
--oss-embedder openai --oss-embedder-key <key> \
--oss-vector qdrant
# Anthropic LLM + OpenAI embeddings + PGVector
openclaw mem0 init --mode open-source \
--oss-llm anthropic --oss-llm-key <key> \
--oss-embedder openai --oss-embedder-key <key> \
--oss-vector pgvector --oss-vector-user postgres --oss-vector-password secret
# JSON output (for LLM agents)
openclaw mem0 init --mode open-source --oss-llm ollama --oss-embedder ollama --oss-vector qdrant --json
```
<details>
<summary>All <code>--oss-*</code> flags</summary>
| Flag | Description |
| ---- | ----------- |
| `--oss-llm <provider>` | `openai`, `ollama`, or `anthropic` |
| `--oss-llm-key <key>` | API key for LLM provider |
| `--oss-llm-model <model>` | Override default LLM model |
| `--oss-llm-url <url>` | Base URL (Ollama only) |
| `--oss-embedder <provider>` | `openai` or `ollama` |
| `--oss-embedder-key <key>` | API key for embedder |
| `--oss-embedder-model <model>` | Override default embedder model |
| `--oss-embedder-url <url>` | Base URL (Ollama only) |
| `--oss-vector <provider>` | `qdrant` or `pgvector` |
| `--oss-vector-url <url>` | Qdrant server URL (default: `http://localhost:6333`) |
| `--oss-vector-host <host>` | PGVector host |
| `--oss-vector-port <port>` | PGVector port |
| `--oss-vector-user <user>` | PGVector user |
| `--oss-vector-password <pw>` | PGVector password |
| `--oss-vector-dbname <db>` | PGVector database name |
| `--oss-vector-dims <n>` | Override embedding dimensions |
</details>
#### Manual Config
Minimal config — uses OpenAI defaults:
```json5
{
@@ -130,8 +199,8 @@ Customize the embedder, vector store, or LLM via the `oss` block:
"userId": "alice",
"oss": {
"embedder": { "provider": "openai", "config": { "model": "text-embedding-3-small" } },
"vectorStore": { "provider": "qdrant", "config": { "host": "localhost", "port": 6333 } },
"llm": { "provider": "openai", "config": { "model": "gpt-5.4" } }
"vectorStore": { "provider": "qdrant", "config": { "url": "http://localhost:6333" } },
"llm": { "provider": "openai", "config": { "model": "gpt-5-mini" } }
}
}
```
@@ -144,11 +213,11 @@ All `oss` fields are optional. See the [Mem0 OSS docs](https://docs.mem0.ai/open
<img src="https://raw.githubusercontent.com/mem0ai/mem0/main/docs/images/openclaw-architecture.png" alt="Architecture" width="800" />
</p>
**Auto-Recall** — Before the agent responds, the plugin searches Mem0 for relevant memories and injects them into context.
**Auto-Recall** (`autoRecall: true`) — Before the agent responds, the plugin searches Mem0 for relevant memories and injects them into context.
**Auto-Capture** — After the agent responds, the conversation is filtered through a noise-removal pipeline and sent to Mem0. New facts get stored, stale ones updated, duplicates merged.
**Auto-Capture** (`autoCapture: true`) — After the agent responds, the conversation is filtered through a noise-removal pipeline and sent to Mem0. New facts get stored, stale ones updated, duplicates merged.
Both run silently. No prompting, no manual calls required.
Both are opt-in. Once enabled, they run silently — no prompting, no manual calls required. Without them, the agent can still use memory tools (`memory_add`, `memory_search`, etc.) explicitly.
### Memory Scopes
@@ -176,7 +245,7 @@ Eight tools are registered for agent use:
## CLI
All commands: `openclaw mem0 <command>`.
All commands: `openclaw mem0 <command>`. All commands support `--json` for machine-readable output (for LLM agents).
```bash
# Memory operations
@@ -191,8 +260,9 @@ openclaw mem0 delete --all --user-id alice --confirm
openclaw mem0 import memories.json
# Management
openclaw mem0 init
openclaw mem0 init --api-key <key> --user-id alice
openclaw mem0 init # interactive setup
openclaw mem0 init --mode open-source --oss-llm ollama # non-interactive OSS
openclaw mem0 init --api-key <key> --user-id alice # non-interactive platform
openclaw mem0 status
openclaw mem0 config show
openclaw mem0 config get api_key
@@ -205,6 +275,12 @@ openclaw mem0 event status <event_id>
# Memory consolidation
openclaw mem0 dream
openclaw mem0 dream --dry-run
# JSON output (any command)
openclaw mem0 search "preferences" --json
openclaw mem0 list --json
openclaw mem0 status --json
openclaw mem0 help --json # discover all commands + flags
```
## Configuration Reference
@@ -215,8 +291,8 @@ openclaw mem0 dream --dry-run
| --- | ---- | ------- | ----------- |
| `mode` | `"platform"` \| `"open-source"` | `"platform"` | Backend mode |
| `userId` | `string` | OS username | User identifier. All memories scoped to this value. |
| `autoRecall` | `boolean` | `true` | Inject relevant memories before each turn |
| `autoCapture` | `boolean` | `true` | Extract and store facts after each turn |
| `autoRecall` | `boolean` | `false` | Inject relevant memories before each turn |
| `autoCapture` | `boolean` | `false` | Extract and store facts after each turn |
| `topK` | `number` | `5` | Max memories returned per recall |
| `searchThreshold` | `number` | `0.5` | Minimum similarity score (0-1) |
@@ -230,7 +306,7 @@ openclaw mem0 dream --dry-run
### Open-Source Mode
All fields optional. Defaults: `text-embedding-3-small` embeddings, local SQLite vector store (`~/.mem0/vector_store.db`), `gpt-5.4` LLM.
All fields optional. Defaults: `text-embedding-3-small` embeddings, local SQLite vector store (`~/.mem0/vector_store.db`), `gpt-5-mini` LLM.
| Key | Type | Default | Description |
| --- | ---- | ------- | ----------- |