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# Config & Working Directory
This page covers:
- **Directory structure** — Where files are stored and the purpose of each directory
- **Environment variables** — How to customize paths and behavior
- **Configuration files** — Complete field description for `config.json` and `agent.json`
From **v0.1.0**, QwenPaw supports **multi-agent**. Configuration is split into two layers:
1. **Global config** (`config.json`) — Model providers, agent list, global settings
2. **Agent config** (`agent.json`) — Independent config for each agent (channels, heartbeat, tools, etc.)
---
## Directory Structure
The default working directory is `~/.qwenpaw`. After running `qwenpaw init`, the complete structure looks like:
```
$QWENPAW_WORKING_DIR/ # Default ~/.qwenpaw
├── config.json # Global config
├── workspaces/
│ ├── default/ # Default agent workspace
│ │ ├── agent.json # Agent config
│ │ ├── chats.json # Conversation history
│ │ ├── jobs.json # Cron jobs
│ │ ├── token_usage.json # Token usage records
│ │ ├── AGENTS.md # Persona file
│ │ ├── SOUL.md # Persona file
│ │ ├── PROFILE.md # Persona file
│ │ ├── BOOTSTRAP.md # Initial setup guide (auto-deleted after completion)
│ │ ├── MEMORY.md # Long-term memory
│ │ ├── skills/ # Workspace-local skills
│ │ ├── skill.json # Skill enabled state and config
│ │ ├── memory/ # Daily memory files
│ │ └── browser/ # Browser user data (cookies, cache, etc.)
│ └── abc123/ # Other agent workspace
│ └── ...
└── skill_pool/ # Local shared skill pool
├── skill.json # Pool metadata
└── ...
$QWENPAW_SECRET_DIR/ # Default ~/.qwenpaw.secret
├── providers.json # Model provider config and API keys
└── envs.json # Environment variables
```
> **Path explanation:** `$QWENPAW_WORKING_DIR` and `$QWENPAW_SECRET_DIR` are environment variables, with default values of `~/.qwenpaw` and `~/.qwenpaw.secret` respectively. They can be customized via environment variables, see "Environment Variables" section below.
### Directory Explanation
**Global Directory (`~/.qwenpaw/`)**
| File / Directory | Purpose |
| ---------------- | ----------------------------------------------------- |
| `config.json` | Global config (model providers, env vars, agent list) |
| `workspaces/` | All agent workspace directories |
**Agent Workspace (`~/.qwenpaw/workspaces/{agent_id}/`)**
| File / Directory | Purpose |
| ------------------ | ------------------------------------------------------------ |
| `agent.json` | Agent config (channels, heartbeat, tools, skills, MCP, etc.) |
| `chats.json` | Conversation history |
| `jobs.json` | Cron job list |
| `token_usage.json` | Token usage records |
| `AGENTS.md` | Persona file (see [Agent Persona](./persona)) |
| `SOUL.md` | Persona file (see [Agent Persona](./persona)) |
| `PROFILE.md` | Persona file (see [Agent Persona](./persona)) |
| `BOOTSTRAP.md` | Initial setup guide (auto-deleted after completion) |
| `MEMORY.md` | Long-term memory (see [Memory](./memory)) |
| `skills/` | Skills available in this workspace |
| `skill.json` | Skill enabled state, channel routing, and config |
| `memory/` | Daily memory files (see [Memory](./memory)) |
| `browser/` | Browser user data (cookies, cache, localStorage, etc.) |
> **Persona files:** Agent behavior and personality are defined by persona files. Running `qwenpaw init` automatically creates template files based on your chosen language (`zh` / `en` / `ru`). For detailed explanation and management, see [Agent Persona](./persona).
> **Multi-Agent:** See the [Multi-Agent](./multi-agent) documentation for details.
---
## Environment Variables
You can customize paths and behavior via environment variables:
**Path-related:**
| Variable | Default | Description |
| -------------------------- | ------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `QWENPAW_WORKING_DIR` | `~/.qwenpaw` | Working directory root path |
| `QWENPAW_SECRET_DIR` | `~/.qwenpaw.secret` | Sensitive data directory (stores `providers.json` and `envs.json`). Docker default is `/app/working.secret` |
| `QWENPAW_KEYRING_ACCOUNT` | _(auto)_ | OS keychain account name for the master key. Defaults to `master_key`; when `QWENPAW_WORKING_DIR`/`QWENPAW_SECRET_DIR` are set (e.g. a dev checkout) it auto-derives a per-install account so a dev install never overwrites the stable install's key. Set explicitly to name a profile. |
| `QWENPAW_CONFIG_FILE` | `config.json` | Config file name (relative to `QWENPAW_WORKING_DIR`) |
| `QWENPAW_HEARTBEAT_FILE` | `HEARTBEAT.md` | Heartbeat file name (relative to agent workspace) |
| `QWENPAW_JOBS_FILE` | `jobs.json` | Cron jobs file name (relative to agent workspace) |
| `QWENPAW_CHATS_FILE` | `chats.json` | Conversation history file name (relative to agent workspace) |
| `QWENPAW_TOKEN_USAGE_FILE` | `token_usage.json` | Token usage record file name (relative to agent workspace) |
**Other configuration:**
| Variable | Default | Description |
| ------------------------------------ | --------------- | ---------------------------------------------------------------------------- |
| `QWENPAW_LOG_LEVEL` | `info` | Log level (`debug` / `info` / `warning` / `error` / `critical`) |
| `QWENPAW_LOG_MAX_SIZE` | `5MiB` | Maximum active log size; accepts bytes or suffixes such as `10MB` and `1GiB` |
| `QWENPAW_LOG_MAX_BACKUPS` | `3` | Number of rotated log backups to retain; `0` disables backups |
| `QWENPAW_MEMORY_COMPACT_THRESHOLD` | `100000` | Character threshold to trigger memory compaction |
| `QWENPAW_MEMORY_COMPACT_KEEP_RECENT` | `3` | Number of recent messages to keep after compaction |
| `QWENPAW_MEMORY_COMPACT_RATIO` | `0.7` | Threshold ratio for triggering compaction (relative to context window size) |
| `QWENPAW_CONSOLE_STATIC_DIR` | _(auto-detect)_ | Console frontend static files path |
**Security & Authentication:**
| Variable | Default | Description |
| ---------------------------- | ------- | -------------------------------------------------- |
| `QWENPAW_AUTH_ENABLED` | `false` | Whether to enable Web console login authentication |
| `QWENPAW_AUTH_USERNAME` | - | Admin username for auto-registration (optional) |
| `QWENPAW_AUTH_PASSWORD` | - | Admin password for auto-registration (optional) |
| `QWENPAW_TOOL_GUARD_ENABLED` | `true` | Whether to enable tool guard |
| `QWENPAW_SKILL_SCAN_MODE` | `warn` | Skill scanning mode (`block` / `warn` / `off`) |
Example — use a different working dir for this shell:
```bash
export QWENPAW_WORKING_DIR=/home/me/my_qwenpaw
qwenpaw app
```
Config, HEARTBEAT, jobs, memory, etc. will be read/written under
`/home/me/my_qwenpaw`.
---
## Configuration File Structure
Starting from **v0.1.0**, configuration is split into two layers:
1. **Global config** - `~/.qwenpaw/config.json` (providers, environment variables, agent list)
2. **Agent config** - `~/.qwenpaw/workspaces/{agent_id}/agent.json` (per-agent settings)
### Global config.json
Stores globally shared configuration:
```json
{
"agents": {
"active_agent": "default",
"profiles": {
"default": {
"id": "default",
"name": "Default Agent",
"description": "Default workspace agent",
"enabled": true
},
"abc123": {
"id": "abc123",
"name": "Code Assistant",
"description": "Focuses on code review and development",
"enabled": true
}
}
},
"last_api": {
"host": "127.0.0.1",
"port": 8088
},
"show_tool_details": true
}
```
**Global config.json field descriptions:**
| Field | Type | Default | Description |
| --------------------- | -------------- | ------------------- | ----------------------------------------------------------------- |
| `agents.active_agent` | string | `"default"` | Currently active agent ID |
| `agents.profiles` | object | `{}` | Agent profile references (key is agent_id) |
| `last_api.host` | string \| null | `null` | Host address from last `qwenpaw app` start |
| `last_api.port` | int \| null | `null` | Port from last `qwenpaw app` start |
| `show_tool_details` | bool | `true` | Whether to show tool call/return details in channel messages |
| `user_timezone` | string | _(system timezone)_ | IANA timezone name (e.g., `"Asia/Shanghai"`) |
| `last_dispatch` | object \| null | `null` | Last message dispatch target (used for heartbeat `target="last"`) |
**`agents.profiles[agent_id]` reference fields:**
| Field | Type | Required | Description |
| --------------- | ------ | -------- | ----------------------------------------------------------------------------- |
| `id` | string | Yes | Agent unique identifier |
| `name` | string | Yes | Agent display name |
| `description` | string | No | Agent description (used for multi-agent collaboration) |
| `enabled` | bool | Yes | Whether to enable this agent |
| `workspace_dir` | string | No | Workspace path (optional, defaults to `$QWENPAW_WORKING_DIR/workspaces/{id}`) |
> **Backward compatibility:** The global config.json still supports `channels`, `mcp`, `tools`, `security` and other fields for backward compatibility with older versions. In multi-agent mode, these configurations should be set in each agent's `agent.json`.
>
> **Configuration priority:** The agent's `agent.json` takes precedence over the global `config.json`. When the same field is configured in both places, the system uses the value from `agent.json`. For multi-agent mode, it's recommended to put all configurations in each agent's `agent.json`.
> **Model provider configuration** is stored in `$QWENPAW_SECRET_DIR/providers.json` (default `~/.qwenpaw.secret/providers.json`).
> **Environment variables** are stored in `$QWENPAW_SECRET_DIR/envs.json` (default `~/.qwenpaw.secret/envs.json`).
### Agent config (agent.json)
Each agent has an independent `agent.json` in its workspace directory (`~/.qwenpaw/workspaces/{agent_id}/`) that stores all of its configuration (channels, tools, heartbeat, MCP, security, etc.). This allows different agents to have completely different configurations without interfering with each other.
```json
{
"id": "default",
"name": "Default Agent",
"description": "Default workspace agent",
"workspace_dir": "",
"channels": {
"console": {
"enabled": true,
"bot_prefix": ""
},
"dingtalk": {
"enabled": false,
"bot_prefix": "",
"client_id": "",
"client_secret": ""
}
},
"mcp": {
"clients": {
"filesystem": {
"name": "Filesystem Access",
"enabled": true,
"command": "npx",
"args": [
"-y",
"@modelcontextprotocol/server-filesystem",
"/path/to/folder"
]
}
}
},
"heartbeat": {
"enabled": false,
"every": "30m",
"target": "main",
"timeoutSeconds": 300,
"activeHours": null
},
"running": {
"max_iters": 50,
"llm_retry_enabled": true,
"llm_max_retries": 3,
"llm_backoff_base": 1.0,
"llm_backoff_cap": 10.0,
"max_input_length": 131072
},
"active_model": null,
"language": "en",
"system_prompt_files": ["AGENTS.md", "SOUL.md", "PROFILE.md"],
"tools": {
"builtin_tools": {}
},
"security": {
"tool_guard": {
"enabled": true,
"shell_evasion_checks": {
"command_substitution": false,
"obfuscated_flags": false,
"backslash_escaped_whitespace": false,
"backslash_escaped_operators": false,
"newlines": false,
"comment_quote_desync": false,
"quoted_newline": false
}
},
"file_guard": {
"enabled": true
},
"skill_scanner": {
"mode": "warn"
},
"allow_no_auth_hosts": ["127.0.0.1", "::1"]
},
"last_dispatch": null
}
```
> **Note:** The complete field list and descriptions are provided in the sections below. Agent configuration can be managed in the Console or by directly editing the `agent.json` file.
---
### agent.json Field Reference
#### `channels` — Messaging channel configs
Each channel has common fields (like `enabled`, `bot_prefix`, access control policies, etc.) and channel-specific fields (like DingTalk's `client_id`, `client_secret`).
**Supported channels:**
- **console** — Console (enabled by default)
- **dingtalk** — DingTalk
- **feishu** — Feishu/Lark
- **discord** — Discord
- **telegram** — Telegram
- **qq** — QQ bot
- **imessage** — iMessage (macOS only)
- **mattermost** — Mattermost
- **matrix** — Matrix
- **wecom** — WeCom (WeChat Work)
- **wechat** — WeChat Personal (iLink)
- **xiaoyi** — Huawei XiaoYi
- **mqtt** — MQTT
- **voice** — Voice
> **Complete configuration:** Common fields, channel-specific fields (like DingTalk's `client_id`, Feishu's `app_id`), and detailed configuration steps for each channel are documented in [Channels](./channels).
Management: Console (Agent → Channels) or directly edit `agent.json`.
> **Hot reload:** The system automatically detects `agent.json` changes every 2 seconds. After modifying channel config, it will auto-reload without restart.
---
#### `mcp` — MCP client configuration
MCP (Model Context Protocol) allows agents to connect to external services (like Filesystem, Git, SQLite MCP servers, etc.).
Each MCP client includes name, enabled state, transport method (stdio/HTTP/SSE), startup command or URL, and other fields.
> **Complete configuration:** Full field descriptions, config formats, examples, and usage for MCP clients are documented in [MCP](./mcp).
Management: Console (Agent → MCP) or directly edit `agent.json`.
---
#### `heartbeat` — Heartbeat configuration
Heartbeat is a scheduled self-check feature that executes tasks from `HEARTBEAT.md` at regular intervals.
| Field | Type | Default | Description |
| ---------------- | -------------- | -------- | ------------------------------------------------------------------------------------------------------------ |
| `enabled` | bool | `false` | Whether to enable heartbeat feature |
| `every` | string | `"30m"` | Run interval. Supports `Nh`, `Nm`, `Ns` combos, e.g. `"1h"`, `"30m"`, `"2h30m"`, `"90s"` |
| `target` | string | `"main"` | `"main"` = run in main session only; `"last"` = dispatch result to the last channel/user that sent a message |
| `timeoutSeconds` | int | `300` | Maximum execution time for one heartbeat run, in seconds. Valid range: `1`–`3600` |
| `activeHours` | object \| null | `null` | Optional time window (if set, heartbeat only runs during this period) |
**`heartbeat.activeHours`** (when not null):
| Field | Type | Default | Description |
| ------- | ------ | --------- | --------------------------- |
| `start` | string | `"08:00"` | Start time (HH:MM, 24-hour) |
| `end` | string | `"22:00"` | End time (HH:MM, 24-hour) |
See [Heartbeat](./heartbeat) for detailed guide.
---
#### `running` — Runtime configuration
Controls agent runtime behavior, retry strategies, context management, and memory configuration.
**Basic Runtime:**
| Field | Type | Default | Description |
| ---------------------------- | ----- | ------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `max_iters` | int | `100` | Maximum number of reasoning-acting iterations for ReAct agent (must be ≥ 1) |
| `shell_command_timeout` | float | `60.0` | Default timeout in seconds for `execute_shell_command`. The LLM may still override this per-call via the timeout parameter |
| `shell_command_executable` | str | `""` | Path to the shell used by `execute_shell_command` on Unix/macOS (e.g. `/bin/bash`, `/bin/zsh`). On Windows, supports `powershell.exe` / `pwsh.exe`. When empty, falls back to `$SHELL`, then `/bin/sh` (or `cmd.exe` on Windows) |
| `auto_continue_on_text_only` | bool | `false` | When enabled, the agent automatically retries up to two extra reasoning passes if the model responds with text but no tools |
**LLM Retry & Rate Limiting:**
| Field | Type | Default | Description |
| ----------------------- | ----- | ------- | ----------------------------------------------------------------------------------------------------- |
| `llm_retry_enabled` | bool | `true` | Whether to auto-retry transient LLM API failures such as rate limits, timeouts, and connection errors |
| `llm_max_retries` | int | `3` | Maximum retry attempts for transient LLM API failures (must be ≥ 1) |
| `llm_backoff_base` | float | `1.0` | Base delay in seconds for exponential retry backoff (must be ≥ 0.1) |
| `llm_backoff_cap` | float | `10.0` | Maximum backoff delay cap in seconds (must be ≥ 0.5 and greater than or equal to `llm_backoff_base`) |
| `llm_max_concurrent` | int | `10` | Maximum concurrent LLM calls (shared across all agents) |
| `llm_max_qpm` | int | `600` | Maximum queries per minute (QPM). 0 = no limit |
| `llm_rate_limit_pause` | float | `5.0` | Global pause duration in seconds after receiving a 429 rate limit response |
| `llm_rate_limit_jitter` | float | `1.0` | Random jitter range in seconds added to rate limit pause to avoid thundering herd |
| `llm_acquire_timeout` | float | `300.0` | Maximum timeout in seconds to wait for acquiring a rate limit slot |
**Context Management:**
| Field | Type | Default | Description |
| -------------------------- | ------ | --------------- | ----------------------------------------------------------------------- |
| `max_input_length` | int | `131072` (128K) | Maximum input length (tokens) for model context window (must be ≥ 1000) |
| `history_max_length` | int | `10000` | Maximum output length (characters) for `/history` command |
| `context_manager_backend` | string | `"light"` | Context manager backend type |
| `memory_manager_backend` | string | `"remelight"` | Memory manager backend type |
| `light_context_config` | object | _(see below)_ | Light context manager configuration |
| `reme_light_memory_config` | object | _(see below)_ | ReMeLight memory manager configuration |
**Light Context Configuration (`light_context_config` object):**
| Field | Type | Default | Description |
| ------------------------------ | ------ | ---------- | ------------------------------------------------------------ |
| `dialog_path` | string | `"dialog"` | Dialog persistence directory (relative to working dir) |
| `token_count_estimate_divisor` | float | `4.0` | Divisor for byte-based token estimation (byte_len / divisor) |
**Light Context Compaction (`light_context_config.context_compact_config` object):**
| Field | Type | Default | Description |
| ------------------------- | ----- | ------- | ------------------------------------------------------------------------- |
| `enabled` | bool | `true` | Whether to enable automatic context compaction |
| `compact_threshold_ratio` | float | `0.8` | Threshold ratio (relative to `max_input_length`) that triggers compaction |
| `reserve_threshold_ratio` | float | `0.1` | Ratio of recent context to preserve after compaction for continuity |
**Light Tool Result Pruning (`light_context_config.tool_result_pruning_config` object):**
| Field | Type | Default | Description |
| ------------------------------ | ---- | ------- | ----------------------------------------------------------------------------------------------------------- |
| `enabled` | bool | `true` | Whether to enable tool result pruning |
| `pruning_recent_n` | int | `2` | Number of recent tool-result-bearing messages kept at the recent preview threshold before scroll compaction |
| `pruning_old_msg_max_bytes` | int | `3000` | Compact preview byte threshold for tool results retained in live context after scroll compaction |
| `pruning_recent_msg_max_bytes` | int | `50000` | Recent/execution preview byte threshold for tool results before and shortly after entering context |
| `offload_retention_days` | int | `5` | Number of days to retain tool result files |
**ReMeLight Memory Configuration (`reme_light_memory_config` object):**
| Field | Type | Default | Description |
| --------------------------- | ----------- | ---------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `metadata_dir` | string | `"mem_metadata"` | Subdirectory for ReMe persistent state |
| `session_dir` | string | `"mem_session"` | Subdirectory for ReMe source conversation logs used by auto-memory |
| `mem_session_dir` | string | `"mem_agent"` | Subdirectory for ReMe internal memory-agent sessions |
| `resource_dir` | string | `"resource"` | Subdirectory for external assets |
| `daily_dir` | string | `"memory"` | Subdirectory for daily memory |
| `digest_dir` | string | `"digest"` | Subdirectory for digest memory |
| `summarize_when_compact` | bool | `true` | Whether to enable memory summarization during compaction |
| `inbox_push_enabled` | bool | `true` | Whether to push auto-memory, auto-dream, and auto-resource job results to the inbox |
| `auto_memory_interval` | int \| null | `5` | Auto memory every N user queries. `None` or `<= 0` disables periodic auto memory |
| `dream_cron_enabled` | bool | `true` | Whether to enable the scheduled dream-based memory optimization job |
| `dream_cron` | string | `"0 23 * * *"` | Valid 5-field cron expression for dream-based memory optimization (required when enabled); scheduled runs start after a random delay of 0–60 seconds to avoid simultaneous calls |
| `auto_memory_search_config` | object | _(see below)_ | Auto memory search configuration |
| `embedding_model_config` | object | _(see below)_ | Embedding model configuration |
> `rebuild_memory_index_on_start` is no longer supported. Rebuild an index only when needed from the Console or the
> maintenance API described in [Rebuilding the Memory Search Index](./memory#rebuilding-the-memory-search-index).
**Auto Memory Search Configuration (`reme_light_memory_config.auto_memory_search_config` object):**
| Field | Type | Default | Description |
| ------------- | ---- | ------- | -------------------------------------------------------- |
| `enabled` | bool | `false` | Whether to auto search memory on every conversation turn |
| `max_results` | int | `2` | Maximum results for auto memory search |
**Embedding Configuration (`reme_light_memory_config.embedding_model_config` object):**
| Field | Type | Default | Description |
| ------------------ | ------ | ---------- | ---------------------------------------------------------------------------------------------- |
| `backend` | string | `"openai"` | Embedding backend type: `openai`, `dashscope`, `dashscope_multimodal`, `gemini`, `ollama` |
| `api_key` | string | `""` | API key for the embedding provider. Required for OpenAI-compatible and Gemini backends |
| `base_url` | string | `""` | Optional custom API URL for OpenAI-compatible backends. For Ollama, this is passed as the host |
| `model_name` | string | `""` | Embedding model name (e.g., `"text-embedding-3-small"`) |
| `dimensions` | int | `1024` | Embedding vector dimensions |
| `enable_cache` | bool | `true` | Whether to enable embedding cache |
| `use_dimensions` | bool | `false` | Whether to use custom dimensions |
| `max_cache_size` | int | `10000` | Maximum cache size |
| `max_input_length` | int | `8192` | Maximum input length for embeddings |
| `max_batch_size` | int | `10` | Maximum batch size for batch processing |
Vector retrieval is enabled only when the selected backend has the minimum runnable configuration. These conditions are aligned with AgentScope credential requirements:
| Backend | Enable condition | Credential mapping |
| ----------------------------------------------- | --------------------------------------------- | ------------------------------- |
| `openai` / `dashscope` / `dashscope_multimodal` | Both `model_name` and `api_key` are non-empty | `api_key`; optional `base_url` |
| `gemini` | Both `model_name` and `api_key` are non-empty | `api_key` |
| `ollama` | `model_name` is non-empty | optional `host` from `base_url` |
When the enable condition is not met, ReMe still keeps keyword indexes and wikilink graph indexes, but the embedding vector index is disabled.
These settings can also be changed in the Console under **Agent → Runtime Config**. Fields read directly from
`agent.json`, such as auto-memory cadence and auto-search limits, apply to later turns after saving. Embedded ReMe
component settings, such as directories and embedding configuration, require restarting the agent process so the ReMe
application is constructed with the new configuration.
---
#### `language` & `system_prompt_files` — Persona file configuration
| Field | Type | Default | Description |
| --------------------- | ------------- | ---------------------------------------- | ----------------------------------------------- |
| `language` | string | `"zh"` | Agent language (`zh` / `en` / `ru`) |
| `system_prompt_files` | array[string] | `["AGENTS.md", "SOUL.md", "PROFILE.md"]` | List of persona files loaded into system prompt |
**Persona files** define agent behavior and personality, stored in the workspace directory. You can:
- Manage persona files in the Console's **Agent → Workspace** page (edit, enable/disable, reorder)
- Directly edit the `system_prompt_files` array to control which files are loaded
- Switch language in the Console's **Agent → Runtime Config** page (overwrites existing persona files)
**Detailed explanation:** See [Agent Persona](./persona) documentation.
---
#### `user_timezone` — User timezone
| Field | Type | Default | Description |
| --------------- | ------ | ------------------- | ---------------------------------------------------------------------------------------------------------------------- |
| `user_timezone` | string | _(system timezone)_ | IANA timezone name (e.g. `"Asia/Shanghai"`, `"America/New_York"`). Defaults to the system timezone detected at startup |
This timezone is used for:
- Displaying the current time in the agent's system prompt
- The `get_current_time` tool
- Default timezone for new cron jobs (CLI and console)
- Heartbeat active hours evaluation
You can also change it via the Console (Agent → Runtime Config).
---
#### `active_model` — Current model in use
Specifies the model used by this agent.
| Field | Type | Default | Description |
| ------------- | ------ | ------- | --------------------------------------------------- |
| `provider_id` | string | `""` | Model provider ID (e.g., `"dashscope"`, `"openai"`) |
| `model` | string | `""` | Model name (e.g., `"qwen-max"`, `"gpt-4"`) |
When `null`, uses the global default model. Can be configured in Console (Agent → Model Settings).
---
#### `approval_level` — Tool execution security level
| Field | Type | Default | Description |
| ---------------- | ------ | -------- | ----------------------------------------------------------------------------------------------- |
| `approval_level` | string | `"AUTO"` | Tool execution security level: `STRICT`, `SMART`, `AUTO`, or `OFF`. See [Security](./security). |
---
#### `tools` — Tool configuration
Controls the built-in tools available to the agent. Each tool can be individually enabled/disabled, configured whether to show to users, and whether to execute asynchronously.
> **Complete configuration:** Detailed field structure, configuration examples, etc. for tools are documented in [MCP & Built-in Tools](./mcp).
Management: Console (Agent → Tool Config) or directly edit `agent.json`.
---
#### `security` — Security configuration
Contains three protection modules:
- **`tool_guard`** — Tool guard (runtime detection of dangerous commands and injection attacks)
- **`file_guard`** — File guard (protects sensitive file access)
- **`skill_scanner`** — Skill scanner (scans for malicious code before enabling skills)
Top-level field:
| Field | Type | Default | Description |
| --------------------- | -------- | ---------------------- | ------------------------------------------------------------------------------ |
| `allow_no_auth_hosts` | string[] | `["127.0.0.1", "::1"]` | IP whitelist that bypasses web authentication. Localhost is allowed by default |
> **Complete configuration:** Detailed field descriptions, security rules, custom rule configuration, etc. for each module are documented in [Security](./security).
Management: Console (Settings → Security Config) or directly edit `agent.json`.
---
#### `last_dispatch` — Last message dispatch target
Records the last user message source, used for sending messages when heartbeat `target = "last"`.
| Field | Type | Default | Description |
| ------------ | ------ | ------- | --------------------------------------------- |
| `channel` | string | `""` | Channel name (e.g. `"discord"`, `"dingtalk"`) |
| `user_id` | string | `""` | User ID in that channel |
| `session_id` | string | `""` | Session/conversation ID |
Auto-updated; no manual configuration needed.
---
## Model Providers
QwenPaw needs an LLM provider to work. You can set it up in three ways:
- **`qwenpaw init`** — interactive wizard, the easiest way
- **Console UI** — in Settings → Models page
- **API** — `PUT /providers/{id}` and `PUT /providers/active_llm`
**Built-in providers:**
| Provider | ID | Default Base URL | API Key Prefix |
| ---------------------------------- | ------------------------ | --------------------------------------------------- | -------------- |
| QwenPaw Local | `qwenpaw-local` | _(local)_ | _(none)_ |
| Ollama | `ollama` | `http://localhost:11434` | _(none)_ |
| LM Studio | `lmstudio` | `http://localhost:1234/v1` | _(none)_ |
| OpenRouter | `openrouter` | `https://openrouter.ai/api/v1` | `sk-or-v1-` |
| ModelScope | `modelscope` | `https://api-inference.modelscope.cn/v1` | `ms` |
| DashScope | `dashscope` | `https://dashscope.aliyuncs.com/compatible-mode/v1` | `sk` |
| Aliyun Coding Plan (China) | `aliyun-codingplan` | `https://coding.dashscope.aliyuncs.com/v1` | `sk-sp` |
| Aliyun Coding Plan (International) | `aliyun-codingplan-intl` | `https://coding-intl.dashscope.aliyuncs.com/v1` | `sk-sp` |
| OpenAI | `openai` | `https://api.openai.com/v1` | _(any)_ |
| Azure OpenAI | `azure-openai` | _(you set it)_ | _(any)_ |
| Anthropic | `anthropic` | `https://api.anthropic.com` | _(any)_ |
| Google Gemini | `gemini` | `https://generativelanguage.googleapis.com` | _(any)_ |
| DeepSeek | `deepseek` | `https://api.deepseek.com` | `sk-` |
| Kimi (China) | `kimi-cn` | `https://api.moonshot.cn/v1` | _(any)_ |
| Kimi (International) | `kimi-intl` | `https://api.moonshot.ai/v1` | _(any)_ |
| MiniMax (China) | `minimax-cn` | `https://api.minimaxi.com/anthropic` | _(any)_ |
| MiniMax (International) | `minimax` | `https://api.minimax.io/anthropic` | _(any)_ |
| Zhipu (BigModel) | `zhipu-cn` | `https://open.bigmodel.cn/api/paas/v4` | _(any)_ |
| Zhipu Coding Plan (BigModel) | `zhipu-cn-codingplan` | `https://open.bigmodel.cn/api/coding/paas/v4` | _(any)_ |
| Zhipu (Z.AI) | `zhipu-intl` | `https://api.z.ai/api/paas/v4` | _(any)_ |
| Zhipu Coding Plan (Z.AI) | `zhipu-intl-codingplan` | `https://api.z.ai/api/coding/paas/v4` | _(any)_ |
| OpenCode | `opencode` | `https://opencode.ai/zen/v1` | _(any)_ |
| SiliconFlow (China) | `siliconflow-cn` | `https://api.siliconflow.cn/v1` | `sk-` |
| SiliconFlow (International) | `siliconflow-intl` | `https://api.siliconflow.com/v1` | `sk-` |
| Custom | `custom` | _(you set it)_ | _(any)_ |
For each provider you need to set:
| Setting | Description |
| ---------- | ------------------------------------------------ |
| `base_url` | API base URL (pre-filled for built-in providers) |
| `api_key` | Your API key |
Then choose which provider + model to activate:
| Setting | Description |
| ------------- | ---------------------------------------- |
| `provider_id` | Which provider to use (e.g. `dashscope`) |
| `model` | Which model to use (e.g. `qwen3-max`) |
> **Tip:** Run `qwenpaw init` and follow the prompts — it will list available
> models for each provider so you can pick one directly.
>
> **Note:** You are responsible for ensuring the API key and base URL are valid.
> QwenPaw does not verify whether the key is correct or has sufficient quota —
> make sure the chosen provider and model are accessible.
---
## Tool Environment Variables
Some tools and MCP services need extra API keys (e.g. `TAVILY_API_KEY` for web search). You can
manage them in three ways:
- **`qwenpaw init`** — prompts "Configure environment variables?" during setup
- **Console UI** — edit on the settings page
- **API** — `GET/PUT/DELETE /envs`
Set variables are auto-loaded at app startup, so all tools and child processes
can read them via `os.environ`.
> **Note:** You are responsible for ensuring the values (e.g. third-party API
> keys) are valid. QwenPaw only stores and injects them — it does not verify
> correctness.
---
## Skills
Skills extend the agent's capabilities. Skill files are distributed across two locations:
| Directory | Purpose |
| ------------------------------------------ | ------------------------------------------------- |
| `~/.qwenpaw/skill_pool/` | Local shared pool for built-ins and shared skills |
| `~/.qwenpaw/workspaces/{agent_id}/skills/` | Skills present in a specific agent's workspace |
Each skill is a directory with a `SKILL.md` file (YAML front matter with `name` and `description`), and optional `references/` and `scripts/` subdirectories.
Skill enabled state and configuration are controlled by `~/.qwenpaw/workspaces/{agent_id}/skill.json`.
**Manage skills via:**
- Console (Agent → Skills) — Visual management, import, create, enable/disable
- `qwenpaw init` (choose all / none / custom during setup)
- `qwenpaw skills config` (interactive toggle)
See [Skills](./skills) for detailed documentation.
---
## Memory
QwenPaw has persistent cross-conversation memory: it automatically compresses context and saves key information to Markdown files for long-term retention.
Memory files are stored in the agent workspace:
| File / Directory | Purpose |
| ------------------------------------------------------- | --------------------------------------------------------------------- |
| `~/.qwenpaw/workspaces/{agent_id}/MEMORY.md` | Long-lived key information (decisions, preferences, persistent facts) |
| `~/.qwenpaw/workspaces/{agent_id}/memory/YYYY-MM-DD.md` | Daily logs (notes, runtime context, auto-generated summaries) |
### Embedding Configuration
Memory search relies on vector embeddings for semantic retrieval.
Configure embeddings in `agent.json` under `running.reme_light_memory_config.embedding_model_config`, which supports backend selection and parameters such as `use_dimensions`:
> The vector-search enable condition is aligned with AgentScope credential requirements: OpenAI-compatible and Gemini backends require `model_name` plus `api_key`; Ollama only requires `model_name`. `base_url` is optional for OpenAI-compatible endpoints and is used as Ollama `host` when set. See [Memory](./memory#embedding-configuration-optional) for full configuration details.
---
## Summary
- Everything lives under **`~/.qwenpaw`** by default; override with `QWENPAW_WORKING_DIR` (and related env vars) if needed.
- From **v0.1.0**, configuration is split into:
- **Global config** (`~/.qwenpaw/config.json`) — providers, environment variables, agent list
- **Agent config** (`~/.qwenpaw/workspaces/{agent_id}/agent.json`) — per-agent settings
- Daily management is primarily done through the **Console**, or by directly editing configuration files.
- Agent personality is defined by Markdown files in the workspace directory. See [Agent Persona](./persona) for details.
- LLM providers are globally configured via `qwenpaw init` or the Console.
- Config changes are **auto-reloaded** without restart (polled every 2 seconds).
- Call the Agent API: **POST** `/api/console/chat` with JSON body, SSE streaming; see [Quick start — Verify install](./quickstart#verify-install-optional) for examples.
---
## Related pages
- [Introduction](./intro) — What the project can do
- [Agent Persona](./persona) — Detailed explanation and management of persona files
- [Channels](./channels) — How to configure messaging channels
- [Heartbeat](./heartbeat) — Heartbeat configuration
- [Multi-Agent](./multi-agent) — Multi-agent setup, management, and collaboration
- [Memory](./memory) — Memory system details
- [Skills](./skills) — Skills system details