XiaoClaw: AI Voice Assistant with Local Agent Brain

ESP32-S3 AI Voice Assistant — Voice I/O + Local LLM Agent

🌐 Official Website

Introduction

XiaoClaw is a unified ESP32-S3 firmware that combines voice interaction with a local AI Agent brain. It integrates:

xiaozhi-esp32 — Voice I/O layer: audio recording, playback, wake word detection, display, and network communication
mimiclaw — Agent brain: LLM inference, tool calling, memory management, autonomous task execution

Core Features:

Voice I/O with local wake word detection
Local LLM inference with ReAct agent loop
Self-learning: Multi-step tasks are automatically crystallized into reusable skills
Skill system with L0-L4 memory hierarchy
Cron scheduler for autonomous tasks
MCP client for dynamic remote tools

All running on a single ESP32-S3 chip with 32MB Flash and 8MB PSRAM.

graph TB



    subgraph Firmware["<b>🏗️ XiaoClaw Firmware</b>"]

        subgraph VoiceIO["<b>🎤 Voice I/O Layer</b><br/><sub>xiaozhi</sub>"]
            direction TB
            A["👂 Wake Word"]
            B["📝 ASR Server"]
            C["🔊 TTS Playback"]
            D["📺 Display"]
            E["📡 WiFi"]
            A --> B --> C
            B -.-> D
            B -.-> E
        end

        subgraph Bridge["<b>🌉 Bridge Layer</b>"]
            direction TB
            BR["📥 Input"] --> BC["⚙️ Route"] --> BG["📤 Output"]
        end

        subgraph Agent["<b>🧠 Agent Brain</b><br/><sub>mimiclaw</sub>"]
            direction TB
            F["🤖 LLM API"]
            G["🔧 Tool Calling"]
            H["💾 Memory"]
            I["📋 Session"]
            J["⏰ Cron"]
            K["🌐 Search"]
            F --> G
            F --> H
            F --> I
            F --> J
            F --> K
        end
    end

    VoiceIO -->|"Text"| Bridge -->|"Command"| Agent
    Agent -.->|"Response"| Bridge

    style Firmware fill:#f8f9fa,stroke:#495057,stroke-width:4px,radius:20px
    style VoiceIO fill:#e3f2fd,stroke:#1565c0,stroke-width:3px,radius:15px
    style Bridge fill:#fff8e1,stroke:#f57c00,stroke-width:4px,radius:15px
    style Agent fill:#f3e5f5,stroke:#7b1fa2,stroke-width:3px,radius:15px
    style A fill:#1976d2,stroke:#0d47a1,stroke-width:2px,color:#fff
    style B fill:#1565c0,stroke:#0d47a1,stroke-width:2px,color:#fff
    style C fill:#1976d2,stroke:#0d47a1,stroke-width:2px,color:#fff
    style D fill:#42a5f5,stroke:#1565c0,stroke-width:2px,color:#fff
    style E fill:#42a5f5,stroke:#1565c0,stroke-width:2px,color:#fff
    style F fill:#7b1fa2,stroke:#4a148c,stroke-width:2px,color:#fff
    style G fill:#9c27b0,stroke:#6a1b9a,stroke-width:2px,color:#fff
    style H fill:#ab47bc,stroke:#7b1fa2,stroke-width:2px,color:#fff
    style I fill:#ba68c8,stroke:#8e24aa,stroke-width:2px,color:#fff
    style J fill:#7b1fa2,stroke:#4a148c,stroke-width:2px,color:#fff
    style K fill:#9575cd,stroke:#7b1fa2,stroke-width:2px,color:#fff
    style BR fill:#ff9800,stroke:#f57c00,stroke-width:2px,color:#fff
    style BC fill:#ffa726,stroke:#fb8c00,stroke-width:2px,color:#fff
    style BG fill:#ff9800,stroke:#f57c00,stroke-width:2px,color:#fff

Features

Voice I/O Layer (xiaozhi)

Offline wake word detection (ESP-SR)
Streaming ASR + TTS via server connection
OPUS audio codec
OLED / LCD display with emoji support
Battery and power management
Multi-language support (Chinese, English, Japanese)
WebSocket / MQTT protocol support

Agent Brain Layer (mimiclaw)

LLM API integration (Anthropic Claude / OpenAI GPT)
Modular ReAct agent loop with AgentRunner execution engine
Hook system for iteration/tool callbacks (before_iteration, after_iteration, on_tool_result, before_tool_execute)
Checkpoint system for crash recovery
Context Builder with modular system prompt construction
Session consolidation with automatic history compression
Long-term memory (SPIFFS-based)
Session management with cursor-based history tracking
Cron scheduler for autonomous tasks
Web search capability (Tavily / Brave)

Hardware Requirements

ESP32-S3 development board
32MB Flash (minimum 16MB)
8MB PSRAM (Octal PSRAM recommended)
Audio codec with microphone and speaker
Optional: LCD/OLED display

Supported Boards

XiaoClaw inherits board support from xiaozhi-esp32, including:

ESP32-S3-BOX3
M5Stack CoreS3 / AtomS3R
LiChuang ESP32-S3 Development Board
LILYGO T-Circle-S3
And 70+ more boards...

Quick Start

Prerequisites

ESP-IDF v5.5 or later
Python 3.10+
CMake 3.16+

Build

# Clone the repository
git clone https://github.com/your-repo/xiaoclaw.git
cd xiaoclaw

# Set target
idf.py set-target esp32s3

# Configure (optional)
idf.py menuconfig

# Build
idf.py build

Flash

# Flash and monitor
idf.py -p PORT flash monitor

# Flash app only (skip SPIFFS to preserve data)
esptool.py -p PORT write_flash 0x20000 ./build/xiaozhi.bin

Configuration

Configure via idf.py menuconfig under Xiaozhi Assistant → Secret Configuration:

Option	Description
`CONFIG_MIMI_SECRET_WIFI_SSID`	WiFi network name
`CONFIG_MIMI_SECRET_WIFI_PASS`	WiFi password
`CONFIG_MIMI_SECRET_API_KEY`	LLM API key
`CONFIG_MIMI_SECRET_MODEL_PROVIDER`	Model provider: `anthropic` or `openai`
`CONFIG_MIMI_SECRET_MODEL`	Model name (e.g., `MiniMax-M2.5`, `claude-opus-4-5`)
`CONFIG_MIMI_SECRET_OPENAI_API_URL`	OpenAI compatible API URL
`CONFIG_MIMI_SECRET_ANTHROPIC_API_URL`	Anthropic API URL (optional)
`CONFIG_MIMI_SECRET_SEARCH_KEY`	Brave Search API key (optional)
`CONFIG_MIMI_SECRET_TAVILY_KEY`	Tavily Search API key (optional)

Example: Alibaba Cloud Coding+ (通义灵码):

CONFIG_MIMI_SECRET_MODEL_PROVIDER="openai"
CONFIG_MIMI_SECRET_MODEL="MiniMax-M2.5"
CONFIG_MIMI_SECRET_OPENAI_API_URL="https://coding.dashscope.aliyuncs.com/v1/chat/completions"
CONFIG_MIMI_SECRET_API_KEY="your-api-key"

Architecture

Bridge Layer

The bridge layer connects the voice I/O layer with the agent brain:

flowchart TB



    subgraph Voice["<b>🔊 Voice Input Layer</b>"]

        A["🎤 User Voice"] --> B["👂 Wake Word"]
        B --> C["📝 ASR Server"]
        C --> D["📄 Text Output"]
    end

    subgraph Bridge["<b>🌉 Bridge Layer</b>"]

        E["📥 Receive"] --> F["⚙️ Route"] --> G["📤 Send"]
    end

    subgraph Agent["<b>🤖 Agent Brain</b>"]

        H["🧠 LLM Inference"]
        I["🔧 Tool Calling"]
        J["📋 Response"]
        K["💾 Memory"]
        H --> I
        H --> K
        I --> J
    end

    subgraph TTS["<b>🔊 Voice Output Layer</b>"]

        L["📝 TTS Synth"] --> M["🔊 Playback"] --> N["🎵 Speaker"]
    end

    D -->|"Text"| E
    G -->|"Command"| H
    J -->|"Text"| G
    G -->|"Text"| L

    style Voice fill:#e3f2fd,stroke:#1565c0,stroke-width:3px,radius:15px
    style Bridge fill:#fff8e1,stroke:#f57c00,stroke-width:4px,radius:15px
    style Agent fill:#f3e5f5,stroke:#7b1fa2,stroke-width:3px,radius:15px
    style TTS fill:#e8f5e9,stroke:#388e3c,stroke-width:3px,radius:15px
    style A fill:#1976d2,stroke:#0d47a1,color:#fff
    style B fill:#1565c0,stroke:#0d47a1,color:#fff
    style C fill:#1976d2,stroke:#0d47a1,color:#fff
    style D fill:#42a5f5,stroke:#1565c0,color:#fff
    style E fill:#f57c00,stroke:#e65100,color:#fff
    style F fill:#ff9800,stroke:#f57c00,color:#fff
    style G fill:#f57c00,stroke:#e65100,color:#fff
    style H fill:#7b1fa2,stroke:#4a148c,color:#fff
    style I fill:#9c27b0,stroke:#6a1b9a,color:#fff
    style J fill:#ab47bc,stroke:#7b1fa2,color:#fff
    style K fill:#ba68c8,stroke:#8e24aa,color:#fff
    style L fill:#388e3c,stroke:#1b5e20,color:#fff
    style M fill:#43a047,stroke:#2e7d32,color:#fff
    style N fill:#66bb6a,stroke:#388e3c,color:#fff

Memory Layout

Partition	Size	Purpose
nvs	32KB	Non-volatile storage
otadata	8KB	OTA data
phy_init	4KB	Physical init data
ota_0	5MB	Main firmware
ota_1	5MB	OTA backup
assets	5MB	Model assets (wake word, etc.)
model	5MB	AI model storage
fatfs	~12MB	Memory, sessions, skills

Task Layout

Task	Core	Priority	Stack	Function
agent_loop	1	6	24KB	LLM processing
tg_poll	0	5	12KB	Telegram bot
feishu_ws	0	5	12KB	Feishu bot
cron	any	4	8KB	Cron scheduler

Tools

The agent can use various tools:

Tool	Description
`web_search`	Search the web for current information
`get_datetime`	Get current date/time
`get_unix_timestamp`	Get current unix timestamp
`lua_eval`	Execute a Lua code string directly
`lua_run`	Execute a Lua script from FATFS
`mcp_connect`	Connect to an MCP server
`mcp_disconnect`	Disconnect from MCP server
`mcp_server.tools_list`	List available remote tools
`mcp_server.tools_call`	Call a remote tool by name
`cron_add`	Schedule a task
`cron_list`	List scheduled tasks
`cron_remove`	Remove a scheduled task
`read_file`	Read file from FATFS
`write_file`	Write file to FATFS
`edit_file`	Edit file (find-and-replace)
`list_dir`	List files in directory

MCP Client (Dynamic Remote Tools)

XiaoClaw supports connecting to remote MCP servers to dynamically discover and call tools.

Configuration:

Kconfig (recommended): Set CONFIG_MIMI_MCP_REMOTE_HOST, CONFIG_MIMI_MCP_REMOTE_PORT, etc. in main/Kconfig.projbuild
SKILL.md (legacy fallback): /fatfs/skills/mcp-servers/SKILL.md

Kconfig Options:

Option	Description	Default
`CONFIG_MIMI_MCP_CLIENT_ENABLE`	Enable MCP client	n
`CONFIG_MIMI_MCP_REMOTE_HOST`	Server hostname/IP	""
`CONFIG_MIMI_MCP_REMOTE_PORT`	Server port	8080
`CONFIG_MIMI_MCP_REMOTE_EP`	HTTP endpoint name	mcp_server
`CONFIG_MIMI_MCP_TIMEOUT_MS`	Tool call timeout	10000

Available tools:

Tool	Description
`mcp_connect`	Connect to an MCP server by name
`mcp_disconnect`	Disconnect from current server
`mcp_server.tools_list`	List available remote tools
`mcp_server.tools_call`	Call a remote tool by name

Python MCP Server Example: scripts/mcp_server.py

pip install "mcp[cli]"
python scripts/mcp_server.py --port 8000

Remote tools are registered with the actual tool names after connection.

mcp-servers SKILL.md (/fatfs/skills/mcp-servers/SKILL.md):

---
name: mcp-servers
description: Connect to MCP servers and use remote tools
always: true
---

How it works:

Configure server via Kconfig or SKILL.md
Use mcp_connect with {"server_name": "default"} to connect
Use mcp_server.tools_list to discover available tools
Use mcp_server.tools_call to execute remote tools

Lua Scripting

XiaoClaw supports Lua scripting for custom logic and HTTP requests. Scripts are stored in /fatfs/lua/ directory.

Built-in functions:

Function	Description
`print(...)`	Print output to log
`http_get(url)`	HTTP GET request, returns `response, status`
`http_post(url, body, content_type)`	HTTP POST request
`http_put(url, body, content_type)`	HTTP PUT request
`http_delete(url)`	HTTP DELETE request

Example script: /fatfs/lua/hello.lua

local greeting = "Hello from Lua!"
local timestamp = os.time()
return string.format("%s (timestamp: %d)", greeting, timestamp)

Example HTTP script: /fatfs/lua/http_example.lua

local response, status = http_get("https://example.com")
print("Status:", status)
print("Response:", response)

Scripts can return values which are serialized as JSON and returned to the agent.

Memory System

XiaoClaw stores data in plain text files on FATFS with session consolidation support:

Path	Purpose
`/fatfs/config/SOUL.md`	AI personality
`/fatfs/config/USER.md`	User info
`/fatfs/memory/MEMORY.md`	Long-term memory (L2)
`/fatfs/memory/skill_index.json`	Skill index (L1)
`/fatfs/skills/auto/`	Auto-crystallized skills (L3)
`/fatfs/sessions/`	Sessions + archive (L4)

Memory Hierarchy (L0-L4)

Session Management

Cursor-based tracking: Each session tracks read position via cursor for efficient history traversal
Consolidation: When session exceeds max_history (default: 50) messages, oldest consolidate_batch (default: 20) messages are archived to /fatfs/sessions/
LRU cache: Active sessions cached in memory (max 8 sessions) for fast access
Checkpoint recovery: Agent can resume from last checkpoint on crash

Skills System

Skills are loaded from /fatfs/skills/ directory with YAML frontmatter support.

Directory Structure:

/fatfs/skills/
├── lua-scripts/SKILL.md      # Manual skill
├── mcp-servers/SKILL.md      # Manual skill (always=true)
└── auto/                     # Auto-crystallized skills
    └── auto_<name>_<hash>/SKILL.md

Skill Index (L1)

Skill metadata is stored in /fatfs/memory/skill_index.json:

{
  "skills": [
    {
      "name": "auto_light_ctrl_a3f2_7d2e",
      "path": "/fatfs/skills/auto/auto_light_ctrl_a3f2_7d2e/SKILL.md",
      "usage_count": 5,
      "success_rate": 0.8,
      "last_used": 1745678901
    }
  ],
  "last_updated": 1745678901
}

Field	Description
`name`	Skill identifier
`path`	Full path to SKILL.md
`usage_count`	Number of times used
`success_rate`	Calculated success rate
`last_used`	Unix timestamp of last use

Auto-Crystallization (L3)

When a multi-step task succeeds, the system automatically creates a skill.

Crystallization Conditions:

Task completed successfully
At least 2 tool calls required
No similar auto-skill exists

Creation Process:

Task ends successfully with 2+ tool calls
learning_hook_on_task_end() triggers crystallization
Creates /fatfs/skills/auto/auto_<intent>_<hash>/SKILL.md
Adds entry to skill_index.json

flowchart TD
    subgraph Input["<b>📥 Input</b>"]
        A["🎯 Task Completes<br/>2+ tool calls"]
    end

    subgraph Validation["<b>✅ Validation</b>"]
        direction TB
        B{Success?}
        D{"🔍 Similar skill<br/>exists?"}
    end

    subgraph Creation["<b>⚙️ Skill Creation</b>"]
        direction TB
        E["📁 Create dir"]
        F["📝 Write SKILL.md"]
        G["📋 Update index"]
    end

    subgraph Output["<b>📤 Output</b>"]
        I["✅ Skill ready"]
    end

    C["❌ Skip"]

    A --> B
    B -->|No| C
    B -->|Yes| D
    D -->|Yes| C
    D -->|No| E
    E --> F --> G --> I

    style Input fill:#e3f2fd,stroke:#1565c0,stroke-width:3px,radius:15px
    style Validation fill:#fff8e1,stroke:#f57c00,stroke-width:3px,radius:15px
    style Creation fill:#e8f5e9,stroke:#388e3c,stroke-width:3px,radius:15px
    style Output fill:#f3e5f5,stroke:#7b1fa2,stroke-width:3px,radius:15px
    style A fill:#1976d2,stroke:#0d47a1,stroke-width:2px,color:#fff
    style B fill:#ff9800,stroke:#f57c00,stroke-width:2px,color:#fff
    style C fill:#ef5350,stroke:#c62828,stroke-width:2px,color:#fff
    style D fill:#ffa726,stroke:#fb8c00,stroke-width:2px,color:#fff
    style E fill:#42a5f5,stroke:#1565c0,stroke-width:2px,color:#fff
    style F fill:#66bb6a,stroke:#388e3c,stroke-width:2px,color:#fff
    style G fill:#26a69a,stroke:#00796b,stroke-width:2px,color:#fff
    style I fill:#7b1fa2,stroke:#4a148c,stroke-width:2px,color:#fff

Auto-Skills:

All auto-skills in /fatfs/skills/auto/ are available for use
Auto-skills with higher usage_count are more frequently used
Skills can be invoked by matching their Tool Sequence

flowchart LR
    subgraph Trigger["<b>🔧 Trigger</b>"]
        A["Tool call"]
    end

    subgraph Match["<b>🔗 Match</b>"]
        B{"Match<br/>Tool Sequence?"}
    end

    subgraph Track["<b>📈 Tracking</b>"]
        C["Record usage"]
        D["usage_count++"]
    end

    Z["➖ No tracking"]

    A --> B
    B -->|No| Z
    B -->|Yes| C --> D

    style Trigger fill:#f3e5f5,stroke:#7b1fa2,stroke-width:3px,radius:15px
    style Match fill:#fff8e1,stroke:#f57c00,stroke-width:3px,radius:15px
    style Track fill:#e8f5e9,stroke:#388e3c,stroke-width:3px,radius:15px
    style A fill:#7b1fa2,stroke:#4a148c,stroke-width:2px,color:#fff
    style B fill:#ff9800,stroke:#f57c00,stroke-width:2px,color:#fff
    style C fill:#66bb6a,stroke:#388e3c,stroke-width:2px,color:#fff
    style D fill:#26a69a,stroke:#00796b,stroke-width:2px,color:#fff
    style Z fill:#ef5350,stroke:#c62828,stroke-width:2px,color:#fff

Note: When a tool call matches an auto-skill's Tool Sequence pattern, that skill's usage_count is incremented.

Frontmatter Format

Manual Skills:

---
name: skill-name
description: Brief description of what the skill does
always: false  # true = always injected into system prompt
---
# Skill Content
...

Auto-Crystallized Skills:

---
name: auto_light_ctrl_a3f2_7d2e
description: Auto-generated skill for: turn on the bedroom light
always: false
auto: true
created_from: 3 tool calls
step_count: 3
success_rate: 1.0
---

# Auto Skill: auto_light_ctrl_a3f2_7d2e

## Intent
turn on the bedroom light

## Tool Sequence
1. tool_name({"arg": "value"})
2. another_tool({"arg": "value"})

## Pitfalls
- Auto-generated from multi-step task execution

Memory Integration (L1-L3)

Layer	Content	Storage	Notes
L1	Skill index	skill_index.json	All skills metadata
L3	Auto-skills	/skills/auto/	All auto skills available

In System Prompt:

L1: Skill index shown as "Available Skills" (names only)
L3: All auto-skills full content available
Always: Skills with always: true always injected

Development

Project Structure

xiaoclaw/
├── main/
│   ├── mimi/                  # Agent brain (from mimiclaw)
│   │   ├── agent/            # Agent loop, runner, hooks, checkpoint
│   │   │   ├── agent_loop.c      # Main agent task loop
│   │   │   ├── runner.c          # ReAct execution engine
│   │   │   ├── context_builder.c # System prompt construction
│   │   │   ├── hook.c            # Agent hooks implementation
│   │   │   ├── learning_hooks.c  # Auto-learning/crystallization hooks
│   │   │   └── checkpoint.c       # Crash recovery checkpoint
│   │   ├── bus/              # Message bus
│   │   ├── channels/         # Telegram, Feishu bot integrations
│   │   │   ├── telegram/
│   │   │   └── feishu/
│   │   ├── cron/             # Cron scheduler service
│   │   ├── gateway/          # WebSocket server
│   │   ├── heartbeat/        # Autonomous task heartbeat
│   │   ├── llm/              # LLM proxy
│   │   ├── memory/           # Memory store, session manager, consolidator
│   │   │   ├── memory_store.c    # Long-term memory
│   │   │   ├── session_manager.c # Session with cursor/consolidation
│   │   │   ├── consolidator.c    # Automatic history compression
│   │   │   └── hierarchy.c       # Memory hierarchy management
│   │   ├── ota/              # OTA updates
│   │   ├── proxy/            # HTTP proxy
│   │   ├── skills/           # Skill loader
│   │   │   ├── skill_loader.c     # Skill loading (frontmatter)
│   │   │   ├── skill_meta.c       # Skill metadata
│   │   │   └── skill_crystallize.c # Auto-crystallization
│   │   ├── tools/            # Tool registry
│   │   │   ├── tool_registry.c    # Tool registration
│   │   │   ├── tool_cron.c        # Cron tools
│   │   │   ├── tool_files.c       # File operation tools
│   │   │   ├── tool_get_time.c    # Time tools
│   │   │   ├── tool_lua.c         # Lua execution tool
│   │   │   ├── tool_mcp_client.c  # MCP client tool
│   │   │   └── tool_web_search.c  # Web search tool
│   │   ├── util/             # Utilities
│   │   │   └── fatfs_util.c
│   │   ├── mimi.c/h          # Module entry
│   │   ├── mimi_config.h     # Configuration
│   │   └── mimi_secrets.h    # Secret keys
│   ├── audio/                # Voice I/O (from xiaozhi)
│   │   ├── audio_codec.cc/h
│   │   ├── audio_service.cc/h
│   │   ├── codecs/           # Audio codecs
│   │   ├── demuxer/          # Audio demuxer
│   │   ├── processors/        # Audio processors (AFE, etc.)
│   │   └── wake_words/        # Wake word detection
│   ├── bridge/               # Bridge layer (voice ↔ Agent)
│   ├── display/              # Display drivers
│   │   ├── display.cc/h
│   │   ├── lcd_display.cc/h
│   │   ├── oled_display.cc/h
│   │   ├── emote_display.cc/h
│   │   └── lvgl_display/     # LVGL graphics
│   ├── protocols/            # Communication protocols
│   │   ├── websocket_protocol.cc/h
│   │   └── mqtt_protocol.cc/h
│   ├── boards/               # Board support (70+ board configs)
│   │   ├── common/          # Common components
│   │   └── <board-name>/    # Per-board configs
│   ├── led/                 # LED control
│   ├── application.cc/h     # Main application entry
│   ├── device_state.h       # Device state
│   ├── device_state_machine.cc/h # State machine
│   ├── main.cc              # Entry point
│   ├── mcp_server.cc/h      # MCP server
│   ├── ota.cc/h             # OTA updates
│   ├── settings.cc/h         # Settings management
│   ├── system_info.cc/h      # System info
│   ├── assets.cc/h           # Assets management
│   └── idf_component.yml     # Component manifest
├── fatfs_data/               # FATFS content (flashed to /fatfs partition)
│   ├── config/
│   │   ├── SOUL.md          # AI personality definition
│   │   └── USER.md          # User information
│   ├── lua/                  # Lua scripts
│   │   ├── hello.lua
│   │   └── http_example.lua
│   ├── memory/
│   │   ├── MEMORY.md        # Long-term memory
│   │   ├── facts.json       # Facts database
│   │   └── skill_index.json # Skill index
│   ├── skills/               # Skills directory
│   │   ├── lua-scripts/
│   │   └── mcp-servers/
│   ├── HEARTBEAT.md          # Runtime heartbeat tasks
│   └── cron.json             # Cron jobs configuration
├── CMakeLists.txt
└── sdkconfig.defaults.esp32s3

Related Projects

XiaoClaw is built upon these excellent projects:

xiaozhi-esp32 — Voice interaction framework
mimiclaw — ESP32 AI agent

License

MIT License

Acknowledgments

xiaozhi-esp32 team for the voice interaction framework
mimiclaw team for the embedded AI agent architecture
Espressif for ESP-IDF and ESP-SR

Name		Name	Last commit message	Last commit date
Latest commit History 1,009 Commits
.claude		.claude
.github/workflows		.github/workflows
docs		docs
main		main
partitions		partitions
scripts		scripts
website		website
.clang-format		.clang-format
.gitignore		.gitignore
CMakeLists.txt		CMakeLists.txt
LICENSE		LICENSE
README.md		README.md
README_zh.md		README_zh.md
sdkconfig.defaults.esp32s3		sdkconfig.defaults.esp32s3

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XiaoClaw: AI Voice Assistant with Local Agent Brain

Introduction

Features

Voice I/O Layer (xiaozhi)

Agent Brain Layer (mimiclaw)

Hardware Requirements

Supported Boards

Quick Start

Prerequisites

Build

Flash

Configuration

Architecture

Bridge Layer

Memory Layout

Task Layout

Tools

MCP Client (Dynamic Remote Tools)

Lua Scripting

Memory System

Memory Hierarchy (L0-L4)

Session Management

Skills System

Skill Index (L1)

Auto-Crystallization (L3)

Frontmatter Format

Memory Integration (L1-L3)

Development

Project Structure

Related Projects

License

Acknowledgments

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