Baize

Speak freely, keep secrets sealed

A security assistant for Vibe Coding and data analysis, inspired by a Chinese mythical beast.

An open-source Coding Agent CLI with strong privacy protection and multilingual interaction. It supports multiple backends (DeepSeek / OpenAI-compatible / Zhipu / Tongyi / Kimi / local Ollama) and provides full capabilities such as tool calling, skill loading, subagent delegation, context compression, and a secure sandbox.

Core Advantages

Privacy Protection

• Bidirectional reversible privacy protection: Sensitive information is replaced with placeholders before being sent to the LLM, and automatically restored after the response is received. The entire process is invisible to users: the history, tool parameters, and final answers you see always contain the original text, while the LLM always sees random, unguessable placeholders such as «PZ_PHONE_a3f8b2c1d4e5f6a7».
• Comprehensive redaction on disk: Session files, compressed transcripts, and audit logs are automatically redacted before being written to disk (enabled by default). Plaintext PII never appears on disk.
• Injection resistance: Placeholders use 64-bit cryptographic random tokens and cannot be enumerated, forged, or replayed. Old placeholders become invalid immediately after switching sessions.
• Multiple ways to enable privacy protection: You can enable it through natural language input in the CLI, or use the more precise /privacy command.
• Local-first agent: This product has no remote database and runs fully locally. It does not collect user information.

Features

• Multi-backend support: DeepSeek, any OpenAI-compatible endpoint (GLM / Qwen / Kimi / OpenAI), and local Ollama. Switch with one configuration change.

• Zero-configuration startup: Configuration files are generated automatically on first run. Users only need to enter their API key once.

• Privacy redaction: Automatically identifies and redacts 30+ types of sensitive information before sending to the LLM (phone numbers, emails, ID cards, passports, bank cards, international numbers, API keys, JWTs, database connection strings, MAC addresses, etc.), and restores them after receiving the response. The same redaction is applied when writing to disk, so no plaintext is left on disk. Supports standard and strict modes, switchable via natural language or the /privacy command.

• Multilingual interaction: Freely switch among nine languages: Chinese, English, Japanese, Korean, Spanish, French, German, Russian, and Arabic. Say English or use /lang ja, and the AI will think and reply in the corresponding language throughout the session.

• Complete toolchain: bash execution, file read/write/edit, glob/grep search, web search and fetch, background tasks, and task/todo management.

• Skill system (Skills): Load domain knowledge on demand (SKILL.md) so the AI can be more professional in specific scenarios.

• Subagents: Delegate complex tasks to subagents with independent contexts to avoid polluting the main session.

• Hooks: Python or Shell hooks that support pre/post tool-call interception, audit logs, automatic formatting, and test gating.

• MCP protocol: Connect to external tool servers (GitHub, Filesystem, etc.) through the Model Context Protocol.

• Context compression: Two-level compression (tool-result truncation + LLM summarization) supports very long conversations.

• Secure sandbox: Command allowlist, path-escape detection, dangerous-command blocking, sensitive-file protection, and script-injection interception.

• Black-gold themed CLI: Chinese-width adaptation, syntax highlighting, diff coloring, and collapsible thinking.

Baize CLI Interface

Installation

Prerequisites

• Python 3.10+ (requires tomllib; built in on 3.11+; on 3.10 install tomli)
• pip

Install from Source

Two download methods

1. pip install https://github.com/Xu123-Bob/Baize.git

   bash -- press Win+R, enter cmd, then run:

   baize

2. On the repository page, click <> Code --> Download ZIP.

(1) After extracting, enter the directory:

bash -- press Win+R, enter cmd

cd path\to\extracted\directory

pip install -r requirements.txt

python -m Baize

After installation, press Win+R, enter cmd, open the CLI, type baize, and run it.

(2) After downloading the ZIP, install locally. Extract it, enter the directory, and run:

bash -- press Win+R, enter cmd

pip install .

After installation, press Win+R, enter cmd, open the CLI, type baize, and run it.

Optional: Install Data Access Dependencies (Important for Data Analysis)

If you need Baize to drive SPSS or SQL databases, install the additional dependencies:

pip install -r requirements-data.txt

requirements-data.txt includes:

• spss-studio-mcp: SPSS statistical analysis MCP server (requires IBM SPSS Statistics installed locally)
• atengk-mcp-server-rdbms: General relational database MCP server (PostgreSQL / MySQL / SQL Server / Oracle / Dameng, etc.)
• pyodbc: ODBC Python binding required by SQL Server

Alternatively, if the project is installed via pyproject.toml, you can use extras:

pip install -e ".[data]"          # Install all data access dependencies
pip install -e ".[spss]"          # SPSS only
pip install -e ".[sql]"           # General SQL only
pip install -e ".[sql-mssql]"     # SQL Server only (includes pyodbc)

• ⚠️ SQL Server users: pyodbc is only the Python binding. You also need Microsoft ODBC Driver 18 for SQL Server installed at the system level.
• ⚠️ SPSS users: spss-studio-mcp is only an MCP bridge. IBM SPSS Statistics (versions 20–31) must be installed locally and properly licensed, and the SPSS_INSTALL_PATH environment variable must point to the SPSS installation directory. Full statistical analysis with the SPSS engine is mainly supported on Windows. On Linux/macOS, it can fall back to "file mode" (read .sav files, inspect metadata, preview data, but no statistical analysis).

Quick Start

Run with an LLM API

1. First run

baize

On first run, Baize automatically generates two configuration files:

~/.baize/config.toml   # Backend configuration (choose DeepSeek / OpenAI / Ollama)

~/.baize/.env          # Key file

On Windows, the path is C:\Users\YourUserName\.baize\.

2. Choose a backend

Open ~/.baize/config.toml and modify active_provider (this step is required):

# Baize configuration file
# Modify active_provider to switch backends
# Possible values: "deepseek" / "qwen" / "kimi" / "glm" / "openai" / "ollama"

active_provider = "deepseek"

[model_providers.deepseek]
name = "DeepSeek"
base_url = "https://api.deepseek.com"
env_key = "DEEPSEEK_API_KEY"
model = "deepseek-flash"

[model_providers.qwen]
name = "Qwen"
base_url = "https://dashscope.aliyuncs.com/compatible-mode/v1"
env_key = "DASHSCOPE_API_KEY"
model = "qwen-plus"

[model_providers.kimi]
name = "Kimi"
base_url = "https://api.moonshot.cn/v1"
env_key = "MOONSHOT_API_KEY"
model = "kimi-k2.7-code"

[model_providers.glm]