gede 0.4.37

A powerful CLI for interacting with multiple LLM providers. Support 10+ providers with smart chat management, encryption, MCP servers, and rich tools ecosystem.

Gede

🚀 A powerful and feature-rich CLI for interacting with multiple LLM providers

Gede is a powerful command-line interface that seamlessly integrates with multiple LLM providers including OpenAI, Anthropic, and DeepSeek. It features local chat history management, built-in tool calling capabilities, and MCP (Model Context Protocol) integration for enhanced AI interactions.

Features

• 🤖 Multi-Provider Support: OpenAI, Anthropic, DeepSeek, Qwen, Baidu, OpenRouter, Moonshot, Ollama, and more
• 💬 Chat Management: Create public, private (encrypted), and cloned conversations
• 🛠️ Rich Tools Ecosystem: Built-in web search, URL reading, and custom tools
• 🔌 MCP Server Integration: Connect to Model Context Protocol servers
• 📦 Profile Support: Manage multiple configurations with profiles
• 🌐 Web Search: Enable AI model's built-in web search capability
• 🖥️ API Server: Built-in HTTP API server for GUI client integration

Quick Start

Prerequisites

• Python 3.10 or higher
• uv package manager

Install

uv tool install gede

Quick Example

# Start a new chat
gede

# Or start with a specific model
gede --model openai:gpt-4o

# Start in private mode
gede --private

# Use with tools enabled
gede --tools web_search,now

Slash Commands

When using Gede, you can use slash commands to perform various operations. Type /help to see all commands, or /help KEYWORD to search for specific commands.

Chat Management

Command	Description
/new	Start a new public chat (plain text)
/new-private	Start a new private chat (password-encrypted)
/chat-info	Display current chat details (ID, title, model, message count, tools, MCP servers)
/clone-chat	Create a new chat with same settings (instruction, model, parameters)
/quit	Exit the application (unsaved private chats won't persist)

Instruction & Prompt Management

Command	Description
/set-instruction <TEXT>	Set system instruction. Use \\ for multi-line mode (Esc+Enter to submit)
/get-instruction	Display current system instruction
/select-instruction	Choose from predefined instructions in ~/.gede/instructions/
/select-prompt	Select a predefined prompt as input message from ~/.gede/prompts/

Model Settings

Command	Description
/select-llm [PROVIDER] [--no-cache]	Switch AI model. Use --no-cache to refresh model list
/set-message-num NUMBER	Control chat history length (0 = all messages)
/set-model-settings KEY VALUE	Adjust parameters: temperature (0-2), top_p (0-1), max_tokens, frequency_penalty (-2 to 2), presence_penalty (-2 to 2), reasoning_effort
/get-model-settings	Display current model parameters
/set-model-reasoning <LEVEL>	Control reasoning depth: minimal, low, medium, high, auto, or off
/set-model-web-search <on|off|auto>	Toggle web search capability

File Operations

Command	Description
/save	Save current chat. Public: auto-saved with generated title. Private: requires password
/load-chat	Load a public chat from ~/.gede/chats/public/ (interactive selection)
/load-private-chat	Load private chat from ~/.gede/chats/private/ (password required)
/export <FILEPATH>	Export chat to text file. Relative paths save to ~/.gede/chats/exports/ or specificed file path.
/search-chats	Search through all saved public chats with a real-time interactive interface (fzf-style). Type a keyword to filter by title or message content, use ↑↓ to navigate, Enter to load the selected chat.

Image Input

Gede supports attaching images to your messages via two methods:

Method	Description
Ctrl+Y	Paste an image from the clipboard (macOS). Can be used multiple times per message.
/add-image <PATH|URL>	Attach a local image file or an HTTP/HTTPS image URL to the next message.

/add-image details:

• Local file: provide an absolute or relative path (supports ~). Supported formats: jpg, jpeg, png, gif, webp. The file is read and stored as base64.
• Remote URL: provide an http:// or https:// URL. The URL is passed directly to the LLM.
• Run the command multiple times to attach several images at once.
• A confirmation line is printed after each successful addition: 🖼 Image added #N | ...
• All pending images are sent together with your next text message, then the queue is cleared.

You: /add-image ~/screenshots/diagram.png
🖼  Image added #1 | diagram.png | image/png | 128.4 KB

You: /add-image https://example.com/chart.jpg
🖼  Image added #2 | URL: https://example.com/chart.jpg

You: What do these images show?

Keyboard Shortcuts

Key	When	Action
Ctrl+Y	Waiting for input	Paste image or text from clipboard
Esc	During streaming LLM response	Interrupt the response immediately
\	At the start of input	Enter multi-line mode (submit with Esc + Enter)

Interrupting a streaming response

Press Esc at any time while the assistant is generating a response to stop it immediately. The partial output will be discarded and will not be added to the conversation history — the next message you send will start from the last complete exchange.

Tools

Command	Description
/select-tools	Enable/disable built-in tools (Space to toggle, Enter to confirm)
/select-mcp	Select enabled MCP servers for the current session

Utility

Command	Description
/cleanup	Clear terminal screen
/help [KEYWORD]	Show all commands or search by keyword

CLI Usage

Command Line Arguments

Gede supports the following command line arguments:

• --profile <profile_name>: Use specified configuration profile (default: default)
• --log-level <level>: Set log level, options: DEBUG, INFO, WARNING, ERROR, CRITICAL
• --model <provider_id:model_id>: Specify default model, e.g.: openai:gpt-4o
• --instruction <text>: Set system prompt
• --private: Start private session
• --reasoning-effort <effort>: Set reasoning mode, options: minimal, low, medium, high, off, auto
• --web-search <mode>: Enable or disable model's built-in web search, options: on, off, auto
• --tools <tool_list>: Set enabled tools list, multiple tools separated by commas, e.g.: web_search,now,read_page
• --workspace-dir <directory>: Set the initial working directory for bash and local_python_execute, and the file-access boundary for workspace-scoped tools (default: the current directory)
• --prompt <text|-> / --prompts <text|->：Run headlessly: send a prompt directly and exit. Use --prompt=- to read the prompt from stdin (pipe mode). On success, stdout contains only the assistant answer; runtime errors are written to stderr with a non-zero exit code.
• --trace: Enable trace mode for analyzing detailed execution information of agent calls. Uses Arize Phoenix if the arize-trace extra is installed, otherwise uses OpenAI's default tracing (requires OPENAI_API_KEY)
• --mcp-servers <server_list>: Set the initially selected MCP servers, separated by commas

Usage Examples

# Start with default configuration
gede

# Start with specified model
gede --model openai:gpt-4o

# Enable tools and private mode
gede --tools web_search,now --private

# Let text_editor access only this project directory
gede --tools text_editor --workspace-dir ~/projects/example

# Set reasoning mode and log level
gede --reasoning-effort high --log-level DEBUG

# Use specific profile
gede --profile my_profile

# Send a prompt directly and exit (headless)
gede --prompt="请用一句话解释量子计算"

# Pipe a prompt via stdin
echo "what is recursion?" | gede --prompt=-

Configuration

Storage

On first launch, Gede will automatically create a configuration directory at ~/.gede/ with:

• config
  • .env - Configuration file for API keys
  • mcp.json - MCP server confirugation
  • profiles.json - Profile confirugation
• chats/public/ - Public chat storage
• chats/private/ - Encrypted private chat storage
• instructions/ - Custom system instructions
• prompts/ - Predefined prompts

Gede uses environment variables to store API keys for various LLM providers. The configuration file is located at ~/.gede/config/.env. Edit this file to add your actual API keys.

Supported Providers

When you first run Gede, a default config file will be automatically created. Supported providers include:

• 302.ai: AI302_API_KEY
• OpenRouter: OPENROUTER_API_KEY
• OpenAI: OPENAI_API_KEY
• Anthropic: ANTHROPIC_API_KEY
• Baidu (ERNIE): WENXIN_API_KEY
• SiliconFlow: SILICONFLOW_API_KEY
• Aliyun (Qwen): QWEN_API_KEY
• VoiceEngine (Doubao): DOUBAO_API_KEY
• DeepSeek: DEEPSEEK_API_KEY
• Moonshot (Kimi): MOONSHOT_API_KEY

The config file also supports:

• Generate Title Model: Use specific model for chat title generation
• Phoenix Tracing: Configure observability with Arize Phoenix

Profile

Gede supports profile management to save and reuse your preferred configurations. The profile configuration file is located at ~/.gede/config/profiles.json.

Profile Structure

Each profile can contain the following settings:

• model: Default model to use (format: provider:model_id)
• instruction: System instruction/prompt
• private: Whether to start in private mode (boolean)
• tools: List of enabled tools (e.g., ["web_search", "now", "read_page"])
• mcp_servers: List of initially selected MCP servers (e.g., ["filesystem"])
• log_level: Logging level (DEBUG, INFO, WARNING, ERROR, CRITICAL)

Example Configuration

{
  "default": {
    "model": "openai:gpt-4o",
    "instruction": "You are a helpful assistant.",
    "private": false,
    "tools": ["web_search", "now", "read_page"],
    "mcp_servers": ["filesystem"],
    "log_level": "INFO"
  },
  "coding": {
    "model": "anthropic:claude-sonnet-4-20250514",
    "instruction": "You are an expert programming assistant.",
    "tools": ["web_search", "read_page"],
    "log_level": "DEBUG"
  },
  "research": {
    "model": "openai:gpt-4o",
    "instruction": "You are a research assistant specialized in finding and analyzing information.",
    "tools": ["web_search", "read_page"]
  }
}

Usage

# Use default profile
gede

# Use specific profile
gede --profile coding

# Use profile and override settings
gede --profile research --model deepseek:deepseek-reasoner

Note: Command-line arguments will override profile settings for the current session.

MCP

The MCP configuration file is located at ~/.gede/config/mcp.json. It allows you to define multiple MCP servers that Gede can connect to.

The enable field is the global availability switch. Only servers with enable: true appear in /select-mcp and can be selected. The --mcp-servers argument and a profile's mcp_servers field define the initial selection, like --tools; Gede connects those servers and caches their tool lists before showing the first prompt. Selecting another server later connects it once and reuses that connection until Gede exits. Only tools from the currently selected servers are sent to the LLM.

STDIO Server

Connects to a local process via standard input/output.

• command (required): The executable command to run.
• args (optional): List of arguments for the command.
• env (optional): Dictionary of environment variables.
• cwd (optional): Working directory for the process.
• tool_timeout (optional, default: 120): Maximum seconds to wait for one MCP tool response. Set to 0 to disable the limit.
• enable (optional, default: true): Whether this server is enabled.

Remote Server (SSE / Streamable HTTP)

Connects to a remote MCP server.

• type (required): Must be either sse or streamable-http.
• url (required): The URL of the server endpoint.
• headers (optional): Dictionary of HTTP headers.
• note (optional): Description or note for the server.
• tool_timeout (optional, default: 120): Maximum seconds to wait for one MCP tool response. Set to 0 to disable the limit.
• enable (optional, default: true): Whether this server is enabled.

Example Configuration

{
  "mcpServers": {
    "filesystem": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-filesystem",
        "/Users/username/Desktop"
      ],
      "enable": true
    },
    "remote-echo": {
      "type": "sse",
      "url": "https://example.com/mcp",
      "headers": {
        "Authorization": "Bearer YOUR_TOKEN"
      },
      "note": "My remote MCP server",
      "enable": true
    }
  }
}

Build-in Tools

Tool	Description
web_search	Search the internet using Exa AI
read_url	Read extracted text or the original HTML source from a URL
now	Get current date, time, and timezone information
bash	Execute bash shell commands on the local system (see below)
image_gpt_tool	Generate images with the GPT image model from text prompts and reference images
image_grok_tool	Generate images with the Grok image model from text prompts and reference images
speech_tool	Generate speech audio from text using Fish Audio
code_execute	Execute stateful Python code in an isolated E2B sandbox
local_python_execute	Execute approved Python locally in a managed uv environment
subagent_tool	Run up to four independent Gede headless subtasks concurrently
text_editor	View, create, and precisely edit UTF-8 text files inside a configured workspace

text_editor

The text_editor tool follows Claude's text-editor command model while remaining a regular Gede built-in tool that works with every supported provider.

Command	Required parameters	Behavior
view	path	View a file with line numbers or list one directory level; optionally accepts view_range: [start, end], where -1 means the final line
create	path, file_text	Create a new UTF-8 file; the parent directory must exist and the target must not
str_replace	path, old_str, new_str	Replace old_str only when it occurs exactly once
insert	path, insert_line, insert_text	Insert text after a line; line 0 means the start of the file

Relative paths are resolved under --workspace-dir. Absolute paths are accepted only when they remain inside that directory after symbolic-link resolution. Path traversal and symbolic links that resolve outside the workspace are rejected. Files must be valid UTF-8 text; binary files are not supported. File and directory views are truncated after 10,000 characters, and file content can be read in smaller sections with view_range.

The workspace directory must already exist. It is resolved once at startup and does not change Gede's process working directory. If the argument is omitted, the startup working directory is used. The same directory is also used as the initial cwd of the bash tool.

gede --tools text_editor --workspace-dir ~/projects/example

Example tool arguments:

{
  "command": "str_replace",
  "path": "src/app.py",
  "old_str": "debug = True",
  "new_str": "debug = False"
}

read_url

The read_url tool can extract webpage text or return the HTML source received from the server.

Parameter	Required	Description
url	Yes	URL of the webpage to read
query	No	In text mode, return only paragraphs relevant to this query; omit it to extract the full body text
output_format	No	text (default) extracts webpage text; html returns the fetched HTML source

When output_format is html, query is ignored. The result is the HTTP response source after redirects and decoding; Gede does not execute JavaScript or return a browser-rendered DOM. HTML mode also bypasses BeautifulSoup and the LLM-based text extraction step. Requests to mp.weixin.qq.com use a WeChat mobile user agent and WeChat referer for compatibility; other sites use a general desktop browser user agent without that referer.

Example tool arguments for reading HTML:

{
  "url": "https://example.com",
  "query": null,
  "output_format": "html"
}

Text extraction uses the model configured by READ_URL_MODEL in ~/.gede/config/.env, using the provider_id:model_id format. HTML mode does not require this setting.

READ_URL_MODEL="openai:gpt-4o"

Enable the tool with:

gede --tools read_url

image_gpt_tool and image_grok_tool

The image_gpt_tool and image_grok_tool use tena to generate images from a text prompt. They can also pass local file paths or HTTP/HTTPS image URLs as reference images.

Set the image model paths in ~/.gede/config/.env:

GEDE_IMAGE_GPT_MODEL_PATH="openrouter/gpt-image-2"
GEDE_IMAGE_GROK_MODEL_PATH="openrouter/x-ai/grok-2-image"
GEDE_IMAGE_ACCESS_ENDPOINT="http://localhost:9127/public/generated_images"

The selected tena model still requires its corresponding API key, such as OPENROUTER_API_KEY, ZENMUX_API_KEY, AI302_API_KEY, or GEMINI_API_KEY.

Generated images are saved under ~/.gede/data/public/generated_images/, and the tool result returns both saved file paths and HTTP URLs when GEDE_IMAGE_ACCESS_ENDPOINT is configured. If gede-server uses --base-path /api/v1, include that prefix in the endpoint, for example http://localhost:9127/api/v1/public/generated_images.

Enable:

gede --tools image_gpt_tool,image_grok_tool

speech_tool

The speech_tool uses the Fish Audio TTS API to generate an MP3 audio file from text.

Set the Fish Audio model, API key, voice reference ID, and optional public URL endpoint in ~/.gede/config/.env:

FISH_AUDIO_MODEL_ID="s2.1-pro-free"
FISH_AUDIO_API_KEY="YOUR_FISH_AUDIO_API_KEY"
FISH_AUDIO_VOICE_ID="fd8438ddf6cc41caafc5cd10ece9a4f1"
GEDE_SPEECH_ACCESS_ENDPOINT="http://localhost:9127/public/generated_audio"

Generated audio files are saved under ~/.gede/data/public/generated_audio/, and the tool result returns both the saved file path and HTTP URL when GEDE_SPEECH_ACCESS_ENDPOINT is configured. If gede-server uses --base-path /api/v1, include that prefix in the endpoint, for example http://localhost:9127/api/v1/public/generated_audio.

Enable:

gede --tools speech_tool

code_execute

The code_execute tool runs Python with E2B Code Interpreter. Variables, imports, functions, and files remain available across tool calls in the same chat while the Gede process is running. It does not expose arbitrary local files or host environment variables to the sandbox.

See the code_execute execution-flow guide for the sandbox lifecycle, attachment transfer, output-file export, and error handling details.

In API server mode, files uploaded through /chat/upload can be sent as type: "file" attachments in /chat. The model can also pass files inside the configured --workspace-dir through the optional workspace_files argument. Gede snapshots those selected workspace files, lazily uploads both input sources into the chat's sandbox, and exposes their metadata and sandbox paths as JSON in os.environ["GEDE_INPUT_FILES"]:

import json
import os
import pandas as pd

inputs = json.loads(os.environ["GEDE_INPUT_FILES"])
df = pd.read_csv(inputs[0]["path"])

For example, a tool call can upload a workspace file before running Python:

{
  "code": "import json, os, pandas as pd\ninputs = json.loads(os.environ['GEDE_INPUT_FILES'])\ndf = pd.read_excel(inputs[0]['path'])\nprint(df.shape)",
  "workspace_files": ["reports/expenses.xlsx"]
}

Workspace paths may be relative to --workspace-dir or absolute paths inside it. Missing files, directories, .. traversal, and symbolic links resolving outside the workspace are rejected. Paths name exact files; glob expansion and recursive directory upload are not supported.

Uploaded inputs are stored under /home/user/gede_inputs/<file_ref>/ in the sandbox. The same content hash is uploaded only once per sandbox, and previously registered inputs remain in the manifest for later calls in that chat.

Each execution receives a unique sandbox output directory in os.environ["GEDE_OUTPUT_DIR"]. Code should save files intended for the user there:

import os
from pathlib import Path

output_dir = Path(os.environ["GEDE_OUTPUT_DIR"])
df.to_csv(output_dir / "result.csv", index=False)
(output_dir / "summary.txt").write_text("Done")

Configure E2B in ~/.gede/config/.env:

E2B_API_KEY="e2b_..."
GEDE_E2B_SANDBOX_TIMEOUT_SECONDS="300"
GEDE_E2B_EXECUTION_TIMEOUT_SECONDS="120"
GEDE_CODE_EXECUTE_ACCESS_ENDPOINT="http://localhost:9127/public/code_interpreter"

The sandbox pauses after the configured idle timeout and resumes automatically on the next call. TUI chat switches and normal process shutdown permanently close the sandbox. API server mode keeps one sandbox per chat_id and serializes concurrent executions for the same chat. Sandbox IDs are not persisted, so restarting Gede starts fresh environments.

Python stdout, stderr, text results, errors, and execution counts are returned as JSON. All regular files under the execution's GEDE_OUTPUT_DIR are downloaded after execution, preserving subdirectories. E2B-rendered PNG/JPEG results are downloaded separately under rendered-images/. Artifacts are saved under ~/.gede/data/public/code_interpreter/; the tool returns local paths, /public/... paths, and HTTP URLs. API server mode automatically builds an absolute URL from the current request, including --base-path. GEDE_CODE_EXECUTE_ACCESS_ENDPOINT overrides that URL when an external reverse-proxy address is required and is also needed for HTTP URLs in standalone TUI mode.

Input and output collection are each limited to 50 files, 50 MiB per file, and 200 MiB total. Symbolic links and paths outside GEDE_OUTPUT_DIR are ignored. Files remain available inside the sandbox for later calls until that chat's sandbox is closed.

Enable:

gede --tools code_execute

The tool does not require approval. Enabling it allows files explicitly selected through workspace_files to be sent to E2B, in addition to uploaded chat attachments. E2B sandboxes have internet access by default. A force-killed Gede process can leave a paused sandbox behind; remove such sandboxes from the E2B dashboard. Because session state is process-local, multi-worker API deployments cannot guarantee that requests for one chat_id reach the same sandbox.

local_python_execute

The local_python_execute tool runs Python on the Gede host after explicit approval. It uses --workspace-dir as its working directory and starts a fresh Python process for every call. Python variables do not persist between calls, but files written to the workspace do. Interactive stdin is unavailable because the tool sends the source code to Python through stdin.

See the local Python execution-flow guide for the uv environment lifecycle, security boundary, output-file protocol, and error handling details.

The first approved call lazily creates a shared uv-managed CPython 3.12 environment under ~/.gede/data/local_python/envs/. Gede invokes that environment's Python directly instead of modifying or activating the system or project environment. The bundled, hash-locked environment includes NumPy, pandas, SciPy, Matplotlib, seaborn, scikit-learn, openpyxl, Pillow, Requests, and pypdf.

Code receives the absolute workspace path in os.environ["GEDE_WORKSPACE_DIR"]. Files that should be returned to the client must be written inside that directory and declared through output_files:

{
  "code": "from pathlib import Path\nimport pandas as pd\nout = Path('reports/result.csv')\nout.parent.mkdir(parents=True, exist_ok=True)\npd.DataFrame({'value': [1, 2]}).to_csv(out, index=False)",
  "output_files": ["reports/result.csv"]
}

In API Server mode each valid declaration receives an authenticated workspace file URL. The file is served directly from the workspace without being copied to ~/.gede/data/public. Configure GEDE_SERVER_API_KEY; workspace file access is disabled when the key is absent. GEDE_WORKSPACE_ACCESS_ENDPOINT can override the generated URL root when Gede is behind a reverse proxy.

GEDE_SERVER_API_KEY="replace-with-a-strong-secret"
GEDE_LOCAL_PYTHON_EXECUTION_TIMEOUT_SECONDS="120"
GEDE_LOCAL_PYTHON_SETUP_TIMEOUT_SECONDS="600"
# Optional:
GEDE_WORKSPACE_ACCESS_ENDPOINT="https://gede.example.com/api/v1/workspace/files"

Enable:

gede --tools local_python_execute --workspace-dir ~/projects/example

This environment isolates Python dependencies only. Approved code still runs as the current operating-system user and can access local files outside the workspace, the network, and local processes. Gede passes only a small environment-variable allowlist and does not forward model API keys or PYTHONPATH, but this is not an operating-system sandbox.

subagent_tool

The subagent_tool concurrently runs up to four independent Gede headless processes with the fixed model openrouter:x-ai/grok-4.5. It accepts a tasks array; each task has a unique name, a prompt, and a list of built-in tools.

Each valid task starts a fresh conversation and inherits only the current process environment and working directory. It does not inherit the parent conversation history, system instruction, attachments, MCP servers, private-session state, or code_execute sandbox. A task's tool list may be empty, but every supplied name must be registered as a built-in tool. Recursive use of subagent_tool is rejected. Gede passes the parent's configured workspace directory to the child process, so bash and local_python_execute start there while text_editor and code_execute.workspace_files keep the same file-access boundary.

All valid tasks run concurrently and each has its own 600-second timeout. Invalid tasks and subprocess failures are isolated and do not stop other tasks. Results are returned as a JSON array in input order with name, status, result, and error fields. Approval-required tools retain the existing headless behavior, so tools such as bash and local_python_execute are rejected inside a subtask rather than bypassing approval.

Enable:

gede --tools subagent_tool

Example tool arguments:

{
  "tasks": [
    {
      "name": "生成密码",
      "prompt": "生成 10 个临时密码，并说明生成规则",
      "tools": ["code_execute"]
    },
    {
      "name": "检查规则",
      "prompt": "总结安全临时密码应满足的规则",
      "tools": []
    }
  ]
}

bash Tool

The bash tool allows the LLM to execute shell commands on your local machine.

Features:

• Confirmation prompt: Before every execution, you will be asked to approve the command — the AI cannot run anything without your explicit y consent
• Unified tool approval: TUI and API server mode both use the shared tool approval flow; headless mode rejects approval-required tools automatically
• Safety restrictions: Dangerous commands are automatically rejected (e.g., rm -rf /, mkfs, dd to disk devices, fork bombs, shutdown/reboot)
• Working directory tracking: Commands start in --workspace-dir; cd persists across calls for that chat without changing another chat's cwd
• Non-interactive mode: Subprocesses run with stdin closed, preventing commands from hanging while waiting for user input
• Timeout protection: Commands are killed after 30 seconds to prevent hangs
• Output truncation: Output is capped at 100 lines to avoid overwhelming the context
• ANSI cleanup: Terminal color codes are stripped from output

Enable:

gede --tools bash --workspace-dir ~/projects/example

--workspace-dir sets the initial cwd; it is not a filesystem sandbox for shell commands. The model can still use absolute paths or cd outside that directory after approval. Use text_editor when access must remain confined to the configured workspace.

Example Profile (~/.gede/config/profiles.json):

{
  "dev": {
    "model": "openai:gpt-4o",
    "tools": ["bash", "read_url"]
  }
}

⚠️ Security note: Only enable the bash tool in sessions where you trust the AI model and the prompts being sent. Always review the command shown before approving execution.

Optional Dependencies

Gede supports optional extensions for enhanced functionality:

Arize Phoenix Tracing (arize-trace)

Enable advanced tracing and observability with Arize Phoenix. This extension is used when you enable trace mode with the --trace flag.

Installation:

uv pip install "gede[arize-trace]"

Usage:

When the arize-trace extension is installed and --trace is enabled, Gede will automatically use Arize Phoenix for tracing:

gede --trace

If the extension is not installed, Gede will fall back to OpenAI's built-in tracing (if OPENAI_API_KEY is set).

Configuration:

To use Arize Phoenix, edit ~/.gede/config/.env and configure:

# Phoenix trace endpoint (customize with your project token if needed)
PHOENIX_COLLECTOR_ENDPOINT=https://app.phoenix.arize.com/s/your-project-token/v1/traces

If not configured, it defaults to https://app.phoenix.arize.com.

Develop

# Clone the repository
git clone https://github.com/adow/gede.git
cd gede

# Install dependencies using uv
uv sync

# Run Gede
python3 -m gede.gede

Project Structure

gede/
├── gede/
│   ├── commands/              # Slash command implementations
│   │   ├── base.py           # Command base class
│   │   ├── chat_commands.py  # Chat management commands
│   │   ├── model_commands.py # Model selection and settings
│   │   ├── file_commands.py  # File operations (save, load, export)
│   │   └── ...              # Other command modules
│   ├── llm/
│   │   ├── providers.py       # LLM provider registry
│   │   ├── *_provider.py     # Individual provider implementations
│   │   │   ├── openai_provider.py
│   │   │   ├── anthropic_provider.py
│   │   │   ├── deepseek_provider.py
│   │   │   └── ...          # Other providers
│   │   ├── tools/            # Built-in tools
│   │   │   ├── web_search.py
│   │   │   ├── read_url_tool.py
│   │   │   └── time_tool.py
│   │   └── mcp/              # Model Context Protocol integration
│   ├── chatcore.py           # Core chat logic
│   ├── gede.py             # Main CLI entry point
│   ├── server.py             # API server entry point
│   ├── config.py             # Configuration management
│   ├── encrypt.py            # Encryption utilities
│   ├── profiles.py           # Profile management
│   └── top.py                # Top-level utilities
├── CONTRIBUTING.md           # Contribution guidelines
├── CODE_OF_CONDUCT.md       # Community code of conduct
├── CHANGELOG.md             # Version history
├── LICENSE                  # MIT License
├── pyproject.toml           # Python project configuration
├── Dockerfile               # Docker configuration
└── README.md               # This file

API Server

Gede includes a built-in HTTP API server (gede-server) built with FastAPI, designed for GUI client integration.

# Start the server (default port: 9127)
gede-server

# Custom port and base path
gede-server --port 8080 --base-path /api/v1 --workspace-dir ~/projects/example --log-level=INFO

See docs/server-api.md for the full API reference.

Technology Stack

• Language: Python 3.10+
• CLI Framework: rich, inquirer, prompt-toolkit,
• Encryption: cryptography
• HTTP Client: httpx
• Agent Framework: OpenAI Agent
• Build: uv

Security

• Password-protected private chats with AES encryption
• User data stays local by default - chat history is ephemeral and only persisted when explicitly saved using /save command

Community

• 📝 Issues & Discussions
• 🤝 Contributing Guidelines
• 📖 Code of Conduct
• 📊 Changelog

License

Acknowledgments

Thanks to all contributors and the open-source community for support and feedback!

Disclaimer

Gede is provided "as-is" for research and personal use. Users are responsible for complying with LLM provider terms of service and applicable laws when using this tool.