agentcode-cli 1.0.0

An open, multi-model agentic coding CLI — inspired by Claude Code

AgentCode

An open, multi-model agentic coding CLI — inspired by Claude Code.

Architecture

┌─────────────────────────────────────────────────┐
│                  cli.py (UI)                    │
│  REPL loop · slash commands · Rich terminal UI  │
│  AGENTCODE.md · session persistence             │
└──────────────────────┬──────────────────────────┘
                       │
┌──────────────────────▼──────────────────────────┐
│               agent.py (Brain)                  │
│  Agentic loop · context management · permissions│
│  Hooks · subagents · LLM-powered compaction     │
│                                                 │
│   while needs_follow_up:                        │
│     1. Send messages + tools → LLM              │
│     2. If tool_calls → execute, append, loop    │
│     3. If text only  → done                     │
│                                                 │
│   LiteLLM ──→ Claude / GPT / Gemini             │
└──────────────────────┬──────────────────────────┘
                       │
          ┌────────────┴────────────┐
          │                         │
┌─────────▼───────────┐   ┌─────────▼───────────┐
│    tools.py (Hands) │   │  mcp_client.py       │
│  read_file          │   │  Connect to MCP      │
│  write_file         │   │  servers and expose  │
│  edit_file          │   │  their tools to the  │
│  run_command        │   │  agent loop.         │
│  list_directory     │   └─────────────────────┘
│  search_files       │
│  search_text        │
│  git_status/diff    │
│  git_log/commit     │
│  git_branch/push    │
│  spawn_subagents    │
└─────────────────────┘

Quick Start

# Option A: Install from PyPI
pip install agentcode-cli

# Option B: Install from source
git clone https://github.com/vigp17/AgentCode.git
cd AgentCode
pip install -r requirements.txt

# Add your API keys to a .env file in your project directory
ANTHROPIC_API_KEY=sk-ant-...
OPENAI_API_KEY=sk-...
GEMINI_API_KEY=...

# Run
agentcode                               # Interactive REPL
agentcode "fix the failing tests"       # One-shot mode
agentcode --model gpt-4o               # Use a different model
agentcode --init-settings              # Create a settings file

Supported Models

Via LiteLLM, AgentCode works with any model that supports function calling — including Claude, GPT, Gemini, and select open-source models via Ollama.

Provider	Model String	API Key Env Var
Anthropic	claude-sonnet-4-6 (default)	ANTHROPIC_API_KEY
OpenAI	gpt-4o	OPENAI_API_KEY
Google	gemini/gemini-2.5-pro	GEMINI_API_KEY

Cost-Aware Routing

AgentCode automatically picks the cheapest model that can handle the task:

Tier	Anthropic	OpenAI	Gemini
Light	Haiku 4.5	GPT-4o Mini	Gemini 2.0 Flash
Medium	Sonnet 4.6	GPT-4o	Gemini 2.5 Flash
Heavy	Opus 4.6	GPT-5.5	Gemini 2.5 Pro

Simple questions go to cheap/fast models. Complex multi-file tasks go to powerful ones. Use --no-route to always use the specified model.

Tools

File & Shell

Tool	Description	Permission
read_file	Read file contents with line numbers	Auto
write_file	Create or overwrite a file	Ask
edit_file	Surgical find-and-replace edit	Ask
run_command	Execute a bash command	Ask
list_directory	Tree view of directory structure	Auto
search_files	Find files by glob pattern	Auto
search_text	Grep for text across files	Auto

Git

Tool	Description	Permission
git_status	Show working tree status	Auto
git_diff	Show staged or unstaged changes	Auto
git_log	Show recent commit history	Auto
git_commit	Stage files and create a commit	Ask
git_branch	List, create, or switch branches	Ask
git_push	Push commits to a remote	Ask

Subagents

Tool	Description	Permission
spawn_subagents	Run multiple agents in parallel on subtasks	Auto

Permission model: Read-only tools auto-approve. Write/execute tools ask before running (unless --auto-approve / -y flag is set).

Slash Commands

Command	Description
/model <name>	Switch LLM model on the fly (disables routing)
/route	Show or toggle cost-aware routing
/cost	Show session cost breakdown
/mcp	Manage MCP server connections
/mcp list	Show connected servers and tool counts
/mcp add <server>	Connect a server (prompts for credentials)
/mcp remove <server>	Disconnect a server
/clear	Reset conversation and delete saved session
/compact	Force LLM-powered context compaction
/tokens	Show estimated token usage
/init	Create an AGENTCODE.md template
/settings	Show resolved settings and active config files
/help	Show help
/exit	Quit

Session Persistence

Conversations are automatically saved to .agentcode_session.json in your project directory and resumed on next launch. Use /clear to start fresh.

Hooks

Run shell commands before or after any tool call. Create .agentcode/hooks.json:

{
  "post_edit_file": "prettier --write \"$AGENTCODE_PATH\"",
  "post_write_file": "prettier --write \"$AGENTCODE_PATH\"",
  "pre_run_command": "echo \"Running: $AGENTCODE_COMMAND\""
}

Supported keys: pre_<toolname>, post_<toolname>, pre_tool / post_tool (wildcard). Tool args are passed as AGENTCODE_<ARG>=value env vars. Global hooks go in ~/.agentcode/hooks.json.

MCP Support

Connect to any MCP server using the /mcp add command — no manual config editing needed:

/mcp add github       # prompts for GitHub token, connects immediately
/mcp add filesystem   # connects to current directory, no credentials needed
/mcp add postgres     # prompts for connection string
/mcp add sqlite       # prompts for database file path
/mcp list             # show connected servers and tool counts
/mcp remove github    # disconnect a server

Servers connect live without restarting. Config is saved to .agentcode/mcp.json and reloaded automatically on next launch.

MCP tools are exposed as mcp__<server>__<toolname> (e.g. mcp__github__create_issue) and available to the LLM alongside built-in tools. Connected servers are shown in the banner on startup.

Advanced: You can also edit .agentcode/mcp.json directly for custom servers:

{
  "mcpServers": {
    "my-server": {
      "command": "npx",
      "args": ["-y", "@myorg/mcp-server"],
      "env": {"API_KEY": "..."}
    }
  }
}

Global config goes in ~/.agentcode/mcp.json.

Subagents

AgentCode can spawn parallel agents to work on independent subtasks simultaneously. Just ask naturally:

"Analyze agent.py, router.py, and tools.py in parallel and summarize each one"

The agent calls spawn_subagents internally, runs up to 5 agents in parallel, and returns combined results.

AGENTCODE.md

AgentCode loads project-level instructions from AGENTCODE.md in your project directory (and global config from ~/.agentcode/AGENTCODE.md), injecting them into the system prompt automatically.

Run /init to generate a starter template, then edit it to define your project's coding standards, preferences, and constraints.

How to Extend

Add a new tool

1. Add the function schema to TOOL_DEFINITIONS in tools.py
2. Implement the function (e.g., _my_tool(...))
3. Register it in TOOL_MAP
4. If it should require user approval, omit it from permissions.auto_approve in .agentcode/settings.json

Environment Variables

Variable	Description	Default
AGENTCODE_MODEL	Default model	claude-sonnet-4-6
AGENTCODE_MAX_ITERATIONS	Max tool-call iterations per turn	25
ANTHROPIC_API_KEY	Anthropic API key	—
OPENAI_API_KEY	OpenAI API key	—
GEMINI_API_KEY	Google Gemini API key	—

Publishing to PyPI

# 1. Install build tools
pip install build twine

# 2. Bump the version in pyproject.toml, then build
python -m build

# 3. Upload to PyPI
twine upload dist/*

You'll be prompted for your PyPI credentials on first upload. Use an API token from pypi.org/manage/account for security.

For test uploads before going live:

twine upload --repository testpypi dist/*
pip install --index-url https://test.pypi.org/simple/ agentcode

License

MIT