agentic-cli 0.2.0

A framework for building domain-specific agentic CLI applications

Agentic CLI

A framework for building domain-specific agentic CLI applications powered by LLM agents.

Overview

Agentic CLI provides the core infrastructure for building interactive CLI applications that leverage LLM agents for complex tasks. It offers:

• Pluggable Orchestration: Choose between Google ADK or LangGraph for agent workflows
• Rich Terminal UI: Thinking boxes, markdown rendering, and streaming responses via thinking-prompt
• Declarative Agents: Define agents with simple configuration objects
• Built-in Tools: Python execution, knowledge base search, web search
• Session Persistence: Save and restore conversation sessions
• Type-safe Configuration: Settings management with pydantic-settings

Architecture

┌─────────────────────────────────────────────────────────────────────┐
│                         BaseCLIApp                                  │
│  - Terminal UI (thinking-prompt)                                    │
│  - Command registry (/help, /status, /clear, etc.)                  │
│  - Message history                                                  │
└─────────────────────────────────────────────────────────────────────┘
                                 │
                                 ▼
┌─────────────────────────────────────────────────────────────────────┐
│                    BaseWorkflowManager                              │
│  - Agent orchestration                                              │
│  - Event streaming                                                  │
│  - Session management                                               │
├─────────────────────────────┬───────────────────────────────────────┤
│   GoogleADKWorkflowManager  │     LangGraphWorkflowManager          │
│   (Default)                 │     (Optional: langgraph extra)       │
└─────────────────────────────┴───────────────────────────────────────┘
                                 │
                                 ▼
┌─────────────────────────────────────────────────────────────────────┐
│                         AgentConfig[]                               │
│  - name, prompt, tools, sub_agents, description                     │
└─────────────────────────────────────────────────────────────────────┘

Requirements

• Python 3.12+
• Google API key (for Gemini models) or Anthropic API key (for Claude models)

Installation

Basic Installation (Google ADK)

pip install agentic-cli

With LangGraph Support

pip install agentic-cli[langgraph]

Development Installation

git clone https://github.com/shoom1/agentic-cli.git
cd agentic-cli
pip install -e ".[dev]"

Using Conda

conda env create -f environment.yml
conda run -n agenticcli pip install -e .

Quick Start

Create a minimal CLI application in just a few lines:

import asyncio
from agentic_cli import BaseCLIApp, BaseSettings, AgentConfig, GoogleADKWorkflowManager
from agentic_cli.cli import AppInfo

# Define your tools
def greet(name: str) -> dict:
    """Greet a person by name."""
    return {"greeting": f"Hello, {name}!"}

# Configure your agent
AGENTS = [
    AgentConfig(
        name="assistant",
        prompt="You are a helpful assistant. Use the greet tool when asked to greet someone.",
        tools=[greet],
    ),
]

# Create your app
class MyApp(BaseCLIApp):
    def get_app_info(self) -> AppInfo:
        return AppInfo(name="My App", version="0.1.0")

    def get_settings(self) -> BaseSettings:
        return BaseSettings()

    def create_workflow_manager(self) -> GoogleADKWorkflowManager:
        return GoogleADKWorkflowManager(agent_configs=AGENTS, settings=self._settings)

if __name__ == "__main__":
    asyncio.run(MyApp().run())

Run with your API key:

export GOOGLE_API_KEY="your-api-key"
python my_app.py

Workflow Managers

GoogleADKWorkflowManager (Default)

Uses Google's Agent Development Kit for orchestration. Best for:

• Simple agent hierarchies
• Google Gemini models with native thinking support
• Quick prototyping

from agentic_cli import GoogleADKWorkflowManager

manager = GoogleADKWorkflowManager(
    agent_configs=AGENTS,
    settings=settings,
    model="gemini-2.0-flash",  # Optional: auto-detected from API keys
)

LangGraphWorkflowManager

Uses LangGraph for orchestration. Best for:

• Cyclical workflows (self-validation, iterative refinement)
• Model-agnostic operation (OpenAI, Anthropic, Google)
• State checkpointing and time-travel debugging
• Complex multi-agent coordination

from agentic_cli import LangGraphWorkflowManager

manager = LangGraphWorkflowManager(
    agent_configs=AGENTS,
    settings=settings,
    checkpointer="memory",  # or "postgres" for persistence
)

Requires: pip install agentic-cli[langgraph]

Comparison

Feature	Google ADK	LangGraph
Setup complexity	Simple	Moderate
Cyclical workflows	Limited	Native
Multi-provider	Google only	OpenAI, Anthropic, Google
State persistence	In-memory	Memory or PostgreSQL
Thinking support	Native (Gemini)	Via model config

Auto-selection via Settings

from agentic_cli import create_workflow_manager_from_settings

settings = BaseSettings(orchestrator="langgraph")  # or "adk"
manager = create_workflow_manager_from_settings(agent_configs=AGENTS, settings=settings)

Configuration

BaseSettings

All settings can be configured via environment variables with the AGENTIC_ prefix or in a .env file:

from agentic_cli import BaseSettings

class MySettings(BaseSettings):
    model_config = SettingsConfigDict(
        env_file=".env",
        env_prefix="MYAPP_",  # Custom prefix
    )
    app_name: str = "my_app"
    workspace_dir: Path = Path.home() / ".my_app"

Key Settings

Setting	Env Variable	Default	Description
google_api_key	GOOGLE_API_KEY	None	Google API key for Gemini
anthropic_api_key	ANTHROPIC_API_KEY	None	Anthropic API key for Claude
default_model	AGENTIC_DEFAULT_MODEL	Auto	Model to use
thinking_effort	AGENTIC_THINKING_EFFORT	"medium"	Thinking level: none, low, medium, high
orchestrator	AGENTIC_ORCHESTRATOR	"adk"	Orchestrator: adk or langgraph
workspace_dir	AGENTIC_WORKSPACE_DIR	~/.agentic	Storage directory
log_level	AGENTIC_LOG_LEVEL	"warning"	Logging level

Settings Context

For multi-tenant or isolated contexts:

from agentic_cli import SettingsContext

with SettingsContext(custom_settings):
    # All code here uses custom_settings
    result = my_tool()

Agent Configuration

Agents are defined declaratively using AgentConfig:

from agentic_cli import AgentConfig

# Simple agent
assistant = AgentConfig(
    name="assistant",
    prompt="You are a helpful assistant.",
    tools=[my_tool],
)

# Agent with dynamic prompt
def get_prompt():
    return f"Today is {datetime.now().strftime('%Y-%m-%d')}. Help the user."

dynamic_agent = AgentConfig(
    name="dynamic",
    prompt=get_prompt,  # Callable for dynamic prompts
    tools=[tool_a, tool_b],
)

# Coordinator with sub-agents
coordinator = AgentConfig(
    name="coordinator",
    prompt="Route requests to the appropriate specialist.",
    tools=[],
    sub_agents=["researcher", "analyst"],  # Names of other agents
    description="Routes work to specialists",
)

researcher = AgentConfig(
    name="researcher",
    prompt="Research topics thoroughly.",
    tools=[web_search],
)

analyst = AgentConfig(
    name="analyst",
    prompt="Analyze data and provide insights.",
    tools=[calculate],
)

# Pass all configs to workflow manager
configs = [coordinator, researcher, analyst]

AgentConfig Fields

Field	Type	Description
name	str	Unique identifier
prompt	str | Callable	System instruction
tools	list[Callable]	Available tool functions
sub_agents	list[str]	Names of agents this one can delegate to
description	str	Short description for routing
model	str | None	Model override (defaults to manager's model)

Tools

Creating Tools

Tools are regular Python functions with type hints and docstrings:

def search_database(query: str, limit: int = 10) -> dict:
    """Search the database for matching records.

    Args:
        query: Search query string
        limit: Maximum number of results (default: 10)

    Returns:
        Dict with results and count
    """
    results = db.search(query, limit=limit)
    return {"results": results, "count": len(results)}

Built-in Tools

SafePythonExecutor

Execute Python code in a sandboxed environment:

from agentic_cli.tools import SafePythonExecutor

executor = SafePythonExecutor(default_timeout=30)
result = executor.execute("""
import numpy as np
data = np.array([1, 2, 3, 4, 5])
np.mean(data)
""")
# result = {"success": True, "result": "3.0", "output": "", "error": ""}

Allowed modules: numpy, pandas, scipy, math, json, datetime, collections, itertools, re, random

KnowledgeBaseManager

Semantic search over documents:

from agentic_cli.knowledge_base import KnowledgeBaseManager, SourceType

kb = KnowledgeBaseManager(settings=settings)

# Ingest a document
doc = kb.ingest_document(
    content="Machine learning is...",
    title="ML Introduction",
    source_type=SourceType.WEB,
    source_url="https://example.com/ml",
)

# Search
results = kb.search("neural networks", top_k=5)

CLI Commands

Built-in slash commands available in all apps:

Command	Aliases	Description
/help		Show available commands
/status		Show session and workflow status
/clear		Clear the screen
/exit	/quit	Exit the application

Adding Custom Commands

from agentic_cli.cli.commands import Command, CommandCategory

class MyCommand(Command):
    def __init__(self):
        super().__init__(
            name="mycommand",
            description="Do something custom",
            category=CommandCategory.GENERAL,
        )

    async def execute(self, args: str, app: Any) -> None:
        app.session.add_response(f"Executed with args: {args}")

# In your app
class MyApp(BaseCLIApp):
    def get_custom_commands(self) -> list[Command]:
        return [MyCommand()]

Events

WorkflowEvent types for UI integration:

EventType	Description	Metadata
TEXT	Final text response	session_id
THINKING	Model reasoning	session_id
TOOL_CALL	Tool invocation	tool_name, tool_args
TOOL_RESULT	Tool result	tool_name, result, success
CODE_EXECUTION	Code execution result	outcome
ERROR	Error message	recoverable, error_code
USER_INPUT_REQUIRED	Tool needs user input	request_id, prompt

Processing Events

async for event in manager.process(message, user_id="user1"):
    if event.type == EventType.TEXT:
        print(event.content)
    elif event.type == EventType.THINKING:
        print(f"[Thinking] {event.content}")
    elif event.type == EventType.TOOL_CALL:
        print(f"Calling: {event.metadata['tool_name']}")
    elif event.type == EventType.TOOL_RESULT:
        print(f"Result: {event.metadata['result']}")

Examples

See the examples/ directory for complete working examples:

• hello_agent.py - Simple assistant using Google ADK
• hello_langgraph.py - Same assistant using LangGraph

Run examples:

export GOOGLE_API_KEY="your-key"
python examples/hello_agent.py

# Or with LangGraph (requires langgraph extra)
pip install agentic-cli[langgraph]
python examples/hello_langgraph.py

Development

Running Tests

# With conda
conda run -n agenticcli python -m pytest tests/ -v

# With pip
pytest tests/ -v

# With coverage
pytest tests/ -v --cov=agentic_cli

Project Structure

agentic-cli/
├── src/agentic_cli/
│   ├── __init__.py           # Package exports
│   ├── config.py             # BaseSettings, SettingsContext
│   ├── cli/
│   │   ├── app.py            # BaseCLIApp
│   │   └── commands.py       # Command, CommandRegistry
│   ├── workflow/
│   │   ├── events.py         # WorkflowEvent, EventType
│   │   ├── config.py         # AgentConfig
│   │   ├── adk_manager.py    # GoogleADKWorkflowManager
│   │   └── langgraph_manager.py  # LangGraphWorkflowManager
│   ├── tools/
│   │   └── executor.py       # SafePythonExecutor
│   └── knowledge_base/
│       └── manager.py        # KnowledgeBaseManager
├── examples/
│   ├── hello_agent.py
│   └── hello_langgraph.py
└── tests/

License

MIT