agentic

The agent/knowledge/orchestration/workflow engine that powers the Powabase stack — a well-documented Python library you can also import on its own.

Features

• Agent: Single LLM-powered agent with a clean run() interface
• Session Management: Built-in conversation history tracking
• Multi-Model Support: Uses litellm for 100+ LLM providers
• Type-Safe: Full type hints and dataclass-based outputs
• Orchestration: Multi-agent coordination (sequential, supervisor, router, parallel)
• Workflow: Multi-step pipelines with conditional logic, loops, and a sandboxed code-execution block

Installation

# Using pip
pip install agentic

# Using uv
uv add agentic

# From source
git clone https://github.com/your-org/agentic.git
cd agentic
pip install -e .

Quick Start

from agentic import Agent

# Create an agent
agent = Agent(
    model="gpt-4o-mini",
    system_prompt="You are a helpful assistant.",
)

# Run the agent
output = agent.run("What is 2+2?")
print(output.content)  # "2 + 2 equals 4."

# Check execution details
print(output.status)  # ExecutionStatus.COMPLETED
print(output.usage)   # {"prompt_tokens": 15, "completion_tokens": 8, ...}

Multi-Turn Conversations

Use AgentSession to maintain conversation history:

from agentic import Agent, AgentSession

agent = Agent(
    model="gpt-4o-mini",
    system_prompt="You are a helpful assistant.",
)

# Create a session
session = AgentSession()

# First turn
output1 = agent.run("My name is Alice", session=session)
session.add_output(output1)

# Second turn - agent remembers the name
output2 = agent.run("What's my name?", session=session)
session.add_output(output2)
print(output2.content)  # "Your name is Alice."

# Get conversation history
messages = session.get_messages()

Async Support

import asyncio
from agentic import Agent

async def main():
    agent = Agent(model="gpt-4o-mini", system_prompt="You are helpful.")
    output = await agent.arun("Hello!")
    print(output.content)

asyncio.run(main())

Model Selection

agentic uses litellm under the hood, supporting 100+ LLM providers:

# OpenAI
agent = Agent(model="gpt-4o")

# Anthropic
agent = Agent(model="claude-3-opus-20240229")

# Azure OpenAI
agent = Agent(model="azure/my-deployment")

# Local models (Ollama)
agent = Agent(model="ollama/llama2")

Set up API keys via environment variables:

export OPENAI_API_KEY="sk-..."
export ANTHROPIC_API_KEY="sk-ant-..."

Core Concepts

Agent

An Agent wraps an LLM with a system prompt. It provides a simple run() method that handles message formatting, LLM calls, and response parsing.

AgentOutput

The result of agent.run(). Contains:

• content: The LLM's response text
• status: Execution status (COMPLETED, FAILED, etc.)
• messages: All messages exchanged
• usage: Token usage statistics
• Timing information (started_at, completed_at)

AgentSession

Container for conversation history. Pass a session to run() to include previous messages in the context.

ExecutionContext

Runtime context carrying execution_id, session_id, and custom metadata. Created automatically or can be provided explicitly.

See docs/CONCEPTS.md for detailed documentation.

Project Structure

agentic/src/agentic/
├── agent/           # Agent implementation
│   ├── agent.py     # Agent class
│   ├── output.py    # AgentOutput dataclass
│   └── session.py   # AgentSession for history
├── orchestration/   # Multi-agent coordination (sequential, supervisor, router, parallel)
├── workflow/        # Pipeline execution (blocks, conditions, sandboxed functions)
└── execution/       # Shared infrastructure
    ├── context.py   # ExecutionContext
    ├── status.py    # ExecutionStatus enum
    └── base.py      # BaseOutput class

Development

# Install with dev dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Type checking
mypy src/agentic

License

Apache-2.0 — see LICENSE.

Contributing

Contributions are welcome! Please read our contributing guidelines and submit a pull request.