zagency 0.6.5

An agentic framework for building AI agents with LLM integration

zagency

A framework for building AI agents with LLM integration, tool discovery, and comprehensive evaluation capabilities.

Installation

pip install zagency

For development:

pip install -e .

Quick Start

from zagency import Agent, LiteLLM, tool

class MyAgent(Agent):
    @tool
    def calculate(self, expression: str) -> float:
        """Evaluate a mathematical expression"""
        return eval(expression)
    
    def step(self, environment):
        # Agent logic here
        state = self.ingest_state(environment)
        if state.get("task"):
            result = self.invoke([{"role": "user", "content": state["task"]}])
            environment.update_state({"result": result}, agent=self)
        return {"status": "completed"}

# Initialize and run
lm = LiteLLM(model="gpt-4")
agent = MyAgent(lm)

Core Components

Agents

Base class for all agents with automatic tool discovery:

• Inherit from Agent class
• Implement step() method for agent logic
• Use @tool decorator to expose methods to LLM
• Access LLM through self.invoke()

Environments

State management between agents:

• Environment: All agents share the same state
• IsolatedEnvironment: Each agent has private state with shared globals
• CodingEnvironment: Specialized for code editing tasks

Language Models

Built on LiteLLM for multi-provider support:

from zagency import LiteLLM

# Supports OpenAI, Anthropic, and more
lm = LiteLLM(model="gpt-4")
lm = LiteLLM(model="claude-3-opus")

Tools

Decorated methods automatically become LLM-callable:

@tool
def search_files(self, pattern: str) -> list:
    """Search for files matching pattern"""
    return Path(".").glob(pattern)

Evaluation Framework

Scorers

Evaluate different aspects of agent behavior:

• AgentScorer: Internal metrics (tokens, decisions)
• EnvironmentScorer: State changes
• TraceScorer: End-to-end performance

Running Evaluations

from zagency.core import Evaluation

eval = Evaluation(
    name="my_eval",
    scorers=[MyScorer(), AnotherScorer()]
)

results = eval.run(
    agent=agent,
    environment_class=Environment,
    dataset=test_cases,
    max_steps_per_datum=50
)

Advanced Usage

Multi-Agent Systems

from zagency.handler import StepHandler

handler = StepHandler(environment)
handler.add_agent(agent1)
handler.add_agent(agent2)
handler.run(max_steps=100)

Custom Environments

class MyEnvironment(Environment):
    def get_state(self, agent=None):
        # Return state for requesting agent
        return self._state
    
    def update_state(self, updates, agent=None):
        # Handle state updates
        self._state.update(updates)

Project Structure

zagency/
├── core/
│   ├── agent.py          # Base Agent class
│   ├── environment.py    # Environment implementations
│   ├── lm.py            # Language model abstraction
│   ├── scorer.py        # Scoring system
│   └── evaluation.py    # Evaluation framework
├── environments/        # Specialized environments
├── handler/            # Agent orchestration
└── tests/             # Test suite

Key Features

• Automatic tool discovery via decorators
• Multi-LLM support through LiteLLM
• Flexible state management with environments
• Comprehensive evaluation framework
• Token usage tracking and cost monitoring
• Rich console output support

Dependencies

• torch, litellm, rich, pydantic
• whisper, pyannote.audio (for audio capabilities)
• ffmpeg-python (for media processing)

Development

# Run tests
pytest tests/

# Build package
make build

# Clean artifacts
make cleanup

License

MIT License

Links

• Full Documentation
• API Reference
• Examples