acorn 0.4.4

LLM agent framework with structured I/O

acorn 🌰

LLM agent framework with structured I/O, heavily influenced by DSPy.

Build AI agents with type-safe inputs and outputs, automatic tool calling, and powerful agentic loops.

---

✨ Features

• 🎯 Structured I/O - Pydantic models for inputs and outputs
• 🤖 Agentic Loops - Multi-turn execution with tool calling
• 🛠️ Auto Tool Schemas - Generate from type hints and docstrings
• 🔄 Dynamic Tools - Add/remove tools during execution
• ✅ Parse Error Recovery - Automatic retry on validation failures
• 📊 Step Callbacks - Full control over loop behavior
• 🔌 LiteLLM Integration - Works with any LLM provider
• 🌊 Streaming Responses - Real-time output with partial structured updates
• 💾 Provider Caching - Reduce latency and cost with prompt caching

---

🚀 Quick Start

Installation

pip install acorn

Set your API key:

# For Anthropic Claude (default)
export ANTHROPIC_API_KEY="your-key-here"

# Or for OpenAI
export OPENAI_API_KEY="your-key-here"

# Or any other LiteLLM-supported provider

Single-Turn Example

from pydantic import BaseModel, Field
from acorn import Module

class Input(BaseModel):
    text: str = Field(description="The text to summarize")
    max_words: int = Field(default=100, description="Maximum words in summary")

class Output(BaseModel):
    summary: str = Field(description="The concise summary")
    word_count: int = Field(description="Number of words in summary")

class Summarizer(Module):
    """Summarize text concisely."""

    initial_input = Input
    final_output = Output

# Use it
summarizer = Summarizer()
result = summarizer(
    text="Long article text here...",
    max_words=50
)

print(result.summary)
print(f"Words: {result.word_count}")

Multi-Turn Agentic Loop

from pydantic import BaseModel, Field
from acorn import Module, tool

class Input(BaseModel):
    topic: str = Field(description="Research topic")
    depth: str = Field(default="shallow", description="Research depth")

class Output(BaseModel):
    findings: str = Field(description="Summary of findings")
    sources: list[str] = Field(description="Sources consulted")

class ResearchAgent(Module):
    """Research assistant with tools."""

    initial_input = Input
    max_steps = 5  # Enable agentic loop
    final_output = Output

    @tool
    def search(self, query: str) -> list:
        """Search for information."""
        # Your search implementation
        return ["result1", "result2"]

    @tool
    def analyze(self, data: str) -> str:
        """Analyze collected data."""
        # Your analysis implementation
        return f"Analysis: {data}"

    def on_step(self, step):
        """Called after each step."""
        print(f"Step {step.counter}")

        # Early termination if done
        if len(step.tool_results) >= 3:
            step.finish(
                findings="Sufficient data collected",
                sources=["source1", "source2"]
            )

        return step

# Use it
agent = ResearchAgent()
result = agent(topic="Large Language Models", depth="shallow")

---

📚 Core Concepts

Module

Base class for LLM agents. Configure with:

• model - LLM to use (default: Claude Sonnet 4.5)
• temperature - Sampling temperature
• max_tokens - Maximum tokens to generate
• max_steps - Max agentic loop iterations (None = single-turn)
• initial_input - Pydantic model for input schema
• final_output - Pydantic model for output schema
• tools - List of available tools
• cache - Enable provider-level prompt caching

Tools

Functions the LLM can call:

@tool
def search(query: str, limit: int = 10) -> list:
    """Search for information.

    Args:
        query: The search query
        limit: Maximum results to return
    """
    return search_api(query, limit)

Schema is automatically generated from type hints and docstring.

Step Callback

Control agentic loop execution:

def on_step(self, step):
    # Access step info
    print(f"Step {step.counter}")
    print(f"Tools called: {[tc.name for tc in step.tool_calls]}")

    # Dynamic tool management
    step.add_tool(new_tool)
    step.remove_tool("old_tool")

    # Early termination
    if condition:
        step.finish(result="Early exit")

    return step

---

🎯 Examples

See examples/ directory:

• single_turn_summarizer.py - Basic single-turn usage
• research_assistant.py - Full agentic loop with tools

---

🧪 Testing

# Run all tests
pytest

# With coverage
pytest --cov=acorn

# Specific test file
pytest tests/test_agentic_loop.py -v

Current status: 128 tests passing, 90% coverage

---

📖 Documentation

• Getting Started - Installation and first steps
• Module Reference - Complete Module API documentation

---

🛣️ Roadmap

✅ Completed

• Single-turn execution
• Multi-turn agentic loops
• Tool calling with auto-schema generation
• Parse error recovery
• Dynamic tool management
• Step callbacks
• Streaming responses with partial structured output
• Forced termination strategies
• Provider caching

📋 Planned

• Branching workflows
• Async support

---

🤝 Contributing

Contributions welcome! Please:

1. Check open issues for areas to help
2. Write tests for new features (maintain >80% coverage)
3. Update documentation
4. Add examples for new features

---

💬 Questions?

Check out:

• Getting Started for installation and examples
• Module Reference for detailed API docs
• Examples for working code
• Tests for usage patterns

---

📄 License

MIT License - see LICENSE for details

---