GitHub - askmanu/acorn: LLM framework for long running agents

Warning

Breaking Change in v0.8.0: Async-First API.

Acorn v0.8.0 makes __call__ async. If you're upgrading from v0.7.x, you need to update your code:

Find all usages of acorn Module subclasses being called directly (e.g. `agent(...)`, `MyModule()(...)`).
- If the call site is in a sync function or top-level code, replace it with `agent.run(...)`.
- If the call site is in an async function, add `await` before the call: `await agent(...)`.
Do NOT change the class definitions themselves, only the call sites.

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

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

# Or any other LiteLLM-supported provider

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
    model = "anthropic/claude-sonnet-4-5-20250514"

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

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

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
    model = "anthropic/claude-sonnet-4-5-20250514"

    @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")

@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)

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

# Run all tests
pytest

# With coverage
pytest --cov=acorn

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