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A modern agent compiler for building and executing LLM-powered agents

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The a1 compiler for safe, reliable, deterministic AI agents.

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a1 is an agent compiler. It takes an Agent (set of tools and a description) and compiles either AOT (ahead-of-time) into a Tool or JIT (just-in-time) for immediate execution tuned to the agent input.

uv pip install a1

🏎️ Why use an agent compiler?

  • Safety a1 generates code for every agent input and isolates LLM contexts as much as possible, reducing the amount of potentially untrusted data an LLM is exposed to.
  • Speed a1 makes codegen practical for agents with aggressive parallelism and static checking.
  • Determinism a1 optimizes for determinism via a swappable cost function.

Agent compilers emerged from frustration with agent frameworks where every agent runs a static while loop program. Slow, unsafe, and highly nondeterministic. An agent compiler can perform the same while loop (just set Verify=IsLoop()) but has the freedom to explore superoptimal execution plans, while subject to engineered constraints.

🚀 How to get started?

from a1 import Agent, tool, Tool, LLM, Done, Runtime
from pydantic import BaseModel

# Define a simple tool
@tool(name="add", description="Add two numbers")
async def add(a: int, b: int) -> int:
    return a + b

# Define input/output schemas
class MathInput(BaseModel):
    problem: str

class MathOutput(BaseModel):
    answer: str

# Create an agent
agent = Agent(
    name="math_agent",
    description="Solves simple math problems",
    input_schema=MathInput,
    output_schema=MathOutput,
    tools=[add, LLM("gpt-4.1")], # LLMs are tools!
)

# Use the agent with AOT compilation
async def main():
    # Compile ahead-of-time
    compiled: Tool = await agent.aot()
    result = await compiled(problem="What is 2 + 2?")
    print(result)

    # Or execute just-in-time
    result = await runtime.jit(agent, problem="What is 5 + 3?")
    print(result)

import asyncio
asyncio.run(main())

See the tests/ directory for extensive examples of everything a1 can do. Docs coming soon to docs.a1project.org

✨ Features

  • Import your Langchain agents
  • Observability via OpenTelemetry
  • Tools instantiated from MCP or OpenAPI
  • RAG instantiated given any SQL database, any fsspec path (e.g. s3://my-place/here or somewhere/local).
  • Skills defined manually or by crawling online docs.
  • Context engineering via a simple API that lets compiled code manage multi-agent behavior.
  • Zero lock-in use any LLM, any secure code execution cloud.
  • Only gets better as researchers develop increasingly powerful methods to Generate, Cost estimate, and Verify agent code.

🤝 Contributing

Awesome! See our Contributing Guide for details.

📄 MIT License

As it should be!

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