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

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-compiler
# or
pip install a1-compiler

🏎️ 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, LLM
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: int

# Create an agent with tools and LLM
agent = Agent(
name="math_agent",
description="Solves simple math problems",
input_schema=MathInput,
output_schema=MathOutput,
tools=[add, LLM(model="gpt-4o")],  # LLMs are tools!
)

async def main():
# Compile ahead-of-time
compiled = await agent.aot()
result = await compiled.execute(problem="What is 2 + 2?")
print(f"AOT result: {result}")

# Or execute just-in-time
result = await agent.jit(problem="What is 5 + 3?")
print(f"JIT result: {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, OpenAPI, or FastAPI servers
• RAG instantiated given any SQL database or fsspec path (e.g. s3://my-place/here, gs://..., or local filesystem)
• 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!