> ## Documentation Index
> Fetch the complete documentation index at: https://docs.a1project.org/llms.txt
> Use this file to discover all available pages before exploring further.

# Compilation

> How to run agents in A1

## Run Agents

Execute agents with JIT (Just-in-Time):

```python theme={null}
from a1 import Agent, LLM

agent = Agent(
    name="calculator",
    tools=[LLM("gpt-4.1")]
)

# Compile and execute immediately
result = await agent.jit(problem="What is 2 + 2?")
```

JIT generates code optimized for each input, then executes it.

## Compile Agents into Tools

AOT (Ahead-of-Time) compilation turns agents into reusable tools:

```python theme={null}
# Compile once
compiled = await agent.aot()

# Use many times
for i in range(1000):
    result = await compiled.execute(problem=f"Input {i}")
```

**Caching**: Compiled agents are cached in `.a1/` directory by default.

```python theme={null}
# Use cached version if available
compiled = await agent.aot(cache=True)

# Force recompilation
compiled = await agent.aot(cache=False)
```

**Agents as tools**: Compiled agents are tools that can be used in other agents.

```python theme={null}
# Compile specialist agent
math_agent = Agent(name="math", tools=[LLM("gpt-4.1")])
compiled_math = await math_agent.aot()

# Use in another agent
coordinator = Agent(
    name="coordinator",
    tools=[compiled_math, LLM("gpt-4.1")]
)

result = await coordinator.jit(task="Calculate and analyze data")
```

## How It Works

Both AOT and JIT follow this pipeline:

```
1. Generate → LLM creates code
2. Verify → Check syntax/logic
3. Cost → Rank candidates
4. Execute → Run best code
```

### Configuration

Control code generation with `Strategy`:

```python theme={null}
from a1 import Strategy

strategy = Strategy(
    num_candidates=5,    # Generate 5 parallel candidates
    max_iterations=3     # Retry up to 3 times on failure
)

compiled = await agent.aot(strategy=strategy)
result = await agent.jit(problem="...", strategy=strategy)
```

See [Strategies](../advanced/strategies.md) for detailed configuration.

### Agentic While Loop

Use `IsLoop` verifier to compile agents into standard agentic while loops (like LangGraph, AutoGen):

```python theme={null}
from a1 import Strategy, IsLoop

strategy = Strategy(verify=IsLoop())

# Compiles to while loop structure
compiled = await agent.aot(strategy=strategy)
```

This creates an ahead-of-time compiled Python function with a while loop, instead of interpreted runtime execution.

### Performance Tuning

**Parallel candidates**: Generate multiple code candidates in parallel and select the best.

```python theme={null}
strategy = Strategy(num_candidates=10)
result = await agent.jit(problem="...", strategy=strategy)
# Generates 10 candidates in parallel, picks lowest cost
```

**Candidate generation** happens in parallel:

```
Candidate 1: [Generate] → [Verify] → [Cost]
Candidate 2: [Generate] → [Verify] → [Cost]  (parallel)
Candidate 3: [Generate] → [Verify] → [Cost]
```

For more advanced configuration (custom verify, cost, generate), see [Strategies](../advanced/strategies.md).