# Signature: async/await throughout
async def run(self):
    result = await self.llm.agenerate(messages)
    return await self.process(result)

# Entry point uses asyncio
asyncio.run(agent.run())

# Signature: sync API wrapping async internals
def run(self):
    return asyncio.run(self._async_run())

# Or using thread pools
def run(self):
    with ThreadPoolExecutor() as pool:
        future = pool.submit(self._blocking_call)
        return future.result()

# Both sync and async APIs exposed
def invoke(self, input):
    return self._sync_invoke(input)

async def ainvoke(self, input):
    return await self._async_invoke(input)

# Signature: Nodes with dependencies
class Node:
    def __init__(self, deps: list[Node]): ...

graph.add_edge(node_a, node_b)
result = graph.execute()  # Topological order

# Signature: Explicit states and transitions
class State(Enum):
    THINKING = "thinking"
    ACTING = "acting"
    DONE = "done"

def transition(self, current: State, event: str) -> State:
    if current == State.THINKING and event == "action_chosen":
        return State.ACTING

# Signature: Sequential step execution
def run(self):
    result = self.step1()
    result = self.step2(result)
    result = self.step3(result)
    return result

# Or pipeline pattern
chain = step1 | step2 | step3
result = chain.invoke(input)

class Callbacks:
    def on_llm_start(self, prompt): ...
    def on_llm_end(self, response): ...
    def on_tool_start(self, tool, input): ...
    def on_tool_end(self, output): ...
    def on_error(self, error): ...

emitter = EventEmitter()
emitter.on('llm:start', handler)
emitter.on('tool:*', wildcard_handler)
emitter.emit('llm:start', {'prompt': prompt})

async def run(self):
    async for chunk in self.llm.astream(prompt):
        yield {"type": "token", "content": chunk}
    yield {"type": "done"}

## Execution Engine Analysis: [Framework Name]

### Concurrency Model
- **Type**: [Native Async / Sync-with-Wrappers / Hybrid]
- **Entry Point**: `path/to/main.py:run()`
- **Thread Safety**: [Yes/No/Partial]

### Execution Topology
- **Model**: [DAG / FSM / Linear Chain]
- **Implementation**: [Description with code refs]
- **Parallelization**: [Supported/Not Supported]

### Event Architecture
- **Pattern**: [Callbacks / Listeners / Generators]
- **Registration**: [Static / Dynamic]
- **Streaming**: [Supported / Not Supported]

### Observability Inventory

| Hook | Location | Async | Modifiable |
|------|----------|-------|------------|
| on_llm_start | callbacks.py:L23 | Yes | Input only |
| on_tool_end | callbacks.py:L45 | Yes | Output |
| ... | ... | ... | ... |

### Scalability Assessment
- **Blocking Operations**: [List any]
- **Resource Limits**: [Token counters, rate limits]
- **Recommended Concurrency**: [Threads/Processes/AsyncIO]