aiskillstore/execution-engine-analysis

Execution Engine Analysis

Analyzes the control flow substrate and concurrency model.

Process

1. Identify async model — Native async, sync-with-wrappers, or hybrid
2. Classify topology — DAG, FSM, or linear chain
3. Catalog events — Callbacks, listeners, generators
4. Map observability — Pre/post hooks, interception points

Concurrency Model Classification

Native Async

# 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())

Indicators: async def, await, asyncio.gather, aiohttp

Sync with Wrappers

# 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()

Indicators: asyncio.run() inside sync methods, ThreadPoolExecutor, run_in_executor

Hybrid

# 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)

Indicators: Paired methods (invoke/ainvoke), sync_to_async decorators

Execution Topology

DAG (Directed Acyclic Graph)

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

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

Indicators: Graph, Node, Edge classes, networkx, topological sort

FSM (Finite State Machine)

# 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

Indicators: State enums, transition tables, current_state, state machine libraries

Linear Chain

# 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)

Indicators: Sequential calls, pipe operators (|), Chain, Pipeline classes

Event Architecture

Callbacks

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): ...

Flexibility: Low — fixed hook points Traceability: Medium — easy to follow

Event Listeners/Emitters

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

Flexibility: High — dynamic registration Traceability: Low — harder to trace

Async Generators (Streaming)

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

Flexibility: Medium — streaming-native Traceability: High — follows data flow

Observability Hooks Inventory

| Hook Point | Purpose | Interception Level | |------------|---------|-------------------| | Pre-LLM | Modify prompt | Input | | Post-LLM | Access raw response | Output | | Pre-Tool | Validate tool input | Input | | Post-Tool | Transform tool output | Output | | Pre-Step | Observe state | Read-only | | Post-Step | Modify next step | Control flow | | On-Error | Handle/transform | Error |

Questions to Answer

• Can you intercept tool input before execution?
• Is the raw LLM response accessible (with token counts)?
• Can you modify control flow from hooks?
• Are hooks sync or async?

Output Template

## 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]

Integration

• Prerequisite: codebase-mapping to identify execution files
• Feeds into: comparative-matrix for async decisions
• Related: control-loop-extraction for agent-specific flow