feat: add LLM wrapper and interceptors for LLM calls

[ankaisen]

No description provided.

[Copilot]

Pull request overview

This PR introduces an LLM wrapper system with interceptors for LLM calls, enabling observability and control over LLM interactions. The implementation adds infrastructure for monitoring, tracing, and intercepting LLM operations before, after, and on error.

Key changes:

• New LLMClientWrapper and LLMInterceptorRegistry for wrapping LLM clients and managing interceptors
• Modified workflow step execution to include step_id in context for better traceability
• Enhanced service layer to integrate LLM wrapper with interceptor registration methods

Reviewed changes

Copilot reviewed 3 out of 3 changed files in this pull request and generated 2 comments.

File	Description
src/memu/llm/wrapper.py	Implements core LLM wrapper infrastructure including interceptor registry, client wrapper, and associated dataclasses for call context, request/response views, and usage tracking
src/memu/app/service.py	Integrates LLM wrapper into the service layer by adding interceptor registry, wrapping LLM clients with metadata, and exposing interceptor registration methods
src/memu/workflow/step.py	Ensures step_id is always included in step context for LLM call traceability

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[Copilot]

Potential IndexError if response is an empty sequence of sequences. The condition checks if response is truthy, but an empty list evaluates to False, while a list containing empty sequences like [[]] would pass the check but fail on response[0]. Consider checking response and response[0] or handling the case where response contains empty sequences.

[Copilot]

The call_fn() invocation is missing await. Since call_fn is expected to return an awaitable (as indicated by lines 363-364), this should be result = await call_fn() to properly await the coroutine. The current code relies on checking inspect.isawaitable(result) afterwards, but this creates an unawaited coroutine that may trigger runtime warnings.