feat: centralize telemetry logging at LLM client level (#8815)

* feat: centralize telemetry logging at LLM client level

Moves telemetry logging from individual adapters to LLMClientBase:
- Add TelemetryStreamWrapper for streaming telemetry on stream close
- Add request_async_with_telemetry() for non-streaming requests
- Add stream_async_with_telemetry() for streaming requests
- Add set_telemetry_context() to configure agent_id, run_id, step_id

Updates adapters and agents to use new pattern:
- LettaLLMAdapter now accepts agent_id/run_id in constructor
- Adapters call set_telemetry_context() before LLM requests
- Removes duplicate telemetry logging from adapters
- Enriches traces with agent_id, run_id, call_type metadata

🐙 Generated with [Letta Code](https://letta.com)

Co-Authored-By: Letta <noreply@letta.com>

* fix: accumulate streaming response content for telemetry

TelemetryStreamWrapper now extracts actual response data from chunks:
- Content text (concatenated from deltas)
- Tool calls (id, name, arguments)
- Model name, finish reason, usage stats

🐙 Generated with [Letta Code](https://letta.com)

Co-Authored-By: Letta <noreply@letta.com>

* refactor: move streaming telemetry to caller (option 3)

- Remove TelemetryStreamWrapper class
- Add log_provider_trace_async() helper to LLMClientBase
- stream_async_with_telemetry() now just returns raw stream
- Callers log telemetry after processing with rich interface data

Updated callers:
- summarizer.py: logs content + usage after stream processing
- letta_agent.py: logs tool_call, reasoning, model, usage

🐙 Generated with [Letta Code](https://letta.com)

Co-Authored-By: Letta <noreply@letta.com>

* fix: pass agent_id and run_id to parent adapter class

LettaLLMStreamAdapter was not passing agent_id/run_id to parent,
causing "unexpected keyword argument" errors.

🐙 Generated with [Letta Code](https://letta.com)

Co-Authored-By: Letta <noreply@letta.com>

---------

Co-authored-by: Letta <noreply@letta.com>

letta/server/rest_api/routers/v1/tools.py

@@ -952,7 +952,13 @@ async def generate_tool_from_prompt(
         llm_config,
         tools=[tool],
     )
-    response_data = await llm_client.request_async(request_data, llm_config)
+    from letta.services.telemetry_manager import TelemetryManager
+
+    llm_client.set_telemetry_context(
+        telemetry_manager=TelemetryManager(),
+        call_type="tool_generation",
+    )
+    response_data = await llm_client.request_async_with_telemetry(request_data, llm_config)
     response = await llm_client.convert_response_to_chat_completion(response_data, input_messages, llm_config)
 
     # Validate that we got a tool call response