feat: add log probabilities from OpenAI-compatible servers and SGLang native endpoint (#9240)

* Add log probabilities support for RL training

This enables Letta server to request and return log probabilities from
OpenAI-compatible providers (including SGLang) for use in RL training.

Changes:
- LLMConfig: Add return_logprobs and top_logprobs fields
- OpenAIClient: Set logprobs in ChatCompletionRequest when enabled
- LettaLLMAdapter: Add logprobs field and extract from response
- LettaResponse: Add logprobs field to return log probs to client
- LettaRequest: Add return_logprobs/top_logprobs for per-request override
- LettaAgentV3: Store and pass logprobs through to response
- agents.py: Handle request-level logprobs override

Usage:
  response = client.agents.messages.create(
      agent_id=agent_id,
      messages=[...],
      return_logprobs=True,
      top_logprobs=5,
  )
  print(response.logprobs)  # Per-token log probabilities

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

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

* Add multi-turn token tracking for RL training via SGLang native endpoint

- Add TurnTokenData schema to track token IDs and logprobs per turn
- Add return_token_ids flag to LettaRequest and LLMConfig
- Create SGLangNativeClient for /generate endpoint (returns output_ids)
- Create SGLangNativeAdapter that uses native endpoint
- Modify LettaAgentV3 to accumulate turns across LLM calls
- Include turns in LettaResponse when return_token_ids=True

* Fix: Add SGLang native adapter to step() method, not just stream()

* Fix: Handle Pydantic Message objects in SGLang native adapter

* Fix: Remove api_key reference from LLMConfig (not present)

* Fix: Add missing 'created' field to ChatCompletionResponse

* Add full tool support to SGLang native adapter

- Format tools into prompt in Qwen-style format
- Parse tool calls from <tool_call> tags in response
- Format tool results as <tool_response> in user messages
- Set finish_reason to 'tool_calls' when tools are called

* Use tokenizer.apply_chat_template for proper tool formatting

- Add tokenizer caching in SGLang native adapter
- Use apply_chat_template when tokenizer available
- Fall back to manual formatting if not
- Convert Letta messages to OpenAI format for tokenizer

* Fix: Use func_response instead of tool_return for ToolReturn content

* Fix: Get output_token_logprobs from meta_info in SGLang response

* Fix: Allow None in output_token_logprobs (SGLang format includes null)

* chore: remove unrelated files from logprobs branch

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

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

* fix: add missing call_type param to adapter constructors in letta_agent_v3

The SGLang refactor dropped call_type=LLMCallType.agent_step when extracting
adapter creation into conditional blocks. Restores it for all 3 spots (SGLang
in step, SimpleLLM in step, SGLang in stream).

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

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

* just stage-api && just publish-api

* fix: update expected LLMConfig fields in schema test for logprobs support

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

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

* chore: remove rllm provider references

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

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

* just stage-api && just publish-api

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

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

---------

Co-authored-by: Ubuntu <ubuntu@ip-172-31-65-206.ec2.internal>
Co-authored-by: Letta <noreply@letta.com>

letta/llm_api/openai_client.py

@@ -511,6 +511,12 @@ class OpenAIClient(LLMClientBase):
         if llm_config.frequency_penalty is not None:
             data.frequency_penalty = llm_config.frequency_penalty
 
+        # Add logprobs configuration for RL training
+        if llm_config.return_logprobs:
+            data.logprobs = True
+            if llm_config.top_logprobs is not None:
+                data.top_logprobs = llm_config.top_logprobs
+
         if tools and supports_parallel_tool_calling(model):
             data.parallel_tool_calls = False