feat(ci): self-hosted provider test for lmstudio (#9404)

* add gpu runners and prod memory_repos

* add lmstudio and vllm in model_settings

* fix llm_configs and change variable name in reusable workflow and change perms for memory_repos to admin in tf

* fix: update self-hosted provider tests to use SDK 1.0 and v2 tests

- Update letta-client from ==0.1.324 to >=1.0.0
- Switch ollama/vllm/lmstudio tests to integration_test_send_message_v2.py

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

* fix: use openai provider_type for self-hosted model settings

ollama/vllm/lmstudio are not valid provider_type values in the SDK
model_settings schema - they use openai-compatible APIs so provider_type
should be openai. The provider routing is determined by the handle prefix.

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

* fix: enable redis for ollama/vllm/lmstudio tests

Background streaming tests require Redis. Add use-redis: true to
self-hosted provider test workflows.

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

* prep for lmstudio and vllm

* used lmstudio_openai client

* change tool call parser from hermes to qwen3_xml

* qwen3_xmlk -> qwen3_coder

* revert to hermes (incompatible with parallel tool calls?) and skipping vllm tests on parallel tool calls

* install uv redis extra

* remove lmstudio

* create lmstudio test

* qwen3-14b on lmstudio

* try with qwen3-4b

* actually update the model config json to use qwen3-4b

* add test_providers::test_lmstudio

* bump timeout from 60 to 120 for slow lmstudio on cpu model

* misc vllm changes

---------

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

tests/integration_test_send_message_v2.py

@@ -213,7 +213,7 @@ def assert_tool_call_response(
         "claude-sonnet-4-5-20250929" in model_handle
         or "claude-opus-4-1" in model_handle
         or model_settings.get("provider_type") == "zai"
-        or model_handle.startswith(("ollama/", "vllm/"))
+        or model_handle.startswith(("ollama/", "vllm/", "lmstudio_openai/"))
     )
     if is_extra_assistant_model and index < len(messages) and isinstance(messages[index], AssistantMessage):
         # Skip the extra AssistantMessage and move to the next message
@@ -444,8 +444,8 @@ def get_expected_message_count_range(
             if model_settings.get("provider_type") == "zai":
                 expected_range += 1
 
-    # Self-hosted models (ollama/vllm) may emit an extra AssistantMessage with thinking content
-    if model_handle.startswith(("ollama/", "vllm/")):
+    # Self-hosted models (ollama/vllm/lmstudio) may emit an extra AssistantMessage with thinking content
+    if model_handle.startswith(("ollama/", "vllm/", "lmstudio/", "lmstudio_openai/")):
         expected_range += 1
 
     if tool_call:
@@ -621,7 +621,7 @@ async def test_greeting(
             agent_id=agent_state.id,
             messages=USER_MESSAGE_FORCE_REPLY,
         )
-        run = await wait_for_run_completion(client, run.id, timeout=60.0)
+        run = await wait_for_run_completion(client, run.id, timeout=120.0)
         messages_page = await client.runs.messages.list(run_id=run.id)
         messages = [m for m in messages_page.items if m.message_type != "user_message"]
         run_id = run.id
@@ -687,6 +687,12 @@ async def test_parallel_tool_calls(
     if provider_type in ["google_ai", "google_vertex"]:
         pytest.skip("Gemini models are flaky for this test so we disable them for now")
 
+    if model_handle.startswith("lmstudio"):
+        pytest.skip("LMStudio runs on CPU and times out on parallel tool call tests")
+
+    if model_handle.startswith("vllm"):
+        pytest.skip("vLLM Qwen3 tool call parsers incompatible with streaming parallel tool calls")
+
     if model_handle.startswith("vllm"):
         pytest.skip("vLLM Qwen3 tool call parsers incompatible with streaming parallel tool calls")
 
@@ -710,7 +716,7 @@ async def test_parallel_tool_calls(
             agent_id=agent_state.id,
             messages=USER_MESSAGE_PARALLEL_TOOL_CALL,
         )
-        await wait_for_run_completion(client, run.id, timeout=60.0)
+        await wait_for_run_completion(client, run.id, timeout=120.0)
     else:
         response = await client.agents.messages.stream(
             agent_id=agent_state.id,
@@ -899,7 +905,7 @@ async def test_tool_call(
             agent_id=agent_state.id,
             messages=USER_MESSAGE_ROLL_DICE,
         )
-        run = await wait_for_run_completion(client, run.id, timeout=60.0)
+        run = await wait_for_run_completion(client, run.id, timeout=120.0)
         messages_page = await client.runs.messages.list(run_id=run.id)
         messages = [m for m in messages_page.items if m.message_type != "user_message"]
         run_id = run.id
@@ -1090,7 +1096,7 @@ async def test_conversation_non_streaming_raw_http(
 
 
 @pytest.mark.skipif(
-    os.getenv("LLM_CONFIG_FILE", "").startswith(("ollama", "vllm")),
+    os.getenv("LLM_CONFIG_FILE", "").startswith(("ollama", "vllm", "lmstudio")),
     reason="Structured output not supported on self-hosted providers in CI",
 )
 @pytest.mark.parametrize(

tests/model_settings/lmstudio.json

@@ -0,0 +1,9 @@
+{
+  "handle": "lmstudio_openai/qwen3-4b",
+  "model_settings": {
+    "provider_type": "openai",
+    "temperature": 0.7,
+    "max_output_tokens": 4096,
+    "parallel_tool_calls": true
+  }
+}

tests/test_providers.py

@@ -233,6 +233,21 @@ async def test_vllm():
     assert len(embedding_models) == 0  # embedding models currently not supported by vLLM
 
 
+@pytest.mark.skipif(model_settings.lmstudio_base_url is None, reason="Only run if LMSTUDIO_BASE_URL is set.")
+@pytest.mark.asyncio
+async def test_lmstudio():
+    from letta.schemas.providers.lmstudio import LMStudioOpenAIProvider
+
+    provider = LMStudioOpenAIProvider(name="lmstudio_openai", base_url=model_settings.lmstudio_base_url)
+    models = await provider.list_llm_models_async()
+    assert len(models) > 0
+    assert models[0].handle == f"{provider.name}/{models[0].model}"
+
+    embedding_models = await provider.list_embedding_models_async()
+    assert len(embedding_models) > 0
+    assert embedding_models[0].handle == f"{provider.name}/{embedding_models[0].embedding_model}"
+
+
 @pytest.mark.skipif(model_settings.sglang_api_base is None, reason="Only run if SGLANG_API_BASE is set.")
 @pytest.mark.asyncio
 async def test_sglang():