diff --git a/.env.example b/.env.example
index 1a6534eb..93c6616f 100644
--- a/.env.example
+++ b/.env.example
@@ -10,3 +10,6 @@
 
 # Dev Server
 # INNGEST_BASE_URL=http://localhost:8288
+
+# Testing models
+# GEMINI_API_KEY=00000000000000000000000000000000000000000000000000
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index fddfafba..b7343c51 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -37,10 +37,12 @@ Run things individually:
 make format-check
 make lint
 make type-check
+make type-check-pyright
 make utest
 
 # Changes code
 make format
+make lint-fix
 
 # Unit tests
 make utest
diff --git a/Makefile b/Makefile
index 0a1d1952..bd0a4c13 100644
--- a/Makefile
+++ b/Makefile
@@ -11,7 +11,7 @@ itest:
 	@cd pkg/inngest && make itest
 	@cd pkg/inngest_encryption && make itest
 
-pre-commit: format-check lint type-check utest
+pre-commit: format-check lint type-check type-check-pyright utest
 
 lint:
 	@cd examples && make lint
@@ -20,6 +20,13 @@ lint:
 	@cd pkg/test_core && make lint
 	@cd tests && make lint
 
+lint-fix:
+	@cd examples && make lint-fix
+	@cd pkg/inngest && make lint-fix
+	@cd pkg/inngest_encryption && make lint-fix
+	@cd pkg/test_core && make lint-fix
+	@cd tests && make lint-fix
+
 type-check:
 	@cd examples && make type-check
 	@cd pkg/inngest && make type-check
diff --git a/examples/Makefile b/examples/Makefile
index ef53f2fd..f2edcb92 100644
--- a/examples/Makefile
+++ b/examples/Makefile
@@ -4,6 +4,10 @@ export MYPYPATH=../pkg/inngest:../pkg/inngest_encryption:../pkg/test_core
 lint:
 	@uv run ruff check .
 
+.PHONY: lint-fix
+lint-fix:
+	@uv run ruff check . --fix
+
 .PHONY: type-check
 type-check:
 	@uv run mypy --config-file=../mypy.ini ./connect
diff --git a/pkg/inngest/Makefile b/pkg/inngest/Makefile
index 0022287b..d58a716a 100644
--- a/pkg/inngest/Makefile
+++ b/pkg/inngest/Makefile
@@ -11,6 +11,10 @@ itest:
 lint:
 	@uv run ruff check .
 
+.PHONY: lint-fix
+lint-fix:
+	@uv run ruff check . --fix
+
 .PHONY: proto
 proto:
 	@uv run protoc --proto_path=./inngest/experimental/connect/ --python_out=./inngest/experimental/connect/ ./inngest/experimental/connect/connect.proto --pyi_out=./inngest/experimental/connect/
diff --git a/pkg/inngest/inngest/experimental/ai/__init__.py b/pkg/inngest/inngest/experimental/ai/__init__.py
index ccf1dc32..9bbe8899 100644
--- a/pkg/inngest/inngest/experimental/ai/__init__.py
+++ b/pkg/inngest/inngest/experimental/ai/__init__.py
@@ -6,7 +6,14 @@
 code.
 """
 
-from . import anthropic, openai
+from . import anthropic, deepseek, gemini, grok, openai
 from .base import BaseAdapter
 
-__all__ = ["BaseAdapter", "anthropic", "openai"]
+__all__ = [
+    "BaseAdapter",
+    "anthropic",
+    "deepseek",
+    "gemini",
+    "grok",
+    "openai",
+]
diff --git a/pkg/inngest/inngest/experimental/ai/gemini.py b/pkg/inngest/inngest/experimental/ai/gemini.py
index 8fc8813d..a9399197 100644
--- a/pkg/inngest/inngest/experimental/ai/gemini.py
+++ b/pkg/inngest/inngest/experimental/ai/gemini.py
@@ -1,72 +1,289 @@
 from __future__ import annotations
 
+import logging
+from typing import Any, Optional
+
 from .base import BaseAdapter
 
+# Set up logging
+logger = logging.getLogger(__name__)
 
-class Adapter(BaseAdapter):
+
+class ThinkingConfig:
     """
-    Gemini adapter
+    Configuration for the 'thinking' feature in Gemini models.
+
+    This allows the model to output its reasoning process (thoughts) before
+    providing the final answer.
     """
 
     def __init__(
         self,
         *,
-        auth_key: str,
-        base_url: str | None = None,
-        headers: dict[str, str] | None = None,
-        model: str,
+        thinking_budget: Optional[int] = None,
+        include_thoughts: Optional[bool] = None,
     ) -> None:
         """
         Args:
-        ----
-            auth_key: Gemini API key.
-            base_url: Gemini API URL.
-            headers: Additional headers to send with the request.
-            model: Gemini model to use.
+            thinking_budget: The token budget allocated for thinking.
+            include_thoughts: Whether to include the thinking process in the output.
         """
+        self.thinking_budget = thinking_budget
+        self.include_thoughts = include_thoughts
+
+    def to_dict(self) -> dict[str, Any]:
+        """Convert to dictionary for API request."""
+        result: dict[str, Any] = {}
+        if self.thinking_budget is not None:
+            result["thinkingBudget"] = self.thinking_budget
+        if self.include_thoughts is not None:
+            result["includeThoughts"] = self.include_thoughts
+        return result
+
+
+class GenerationConfig:
+    r"""
+    Configuration options for model generation and outputs.
+
+    Use this class to control how Gemini generates responses, from basic parameters
+    like temperature and token limits to advanced features like thinking, structured output,
+    and multimodal responses.
+
+    **Example Basic Usage:**
+    ```python
+    config = GenerationConfig(
+        temperature=0.7,
+        max_output_tokens=1000,
+        stop_sequences=["\n\n"]
+    )
+    ```
+
+    **Example Structured JSON Output:**
+    ```python
+    config = GenerationConfig(
+        response_mime_type="application/json",
+        response_schema={
+            "type": "object",
+            "properties": {
+                "name": {"type": "string"},
+                "age": {"type": "number"}
+            }
+        }
+    )
+    ```
 
+    **Example Thinking Mode:**
+    ```python
+    config = GenerationConfig(
+        thinking_config=ThinkingConfig(
+            thinking_budget=2048,  # Higher budget for complex tasks
+            include_thoughts=True   # Get reasoning insights
+        )
+    )
+    ```
+    """
+
+    def __init__(
+        self,
+        *,
+        temperature: Optional[float] = None,
+        top_p: Optional[float] = None,
+        top_k: Optional[int] = None,
+        max_output_tokens: Optional[int] = None,
+        stop_sequences: Optional[list[str]] = None,
+        presence_penalty: Optional[float] = None,
+        frequency_penalty: Optional[float] = None,
+        response_mime_type: Optional[str] = None,
+        response_schema: Optional[dict[str, Any]] = None,
+        response_json_schema: Optional[Any] = None,
+        response_modalities: Optional[list[str]] = None,
+        candidate_count: Optional[int] = None,
+        seed: Optional[int] = None,
+        logprobs: Optional[int] = None,
+        enable_enhanced_civic_answers: Optional[bool] = None,
+        speech_config: Optional[dict[str, Any]] = None,
+        thinking_config: Optional[ThinkingConfig] = None,
+        media_resolution: Optional[str] = None,
+    ) -> None:
+        """Initializes the GenerationConfig."""
+        self.temperature = temperature
+        self.top_p = top_p
+        self.top_k = top_k
+        self.max_output_tokens = max_output_tokens
+        self.stop_sequences = stop_sequences
+        self.presence_penalty = presence_penalty
+        self.frequency_penalty = frequency_penalty
+        self.response_mime_type = response_mime_type
+        self.response_schema = response_schema
+        self.response_json_schema = response_json_schema
+        self.response_modalities = response_modalities
+        self.candidate_count = candidate_count
+        self.seed = seed
+        self.logprobs = logprobs
+        self.enable_enhanced_civic_answers = enable_enhanced_civic_answers
+        self.speech_config = speech_config
+        self.thinking_config = thinking_config
+        self.media_resolution = media_resolution
+
+    def to_dict(self) -> dict[str, Any]:
+        """Convert to dictionary for API request."""
+        result: dict[str, Any] = {}
+        if self.temperature is not None:
+            result["temperature"] = self.temperature
+        if self.top_p is not None:
+            result["topP"] = self.top_p
+        if self.top_k is not None:
+            result["topK"] = self.top_k
+        if self.max_output_tokens is not None:
+            result["maxOutputTokens"] = self.max_output_tokens
+        if self.stop_sequences is not None:
+            result["stopSequences"] = self.stop_sequences
+        if self.presence_penalty is not None:
+            result["presencePenalty"] = self.presence_penalty
+        if self.frequency_penalty is not None:
+            result["frequencyPenalty"] = self.frequency_penalty
+        if self.response_mime_type is not None:
+            result["responseMimeType"] = self.response_mime_type
+        if self.response_schema is not None:
+            result["responseSchema"] = self.response_schema
+        if self.response_json_schema is not None:
+            result["response_json_schema"] = self.response_json_schema
+        if self.response_modalities is not None:
+            result["responseModalities"] = self.response_modalities
+        if self.candidate_count is not None:
+            result["candidateCount"] = self.candidate_count
+        if self.seed is not None:
+            result["seed"] = self.seed
+        if self.logprobs is not None:
+            result["logprobs"] = self.logprobs
+        if self.enable_enhanced_civic_answers is not None:
+            result["enableEnhancedCivicAnswers"] = (
+                self.enable_enhanced_civic_answers
+            )
+        if self.speech_config is not None:
+            result["speechConfig"] = self.speech_config
+        if self.thinking_config is not None:
+            result["thinkingConfig"] = self.thinking_config.to_dict()
+        if self.media_resolution is not None:
+            result["mediaResolution"] = self.media_resolution
+        return result
+
+
+class Adapter(BaseAdapter):
+    """An adapter for the Gemini API."""
+
+    def __init__(
+        self,
+        *,
+        auth_key: str,
+        model: str,
+        base_url: Optional[str] = None,
+        headers: Optional[dict[str, str]] = None,
+        generation_config: Optional[GenerationConfig] = None,
+        tools: Optional[list[dict[str, Any]]] = None,
+        tool_config: Optional[dict[str, Any]] = None,
+        safety_settings: Optional[list[dict[str, Any]]] = None,
+        system_instruction: Optional[dict[str, Any]] = None,
+        cached_content: Optional[str] = None,
+    ) -> None:
+        """Initializes the Adapter."""
         self._auth_key = auth_key
-        self._headers = headers or {}
         self._model = model
-        self._url = (
-            base_url or "https://generativelanguage.googleapis.com/v1beta/"
+        self._base_url = (
+            base_url or "https://generativelanguage.googleapis.com/v1beta"
+        )
+        self._headers = headers or {}
+        self._generation_config = generation_config
+        self._tools = tools
+        self._tool_config = tool_config
+        self._safety_settings = safety_settings
+        self._system_instruction = system_instruction
+        self._cached_content = cached_content
+
+        # Debug logging
+        masked_key = (
+            f"{auth_key[:10]}...{auth_key[-4:]}"
+            if len(auth_key) > 14
+            else "EMPTY_OR_SHORT_KEY"
         )
+        logger.info(f"Gemini adapter initialized with model: {model}")
+        logger.info(f"Gemini API key (masked): {masked_key}")
+        logger.info(f"Gemini base URL: {self._base_url}")
+
+    @property
+    def provider(self) -> str:
+        """Returns the provider name."""
+        return "gemini"
 
     def auth_key(self) -> str:
         """
         Return the authentication key for the adapter.
         """
-
         return self._auth_key
 
     def format(self) -> str:
         """
         Return the format for the adapter.
         """
-
         return "gemini"
 
     def headers(self) -> dict[str, str]:
         """
         Return the headers for the adapter.
         """
-
         return self._headers
 
     def on_call(self, body: dict[str, object]) -> None:
         """
-        Modify the request body.
+        Modify the request body to include all Gemini-specific parameters.
         """
+        # Convert OpenAI-style 'messages' to Gemini's 'contents' format if necessary
+        if "messages" in body and "contents" not in body:
+            messages = body.pop("messages")
+            if isinstance(messages, list):
+                body["contents"] = self._convert_messages_to_contents(messages)
+
+        # Add all generation config parameters
+        if self._generation_config:
+            body["generationConfig"] = self._generation_config.to_dict()
 
-        if not body.get("model"):
-            body["model"] = self._model
+        # Add other Gemini-specific parameters
+        if self._tools:
+            body["tools"] = self._tools
+        if self._tool_config:
+            body["toolConfig"] = self._tool_config
+        if self._safety_settings:
+            body["safetySettings"] = self._safety_settings
+        if self._system_instruction:
+            body["systemInstruction"] = self._system_instruction
+        if self._cached_content:
+            body["cachedContent"] = self._cached_content
 
     def url_infer(self) -> str:
+        """Returns the full URL for the API request."""
+        # For Gemini, the API key is passed as a query parameter
+        url = f"{self._base_url}/models/{self._model}:generateContent?key={self._auth_key}"
+        logger.info(
+            f"Gemini URL for inference: {url[: url.find('key=') + 4]}{self._auth_key[:10]}...{self._auth_key[-4:]}"
+        )
+        return url
+
+    def _convert_messages_to_contents(
+        self, messages: list[dict[str, Any]]
+    ) -> list[dict[str, Any]]:
         """
-        Return the URL for generating text with the model included in the path.
+        Convert a list of OpenAI-style messages to Gemini's 'contents' format.
         """
+        contents = []
+        for message in messages:
+            role = message.get("role")
+            content = message.get("content")
 
-        return (
-            self._url.rstrip("/")
-            + f"/models/{self._model}:generateContent?key={self._auth_key}"
-        )
+            # Map 'assistant' role to 'model' for Gemini
+            if role == "assistant":
+                role = "model"
+
+            if role in ["user", "model"]:
+                contents.append({"role": role, "parts": [{"text": content}]})
+
+        return contents
diff --git a/pkg/inngest/pyproject.toml b/pkg/inngest/pyproject.toml
index 4c4a4a00..a8a7efdf 100644
--- a/pkg/inngest/pyproject.toml
+++ b/pkg/inngest/pyproject.toml
@@ -1,6 +1,6 @@
 [project]
 name = "inngest"
-version = "0.5.1"
+version = "0.5.2"
 authors = [{ name = "Inngest Inc.", email = "hello@inngest.com" }]
 description = "Python SDK for Inngest"
 readme = "README.md"
diff --git a/pkg/inngest_encryption/Makefile b/pkg/inngest_encryption/Makefile
index 3fa61122..fd4eea4f 100644
--- a/pkg/inngest_encryption/Makefile
+++ b/pkg/inngest_encryption/Makefile
@@ -13,6 +13,10 @@ itest:
 lint:
 	@uv run ruff check .
 
+.PHONY: lint-fix
+lint-fix:
+	@uv run ruff check . --fix
+
 release:
 	@grep "version = \"$${VERSION}\"" pyproject.toml && git tag inngest_encryption@$${VERSION} && git push origin inngest_encryption@$${VERSION} || echo "pyproject.toml version does not match"
 
diff --git a/pkg/test_core/Makefile b/pkg/test_core/Makefile
index 69a0cbf1..57e44328 100644
--- a/pkg/test_core/Makefile
+++ b/pkg/test_core/Makefile
@@ -4,6 +4,10 @@ export MYPYPATH=../inngest
 lint:
 	@uv run ruff check .
 
+.PHONY: lint-fix
+lint-fix:
+	@uv run ruff check . --fix
+
 .PHONY: type-check
 type-check:
 	@uv run mypy --config-file=../../mypy.ini .
diff --git a/tests/Makefile b/tests/Makefile
index 00dbabdf..c9104777 100644
--- a/tests/Makefile
+++ b/tests/Makefile
@@ -4,6 +4,10 @@ export MYPYPATH=../pkg/inngest:../pkg/inngest_encryption:../pkg/test_core
 lint:
 	@uv run ruff check .
 
+.PHONY: lint-fix
+lint-fix:
+	@uv run ruff check . --fix
+
 .PHONY: type-check
 type-check:
 	@uv run mypy --config-file=../mypy.ini .
diff --git a/tests/smoke_tests/model_adapters/Makefile b/tests/smoke/models/Makefile
similarity index 100%
rename from tests/smoke_tests/model_adapters/Makefile
rename to tests/smoke/models/Makefile
diff --git a/tests/smoke/models/README.md b/tests/smoke/models/README.md
new file mode 100644
index 00000000..e4173133
--- /dev/null
+++ b/tests/smoke/models/README.md
@@ -0,0 +1,218 @@
+# AI Model Adapters Smoke Test
+
+This smoke test validates that all AI model adapters work correctly with their respective APIs. It runs comprehensive tests across multiple AI providers in parallel for fast execution.
+
+## What it tests
+
+This test suite includes **one main orchestrator function** that runs all model adapter tests in parallel:
+
+### `test_all_adapters` (triggered by `test-all-adapters` event)
+
+This comprehensive test runs **6 model adapter tests simultaneously**:
+
+1.  **OpenAI** - Basic text generation (o4-mini-2025-04-16)
+2.  **Anthropic** - Basic text generation (claude-3-5-sonnet-latest)
+3.  **Gemini Basic** - Basic text generation (gemini-2.0-flash-exp)
+4.  **Gemini Enhanced** - 7 comprehensive feature tests (gemini-2.0-flash-thinking-exp)
+5.  **Grok** - Basic text generation (grok-3-latest)
+6.  **DeepSeek** - Basic text generation (deepseek-chat)
+
+## Setup
+
+1.  **Environment Variables**: Copy the example environment variables into a `.env` file located in the root of the inngest-py project and fill in your API keys. Your `.env` file should look like this:
+
+    ```
+    INNGEST_BASE_URL=http://localhost:8288
+    OPENAI_API_KEY=your_openai_api_key_here
+    ANTHROPIC_API_KEY=your_anthropic_api_key_here
+    GEMINI_API_KEY=your_gemini_api_key_here
+    GROK_API_KEY=your_grok_api_key_here
+    DEEPSEEK_API_KEY=your_deepseek_api_key_here
+    ```
+
+2.  **Virtual Environment**: Make sure you have activated your virtual environment from the project root:
+
+    ```bash
+    source .venv/bin/activate
+    ```
+
+3.  **Install Dependencies**: Make sure all dependencies are installed:
+    ```bash
+    make install
+    ```
+
+## Running the test
+
+Navigate to the smoke test directory and run:
+
+```bash
+cd tests/smoke/models
+make dev
+```
+
+This will:
+
+- Start a FastAPI server on port 8000
+- Load your environment variables from the project root `.env` file
+- Expose the orchestrator function
+
+## Triggering the tests
+
+Once the server is running, trigger the comprehensive test by sending a `test-all-adapters` event via the Inngest dev server at `http://localhost:8288`.
+
+## What to expect in the Inngest Dev Console
+
+### ✅ **Successful Test Run**
+
+You should see **one function execution** with the following structure:
+
+```
+Function: all-model-adapters-test
+Event: test-all-adapters
+Status: ✅ Completed
+Duration: ~5-10 seconds (parallel execution)
+```
+
+### **Expected Response Structure**
+
+The function returns a comprehensive results object with 6 top-level keys:
+
+```json
+{
+  "openai": { "success": true, "response": "Hello! I'm OpenAI's GPT..." },
+  "anthropic": { "success": true, "response": "Hello! I'm Claude..." },
+  "gemini": { "success": true, "response": "Hello! I'm Gemini..." },
+  "gemini_enhanced": {
+    "pure_thinking_test": {
+      "success": true,
+      "response": "🧠 Reasoning: ... 💡 Answer: ..."
+    },
+    "mixed_thinking_test": { "success": true, "response": "..." },
+    "workflow_test": { "success": true, "response": "..." },
+    "structured_test": {
+      "success": true,
+      "response": "{\"name\":\"...\",\"age\":25,...}"
+    },
+    "stop_sequences_test": {
+      "success": true,
+      "response": "1 2 3 4 5",
+      "contains_stop": false
+    },
+    "conversion_test": { "success": true, "response": "8" },
+    "error_handling_test": {
+      "success": true,
+      "error_caught": "API key not valid..."
+    }
+  },
+  "grok": { "success": true, "response": "Hello! I'm Grok..." },
+  "deepseek": { "success": true, "response": "Hello! I'm DeepSeek..." }
+}
+```
+
+## Test Case Details
+
+### **Basic Model Tests (OpenAI, Anthropic, Gemini Basic, Grok, DeepSeek)**
+
+**What they test:**
+
+- ✅ Successful API connection
+- ✅ Request format handling
+- ✅ Response parsing
+- ✅ Basic text generation
+
+**Expected success criteria:**
+
+- `"success": true`
+- `"response"` contains meaningful text about the AI model
+- `"structure_validation"` shows proper response structure
+- `"usage_validation"` shows token count metadata
+
+### **Enhanced Gemini Test Suite**
+
+#### **1. Pure Thinking Test**
+
+- **What it tests**: Gemini's step-by-step reasoning with `thinkingConfig`
+- **Expected success**: Response contains both reasoning (🧠) and answer (💡) sections
+- **Question**: "Think through this step by step: If I have 15 apples and give away 7, then buy 12 more, how many do I have?"
+
+#### **2. Mixed Thinking Test**
+
+- **What it tests**: Both internal thoughts and final answers
+- **Expected success**: Shows thinking process + clear final answer
+- **Question**: "Calculate 15 - 7 + 12. Think about it first, then give me the final answer clearly."
+
+#### **3. Workflow Test**
+
+- **What it tests**: Complete thinking workflow with sufficient token budget
+- **Expected success**: Contains "20" or "twenty" in response
+- **Question**: "What's 8 + 12? Think through it step by step, then give me just the number."
+
+#### **4. Structured Test**
+
+- **What it tests**: JSON schema-based responses
+- **Expected success**: Valid JSON with `name`, `age`, `hobbies` fields
+- **Question**: "Generate a fictional person profile with name, age, and hobbies."
+
+#### **5. Stop Sequences Test**
+
+- **What it tests**: Proper handling of stop sequences
+- **Expected success**: Contains "5" but not "6" or "STOP"
+- **Question**: "Count from 1 to 10, but write STOP after 5."
+
+#### **6. Conversion Test**
+
+- **What it tests**: OpenAI-style message format conversion
+- **Expected success**: Properly handles conversation history
+- **Input**: Multi-turn conversation ending with "What's 5 + 3?"
+
+#### **7. Error Handling Test**
+
+- **What it tests**: Invalid API key handling
+- **Expected success**: `"success": true` because error was caught properly
+- **Note**: This test intentionally uses invalid credentials
+
+## Expected Behavior Summary
+
+### ✅ **All Tests Pass**
+
+- **Total duration**: ~5-10 seconds (parallel execution)
+- **6 model adapters tested simultaneously**
+- **All basic tests return `"success": true`**
+- **Enhanced Gemini shows 7 sub-tests all passing**
+- **Error handling test shows `"success": true` with caught error**
+
+### 🔍 **Key Success Indicators**
+
+1.  **Parallel Execution**: All tests complete in ~5-10 seconds (not 30+ seconds)
+2.  **API Connectivity**: All providers return valid responses
+3.  **Response Parsing**: All responses properly extracted and formatted
+4.  **Gemini Features**: Thinking, structured output, and stop sequences work
+5.  **Error Handling**: Invalid credentials properly caught and handled
+6.  **Token Validation**: Usage metadata present and valid for all providers
+
+### ❌ **Common Issues**
+
+- **Missing API keys**: Tests return `"success": false` with authentication errors
+- **Invalid API keys**: Similar to above, but specific to one provider
+- **Rate limits**: Temporary failures, retry after waiting
+- **Model availability**: Some models may not be available in all regions
+
+## Troubleshooting
+
+- **Missing or Invalid API keys**: Make sure all required API keys are set in your `.env` file and are valid
+- **Rate Limits**: Some providers may have rate limits; wait a moment and try again
+- **API Credits**: Most providers require that you purchase credits. After purchasing, you may need to wait some time for the credits to register on their system.
+- **Thinking Models**: Enhanced thinking features require Gemini 2.0 series models with thinking capabilities
+- **Structured Output**: JSON schema responses may require specific prompting for older models
+
+## New Features Demonstrated
+
+The enhanced Gemini adapter now supports:
+
+✅ **Thinking Features** - Enable step-by-step reasoning with `ThinkingConfig`
+✅ **Structured Output** - Get JSON responses with schema validation
+✅ **Advanced Generation Controls** - Temperature, top-p, token limits, stop sequences
+✅ **System Instructions** - Custom behavior and personality configuration
+✅ **Message Format Compatibility** - Seamless OpenAI-to-Gemini conversion
+✅ **Comprehensive Error Handling** - Proper API error detection and reporting
+✅ **Parallel Execution** - Fast test execution using Inngest's parallel capabilities
diff --git a/tests/smoke/models/app.py b/tests/smoke/models/app.py
new file mode 100644
index 00000000..d6b11626
--- /dev/null
+++ b/tests/smoke/models/app.py
@@ -0,0 +1,206 @@
+from typing import Any
+
+import fastapi
+import inngest
+import inngest.fast_api
+from inngest.experimental.ai.base import BaseAdapter
+
+# Import all the individual model test helpers
+from models.test_anthropic import (
+    handle_test_anthropic_response,
+    prepare_test_anthropic,
+)
+from models.test_deepseek import (
+    handle_test_deepseek_response,
+    prepare_test_deepseek,
+)
+from models.test_gemini import (
+    handle_gemini_basic_test,
+    handle_gemini_conversion_response,
+    handle_gemini_error_handling_response,
+    handle_gemini_stop_sequences_response,
+    handle_gemini_structured_response,
+    handle_gemini_thinking_response,
+    prepare_gemini_basic_test,
+    prepare_gemini_conversion_test,
+    prepare_gemini_error_handling_test,
+    prepare_gemini_mixed_thinking_test,
+    prepare_gemini_pure_thinking_test,
+    prepare_gemini_stop_sequences_test,
+    prepare_gemini_structured_test,
+    prepare_gemini_workflow_test,
+)
+from models.test_grok import handle_test_grok_response, prepare_test_grok
+from models.test_openai import handle_test_openai_response, prepare_test_openai
+
+inngest_client = inngest.Inngest(app_id="smoke-test-model-adapters-app")
+
+
+@inngest_client.create_function(
+    fn_id="all-model-adapters-test",
+    trigger=inngest.TriggerEvent(event="test-all-adapters"),
+    retries=0,
+)
+async def test_all_adapters(ctx: inngest.Context) -> dict[str, Any]:
+    """
+    Orchestrates all model adapter tests sequentially.
+    """
+    results: dict[str, Any] = {}
+    adapter: BaseAdapter
+
+    # OpenAI
+    try:
+        adapter, body = prepare_test_openai()
+        response = await ctx.step.ai.infer(
+            "test-openai", adapter=adapter, body=body
+        )
+        results["openai"] = handle_test_openai_response(response)
+    except Exception as e:
+        results["openai"] = {"success": False, "error": str(e)}
+
+    # Anthropic
+    try:
+        adapter, body = prepare_test_anthropic()
+        response = await ctx.step.ai.infer(
+            "test-anthropic", adapter=adapter, body=body
+        )
+        results["anthropic"] = handle_test_anthropic_response(response)
+    except Exception as e:
+        results["anthropic"] = {"success": False, "error": str(e)}
+
+    # Grok
+    try:
+        adapter, body = prepare_test_grok()
+        response = await ctx.step.ai.infer(
+            "test-grok", adapter=adapter, body=body
+        )
+        results["grok"] = handle_test_grok_response(response)
+    except Exception as e:
+        results["grok"] = {"success": False, "error": str(e)}
+
+    # DeepSeek
+    try:
+        adapter, body = prepare_test_deepseek()
+        response = await ctx.step.ai.infer(
+            "test-deepseek", adapter=adapter, body=body
+        )
+        results["deepseek"] = handle_test_deepseek_response(response)
+    except Exception as e:
+        results["deepseek"] = {"success": False, "error": str(e)}
+
+    # Gemini Basic
+    try:
+        adapter, body = prepare_gemini_basic_test()
+        response = await ctx.step.ai.infer(
+            "test-gemini-basic", adapter=adapter, body=body
+        )
+        results["gemini_basic"] = handle_gemini_basic_test(response)
+    except Exception as e:
+        results["gemini_basic"] = {"success": False, "error": str(e)}
+
+    # Gemini Enhanced
+    results["gemini_enhanced"] = {}
+    try:
+        adapter, body = prepare_gemini_pure_thinking_test()
+        response = await ctx.step.ai.infer(
+            "test-gemini-pure-thinking", adapter=adapter, body=body
+        )
+        results["gemini_enhanced"]["pure_thinking"] = (
+            handle_gemini_thinking_response(response)
+        )
+    except Exception as e:
+        results["gemini_enhanced"]["pure_thinking"] = {
+            "success": False,
+            "error": str(e),
+        }
+
+    try:
+        adapter, body = prepare_gemini_mixed_thinking_test()
+        response = await ctx.step.ai.infer(
+            "test-gemini-mixed-thinking", adapter=adapter, body=body
+        )
+        results["gemini_enhanced"]["mixed_thinking"] = (
+            handle_gemini_thinking_response(response)
+        )
+    except Exception as e:
+        results["gemini_enhanced"]["mixed_thinking"] = {
+            "success": False,
+            "error": str(e),
+        }
+
+    try:
+        adapter, body = prepare_gemini_workflow_test()
+        response = await ctx.step.ai.infer(
+            "test-gemini-workflow", adapter=adapter, body=body
+        )
+        results["gemini_enhanced"]["workflow"] = (
+            handle_gemini_thinking_response(response)
+        )
+    except Exception as e:
+        results["gemini_enhanced"]["workflow"] = {
+            "success": False,
+            "error": str(e),
+        }
+
+    try:
+        adapter, body = prepare_gemini_structured_test()
+        response = await ctx.step.ai.infer(
+            "test-gemini-structured", adapter=adapter, body=body
+        )
+        results["gemini_enhanced"]["structured"] = (
+            handle_gemini_structured_response(response)
+        )
+    except Exception as e:
+        results["gemini_enhanced"]["structured"] = {
+            "success": False,
+            "error": str(e),
+        }
+
+    try:
+        adapter, body = prepare_gemini_stop_sequences_test()
+        response = await ctx.step.ai.infer(
+            "test-gemini-stop-sequences", adapter=adapter, body=body
+        )
+        results["gemini_enhanced"]["stop_sequences"] = (
+            handle_gemini_stop_sequences_response(response)
+        )
+    except Exception as e:
+        results["gemini_enhanced"]["stop_sequences"] = {
+            "success": False,
+            "error": str(e),
+        }
+
+    try:
+        adapter, body = prepare_gemini_conversion_test()
+        response = await ctx.step.ai.infer(
+            "test-gemini-conversion", adapter=adapter, body=body
+        )
+        results["gemini_enhanced"]["conversion"] = (
+            handle_gemini_conversion_response(response)
+        )
+    except Exception as e:
+        results["gemini_enhanced"]["conversion"] = {
+            "success": False,
+            "error": str(e),
+        }
+
+    # Gemini Error Handling (run last)
+    try:
+        adapter, body = prepare_gemini_error_handling_test()
+        response = await ctx.step.ai.infer(
+            "test-gemini-error-handling", adapter=adapter, body=body
+        )
+        results["gemini_error_handling"] = (
+            handle_gemini_error_handling_response(response)
+        )
+    except Exception as e:
+        results["gemini_error_handling"] = {
+            "success": True,
+            "error_caught": str(e),
+        }
+
+    return results
+
+
+app = fastapi.FastAPI()
+inngest.fast_api.serve(app, inngest_client, [test_all_adapters])
diff --git a/tests/smoke_tests/model_adapters/env.example b/tests/smoke/models/env.example
similarity index 100%
rename from tests/smoke_tests/model_adapters/env.example
rename to tests/smoke/models/env.example
diff --git a/tests/smoke/models/models/__init__.py b/tests/smoke/models/models/__init__.py
new file mode 100644
index 00000000..e18449d7
--- /dev/null
+++ b/tests/smoke/models/models/__init__.py
@@ -0,0 +1 @@
+# Model test modules
diff --git a/tests/smoke/models/models/test_anthropic.py b/tests/smoke/models/models/test_anthropic.py
new file mode 100644
index 00000000..ba9c9d6d
--- /dev/null
+++ b/tests/smoke/models/models/test_anthropic.py
@@ -0,0 +1,30 @@
+import os
+from typing import Any
+
+from inngest.experimental.ai.anthropic import Adapter as AnthropicAdapter
+from utils import extract_anthropic_response
+
+# Create Anthropic adapter
+anthropic_adapter = AnthropicAdapter(
+    auth_key=os.getenv("ANTHROPIC_API_KEY") or "",
+    model="claude-3-5-sonnet-latest",
+)
+
+
+def prepare_test_anthropic() -> tuple[AnthropicAdapter, dict[str, Any]]:
+    """Prepare the request for the Anthropic adapter test."""
+    question = "What is the capital of Texas?"
+    body = {
+        "messages": [{"role": "user", "content": question}],
+        "max_tokens": 1024,
+    }
+    return (anthropic_adapter, body)
+
+
+def handle_test_anthropic_response(response: dict[str, Any]) -> dict[str, Any]:
+    """Handle the response from the Anthropic adapter test."""
+    return {
+        "success": True,
+        "response": extract_anthropic_response(response),
+        "raw_response": str(response),
+    }
diff --git a/tests/smoke/models/models/test_deepseek.py b/tests/smoke/models/models/test_deepseek.py
new file mode 100644
index 00000000..37663a07
--- /dev/null
+++ b/tests/smoke/models/models/test_deepseek.py
@@ -0,0 +1,27 @@
+import os
+from typing import Any
+
+from inngest.experimental.ai.deepseek import Adapter as DeepSeekAdapter
+from utils import extract_openai_response
+
+# Create DeepSeek adapter
+deepseek_adapter = DeepSeekAdapter(
+    auth_key=os.getenv("DEEPSEEK_API_KEY") or "",
+    model="deepseek-chat",
+)
+
+
+def prepare_test_deepseek() -> tuple[DeepSeekAdapter, dict[str, Any]]:
+    """Prepare the request for the DeepSeek adapter test."""
+    question = "What is the capital of Illinois?"
+    body = {"messages": [{"role": "user", "content": question}]}
+    return (deepseek_adapter, body)
+
+
+def handle_test_deepseek_response(response: dict[str, Any]) -> dict[str, Any]:
+    """Handle the response from the DeepSeek adapter test."""
+    return {
+        "success": True,
+        "response": extract_openai_response(response),
+        "raw_response": str(response),
+    }
diff --git a/tests/smoke/models/models/test_gemini.py b/tests/smoke/models/models/test_gemini.py
new file mode 100644
index 00000000..714313be
--- /dev/null
+++ b/tests/smoke/models/models/test_gemini.py
@@ -0,0 +1,229 @@
+import os
+from typing import Any
+
+from inngest.experimental.ai.gemini import (
+    Adapter as GeminiAdapter,
+)
+from inngest.experimental.ai.gemini import (
+    GenerationConfig,
+    ThinkingConfig,
+)
+from utils import extract_gemini_response, extract_gemini_thinking_response
+
+api_key = os.getenv("GEMINI_API_KEY") or ""
+
+# Create Gemini adapters
+gemini_adapter = GeminiAdapter(
+    auth_key=api_key,
+    model="gemini-2.0-flash-exp",
+)
+
+gemini_enhanced_adapter = GeminiAdapter(
+    auth_key=api_key,
+    model="gemini-2.0-flash-thinking-exp",
+    generation_config=GenerationConfig(
+        temperature=0.7,
+        max_output_tokens=2048,
+        thinking_config=ThinkingConfig(
+            thinking_budget=1024, include_thoughts=True
+        ),
+    ),
+)
+
+gemini_structured_adapter = GeminiAdapter(
+    auth_key=api_key,
+    model="gemini-2.0-flash-exp",
+    generation_config=GenerationConfig(
+        temperature=0.3,
+        response_mime_type="application/json",
+        response_schema={
+            "type": "object",
+            "properties": {
+                "capital": {"type": "string"},
+                "state": {"type": "string"},
+                "confidence": {"type": "number", "minimum": 0, "maximum": 1},
+                "reasoning": {"type": "string"},
+            },
+            "required": ["capital", "state", "confidence"],
+        },
+    ),
+)
+
+
+# Basic Test
+def prepare_gemini_basic_test() -> tuple[GeminiAdapter, dict[str, Any]]:
+    question = "Say hello and explain what you are in one sentence."
+    body = {
+        "contents": [{"parts": [{"text": question}]}],
+        "generationConfig": {"temperature": 0.7, "maxOutputTokens": 100},
+    }
+    return (gemini_adapter, body)
+
+
+def handle_gemini_basic_test(response: dict[str, Any]) -> dict[str, Any]:
+    """Handle the response from the Gemini adapter test."""
+    return {
+        "success": True,
+        "response": extract_gemini_response(response),
+        "raw_response": str(response),
+    }
+
+
+# Enhanced Tests
+def prepare_gemini_pure_thinking_test() -> tuple[GeminiAdapter, dict[str, Any]]:
+    question = "Think through this step by step: If I have 15 apples and give away 7, then buy 12 more, how many do I have? Show your reasoning process."
+    body = {
+        "contents": [{"parts": [{"text": question}]}],
+        "generationConfig": {
+            "temperature": 0.3,
+            "maxOutputTokens": 300,
+            "thinkingConfig": {"thinkingBudget": 1024, "includeThoughts": True},
+        },
+    }
+    return (gemini_enhanced_adapter, body)
+
+
+def prepare_gemini_mixed_thinking_test() -> tuple[
+    GeminiAdapter, dict[str, Any]
+]:
+    question = "Calculate 15 - 7 + 12. Think about it first, then give me the final answer clearly."
+    body = {
+        "contents": [{"parts": [{"text": question}]}],
+        "generationConfig": {
+            "temperature": 0.1,
+            "maxOutputTokens": 400,
+            "thinkingConfig": {"thinkingBudget": 512, "includeThoughts": True},
+        },
+    }
+    return (gemini_enhanced_adapter, body)
+
+
+def prepare_gemini_workflow_test() -> tuple[GeminiAdapter, dict[str, Any]]:
+    question = "What's 8 + 12? Think through it step by step, then give me just the number."
+    body = {
+        "contents": [{"parts": [{"text": question}]}],
+        "generationConfig": {
+            "temperature": 0.1,
+            "maxOutputTokens": 500,
+            "thinkingConfig": {"thinkingBudget": 300, "includeThoughts": True},
+        },
+    }
+    return (gemini_enhanced_adapter, body)
+
+
+def handle_gemini_thinking_response(response: dict[str, Any]) -> dict[str, Any]:
+    """Handle the structured response from the Gemini adapter test."""
+    return {
+        "success": True,
+        "response": extract_gemini_thinking_response(response),
+        "raw_response": str(response),
+    }
+
+
+def prepare_gemini_structured_test() -> tuple[GeminiAdapter, dict[str, Any]]:
+    question = (
+        "Generate a fictional person profile with name, age, and hobbies."
+    )
+    body = {
+        "contents": [{"parts": [{"text": question}]}],
+        "generationConfig": {
+            "temperature": 0.1,
+            "maxOutputTokens": 200,
+            "responseMimeType": "application/json",
+            "responseSchema": {
+                "type": "object",
+                "properties": {
+                    "name": {"type": "string"},
+                    "age": {"type": "number"},
+                    "hobbies": {"type": "array", "items": {"type": "string"}},
+                },
+                "required": ["name", "age", "hobbies"],
+            },
+        },
+    }
+    return (gemini_structured_adapter, body)
+
+
+def handle_gemini_structured_response(
+    response: dict[str, Any],
+) -> dict[str, Any]:
+    """Handle the structured response from the Gemini adapter test."""
+    return {
+        "success": True,
+        "response": extract_gemini_response(response),
+        "raw_response": str(response),
+    }
+
+
+def prepare_gemini_stop_sequences_test() -> tuple[
+    GeminiAdapter, dict[str, Any]
+]:
+    question = "Count from 1 to 10, but write STOP after 5."
+    body = {
+        "contents": [{"parts": [{"text": question}]}],
+        "generationConfig": {
+            "temperature": 0.5,
+            "maxOutputTokens": 500,
+            "stopSequences": ["STOP"],
+        },
+    }
+    return (gemini_adapter, body)
+
+
+def handle_gemini_stop_sequences_response(
+    response: dict[str, Any],
+) -> dict[str, Any]:
+    """Handle the structured response from the Gemini adapter test."""
+    return {
+        "success": True,
+        "response": extract_gemini_response(response),
+        "raw_response": str(response),
+    }
+
+
+def prepare_gemini_conversion_test() -> tuple[GeminiAdapter, dict[str, Any]]:
+    adapter = GeminiAdapter(auth_key=api_key, model="gemini-2.0-flash-exp")
+    body = {
+        "messages": [
+            {"role": "user", "content": "Hello, how are you?"},
+            {"role": "assistant", "content": "I'm doing well, thank you!"},
+            {"role": "user", "content": "What's 5 + 3?"},
+        ]
+    }
+    return (adapter, body)
+
+
+def handle_gemini_conversion_response(
+    response: dict[str, Any],
+) -> dict[str, Any]:
+    """Handle the structured response from the Gemini adapter test."""
+    return {
+        "success": True,
+        "response": extract_gemini_response(response),
+        "raw_response": str(response),
+    }
+
+
+# Error Handling Test
+def prepare_gemini_error_handling_test() -> tuple[
+    GeminiAdapter, dict[str, Any]
+]:
+    adapter = GeminiAdapter(
+        auth_key="invalid-key-for-testing", model="gemini-2.0-flash-exp"
+    )
+    body = {
+        "contents": [{"parts": [{"text": "Hello"}]}],
+        "generationConfig": {"maxOutputTokens": 10},
+    }
+    return (adapter, body)
+
+
+def handle_gemini_error_handling_response(
+    response: dict[str, Any],
+) -> dict[str, Any]:
+    """Handle the structured response from the Gemini adapter test."""
+    return {
+        "success": False,
+        "error": "Expected error not thrown",
+        "unexpected_response": str(response),
+    }
diff --git a/tests/smoke/models/models/test_grok.py b/tests/smoke/models/models/test_grok.py
new file mode 100644
index 00000000..8a85b4f5
--- /dev/null
+++ b/tests/smoke/models/models/test_grok.py
@@ -0,0 +1,27 @@
+import os
+from typing import Any
+
+from inngest.experimental.ai.grok import Adapter as GrokAdapter
+from utils import extract_openai_response
+
+# Create Grok adapter
+grok_adapter = GrokAdapter(
+    auth_key=os.getenv("GROK_API_KEY") or "",
+    model="grok-3-latest",
+)
+
+
+def prepare_test_grok() -> tuple[GrokAdapter, dict[str, Any]]:
+    """Prepare the request for the Grok adapter test."""
+    question = "What is the capital of Florida?"
+    body = {"messages": [{"role": "user", "content": question}]}
+    return (grok_adapter, body)
+
+
+def handle_test_grok_response(response: dict[str, Any]) -> dict[str, Any]:
+    """Handle the response from the Grok adapter test."""
+    return {
+        "success": True,
+        "response": extract_openai_response(response),
+        "raw_response": str(response),
+    }
diff --git a/tests/smoke/models/models/test_openai.py b/tests/smoke/models/models/test_openai.py
new file mode 100644
index 00000000..c7c68b69
--- /dev/null
+++ b/tests/smoke/models/models/test_openai.py
@@ -0,0 +1,27 @@
+import os
+from typing import Any
+
+from inngest.experimental.ai.openai import Adapter as OpenAIAdapter
+from utils import extract_openai_response
+
+# Create OpenAI adapter
+openai_adapter = OpenAIAdapter(
+    auth_key=os.getenv("OPENAI_API_KEY") or "",
+    model="o4-mini-2025-04-16",
+)
+
+
+def prepare_test_openai() -> tuple[OpenAIAdapter, dict[str, Any]]:
+    """Prepare the request for the OpenAI adapter test."""
+    question = "What is the capital of California?"
+    body = {"messages": [{"role": "user", "content": question}]}
+    return (openai_adapter, body)
+
+
+def handle_test_openai_response(response: dict[str, Any]) -> dict[str, Any]:
+    """Handle the response from the OpenAI adapter test."""
+    return {
+        "success": True,
+        "response": extract_openai_response(response),
+        "raw_response": str(response),
+    }
diff --git a/tests/smoke_tests/model_adapters/scripts/start.sh b/tests/smoke/models/scripts/start.sh
similarity index 100%
rename from tests/smoke_tests/model_adapters/scripts/start.sh
rename to tests/smoke/models/scripts/start.sh
diff --git a/tests/smoke/models/utils.py b/tests/smoke/models/utils.py
new file mode 100644
index 00000000..8274a6e2
--- /dev/null
+++ b/tests/smoke/models/utils.py
@@ -0,0 +1,91 @@
+from typing import Any
+
+from inngest._internal import types
+
+
+def extract_openai_response(response: dict[str, Any]) -> str:
+    """Extract text from OpenAI/DeepSeek/Grok format response."""
+    choices = response.get("choices", [])
+    if not choices or not types.is_list(choices):
+        return str(response)
+
+    choice = choices[0]
+    if not types.is_dict(choice):
+        return str(choice)
+
+    message = choice.get("message", {})
+    if types.is_dict(message):
+        return str(message.get("content", ""))
+
+    return str(choice)
+
+
+def extract_anthropic_response(response: dict[str, Any]) -> str:
+    """Extract text from Anthropic format response."""
+    content = response.get("content", [])
+    if not content or not types.is_list(content):
+        return str(response)
+
+    if len(content) > 0 and types.is_dict(content[0]):
+        return str(content[0].get("text", ""))
+
+    return str(content)
+
+
+def extract_gemini_response(response: dict[str, Any]) -> str:
+    """Extract text from Gemini format response."""
+    candidates = response.get("candidates", [])
+    if not candidates or not types.is_list(candidates) or len(candidates) == 0:
+        return str(response)
+
+    candidate = candidates[0]
+    if not types.is_dict(candidate):
+        return str(candidate)
+
+    content = candidate.get("content", {})
+    if not types.is_dict(content):
+        return str(candidate)
+
+    parts = content.get("parts", [])
+    if types.is_list(parts) and len(parts) > 0 and types.is_dict(parts[0]):
+        return str(parts[0].get("text", ""))
+
+    return str(candidate)
+
+
+def extract_gemini_thinking_response(response: dict[str, Any]) -> str:
+    """Extract text from Gemini format response, separating thoughts from final answer."""
+    candidates = response.get("candidates", [])
+    if not candidates or not types.is_list(candidates) or len(candidates) == 0:
+        return str(response)
+
+    candidate = candidates[0]
+    if not types.is_dict(candidate):
+        return str(candidate)
+
+    content = candidate.get("content", {})
+    if not types.is_dict(content):
+        return str(candidate)
+
+    parts = content.get("parts", [])
+    if not types.is_list(parts):
+        return str(candidate)
+
+    thoughts: list[str] = []
+    final_answers: list[str] = []
+
+    for part in parts:
+        if types.is_dict(part) and "text" in part:
+            text = str(part.get("text", ""))
+            if part.get("thought", False):
+                thoughts.append(f"🧠 Reasoning: {text}")
+            else:
+                final_answers.append(f"💡 Answer: {text}")
+
+    result_parts: list[str] = []
+    if thoughts:
+        result_parts.append("\n".join(thoughts))
+    if final_answers:
+        result_parts.append("\n".join(final_answers))
+
+    return "\n\n".join(result_parts) if result_parts else str(candidate)
diff --git a/tests/smoke_tests/model_adapters/README.md b/tests/smoke_tests/model_adapters/README.md
deleted file mode 100644
index 45e857ec..00000000
--- a/tests/smoke_tests/model_adapters/README.md
+++ /dev/null
@@ -1,76 +0,0 @@
-# AI Model Adapters Smoke Test
-
-This smoke test validates that all AI model adapters work correctly with their respective APIs.
-
-## What it tests
-
-This test calls multiple AI providers to answer a simple question. The purpose of this is to ensure that we:
-
-- Make successful API calls
-- Handle provider-specific request formats
-- Parse responses correctly
-
-### Models tested:
-
-- **OpenAI** (o4 Mini)
-- **Anthropic** (Claude 3 Sonnet)
-- **Google Gemini** (Gemini 2.5 Pro)
-- **xAI Grok** (Grok 3)
-- **DeepSeek** (DeepSeek Chat)
-
-## Setup
-
-1. **Environment Variables**: Copy the example environment variables into a `.env` file located in the root of the inngest-py project and fill in your API keys. Your `.env` file should look like this:
-
-   ```
-   INNGEST_BASE_URL=http://localhost:8288
-   OPENAI_API_KEY=your_openai_api_key_here
-   ANTHROPIC_API_KEY=your_anthropic_api_key_here
-   GEMINI_API_KEY=your_gemini_api_key_here
-   GROK_API_KEY=your_grok_api_key_here
-   DEEPSEEK_API_KEY=your_deepseek_api_key_here
-   ```
-
-2. **Virtual Environment**: Make sure you have activated your virtual environment:
-
-   ```bash
-   source .venv/bin/activate
-   ```
-
-3. **Install Dependencies**: Make sure all dependencies are installed:
-   ```bash
-   make install
-   ```
-
-## Running the test
-
-Navigate to the smoke test directory and run:
-
-```bash
-cd tests/smoke_tests/model_adapters
-make dev
-```
-
-This will:
-
-- Start a FastAPI server on port 8000
-- Load your environment variables from the project root `.env` file
-- Expose the Inngest function
-
-## Triggering the test
-
-Once the server is running, you can trigger the smoke test by sending an event via the Inngest dev server
-
-## Expected behavior
-
-The test should:
-
-- Successfully call all 5 AI providers
-- Return responses from each provider
-- Handle any API errors gracefully (displaying error messages instead of crashing)
-
-## Troubleshooting
-
-- **Missing or Invalid API keys**: Make sure all required API keys are set in your `.env` file and are valid
-- **Rate Limits**: Some providers may have rate limits; wait a moment and try again
-- **API Credits**: Most providers require that you purchase credits. After purchasing, you may need to wait some time for the credits to register on their system.
diff --git a/tests/smoke_tests/model_adapters/app.py b/tests/smoke_tests/model_adapters/app.py
deleted file mode 100644
index c832cc01..00000000
--- a/tests/smoke_tests/model_adapters/app.py
+++ /dev/null
@@ -1,184 +0,0 @@
-import os
-from typing import Any, Callable, TypedDict
-
-import fastapi
-import inngest
-import inngest.fast_api
-from inngest._internal import types
-from inngest.experimental.ai.anthropic import Adapter as AnthropicAdapter
-from inngest.experimental.ai.base import BaseAdapter
-from inngest.experimental.ai.deepseek import Adapter as DeepSeekAdapter
-from inngest.experimental.ai.gemini import Adapter as GeminiAdapter
-from inngest.experimental.ai.grok import Adapter as GrokAdapter
-from inngest.experimental.ai.openai import Adapter as OpenAIAdapter
-
-
-class ProviderConfig(TypedDict):
-    adapter: BaseAdapter
-    parser: Callable[[dict[str, Any]], str]
-
-
-inngest_client = inngest.Inngest(app_id="smoke-test-model-adapters-app")
-
-# Create AI adapters
-openai_adapter = OpenAIAdapter(
-    auth_key=os.getenv("OPENAI_API_KEY") or "",
-    model="o4-mini-2025-04-16",
-)
-
-anthropic_adapter = AnthropicAdapter(
-    auth_key=os.getenv("ANTHROPIC_API_KEY") or "",
-    model="claude-3-5-sonnet-latest",
-)
-
-gemini_adapter = GeminiAdapter(
-    auth_key=os.getenv("GEMINI_API_KEY") or "",
-    model="gemini-2.5-pro",
-)
-
-grok_adapter = GrokAdapter(
-    auth_key=os.getenv("GROK_API_KEY") or "",
-    model="grok-3-latest",
-)
-
-deepseek_adapter = DeepSeekAdapter(
-    auth_key=os.getenv("DEEPSEEK_API_KEY") or "",
-    model="deepseek-chat",
-)
-
-
-def extract_openai_response(response: dict[str, Any]) -> str:
-    """Extract text from OpenAI/DeepSeek/Grok format response."""
-    choices = response.get("choices", [])
-    if not choices or not types.is_list(choices):
-        return str(response)
-
-    choice = choices[0]
-    if not types.is_dict(choice):
-        return str(choice)
-
-    message = choice.get("message", {})
-    if types.is_dict(message):
-        return str(message.get("content", ""))
-
-    return str(choice)
-
-
-def extract_anthropic_response(response: dict[str, Any]) -> str:
-    """Extract text from Anthropic format response."""
-    content = response.get("content", [])
-    if not content or not types.is_list(content):
-        return str(response)
-
-    if len(content) > 0 and types.is_dict(content[0]):
-        return str(content[0].get("text", ""))
-
-    return str(content)
-
-
-def extract_gemini_response(response: dict[str, Any]) -> str:
-    """Extract text from Gemini format response."""
-    candidates = response.get("candidates", [])
-    if not candidates or not types.is_list(candidates) or len(candidates) == 0:
-        return str(response)
-
-    candidate = candidates[0]
-    if not types.is_dict(candidate):
-        return str(candidate)
-
-    content = candidate.get("content", {})
-    if not types.is_dict(content):
-        return str(candidate)
-
-    parts = content.get("parts", [])
-    if types.is_list(parts) and len(parts) > 0 and types.is_dict(parts[0]):
-        return str(parts[0].get("text", ""))
-
-    return str(candidate)
-
-
-@inngest_client.create_function(
-    fn_id="model-adapter-test",
-    trigger=inngest.TriggerEvent(event="test-adapters"),
-)
-async def test_adapters(ctx: inngest.Context) -> dict[str, str]:
-    # Map each provider to its adapter and parser
-    provider_config: dict[str, ProviderConfig] = {
-        "openai": {
-            "adapter": openai_adapter,
-            "parser": extract_openai_response,
-        },
-        "anthropic": {
-            "adapter": anthropic_adapter,
-            "parser": extract_anthropic_response,
-        },
-        "gemini": {
-            "adapter": gemini_adapter,
-            "parser": extract_gemini_response,
-        },
-        "grok": {
-            "adapter": grok_adapter,
-            "parser": extract_openai_response,  # Uses OpenAI format
-        },
-        "deepseek": {
-            "adapter": deepseek_adapter,
-            "parser": extract_openai_response,  # Uses OpenAI format
-        },
-    }
-
-    # Different state capital questions for each provider
-    questions = {
-        "openai": "What is the capital of California?",
-        "anthropic": "What is the capital of Texas?",
-        "gemini": "What is the capital of New York?",
-        "grok": "What is the capital of Florida?",
-        "deepseek": "What is the capital of Illinois?",
-    }
-
-    responses: dict[str, str] = {}
-
-    for provider_name, config in provider_config.items():
-        try:
-            question = questions[provider_name]
-            adapter = config["adapter"]
-            parser = config["parser"]
-
-            # Prepare the request body based on provider
-            if provider_name == "gemini":
-                # Gemini uses a different format
-                body: dict[str, Any] = {
-                    "contents": [{"parts": [{"text": question}]}]
-                }
-            else:
-                # Standard OpenAI-style format for other providers
-                body = {"messages": [{"role": "user", "content": question}]}
-
-            # Add max_tokens for Anthropic (required parameter)
-            if provider_name == "anthropic":
-                body["max_tokens"] = 1024
-
-            print(f"Calling {provider_name} with question: {question}")
-
-            ai_response = await ctx.step.ai.infer(
-                step_id=f"ai-call-{provider_name}", adapter=adapter, body=body
-            )
-
-            print(f"{provider_name} response: {ai_response}")
-
-            # Use the specific parser for this provider
-            responses[provider_name] = parser(ai_response)
-
-        except Exception as e:
-            print(f"Error with {provider_name}: {e!s}")
-            responses[provider_name] = f"Error: {e!s}"
-
-    return responses
-
-
-app = fastapi.FastAPI()
-
-inngest.fast_api.serve(
-    app,
-    inngest_client,
-    [test_adapters],
-)
diff --git a/uv.lock b/uv.lock
index 80ffe2ed..80f4aa81 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,4 +1,5 @@
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+revision = 2
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