fix(tests): wrap callbacks cleanup in try/finally and resolve merge conflict

- test_litellm_pre_call_utils.py: wrap test body in try/finally so
  litellm.callbacks is always restored even when an assertion fails,
  addressing greptile review comment
- test_langfuse_otel.py: resolve trivial merge conflict in comment
  ("unpatched" vs "unpatch-ed"), keeping correct spelling

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

Author: jquinter

.github/workflows/test-litellm-matrix.yml

@@ -102,6 +102,10 @@ jobs:
         run: |
           cd enterprise && poetry run pip install -e . && cd ..
 
+      - name: Generate Prisma client
+        run: |
+          poetry run prisma generate --schema litellm/proxy/schema.prisma
+
       - name: Run tests - ${{ matrix.test-group.name }}
         run: |
           poetry run pytest ${{ matrix.test-group.path }} \

cookbook/mock_prompt_management_server/README.md

@@ -0,0 +1,293 @@
+# Mock Prompt Management Server
+
+A reference implementation of the [LiteLLM Generic Prompt Management API](https://docs.litellm.ai/docs/adding_provider/generic_prompt_management_api).
+
+This FastAPI server demonstrates how to build a prompt management API that integrates with LiteLLM without requiring a PR to the LiteLLM repository.
+
+## Quick Start
+
+### 1. Install Dependencies
+
+```bash
+pip install fastapi uvicorn pydantic
+```
+
+### 2. Start the Server
+
+```bash
+python mock_prompt_management_server.py
+```
+
+The server will start on `http://localhost:8080`
+
+### 3. Test the Endpoint
+
+```bash
+# Get a prompt
+curl "http://localhost:8080/beta/litellm_prompt_management?prompt_id=hello-world-prompt"
+
+# Get a prompt with authentication
+curl "http://localhost:8080/beta/litellm_prompt_management?prompt_id=hello-world-prompt" \
+  -H "Authorization: Bearer test-token-12345"
+
+# List all prompts
+curl "http://localhost:8080/prompts"
+
+# Get prompt variables
+curl "http://localhost:8080/prompts/hello-world-prompt/variables"
+```
+
+## Using with LiteLLM
+
+### Configuration
+
+Create a `config.yaml` file:
+
+```yaml
+model_list:
+  - model_name: gpt-3.5-turbo
+    litellm_params:
+      model: openai/gpt-3.5-turbo
+      api_key: os.environ/OPENAI_API_KEY
+
+prompts:
+  - prompt_id: "hello-world-prompt"
+    litellm_params:
+      prompt_integration: "generic_prompt_management"
+      api_base: http://localhost:8080
+      api_key: test-token-12345
+```
+
+### Start LiteLLM Proxy
+
+```bash
+litellm --config config.yaml
+```
+
+### Make a Request
+
+```bash
+curl http://0.0.0.0:4000/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -H "Authorization: Bearer sk-1234" \
+  -d '{
+    "model": "gpt-3.5-turbo",
+    "prompt_id": "hello-world-prompt",
+    "prompt_variables": {
+      "domain": "data science",
+      "task": "analyzing customer behavior"
+    },
+    "messages": [
+      {"role": "user", "content": "Please help me get started"}
+    ]
+  }'
+```
+
+## Available Prompts
+
+The server includes several example prompts:
+
+| Prompt ID | Description | Variables |
+|-----------|-------------|-----------|
+| `hello-world-prompt` | Basic helpful assistant | `domain`, `task` |
+| `code-review-prompt` | Code review assistant | `years_experience`, `language`, `code` |
+| `customer-support-prompt` | Customer support agent | `company_name`, `customer_message` |
+| `data-analysis-prompt` | Data analysis expert | `analysis_type`, `dataset_name`, `data` |
+| `creative-writing-prompt` | Creative writing assistant | `genre`, `length`, `topic` |
+
+## Authentication
+
+The server supports optional Bearer token authentication. Valid tokens for testing:
+
+- `test-token-12345`
+- `dev-token-67890`
+- `prod-token-abcdef`
+
+If no `Authorization` header is provided, requests are allowed (for testing purposes).
+
+## API Endpoints
+
+### LiteLLM Spec Endpoints
+
+#### `GET /beta/litellm_prompt_management`
+
+Get a prompt by ID (required by LiteLLM).
+
+**Query Parameters:**
+- `prompt_id` (required): The prompt ID
+- `project_name` (optional): Project filter
+- `slug` (optional): Slug filter
+- `version` (optional): Version filter
+
+**Response:**
+```json
+{
+  "prompt_id": "hello-world-prompt",
+  "prompt_template": [
+    {
+      "role": "system",
+      "content": "You are a helpful assistant specialized in {domain}."
+    },
+    {
+      "role": "user",
+      "content": "Help me with: {task}"
+    }
+  ],
+  "prompt_template_model": "gpt-4",
+  "prompt_template_optional_params": {
+    "temperature": 0.7,
+    "max_tokens": 500
+  }
+}
+```
+
+### Convenience Endpoints (Not in LiteLLM Spec)
+
+#### `GET /health`
+
+Health check endpoint.
+
+#### `GET /prompts`
+
+List all available prompts.
+
+#### `GET /prompts/{prompt_id}/variables`
+
+Get all variables used in a prompt template.
+
+#### `POST /prompts`
+
+Create a new prompt (in-memory only, for testing).
+
+## Example: Full Integration Test
+
+### 1. Start the Mock Server
+
+```bash
+python mock_prompt_management_server.py
+```
+
+### 2. Test with Python
+
+```python
+from litellm import completion
+
+# The completion will:
+# 1. Fetch the prompt from your API
+# 2. Replace {domain} with "machine learning"
+# 3. Replace {task} with "building a recommendation system"
+# 4. Merge with your messages
+# 5. Use the model and params from the prompt
+
+response = completion(
+    model="gpt-4",
+    prompt_id="hello-world-prompt",
+    prompt_variables={
+        "domain": "machine learning",
+        "task": "building a recommendation system"
+    },
+    messages=[
+        {"role": "user", "content": "I have user behavior data from the past year."}
+    ],
+    # Configure the generic prompt manager
+    generic_prompt_config={
+        "api_base": "http://localhost:8080",
+        "api_key": "test-token-12345",
+    }
+)
+
+print(response.choices[0].message.content)
+```
+
+## Customization
+
+### Adding New Prompts
+
+Edit the `PROMPTS_DB` dictionary in `mock_prompt_management_server.py`:
+
+```python
+PROMPTS_DB = {
+    "my-custom-prompt": {
+        "prompt_id": "my-custom-prompt",
+        "prompt_template": [
+            {
+                "role": "system",
+                "content": "You are a {role}."
+            },
+            {
+                "role": "user",
+                "content": "{user_input}"
+            }
+        ],
+        "prompt_template_model": "gpt-4",
+        "prompt_template_optional_params": {
+            "temperature": 0.8,
+            "max_tokens": 1000
+        }
+    }
+}
+```
+
+### Using a Database
+
+Replace the `PROMPTS_DB` dictionary with database queries:
+
+```python
+@app.get("/beta/litellm_prompt_management")
+async def get_prompt(prompt_id: str):
+    # Fetch from database
+    prompt = await db.prompts.find_one({"prompt_id": prompt_id})
+    
+    if not prompt:
+        raise HTTPException(status_code=404, detail="Prompt not found")
+    
+    return PromptResponse(**prompt)
+```
+
+### Adding Access Control
+
+Use the custom query parameters for access control:
+
+```python
+@app.get("/beta/litellm_prompt_management")
+async def get_prompt(
+    prompt_id: str,
+    project_name: Optional[str] = None,
+    user_id: Optional[str] = None,
+    authorization: Optional[str] = Header(None)
+):
+    token = verify_api_key(authorization)
+    
+    # Check if user has access to this project
+    if not has_project_access(token, project_name):
+        raise HTTPException(status_code=403, detail="Access denied")
+    
+    # Fetch and return prompt
+    ...
+```
+
+## Production Considerations
+
+Before deploying to production:
+
+1. **Use a real database** instead of in-memory storage
+2. **Implement proper authentication** with JWT tokens or API keys
+3. **Add rate limiting** to prevent abuse
+4. **Use HTTPS** for encrypted communication
+5. **Add logging and monitoring** for observability
+6. **Implement caching** for frequently accessed prompts
+7. **Add versioning** for prompt management
+8. **Implement access control** based on teams/users
+9. **Add input validation** for all parameters
+10. **Use environment variables** for configuration
+
+## Related Documentation
+
+- [Generic Prompt Management API Documentation](https://docs.litellm.ai/docs/adding_provider/generic_prompt_management_api)
+- [LiteLLM Prompt Management](https://docs.litellm.ai/docs/proxy/prompt_management)
+- [Generic Guardrail API](https://docs.litellm.ai/docs/adding_provider/generic_guardrail_api)
+
+## Questions?
+
+This is a reference implementation for the LiteLLM Generic Prompt Management API. For questions or issues, please open an issue on the [LiteLLM GitHub repository](https://github.com/BerriAI/litellm).
+