Add CI job to run lint (tests will come later) and resolve lint issues

.github/workflows/ci.yml

@@ -0,0 +1,31 @@
+name: CI
+
+on:
+  push:
+    branches: [main]
+  pull_request:
+
+jobs:
+  lint-and-test:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: ["3.11", "3.12", "3.13"]
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: ${{ matrix.python-version }}
+
+      - name: Install dependencies
+        run: make sync
+
+      - name: Lint with ruff
+        run: make lint
+
+      # TODO: enable this once all the tests pass
+      # - name: Run tests
+      #   run: make tests

README.md

@@ -17,7 +17,7 @@ For full details, advanced usage, and API reference, see here: [OpenAI Guardrail
 2. **Install dependencies**
    - **Install from this repo:**
      ```bash
-     pip install -e .[presidio]
+     pip install -e '.[presidio]'
      ```
    - **Eventually this will be:**
      ```bash

examples/basic/azure_implementation.py

@@ -8,12 +8,14 @@
 
 import asyncio
 import os
+
+from dotenv import load_dotenv
 from openai import BadRequestError
+
 from guardrails import (
     GuardrailsAsyncAzureOpenAI,
     GuardrailTripwireTriggered,
 )
-from dotenv import load_dotenv
 
 load_dotenv()
 
@@ -72,14 +74,14 @@ async def process_input(
     except GuardrailTripwireTriggered as e:
         # Extract information from the triggered guardrail
         triggered_result = e.guardrail_result
-        print(f"   Input blocked. Please try a different message.")
+        print("   Input blocked. Please try a different message.")
         print(f"   Full result: {triggered_result}")
         raise
     except BadRequestError as e:
         # Handle Azure's built-in content filter errors
         # Will be triggered not when the guardrail is tripped, but when the LLM is filtered by Azure.
         if "content_filter" in str(e):
-            print(f"\n🚨 Third party content filter triggered during LLM call.")
+            print("\n🚨 Third party content filter triggered during LLM call.")
             print(f"   Error: {e}")
             raise
         else:

examples/basic/custom_context.py

@@ -11,7 +11,6 @@
 from guardrails import GuardrailsAsyncOpenAI, GuardrailTripwireTriggered
 from guardrails.context import GuardrailsContext, set_context
 
-
 # Pipeline config with an LLM-based guardrail using Gemma3 via Ollama
 PIPELINE_CONFIG = {
     "version": 1,

examples/basic/hello_world.py

@@ -2,6 +2,7 @@
 
 import asyncio
 from contextlib import suppress
+
 from rich.console import Console
 from rich.panel import Panel
 
@@ -55,7 +56,7 @@ async def process_input(
 
         return response.llm_response.id
 
-    except GuardrailTripwireTriggered as exc:
+    except GuardrailTripwireTriggered:
         raise
 
 

examples/basic/local_model.py

@@ -2,10 +2,10 @@
 
 import asyncio
 from contextlib import suppress
-from rich.console import Console
-from rich.panel import Panel
 
 from openai.types.chat import ChatCompletionMessageParam
+from rich.console import Console
+from rich.panel import Panel
 
 from guardrails import GuardrailsAsyncOpenAI, GuardrailTripwireTriggered
 
@@ -55,7 +55,7 @@ async def process_input(
         input_data.append({"role": "user", "content": user_input})
         input_data.append({"role": "assistant", "content": response_content})
 
-    except GuardrailTripwireTriggered as exc:
+    except GuardrailTripwireTriggered:
         # Handle guardrail violations
         raise
 

examples/basic/multi_bundle.py

@@ -6,6 +6,7 @@
 from rich.console import Console
 from rich.live import Live
 from rich.panel import Panel
+
 from guardrails import GuardrailsAsyncOpenAI, GuardrailTripwireTriggered
 
 console = Console()
@@ -79,7 +80,7 @@ async def process_input(
 
             return response_id_to_return
 
-        except GuardrailTripwireTriggered as exc:
+        except GuardrailTripwireTriggered:
             # Clear the live display when output guardrail is triggered
             live.update("")
             console.clear()

examples/basic/multiturn_chat_with_alignment.py

@@ -19,9 +19,9 @@
 from __future__ import annotations
 
 import argparse
-import json
-from typing import Iterable
 import asyncio
+import json
+from collections.abc import Iterable
 
 from rich.console import Console
 from rich.panel import Panel
@@ -177,10 +177,10 @@ def _stage_lines(stage_name: str, stage_results: Iterable) -> list[str]:
             # Add interpretation
             if r.tripwire_triggered:
                 lines.append(
-                    f"  ⚠️  PROMPT INJECTION DETECTED: Action does not serve user's goal!"
+                    "  ⚠️  PROMPT INJECTION DETECTED: Action does not serve user's goal!"
                 )
             else:
-                lines.append(f"  ✨ ALIGNED: Action serves user's goal")
+                lines.append("  ✨ ALIGNED: Action serves user's goal")
         else:
             # Other guardrails - show basic info
             for key, value in info.items():

examples/basic/pii_mask_example.py

@@ -11,6 +11,7 @@
 
 import asyncio
 from contextlib import suppress
+
 from rich.console import Console
 from rich.panel import Panel
 

examples/basic/structured_outputs_example.py

@@ -1,6 +1,7 @@
 """Simple example demonstrating structured outputs with GuardrailsClient."""
 
 import asyncio
+
 from pydantic import BaseModel, Field
 
 from guardrails import GuardrailsAsyncOpenAI, GuardrailTripwireTriggered
@@ -37,13 +38,13 @@ async def extract_user_info(guardrails_client: GuardrailsAsyncOpenAI, text: str)
             model="gpt-4.1-nano",
             text_format=UserInfo
         )
-        
+
         # Access the parsed structured output
         user_info = response.llm_response.output_parsed
         print(f"✅ Successfully extracted: {user_info.name}, {user_info.age}, {user_info.email}")
-        
+
         return user_info
-        
+
     except GuardrailTripwireTriggered as exc:
         print(f"❌ Guardrail triggered: {exc}")
         raise
@@ -75,4 +76,4 @@ async def main() -> None:
 
 
 if __name__ == "__main__":
-    asyncio.run(main()) 
+    asyncio.run(main())

examples/basic/suppress_tripwire.py

@@ -3,6 +3,7 @@
 import asyncio
 from contextlib import suppress
 from typing import Any
+
 from rich.console import Console
 from rich.panel import Panel
 

examples/hallucination_detection/run_hallucination_detection.py

@@ -1,8 +1,10 @@
 import asyncio
-from guardrails import GuardrailsAsyncOpenAI, GuardrailTripwireTriggered
+
 from rich.console import Console
 from rich.panel import Panel
 
+from guardrails import GuardrailsAsyncOpenAI, GuardrailTripwireTriggered
+
 # Initialize Rich console
 console = Console()
 
@@ -43,14 +45,14 @@ async def main():
             messages=[{"role": "user", "content": candidate}],
             model="gpt-4.1-mini",
         )
-        
+
         console.print(Panel(
             f"[bold green]Tripwire not triggered[/bold green]\n\n"
             f"Response: {response.llm_response.choices[0].message.content}",
             title="✅ Guardrail Check Passed",
             border_style="green"
         ))
-        
+
     except GuardrailTripwireTriggered as exc:
         # Make the guardrail triggered message stand out with Rich
         console.print(Panel(

examples/implementation_code/blocking/blocking_agents.py

@@ -8,28 +8,28 @@
     OutputGuardrailTripwireTriggered,
     Runner,
 )
-from agents.run import RunConfig
 
 from guardrails import GuardrailAgent
 
+
 async def main():
     # Create agent with guardrails configured from config file
     agent = GuardrailAgent(
         config=Path("guardrails_config.json"),
         name="Customer support agent",
         instructions="You are a customer support agent. You help customers with their questions.",
     )
-    
+
     while True:
         try:
             prompt = input("\nEnter a message: ")
             result = await Runner.run(
                 agent,
                 prompt
             )
-            
+
             print(f"\nAssistant: {result.final_output}")
-            
+
         except (EOFError, KeyboardInterrupt):
             break
         except (InputGuardrailTripwireTriggered, OutputGuardrailTripwireTriggered) as exc:

examples/implementation_code/blocking/blocking_completions.py

@@ -2,13 +2,15 @@
 
 # Please integrate openai-guardrails into my LLM calls.
 
-# Use "guardrails_config.json" as the config file name, 
+# Use "guardrails_config.json" as the config file name,
 # and follow the example below that illustrates a sample installation.
 
 import asyncio
 from pathlib import Path
+
 from guardrails import GuardrailsAsyncOpenAI, GuardrailTripwireTriggered
 
+
 async def process_input(guardrails_client: GuardrailsAsyncOpenAI, user_input: str) -> None:
     """Process user input with complete response validation using the new GuardrailsClient."""
     try:
@@ -18,17 +20,17 @@ async def process_input(guardrails_client: GuardrailsAsyncOpenAI, user_input: st
             messages=[{"role": "user", "content": user_input}],
             model="gpt-4.1-nano",
         )
-        
+
         print(f"\nAssistant: {response.llm_response.choices[0].message.content}")
-        
-    except GuardrailTripwireTriggered as exc:
+
+    except GuardrailTripwireTriggered:
         # GuardrailsClient automatically handles tripwire exceptions
         raise
 
 async def main():
     # Initialize GuardrailsAsyncOpenAI with the config file
     guardrails_client = GuardrailsAsyncOpenAI(config=Path("guardrails_config.json"))
-    
+
     while True:
         try:
             prompt = input("\nEnter a message: ")

examples/implementation_code/blocking/blocking_responses.py

@@ -2,13 +2,15 @@
 
 # Please integrate openai-guardrails into my LLM calls.
 
-# Use "guardrails_config.json" as the config file name, 
+# Use "guardrails_config.json" as the config file name,
 # and follow the example below that illustrates a sample installation.
 
 import asyncio
 from pathlib import Path
+
 from guardrails import GuardrailsAsyncOpenAI, GuardrailTripwireTriggered
 
+
 async def process_input(guardrails_client: GuardrailsAsyncOpenAI, user_input: str, response_id: str | None = None) -> str | None:
     """Process user input with complete response validation using the new GuardrailsClient."""
     try:
@@ -19,21 +21,21 @@ async def process_input(guardrails_client: GuardrailsAsyncOpenAI, user_input: st
             model="gpt-4.1-nano",
             previous_response_id=response_id
         )
-        
+
         print(f"\nAssistant: {response.llm_response.output_text}")
-        
+
         return response.llm_response.id
-        
-    except GuardrailTripwireTriggered as exc:
+
+    except GuardrailTripwireTriggered:
         # GuardrailsClient automatically handles tripwire exceptions
         raise
 
 async def main():
     # Initialize GuardrailsAsyncOpenAI with the config file
     guardrails_client = GuardrailsAsyncOpenAI(config=Path("guardrails_config.json"))
-    
+
     response_id: str | None = None
-    
+
     while True:
         try:
             prompt = input("\nEnter a message: ")

examples/implementation_code/streaming/streaming_completions.py

@@ -2,14 +2,16 @@
 
 # Please integrate openai-guardrails into my LLM calls.
 
-# Use "guardrails_config.json" as the config file name, 
+# Use "guardrails_config.json" as the config file name,
 # and follow the example below that illustrates a sample installation.
 
 import asyncio
 import os
 from pathlib import Path
+
 from guardrails import GuardrailsAsyncOpenAI, GuardrailTripwireTriggered
 
+
 async def process_input(guardrails_client: GuardrailsAsyncOpenAI, user_input: str) -> str:
     """Process user input with streaming output and guardrails using the GuardrailsClient."""
     try:
@@ -20,20 +22,20 @@ async def process_input(guardrails_client: GuardrailsAsyncOpenAI, user_input: st
             model="gpt-4.1-nano",
             stream=True,
         )
-        
+
         # Stream with output guardrail checks
         async for chunk in stream:
             if chunk.llm_response.choices[0].delta.content:
                 print(chunk.llm_response.choices[0].delta.content, end="", flush=True)
         return "Stream completed successfully"
-        
-    except GuardrailTripwireTriggered as exc:
+
+    except GuardrailTripwireTriggered:
         raise
 
 async def main():
     # Initialize GuardrailsAsyncOpenAI with the config file
     guardrails_client = GuardrailsAsyncOpenAI(config=Path("guardrails_config.json"))
-    
+
     while True:
         try:
             prompt = input("\nEnter a message: ")