[feat]: LangSmith tracing integration #72
Conversation
WalkthroughThis update introduces a new modular backend architecture for agent orchestration, message classification, and Discord bot integration. It adds a DevRel agent with workflow nodes, state management, and tools for FAQ and web search. An asynchronous queue manager and agent coordinator handle message routing. The Discord bot is refactored to interact with these components, replacing the prior monolithic implementation. Changes
Sequence Diagram(s)sequenceDiagram
participant DiscordUser
participant DiscordBot
participant QueueManager
participant AgentCoordinator
participant DevRelAgent
participant LLM/Tools
DiscordUser->>DiscordBot: Sends message
DiscordBot->>DiscordBot: Classify message
alt Needs DevRel
DiscordBot->>QueueManager: Enqueue DevRel request (priority)
QueueManager->>AgentCoordinator: Dispatch message
AgentCoordinator->>DevRelAgent: Run agent with AgentState
DevRelAgent->>LLM/Tools: (Various node calls: FAQ, Search, etc.)
LLM/Tools-->>DevRelAgent: Task results
DevRelAgent-->>AgentCoordinator: Final response
AgentCoordinator->>QueueManager: Enqueue response for Discord
QueueManager->>DiscordBot: Deliver response
DiscordBot->>DiscordUser: Reply in thread
else Not DevRel
DiscordBot-->>DiscordUser: (No action / other handling)
end
Poem
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Hello @smokeyScraper, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!
Summary of Changes
Hello team! Gemini here, providing a summary of this pull request. This PR introduces a significant architectural change by implementing a LangGraph-based agent system for handling user interactions, specifically starting with a DevRel agent. It integrates key components like a message classification router, various handler nodes (for FAQs, web search, etc.), and a queue manager for asynchronous processing. A major feature added is the integration of LangSmith tracing across core components (AgentCoordinator, ClassificationRouter, TavilySearchTool) to provide visibility into agent execution flows. The previous FastAPI/Uvicorn entry point has been replaced with a pure asyncio application structure centered around the new queue and agent coordination.
Highlights
- LangGraph Agent Architecture: Introduces a new agent-based system using LangGraph, starting with a
DevRelAgentthat defines a workflow for handling different types of user queries. - LangSmith Tracing Integration: Adds LangSmith tracing capabilities to key parts of the agent and orchestration system, allowing for better debugging and monitoring of agent runs. This includes adding relevant environment variables and
@traceabledecorators. - Asynchronous Queue Management: Implements an
AsyncQueueManagerto handle incoming messages and coordinate agent execution asynchronously with priority support. - Message Classification and Routing: Adds a
ClassificationRouterto categorize incoming messages (FAQ, Technical Support, Web Search, etc.) and route them to the appropriate agent nodes within the LangGraph workflow. - Discord Bot Integration: Updates the Discord bot to use the new classification and queue management system, enabling it to process user messages through the DevRel agent workflow and respond in dedicated threads.
- New Tools and Nodes: Includes implementations for a
TavilySearchToolfor web lookups and a simpleFAQTool, along with corresponding LangGraph nodes to utilize these tools. - Application Structure Refactor: The main application entry point (
main.py) is refactored to manage the lifecycle of the new queue manager, agent coordinator, and Discord bot, moving away from the previous FastAPI/Uvicorn setup.
Changelog
Click here to see the changelog
- backend/.env.example
- Added environment variables for LangSmith tracing (
LANGSMITH_TRACING,LANGSMITH_ENDPOINT,LANGSMITH_API_KEY,LANGSMITH_PROJECT). - Reorganized and commented existing environment variables.
- Added environment variables for LangSmith tracing (
- backend/app/agents/init.py
- Added imports and
__all__for new agent components (DevRelAgent,BaseAgent,AgentState,ClassificationRouter).
- Added imports and
- backend/app/agents/devrel/agent.py
- New file: Implements the
DevRelAgentclass, inheriting fromBaseAgent. - Defines the LangGraph workflow with nodes for intent classification, context gathering, handling various request types (FAQ, web search, tech support, onboarding), and response generation.
- Includes logic for routing based on message intent.
- New file: Implements the
- backend/app/agents/devrel/nodes/classify_intent_node.py
- New file: Implements the
classify_intent_nodefunction to classify user messages using theClassificationRouter.
- New file: Implements the
- backend/app/agents/devrel/nodes/gather_context_node.py
- New file: Implements the
gather_context_nodefunction (currently a placeholder for future context retrieval).
- New file: Implements the
- backend/app/agents/devrel/nodes/generate_response_node.py
- New file: Implements the
generate_response_nodefunction to format the final response based on the task result. - Includes helper functions
_create_search_responseand_create_llm_response.
- New file: Implements the
- backend/app/agents/devrel/nodes/handle_faq_node.py
- New file: Implements the
handle_faq_nodefunction to process FAQ requests using theFAQTool.
- New file: Implements the
- backend/app/agents/devrel/nodes/handle_onboarding_node.py
- New file: Implements the
handle_onboarding_nodefunction (currently a placeholder).
- New file: Implements the
- backend/app/agents/devrel/nodes/handle_technical_support_node.py
- New file: Implements the
handle_technical_support_nodefunction (currently a placeholder).
- New file: Implements the
- backend/app/agents/devrel/nodes/handle_web_search_node.py
- New file: Implements the
handle_web_search_nodefunction to handle web search requests. - Includes
_extract_search_queryusing an LLM and utilizes theTavilySearchTool. - Adds
@traceabledecorator for LangSmith tracing.
- New file: Implements the
- backend/app/agents/devrel/prompts/base_prompt.py
- New file: Defines the
GENERAL_LLM_RESPONSE_PROMPTstring template.
- New file: Defines the
- backend/app/agents/devrel/prompts/search_prompt.py
- New file: Defines the
EXTRACT_SEARCH_QUERY_PROMPTstring template.
- New file: Defines the
- backend/app/agents/devrel/state.py
- New file: Placeholder file.
- backend/app/agents/devrel/tools/faq_tool.py
- New file: Implements the
FAQToolwith a basic dictionary lookup and simple fuzzy matching.
- New file: Implements the
- backend/app/agents/devrel/tools/search_tool.py
- New file: Implements the
TavilySearchToolusing the Tavily API. - Adds
@traceabledecorator for LangSmith tracing.
- New file: Implements the
- backend/app/agents/shared/base_agent.py
- New file: Defines the abstract
BaseAgentclass withrunandstream_runmethods and an abstract_build_graph.
- New file: Defines the abstract
- backend/app/agents/shared/classification_router.py
- New file: Implements the
ClassificationRouterfor message categorization and DevRel need assessment. - Includes quick pattern matching and LLM-based classification.
- Adds
@traceabledecorator for LangSmith tracing.
- New file: Implements the
- backend/app/agents/shared/state.py
- New file: Defines the
AgentStatePydantic model for managing agent state.
- New file: Defines the
- backend/app/core/config.py
- New file: Implements configuration loading using
pydantic-settingsanddotenv. - Adds settings for Gemini, Tavily, LangSmith, and agent models/timeouts.
- New file: Implements configuration loading using
- backend/app/core/orchestration/agent_coordinator.py
- New file: Implements the
AgentCoordinatorto manage agent sessions and coordinate execution via the queue manager. - Handles DevRel agent requests and sends responses back to platforms.
- Adds
@traceabledecorator for LangSmith tracing.
- New file: Implements the
- backend/app/core/orchestration/queue_manager.py
- New file: Implements the
AsyncQueueManagerfor priority-based asynchronous message processing.
- New file: Implements the
- backend/bots/discord/discord_bot.py
- New file: Implements the
DiscordBotusingpy-cord. - Integrates with the
ClassificationRouterandAsyncQueueManager. - Handles message classification, thread creation/management, enqueuing messages for agents, and sending agent responses.
- Adds
!resetand!help_devrelcommands.
- New file: Implements the
- backend/main.py
- Rewritten to initialize and run the
DevRAIApplicationclass. - Removes previous FastAPI/Uvicorn setup.
- Adds signal handling for graceful shutdown.
- Includes checks for required environment variables.
- Configures basic logging.
- Rewritten to initialize and run the
- pyproject.toml
- Removed
langchain-groqandgroqdependencies. - Added
langgraph,langchain-tavily,tavily-python,langchain[google-genai],pydantic-settings, andlangsmithdependencies.
- Removed
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Code Review
This pull request introduces a comprehensive and well-architected framework for DevRel AI agents, including LangGraph-based agent workflows, asynchronous queue management, agent coordination, and a Discord bot integration. The integration of LangSmith for tracing is also a valuable addition. The overall structure is impressive and lays a strong foundation for future development.
I've identified a few areas for improvement, including a critical issue in the agent routing logic and a high-severity concern regarding JSON parsing from LLM responses. Addressing these will significantly enhance the robustness and correctness of the system. There are also some medium-severity suggestions related to the Discord bot's user experience.
Great work on building out this substantial feature set!
Summary of Findings
- Agent Routing Logic: A critical issue was found in
DevRelAgent._route_to_handlerwhere the returned values for routing do not match the expected node names in the LangGraph workflow. This will likely break the agent's conditional path execution. - LLM JSON Parsing Robustness: The JSON parsing in
ClassificationRouter._parse_llm_responseis potentially brittle. Using LangChain's output parsers is recommended for improved robustness against LLM output variations. - Discord Bot Thread Management: The auto-archive duration for Discord threads is short (60 minutes), which might impact user experience for longer conversations. Additionally, the fallback mechanism when thread creation fails could be improved to provide better user feedback.
- Scope of Changes: This PR implements a significant new agent framework, far exceeding just LangSmith tracing integration. This is a major and positive contribution.
- Placeholder Implementations (Low Severity - Not Commented): Several components like
FAQTool's similarity matching and some agent nodes (handle_onboarding_node,handle_technical_support_node) are currently basic placeholders. This is understandable for an initial version, and TODOs indicate planned improvements. - DevRel Agent State File (Low Severity - Not Commented): The file
backend/app/agents/devrel/state.pycurrently only contains a placeholder comment. This might be intentional if the sharedAgentStateis sufficient for now.
Merge Readiness
This pull request introduces a substantial and well-designed agent framework. However, due to the critical issue identified in the DevRelAgent routing logic and the high-severity concern with LLM JSON parsing, I recommend that these changes be addressed before merging. The medium-severity issues related to the Discord bot are also worth considering for an improved user experience.
As an AI, I am not authorized to approve pull requests. Please ensure these changes are reviewed and approved by other team members after the necessary modifications are made.
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Actionable comments posted: 28
🔭 Outside diff range comments (2)
backend/app/agents/devrel/nodes/generate_response_node.py (1)
1-77:⚠️ Potential issueAdd LangSmith tracing to response generation functions.
This PR is about LangSmith tracing integration, but this file lacks any tracing decorators. Add tracing to monitor response generation performance and behavior.
Add the import and decorators:
import logging from typing import Dict, Any from app.agents.shared.state import AgentState from langchain_core.messages import HumanMessage from ..prompts.base_prompt import GENERAL_LLM_RESPONSE_PROMPT +from langsmith import traceableThen add tracing decorators to the functions:
+@traceable(name="create_search_response", run_type="chain") async def _create_search_response(task_result: Dict[str, Any]) -> str:+@traceable(name="create_llm_response", run_type="llm") async def _create_llm_response(state: AgentState, task_result: Dict[str, Any], llm) -> str:+@traceable(name="generate_response", run_type="chain") async def generate_response_node(state: AgentState, llm) -> AgentState:backend/bots/discord/discord_bot.py (1)
1-236:⚠️ Potential issueAdd LangSmith tracing to Discord bot operations.
This file lacks LangSmith tracing despite being part of a tracing integration PR. Add tracing to key operations for better observability.
Add the import:
import discord from discord.ext import commands import logging from typing import Dict, Any, Optional from app.core.orchestration.queue_manager import AsyncQueueManager, QueuePriority from app.agents.shared.classification_router import ClassificationRouter +from langsmith import traceableThen add tracing decorators to key methods:
@traceable(name="discord_message_handler", run_type="chain") async def on_message(self, message): @traceable(name="handle_devrel_message", run_type="chain") async def _handle_devrel_message(self, message, classification: Dict[str, Any]): @traceable(name="handle_agent_response", run_type="chain") async def _handle_agent_response(self, response_data: Dict[str, Any]):
🧹 Nitpick comments (16)
backend/app/agents/devrel/state.py (1)
1-1: Consider removing this placeholder file if no DevRel-specific state is needed.The shared
AgentStateclass inbackend/app/agents/shared/state.pyappears comprehensive with fields for session management, conversation context, task processing, and error handling. Unless there are specific DevRel state requirements that extend beyond the shared state model, this placeholder file may be unnecessary.If DevRel-specific state extensions are planned, document the intended enhancements in the comment. Otherwise, consider removing this file to maintain a cleaner codebase structure.
backend/app/agents/devrel/prompts/search_prompt.py (1)
1-4: Enhance prompt specificity for more reliable query extraction.The prompt template is well-structured, but could benefit from more specific guidance to improve consistency in query extraction.
Consider enhancing the prompt with more specific instructions:
-EXTRACT_SEARCH_QUERY_PROMPT = """Extract the core search query from the following user message. +EXTRACT_SEARCH_QUERY_PROMPT = """Extract the core search query from the following user message. +Remove conversational elements, focus on key terms and concepts. +Keep technical terms intact. Limit to 10 words or less. + User Message: "{message}" -Focus on the main topic or question. Be concise. + +Examples: +- "Can you search for the latest React updates?" → "latest React updates" +- "I need help finding documentation for Python async" → "Python async documentation" + Search Query: """backend/app/agents/devrel/nodes/handle_onboarding_node.py (1)
10-14: Consider making the onboarding response more dynamic and personalized.The onboarding response is completely static with hardcoded next steps. Consider personalizing the response based on the user's platform, previous interactions, or profile information available in the state.
state.task_result = { "type": "onboarding", "action": "welcome_and_guide", - "next_steps": ["setup_environment", "first_contribution", "join_community"] + "next_steps": _get_personalized_next_steps(state), + "user_platform": state.platform, + "personalized_message": f"Welcome to our {state.platform} community!" }backend/app/agents/devrel/prompts/base_prompt.py (1)
1-16: Consider prompt length and token efficiency.The prompt is comprehensive but quite lengthy. Consider breaking it into smaller, more focused prompts or using prompt compression techniques to avoid token limits.
+# Base prompt components for modularity +BASE_ASSISTANT_PROMPT = "You are a helpful DevRel assistant focused on technical questions, onboarding, and community engagement." + +CONTEXT_TEMPLATE = """ +User's message: "{latest_message}" +Recent conversation: {conversation_history} +Current context: {current_context} +""" + +TASK_SPECIFIC_INSTRUCTIONS = { + "technical_support": "If no specific solution was found, offer to escalate or suggest resources.", + "onboarding": "Provide welcoming and guiding information." +} + GENERAL_LLM_RESPONSE_PROMPT = ( - "You are a helpful DevRel assistant. " - "Your goal is to assist users with their technical questions, onboarding, and community engagement.\n\n" - "User's message: \"{latest_message}\"\n" - "Conversation history (last 5): \n" - "{conversation_history}\n\n" - "Current context:\n" - "{current_context}\n\n" - "Task that was just handled: {task_type}\n" - "Details of task result: \n" - "{task_details}\n\n" - "Based on all this information, provide a helpful and concise response.\n" - "If the task was 'technical_support' and no specific solution was found, offer to escalate or suggest resources.\n" - "If the task was 'onboarding', provide welcoming and guiding information.\n" - "Response: " + f"{BASE_ASSISTANT_PROMPT}\n\n" + f"{CONTEXT_TEMPLATE}\n" + "Task handled: {task_type}\nTask details: {task_details}\n\n" + "Provide a helpful, concise response. {task_specific_instruction}\n" + "Response: " )backend/app/agents/devrel/nodes/gather_context_node.py (1)
10-12: Address the TODO comment for database integration.The TODO comment indicates this is an incomplete implementation. Consider implementing database queries for user history or creating a more comprehensive context gathering strategy.
Do you want me to help design a more comprehensive context gathering implementation that includes user history, preferences, and previous interactions?
backend/app/core/config.py (1)
6-16: Consider adding validation for required API keys.All API keys default to empty strings, which could lead to runtime failures. Consider adding validators to ensure required keys are present.
class Settings(BaseSettings): # Gemini LLM API Key - gemini_api_key: str = "" + gemini_api_key: str = Field(default="", description="Gemini API key (required)") # Tavily API Key - tavily_api_key: str = "" + tavily_api_key: str = Field(default="", description="Tavily API key (required for web search)") # Platforms - github_token: str = "" - discord_bot_token: str = "" + github_token: str = Field(default="", description="GitHub token (optional)") + discord_bot_token: str = Field(default="", description="Discord bot token (required for Discord bot)") + + @model_validator(mode='after') + def validate_required_keys(self): + if not self.gemini_api_key: + logger.warning("Gemini API key not configured") + if not self.tavily_api_key: + logger.warning("Tavily API key not configured - web search will not work") + return selfDon't forget to import
Fieldandmodel_validatorfrom pydantic.🧰 Tools
🪛 Pylint (3.3.7)
[refactor] 6-6: Too few public methods (0/2)
(R0903)
backend/app/agents/devrel/nodes/handle_web_search_node.py (1)
10-10: Avoid logging potentially sensitive user data.The f-strings in logging statements could expose sensitive user information. Consider limiting what's logged or using structured logging with appropriate filtering.
- logger.info(f"Extracting search query from: {message[:100]}") + logger.info("Extracting search query from message (length: %d)", len(message)) # ... rest of the code ... - logger.info(f"Extracted search query: {search_query}") + logger.info("Successfully extracted search query")Also applies to: 18-18
backend/app/agents/devrel/tools/faq_tool.py (2)
9-9: Track the TODO for database integration.The TODO comment indicates missing database integration for organization and repository FAQs. This should be tracked to ensure it's implemented.
Would you like me to create an issue to track this TODO item for database integration?
37-44: Improve fuzzy matching to reduce false positives.The current implementation could match unrelated questions that share common words like "how", "do", "i". Consider using more sophisticated similarity metrics.
Consider using a more robust similarity approach:
+from difflib import SequenceMatcher + def _is_similar_question(self, question: str, faq_key: str) -> bool: """Check if question is similar to FAQ key""" - # Simple keyword matching - in production, use better similarity - question_words = set(question.split()) - faq_words = set(faq_key.split()) - - common_words = question_words.intersection(faq_words) - return len(common_words) >= 2 # At least 2 common words + # Use sequence matching for better similarity detection + similarity = SequenceMatcher(None, question, faq_key).ratio() + return similarity > 0.6 # 60% similarity thresholdAlternatively, consider using NLP libraries like
fuzzywuzzyorsentence-transformersfor semantic similarity.backend/app/core/orchestration/queue_manager.py (1)
97-119: Simplify queue retrieval logic.The repeated try-except blocks can be simplified using a loop.
async def _get_next_item(self) -> Optional[Dict[str, Any]]: """Get the next item from queues (priority-based)""" - # Try high priority first - try: - return self.queues[QueuePriority.HIGH].get_nowait() - except asyncio.QueueEmpty: - pass - - # Then medium priority - try: - return self.queues[QueuePriority.MEDIUM].get_nowait() - except asyncio.QueueEmpty: - pass - - # Finally low priority - try: - return self.queues[QueuePriority.LOW].get_nowait() - except asyncio.QueueEmpty: - pass - - return None + # Try queues in priority order + for priority in [QueuePriority.HIGH, QueuePriority.MEDIUM, QueuePriority.LOW]: + try: + return self.queues[priority].get_nowait() + except asyncio.QueueEmpty: + continue + + return Nonebackend/app/agents/devrel/nodes/generate_response_node.py (1)
17-17: Make the search results limit configurable.The limit of 3 search results is hardcoded. Consider making this configurable through a constant or parameter for better flexibility.
+SEARCH_RESULTS_LIMIT = 3 + async def _create_search_response(task_result: Dict[str, Any]) -> str: """Create a response string from search results.""" query = task_result.get("query") results = task_result.get("results", []) if not results: return f"I couldn't find any information for '{query}'. You might want to try rephrasing your search." response_parts = [f"Here's what I found for '{query}':"] - for i, result in enumerate(results[:3]): + for i, result in enumerate(results[:SEARCH_RESULTS_LIMIT]):backend/app/core/orchestration/agent_coordinator.py (1)
89-111: Add tracing to response handling methods.For comprehensive observability, add LangSmith tracing to the response handling methods as well.
+@traceable(name="send_response_to_platform", run_type="chain") async def _send_response_to_platform(self, original_message: Dict[str, Any], response: str):+@traceable(name="send_error_response", run_type="chain") async def _send_error_response(self, original_message: Dict[str, Any], error_message: str):backend/bots/discord/discord_bot.py (3)
47-47: Remove outdated TODO comment.The TODO comment about adding support for commands is outdated since command support is already implemented (see lines 48-50 and the command handlers starting at line 184).
- # TODO: Add support for commands if message.content.startswith('!'):
172-177: Use Discord's built-in message length constant.Instead of hardcoding the 2000 character limit, use Discord's built-in constant for better maintainability.
# Split long responses into multiple messages - if len(response_text) > 2000: - chunks = [response_text[i:i+2000] for i in range(0, len(response_text), 2000)] + max_length = 2000 # Discord's message character limit + if len(response_text) > max_length: + chunks = [response_text[i:i+max_length] for i in range(0, len(response_text), max_length)] for chunk in chunks: await thread.send(chunk)
198-201: Add color to the Discord embed for better visual presentation.The embed is missing a color property which would improve its visual appearance.
embed = discord.Embed( title="DevRel Assistant Help", description="I'm here to help you with Devr.AI related questions!", + color=discord.Color.blue() )backend/app/agents/shared/classification_router.py (1)
103-162: Consider pre-compiling regex patterns for better performance.The pattern matching logic is well-structured, but compiling regex patterns at initialization would improve performance for frequently called methods.
Add a method to compile patterns during initialization:
def __init__(self, llm_client=None): self.llm = llm_client or ChatGoogleGenerativeAI( model=settings.classification_agent_model, temperature=0.1, google_api_key=settings.gemini_api_key ) self._setup_prompts() + self._compile_patterns() + +def _compile_patterns(self): + self.search_patterns = [re.compile(pattern) for pattern in [ + r"search for", r"look up", r"find information about", + r"what's the latest", r"recent news about", r"research", + r"investigate", r"google", r"find out about" + ]] + self.faq_patterns = [re.compile(pattern) for pattern in [ + r"what is devr\.?ai", + r"how do i contribute", + r"how to get started", + r"what platforms.*support" + ]] + self.bug_patterns = [re.compile(pattern) for pattern in [ + r"bug", r"error", r"broken", r"not working", r"issue with" + ]]Then update the pattern matching to use compiled patterns:
-if any(re.search(pattern, message_lower) for pattern in search_patterns): +if any(pattern.search(message_lower) for pattern in self.search_patterns):
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📒 Files selected for processing (25)
backend/.env.example(1 hunks)backend/app/agents/__init__.py(1 hunks)backend/app/agents/devrel/agent.py(1 hunks)backend/app/agents/devrel/nodes/classify_intent_node.py(1 hunks)backend/app/agents/devrel/nodes/gather_context_node.py(1 hunks)backend/app/agents/devrel/nodes/generate_response_node.py(1 hunks)backend/app/agents/devrel/nodes/handle_faq_node.py(1 hunks)backend/app/agents/devrel/nodes/handle_onboarding_node.py(1 hunks)backend/app/agents/devrel/nodes/handle_technical_support_node.py(1 hunks)backend/app/agents/devrel/nodes/handle_web_search_node.py(1 hunks)backend/app/agents/devrel/prompts/base_prompt.py(1 hunks)backend/app/agents/devrel/prompts/search_prompt.py(1 hunks)backend/app/agents/devrel/state.py(1 hunks)backend/app/agents/devrel/tools/faq_tool.py(1 hunks)backend/app/agents/devrel/tools/search_tool.py(1 hunks)backend/app/agents/shared/base_agent.py(1 hunks)backend/app/agents/shared/classification_router.py(1 hunks)backend/app/agents/shared/state.py(1 hunks)backend/app/core/config.py(1 hunks)backend/app/core/orchestration/agent_coordinator.py(1 hunks)backend/app/core/orchestration/queue_manager.py(1 hunks)backend/bots/discord/discord_bot.py(1 hunks)backend/bots/discord_bot/discordBot.py(0 hunks)backend/main.py(1 hunks)pyproject.toml(1 hunks)
💤 Files with no reviewable changes (1)
- backend/bots/discord_bot/discordBot.py
🧰 Additional context used
🧬 Code Graph Analysis (9)
backend/app/agents/__init__.py (4)
backend/app/agents/devrel/agent.py (1)
DevRelAgent(21-95)backend/app/agents/shared/base_agent.py (1)
BaseAgent(7-38)backend/app/agents/shared/state.py (1)
AgentState(4-42)backend/app/agents/shared/classification_router.py (1)
ClassificationRouter(30-190)
backend/app/agents/devrel/nodes/handle_technical_support_node.py (1)
backend/app/agents/shared/state.py (1)
AgentState(4-42)
backend/app/agents/devrel/nodes/handle_onboarding_node.py (1)
backend/app/agents/shared/state.py (1)
AgentState(4-42)
backend/app/agents/devrel/nodes/handle_web_search_node.py (1)
backend/app/agents/shared/state.py (1)
AgentState(4-42)
backend/app/agents/devrel/nodes/handle_faq_node.py (2)
backend/app/agents/shared/state.py (1)
AgentState(4-42)backend/app/agents/devrel/tools/faq_tool.py (1)
get_response(22-35)
backend/app/agents/shared/base_agent.py (2)
backend/app/agents/shared/state.py (1)
AgentState(4-42)backend/app/agents/devrel/agent.py (1)
_build_graph(35-76)
backend/app/agents/devrel/nodes/generate_response_node.py (1)
backend/app/agents/shared/state.py (1)
AgentState(4-42)
backend/app/agents/devrel/nodes/gather_context_node.py (1)
backend/app/agents/shared/state.py (1)
AgentState(4-42)
backend/app/core/orchestration/queue_manager.py (1)
backend/main.py (2)
start(26-47)stop(49-62)
🪛 Pylint (3.3.7)
backend/app/agents/shared/state.py
[refactor] 41-41: Too few public methods (0/2)
(R0903)
[refactor] 4-4: Too few public methods (0/2)
(R0903)
backend/app/core/config.py
[refactor] 35-35: Too few public methods (0/2)
(R0903)
[refactor] 6-6: Too few public methods (0/2)
(R0903)
backend/app/agents/devrel/tools/search_tool.py
[refactor] 10-10: Too few public methods (1/2)
(R0903)
backend/main.py
[refactor] 97-97: Consider using 'sys.exit' instead
(R1722)
backend/app/agents/devrel/tools/faq_tool.py
[refactor] 6-6: Too few public methods (1/2)
(R0903)
backend/app/core/orchestration/agent_coordinator.py
[refactor] 13-13: Too few public methods (0/2)
(R0903)
backend/app/agents/shared/classification_router.py
[refactor] 30-30: Too few public methods (1/2)
(R0903)
🔇 Additional comments (20)
pyproject.toml (1)
19-24:✅ Verification successful
Verify dependency version compatibility for the new LangChain ecosystem packages.
The dependency updates correctly support the LangSmith tracing integration and LangGraph agent framework. The version constraints appear reasonable but should be verified for compatibility.
Run the following script to verify the latest versions and compatibility:
🏁 Script executed:
#!/bin/bash # Description: Check latest versions and potential conflicts for new dependencies echo "Checking latest versions for new dependencies..." for pkg in "langgraph" "langchain-tavily" "tavily-python" "langchain" "pydantic-settings" "langsmith"; do echo "=== $pkg ===" pip index versions "$pkg" 2>/dev/null | head -5 || echo "Package not found or error" done echo -e "\nChecking for potential dependency conflicts..." # Create a temporary requirements file to test resolution cat > /tmp/test_deps.txt << EOF langgraph>=0.4.7,<0.5.0 langchain-tavily>=0.2.0,<0.3.0 tavily-python>=0.7.3,<0.8.0 langchain[google-genai]>=0.3.25,<0.4.0 pydantic-settings>=2.9.1,<3.0.0 langsmith>=0.3.45,<0.4.0 EOF pip-compile --dry-run /tmp/test_deps.txt 2>&1 | grep -E "(conflict|incompatible|error)" || echo "No obvious conflicts detected" rm -f /tmp/test_deps.txtLength of output: 9669
Dependency version constraints are valid and compatible
All new packages satisfy the specified ranges and a dry-run resolution (
pip-compile) reported no conflicts. These updates properly support the LangSmith tracing integration and LangGraph agent framework—no further changes needed.backend/app/agents/shared/state.py (1)
4-42: Well-designed AgentState model.The AgentState model is well-structured with appropriate field types, proper use of default factories for mutable fields, and comprehensive coverage of agent workflow needs. The static analysis warning about "too few public methods" can be safely ignored as this is a data model, not a service class.
🧰 Tools
🪛 Pylint (3.3.7)
[refactor] 41-41: Too few public methods (0/2)
(R0903)
[refactor] 4-4: Too few public methods (0/2)
(R0903)
backend/.env.example (2)
18-22: LangSmith environment variables align with PR objectives.The addition of LangSmith tracing configuration variables properly supports the main feature of this PR - LangSmith tracing integration.
15-16:✅ Verification successful
Verify API key security practices.
Ensure that the actual API keys for Gemini and Tavily are properly secured and not committed to version control.
🏁 Script executed:
#!/bin/bash # Description: Check if any actual API keys might have been accidentally committed # Expected: No results showing actual API keys in tracked files echo "Checking for potential API key leaks in tracked files..." rg -i "AIza[0-9A-Za-z_-]{35}" --type py rg -i "tvly-[0-9A-Za-z_-]{40}" --type py rg -i "sk-[0-9A-Za-z_-]{40,}" --type pyLength of output: 249
🏁 Script executed:
#!/bin/bash # Broad search for any non-empty API key assignments across all tracked files echo "Searching for any non-empty API key assignments across all files..." rg -E "GEMINI_API_KEY=[^[:space:]]+" rg -E "TAVILY_API_KEY=[^[:space:]]+"Length of output: 1016
🏁 Script executed:
#!/bin/bash # Re-run search for any non-empty API key assignments across the repository echo "Re-running search for any non-empty API key assignments..." rg -e 'GEMINI_API_KEY=[^[:space:]]+' . rg -e 'TAVILY_API_KEY=[^[:space:]]+' . # Additionally search in any .env files echo "Checking in .env files for accidental keys..." rg -e 'GEMINI_API_KEY=[^[:space:]]+' --glob '*.env*' rg -e 'TAVILY_API_KEY=[^[:space:]]+' --glob '*.env*'Length of output: 419
All API key placeholders only – no secrets found
Scans across tracked files and all.env*files returned no matches forGEMINI_API_KEY=orTAVILY_API_KEY=with non-empty values.• Confirmed that only placeholder entries exist in
backend/.env.example.
• No actual Gemini or Tavily keys are present in version control.
• Ensure your real.envis listed in.gitignoreand never committed.backend/app/agents/devrel/tools/search_tool.py (3)
16-16: Excellent use of @Traceable decorator.The
@traceabledecorator properly integrates with LangSmith tracing, directly supporting the main objective of this PR.
24-29: Good async pattern using asyncio.to_thread.The use of
asyncio.to_threadto wrap the synchronous Tavily client call is the correct approach to avoid blocking the event loop.
47-52: Comprehensive error handling.The error handling covers the appropriate exception types (AttributeError for initialization issues, ConnectionError/TimeoutError for network issues) and provides meaningful logging.
backend/app/core/config.py (1)
22-26: LangSmith tracing integration properly configured.The LangSmith tracing configuration is correctly set up with endpoint, API key, and project name, aligning with the PR objectives for tracing integration.
backend/app/agents/devrel/agent.py (1)
35-77: Well-structured workflow graph implementation.The graph building logic is well-organized with clear node definitions, proper parameter binding using
partial, and comprehensive routing logic. All paths correctly lead to response generation before ending.backend/app/agents/devrel/tools/faq_tool.py (1)
22-35: Well-implemented FAQ lookup with fallback mechanism.The method correctly handles case-insensitive matching and provides a fuzzy matching fallback. The implementation is clear and efficient.
backend/main.py (2)
17-25: Well-structured application initialization.The DevRAIApplication class properly initializes components in dependency order and maintains clean separation of concerns.
78-79:⚠️ Potential issueFix signal handler registration.
The
add_signal_handlermethod signature is incorrect. It doesn't pass the signal number to the handler function.for sig in (signal.SIGINT, signal.SIGTERM): - loop.add_signal_handler(sig, lambda s=sig: signal_handler(s), sig) + loop.add_signal_handler(sig, lambda s=sig: signal_handler(s))Likely an incorrect or invalid review comment.
backend/app/core/orchestration/queue_manager.py (3)
72-96: Well-implemented worker with robust error handling.The worker coroutine properly handles different error types with appropriate retry delays and graceful cancellation.
120-136: Robust message processing with proper error isolation.The method correctly handles missing handlers and isolates errors to prevent worker crashes.
1-136: Missing LangSmith tracing integration.The PR title mentions "LangSmith tracing integration" but this file doesn't include any LangSmith tracing code. Consider adding tracing to monitor queue operations, worker performance, and message processing.
Would you like me to provide an example of how to integrate LangSmith tracing into this queue manager?
Likely an incorrect or invalid review comment.
backend/bots/discord/discord_bot.py (1)
98-104:⚠️ Potential issueFix potential AttributeError when classification lacks priority.
The code assumes
classification.get("priority")returns a value that has a.get()method, but it could returnNone.# Determine priority based on classification priority = { "high": QueuePriority.HIGH, "medium": QueuePriority.MEDIUM, "low": QueuePriority.LOW } - priority = priority.get(classification.get("priority"), QueuePriority.MEDIUM) + priority_level = classification.get("priority", "medium") + priority = priority.get(priority_level, QueuePriority.MEDIUM)Likely an incorrect or invalid review comment.
backend/app/agents/shared/classification_router.py (4)
1-10: LGTM! Imports are well-organized and appropriate.The LangSmith tracing integration via the
traceabledecorator aligns perfectly with the PR objectives.
13-29: Well-structured enum definitions.The enums provide clear categorization for messages and DevRel intervention levels, with proper string inheritance for serialization.
30-76: Excellent initialization and prompt design.The class supports dependency injection, uses appropriate LLM settings for classification tasks, and has a well-structured prompt with clear guidelines and expected JSON output format.
🧰 Tools
🪛 Pylint (3.3.7)
[refactor] 30-30: Too few public methods (1/2)
(R0903)
77-102: Robust classification implementation with proper tracing.The method efficiently combines pattern matching with LLM classification, includes comprehensive error handling, and correctly implements LangSmith tracing via the
@traceabledecorator.
| def _parse_llm_response(self, response: str, original_message: str) -> Dict[str, Any]: | ||
| """Parse LLM response""" | ||
| try: | ||
| # Extract JSON from response | ||
| json_start = response.find('{') | ||
| json_end = response.rfind('}') + 1 | ||
|
|
||
| if json_start != -1 and json_end != -1: | ||
| json_str = response[json_start:json_end] | ||
| parsed = json.loads(json_str) | ||
| return parsed | ||
| raise ValueError("No JSON found in response") | ||
|
|
||
| except Exception as e: | ||
| logger.error(f"Error parsing LLM response: {str(e)}") | ||
| return self._fallback_classification(original_message) | ||
|
|
There was a problem hiding this comment.
🛠️ Refactor suggestion
Improve JSON extraction robustness.
The current JSON extraction using string find/rfind is fragile and may fail with nested JSON objects or arrays.
Consider using a more robust approach:
def _parse_llm_response(self, response: str, original_message: str) -> Dict[str, Any]:
"""Parse LLM response"""
try:
- # Extract JSON from response
- json_start = response.find('{')
- json_end = response.rfind('}') + 1
-
- if json_start != -1 and json_end != -1:
- json_str = response[json_start:json_end]
- parsed = json.loads(json_str)
- return parsed
- raise ValueError("No JSON found in response")
+ # Try to parse the entire response first
+ try:
+ return json.loads(response.strip())
+ except json.JSONDecodeError:
+ # Extract JSON using regex for more robust parsing
+ import re
+ json_match = re.search(r'\{[^{}]*(?:\{[^{}]*\}[^{}]*)*\}', response, re.DOTALL)
+ if json_match:
+ return json.loads(json_match.group())
+ raise ValueError("No valid JSON found in response")
except Exception as e:
logger.error(f"Error parsing LLM response: {str(e)}")
return self._fallback_classification(original_message)📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| def _parse_llm_response(self, response: str, original_message: str) -> Dict[str, Any]: | |
| """Parse LLM response""" | |
| try: | |
| # Extract JSON from response | |
| json_start = response.find('{') | |
| json_end = response.rfind('}') + 1 | |
| if json_start != -1 and json_end != -1: | |
| json_str = response[json_start:json_end] | |
| parsed = json.loads(json_str) | |
| return parsed | |
| raise ValueError("No JSON found in response") | |
| except Exception as e: | |
| logger.error(f"Error parsing LLM response: {str(e)}") | |
| return self._fallback_classification(original_message) | |
| def _parse_llm_response(self, response: str, original_message: str) -> Dict[str, Any]: | |
| """Parse LLM response""" | |
| try: | |
| # Try to parse the entire response first | |
| try: | |
| return json.loads(response.strip()) | |
| except json.JSONDecodeError: | |
| # Extract JSON using regex for more robust parsing | |
| import re | |
| json_match = re.search(r'\{[^{}]*(?:\{[^{}]*\}[^{}]*)*\}', response, re.DOTALL) | |
| if json_match: | |
| return json.loads(json_match.group()) | |
| raise ValueError("No valid JSON found in response") | |
| except Exception as e: | |
| logger.error(f"Error parsing LLM response: {str(e)}") | |
| return self._fallback_classification(original_message) |
🤖 Prompt for AI Agents
In backend/app/agents/shared/classification_router.py around lines 163 to 179,
the current method of extracting JSON from the LLM response using string find
and rfind is fragile and can fail with nested JSON or arrays. Replace this
approach with a more robust JSON extraction method, such as using a JSON parser
that can handle partial strings or employing a regex pattern to accurately
capture the JSON object. Ensure the extraction correctly handles nested
structures and returns the parsed JSON or falls back gracefully if parsing
fails.
There was a problem hiding this comment.
Will later migrate to Pydantic parser.
Thanks!!!
There was a problem hiding this comment.
@smokeyScraper That sounds like an excellent architectural choice! Pydantic parsers will provide much better validation, type safety, and error handling compared to manual JSON parsing. Looking forward to seeing that implementation when you get to it.
Thanks for letting me know about your plans!
✏️ Learnings added
Learnt from: smokeyScraper
PR: AOSSIE-Org/Devr.AI#72
File: backend/app/agents/shared/classification_router.py:0-0
Timestamp: 2025-06-08T13:08:48.438Z
Learning: The user plans to migrate the JSON parsing in backend/app/agents/shared/classification_router.py from manual JSON extraction to using Pydantic parser for better validation and type safety.
Note: Learnings are effective only in the context of similar code segments. To apply general coding guidelines, please configure review instructions. You can manage existing learnings in the app.
There was a problem hiding this comment.
Actionable comments posted: 0
♻️ Duplicate comments (1)
backend/app/agents/devrel/nodes/gather_context_node.py (1)
7-22: The error handling concerns from previous reviews remain unaddressed.The function still lacks input validation and error handling as highlighted in past review comments. Consider implementing the suggested validation for required state fields and exception handling to make the function more robust.
🧹 Nitpick comments (3)
backend/app/agents/devrel/nodes/gather_context_node.py (1)
3-3: Remove unused import.The
MessageCategoryimport is not used in this module and should be removed to clean up the code.-from app.agents.shared.classification_router import MessageCategory🧰 Tools
🪛 Ruff (0.11.9)
3-3:
app.agents.shared.classification_router.MessageCategoryimported but unusedRemove unused import:
app.agents.shared.classification_router.MessageCategory(F401)
backend/app/agents/devrel/agent.py (1)
25-32: Consider adding error handling for LLM initialization.The LLM initialization could fail if the API key is missing or invalid. Consider adding validation or error handling around the
ChatGoogleGenerativeAIinitialization to provide better error messages.def __init__(self, config: Dict[str, Any] = None): self.config = config or {} + + if not settings.gemini_api_key: + raise ValueError("Gemini API key is required for DevRel agent") + self.llm = ChatGoogleGenerativeAI( model=settings.devrel_agent_model, temperature=0.3, google_api_key=settings.gemini_api_key )backend/bots/discord/discord_bot.py (1)
125-130: Remove unnecessary else after return.The code can be simplified by removing the else clause after the return statement, as suggested by the static analysis tool.
# Verify thread still exists and is active if thread and not thread.archived: return thread_id - else: - del self.active_threads[user_id] - logger.info(f"Cleaned up archived thread for user {user_id}") + + del self.active_threads[user_id] + logger.info(f"Cleaned up archived thread for user {user_id}")🧰 Tools
🪛 Pylint (3.3.7)
[refactor] 126-130: Unnecessary "else" after "return", remove the "else" and de-indent the code inside it
(R1705)
📜 Review details
Configuration used: CodeRabbit UI
Review profile: CHILL
Plan: Pro
⛔ Files ignored due to path filters (1)
poetry.lockis excluded by!**/*.lock
📒 Files selected for processing (9)
backend/.env.example(1 hunks)backend/app/agents/devrel/agent.py(1 hunks)backend/app/agents/devrel/nodes/gather_context_node.py(1 hunks)backend/app/agents/devrel/tools/search_tool.py(1 hunks)backend/app/agents/shared/classification_router.py(1 hunks)backend/app/core/config.py(1 hunks)backend/app/core/orchestration/agent_coordinator.py(1 hunks)backend/bots/discord/discord_bot.py(1 hunks)pyproject.toml(1 hunks)
🚧 Files skipped from review as they are similar to previous changes (2)
- pyproject.toml
- backend/.env.example
🧰 Additional context used
🧬 Code Graph Analysis (2)
backend/app/agents/devrel/agent.py (11)
backend/app/agents/shared/base_agent.py (2)
BaseAgent(7-38)_build_graph(16-18)backend/app/agents/shared/state.py (1)
AgentState(4-42)backend/app/agents/shared/classification_router.py (1)
MessageCategory(13-22)backend/app/agents/devrel/tools/search_tool.py (1)
TavilySearchTool(10-52)backend/app/agents/devrel/tools/faq_tool.py (1)
FAQTool(6-44)backend/app/agents/devrel/nodes/gather_context_node.py (1)
gather_context_node(7-22)backend/app/agents/devrel/nodes/handle_faq_node.py (1)
handle_faq_node(6-26)backend/app/agents/devrel/nodes/handle_web_search_node.py (1)
handle_web_search_node(21-42)backend/app/agents/devrel/nodes/handle_technical_support_node.py (1)
handle_technical_support_node(6-17)backend/app/agents/devrel/nodes/handle_onboarding_node.py (1)
handle_onboarding_node(6-17)backend/app/agents/devrel/nodes/generate_response_node.py (1)
generate_response_node(60-76)
backend/app/agents/devrel/nodes/gather_context_node.py (2)
backend/app/agents/shared/state.py (1)
AgentState(4-42)backend/app/agents/shared/classification_router.py (1)
MessageCategory(13-22)
🪛 Pylint (3.3.7)
backend/bots/discord/discord_bot.py
[refactor] 126-130: Unnecessary "else" after "return", remove the "else" and de-indent the code inside it
(R1705)
backend/app/core/orchestration/agent_coordinator.py
[refactor] 13-13: Too few public methods (0/2)
(R0903)
backend/app/agents/devrel/tools/search_tool.py
[refactor] 10-10: Too few public methods (1/2)
(R0903)
backend/app/agents/shared/classification_router.py
[refactor] 30-30: Too few public methods (1/2)
(R0903)
backend/app/core/config.py
[refactor] 35-35: Too few public methods (0/2)
(R0903)
[refactor] 6-6: Too few public methods (0/2)
(R0903)
🪛 Ruff (0.11.9)
backend/app/agents/devrel/nodes/gather_context_node.py
3-3: app.agents.shared.classification_router.MessageCategory imported but unused
Remove unused import: app.agents.shared.classification_router.MessageCategory
(F401)
🔇 Additional comments (13)
backend/app/agents/devrel/tools/search_tool.py (1)
10-52: Well-implemented search tool with robust error handling.The implementation demonstrates good practices:
- Proper use of
asyncio.to_threadto avoid blocking the event loop- Comprehensive error handling for different failure scenarios
- Good integration with LangSmith tracing for observability
- Appropriate logging for debugging and monitoring
The pylint warning about too few public methods is acceptable for a focused tool class like this.
🧰 Tools
🪛 Pylint (3.3.7)
[refactor] 10-10: Too few public methods (1/2)
(R0903)
backend/app/core/config.py (1)
6-40: Well-structured configuration module using Pydantic BaseSettings.The configuration design is solid with:
- Logical grouping of related settings (LLM, platforms, agents, etc.)
- Appropriate use of Pydantic BaseSettings for environment variable management
- Reasonable defaults and clear field naming
- Proper handling of extra environment variables with
extra = "ignore"The pylint warnings about too few public methods are false positives when using Pydantic BaseSettings, which is designed to be a data container rather than a behavior-rich class.
🧰 Tools
🪛 Pylint (3.3.7)
[refactor] 35-35: Too few public methods (0/2)
(R0903)
[refactor] 6-6: Too few public methods (0/2)
(R0903)
backend/app/agents/devrel/agent.py (2)
48-62: Good fix for the routing inconsistency issue.The routing configuration now correctly uses
MessageCategoryenum values as keys, which matches what_route_to_handlerreturns. This addresses the critical routing issue identified in previous reviews.
75-100: Routing logic now correctly returns MessageCategory enums.The
_route_to_handlermethod has been properly updated to:
- Handle string-to-enum conversion with error handling
- Return
MessageCategoryenum values directly instead of string keys- Provide appropriate fallback behavior for unknown intents
This resolves the routing inconsistency that was flagged in previous reviews where the conditional edges expected enum values but the routing function returned strings.
backend/app/core/orchestration/agent_coordinator.py (3)
16-29: Well-structured initialization and handler registration.The initialization follows good practices with dependency injection and clear separation of concerns. The handler registration pattern provides a clean abstraction for message routing.
31-62: Excellent tracing integration and error handling.The method properly implements LangSmith tracing with the
@traceabledecorator, which aligns perfectly with the PR objectives. The AgentState construction is comprehensive, and the error handling provides graceful fallbacks with proper logging.
89-111: Solid response handling with platform abstraction.The response handling provides good platform abstraction and uses the queue manager effectively for decoupled communication. The Discord response message structure includes all necessary routing information.
backend/bots/discord/discord_bot.py (3)
13-30: Well-configured Discord bot initialization.The bot initialization properly sets up Discord intents, integrates cleanly with the queue manager and classification router, and establishes good conversation tracking with the active_threads mapping.
41-71: Intelligent message handling with classification-based routing.The message handling effectively uses classification to determine DevRel intervention needs, properly filters bot messages and commands, and includes appropriate error handling with logging.
162-238: Comprehensive response handling and user commands.The agent response handling properly splits long messages to comply with Discord limits, and the commands provide good user experience with clear help information and thread management capabilities.
backend/app/agents/shared/classification_router.py (3)
13-40: Well-designed enums and initialization.The enum definitions comprehensively cover developer interaction types, and the class initialization properly configures the LLM with dependency injection support for testing. The low temperature setting (0.1) is appropriate for classification tasks.
🧰 Tools
🪛 Pylint (3.3.7)
[refactor] 30-30: Too few public methods (1/2)
(R0903)
41-102: Excellent prompt design and tracing integration.The classification prompt provides comprehensive guidelines for DevRel assessment, and the tracing integration with
@traceabledecorator perfectly aligns with the PR objectives. The two-tier approach (quick patterns + LLM) optimizes performance while maintaining accuracy.
103-161: Comprehensive pattern matching for common cases.The quick classification patterns effectively handle common scenarios like web searches, FAQs, and bug reports with appropriate confidence levels and reasoning. This optimization reduces LLM calls for obvious cases while maintaining classification quality.
|
@chandansgowda can you please review and merge. Attaching a few latest interactions Details
|
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Actionable comments posted: 0
♻️ Duplicate comments (1)
backend/main.py (1)
40-44: Success log still placed incorrectly (duplicate issue).This is the same issue from the previous review - the success message is logged before confirming the Discord bot has started successfully. If the bot fails to start, the application will report success then immediately fail.
Move the success log after the Discord bot startup confirmation:
self.running = True -logger.info("Devr.AI Application started successfully!") # Wait for the Discord bot await discord_task + +logger.info("Devr.AI Application started successfully!")
🧹 Nitpick comments (1)
backend/main.py (1)
82-83: Improve signal handler registration to avoid closure issues.The current lambda approach could have closure-related issues. Use a more explicit approach for better reliability.
-for sig in (signal.SIGINT, signal.SIGTERM): - loop.add_signal_handler(sig, lambda s=sig: signal_handler(s), sig) +for sig in (signal.SIGINT, signal.SIGTERM): + loop.add_signal_handler(sig, signal_handler, sig)
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Plan: Pro
📒 Files selected for processing (2)
backend/app/agents/shared/base_agent.py(1 hunks)backend/main.py(1 hunks)
🚧 Files skipped from review as they are similar to previous changes (1)
- backend/app/agents/shared/base_agent.py
🧰 Additional context used
🧬 Code Graph Analysis (1)
backend/main.py (4)
backend/app/core/orchestration/queue_manager.py (3)
AsyncQueueManager(14-135)start(27-35)stop(37-46)backend/app/core/orchestration/agent_coordinator.py (1)
AgentCoordinator(13-111)backend/bots/discord/discord_bot.py (1)
DiscordBot(10-238)backend/app/agents/shared/base_agent.py (1)
run(22-37)
🔇 Additional comments (6)
backend/main.py (6)
1-10: LGTM! Well-organized imports for the new architecture.All necessary imports are present and properly organized for the async application framework.
11-16: LGTM! Appropriate logging configuration.The logging setup is clear and provides sufficient detail for monitoring the application.
57-61: LGTM! Discord bot error handling improved.The error handling for Discord bot closure has been properly implemented as suggested in the previous review.
85-92: LGTM! Comprehensive error handling and cleanup.The error handling properly covers keyboard interrupts and general exceptions, with guaranteed cleanup in the finally block.
95-101: LGTM! Environment validation improved.The environment variable validation is comprehensive, and the use of
sys.exit(1)correctly addresses the previous review feedback about clarity.
104-104: LGTM! Clean application entry point.Using
asyncio.run(main())is the proper way to start the async application.
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New Features
Refactor
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