Usage Guide
Once you've successfully set up Qodo Aware, you can start using it in different tools to ask complicated questions about your code.
What can you do with Qodo Aware?
Get more information about your code
Qodo Aware supports a few agents that act as modes for different levels of depth and analysis:
Context Ask Agent: Fast, context-aware responses from your indexed codebase.
Deep Research Agent: Thorough, multi-repo answers to complex technical queries.
Focus Repo Context: Select specific repositories to define scope of context
Repo tagging: Tag and group repositories under custom labels
What are the core capabilities of Qodo Aware?
Context Engine (RAG Indexer)
Provides AI agents with accurate, ranked, and scoped context for coding tasks like debugging, refactoring, or explaining unfamiliar logic:
Indexes codebases in multiple ways—structural, semantic, embedding-based
Creates vector spaces that represent code meaning and relationships
Supports high-recall, task-specific retrieval pipelines
Deep Ask
An advanced context retrieval agent that performs multi-step reasoning and reflection to solve complex problems by extending the capabilities of standard RAG. Enables the AI to solve more complex tasks that span multiple files, modules, or systems—not just answer short prompts.
Context Retriever API and MCP
A flexible API layer that exposes Qodo Aware’s functionality to internal and external tools, allowing organizations to embed Qodo Aware into existing workflows, from developer terminals to CI pipelines and internal tools.
Code Embedding
Improves retrieval precision and makes the system capable of understanding meaning, not just keywords.
Converts code into vector form
Captures intent, structure, and relationships
Powers search, summarization, and reasoning features
What are the core use cases?
Understand how a feature works across frontend, backend, and data layers
Identify the impact of a code change across teams and services
Refactor core libraries with full visibility into usage
Plan architectural changes with confidence
Surface relevant context during code reviews
Automatically validate test coverage and suggest improvements
Retrieve documentation and usage examples for internal APIs
Example Usage
Comparison Based on Code Behavior
Make informed decisions based on actual implementation rather than just documentation:
use deep_research:
Investigate repositories ["langchain-ai/langchain", "BerriAI/litellm"].
I don't know which one to use for LLM API calling. Create a comparison and help me decide.Expected Output: Detailed comparison of both libraries' implementation approaches, performance characteristics, and suitability for your use case.
Research for Implementation and Planning
Sometimes you need a feature that doesn't exist in a repository. This example shows how to research and plan a contribution:
use deep_research:
Investigate repository ["pallets/flask"], is there capability to manage requests queue?
If not, I'd like to submit a PR for the Flask repo to suggest adding a queue for requests.
Therefore, investigate and plan how to do it and create a .md file plan for me to execute.What this does:
First, the agent verifies if the requested feature already exists
If not, it analyzes the codebase to understand current implementation patterns
Creates a detailed plan aligned with the project's architecture
Ensures the changes won't break existing functionality
More Use Cases
🏛️ Understanding system architecture
deep_research
"Explain how our microservices communicate and what protocols they use"
Detailed explanation of service communication patterns, protocols, and data flow
🎨 Finding design patterns
get_context
"singleton pattern implementation"
Code examples of singleton patterns used in the codebase
🚨 Locating error handling patterns
get_context
"try catch error handling with logging"
Examples of error handling patterns with logging
💰 Understanding business logic
deep_research
"How is pricing calculated for premium users?"
Detailed explanation of pricing logic and rules
🔐 Analyzing authentication flow
deep_research
"Trace the complete OAuth2 authentication flow"
Step-by-step authentication process across services
⚠️ Identifying security vulnerabilities
issues
[code diff with auth changes]
Potential security issues in authentication changes
⚡ Planning feature additions
deep_research
"Where should we add caching for better performance?"
Strategic caching recommendations
🔥 Understanding error sources
deep_research
"What could cause a 500 error in the checkout process?"
Potential failure points and error conditions
🔌 Planning third-party integrations
get_context
"stripe payment integration"
Existing integration patterns and implementations
✅ Validating best practices
deep_research
"Are we following REST best practices in our API design?"
Analysis of REST compliance and recommendations
Last updated
Was this helpful?