Overview
Codebase Chat is an AI consultant that knows your entire codebase. Unlike semantic search which returns file locations and snippets, Codebase Chat provides synthesized, ready-to-use answers — architecture explanations, debugging analysis, implementation guidance, and code reviews grounded in your actual code. This is powered by CodeAlive’s GraphRAG approach: the AI has access to the full knowledge graph of your codebase, not just individual files.How It Works
Ask a Question
Ask any question about your codebase in natural language. The AI retrieves relevant context from the knowledge graph before generating a response.
Get a Synthesized Answer
The response combines information from across your codebase — referencing multiple files, tracing data flows, and explaining architectural patterns.
Example Conversations
- Architecture
- Debugging
- Implementation
- Code Review
Access Methods
- MCP Tool
- REST API
- Skill CLI
The
Your AI agent calls this tool automatically when you ask in-depth questions about your codebase.
codebase_consultant tool is available through any CodeAlive-connected agent:| Parameter | Required | Description |
|---|---|---|
query | Yes | Your question about the codebase |
data_sources | Yes | Repository or workspace names |
conversation_id | No | Continue a previous conversation |
Conversation Continuity
Every chat response includes aconversation_id. Pass it in follow-up questions to maintain context:
Best Practices
Search First
Use semantic search for locating code. Use chat when you need explanations or analysis — it’s more expensive per call
Use Follow-ups
Continue conversations with
conversation_id instead of starting fresh each timeBe Specific
“Explain the payment retry logic” gets better results than “tell me about payments”
Scope by Repo
Target specific repositories for more focused, accurate answers