Codebase Chat

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.

Iterate

Ask another self-contained question that includes the findings and constraints the next answer needs.

Example Conversations

Architecture
Debugging
Implementation
Code Review

You: "Explain how the authentication system works in this project"

AI: The authentication system uses a JWT-based flow with three main components:
    1. AuthController (src/controllers/auth.ts) handles login/signup endpoints
    2. JwtService (src/services/jwt.ts) manages token generation and validation
    3. AuthMiddleware (src/middleware/auth.ts) protects routes...
    [continues with specific code references]

You: "Why might payments be failing intermittently?"

AI: Based on the payment processing code, there are several potential causes:
    1. Race condition in PaymentProcessor.process() (src/services/payment.ts:142)
       - The lock acquisition doesn't cover the full transaction...
    2. Timeout configuration mismatch between the gateway client and...
    [continues with specific code analysis]

You: "How should I add rate limiting to the API?"

AI: Based on your existing middleware pattern in src/middleware/:
    1. Create a new RateLimitMiddleware following the pattern in AuthMiddleware
    2. The existing Redis connection in src/config/redis.ts can be reused...
    3. Your API routes in src/routes/index.ts use the middleware chain pattern...
    [continues with implementation guidance matching your codebase patterns]

You: "Review the error handling in the user service"

AI: The UserService has inconsistent error handling:
    1. createUser() (line 45) catches all errors but only logs generic messages
    2. updateUser() (line 89) correctly throws typed errors
    3. deleteUser() (line 112) has no error handling at all...
    [continues with specific recommendations]

Access Methods

MCP Tool
REST API
Skill CLI

The chat tool is available through any CodeAlive-connected agent:

Parameter	Required	Description
`question`	Yes	Your question about the codebase
`data_sources`	Yes	Repository or workspace names
Your AI agent may call this tool for synthesized analysis after search. For the highest reliability and depth, prefer `semantic_search` and `grep_search` first.

Chat directly via the CodeAlive API:

curl -X POST https://app.codealive.ai/api/tools/chat \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "question": "Explain the authentication flow",
    "data_sources": ["my-backend"]
  }'

See the API reference for the request and response schemas.

If you have the CodeAlive skill installed:

python scripts/chat.py "Explain the authentication flow" my-backend

Stateless requests

Tool API chat does not retain conversation state. Include prior findings, artifact identifiers, assumptions, scope, and constraints in every question. This keeps each request reproducible across agents and integrations.

Best Practices

Search First

Use semantic_search and grep_search for locating code. Use chat when you need explanations or analysis — it’s more expensive per call and can take up to 30 seconds.

Include context

Make every question self-contained by including the relevant findings and constraints

Be 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

Semantic Search

Fast code search for finding file locations and snippets

Multi-Repository

Chat across multiple repositories and workspaces

MCP Integration

Connect chat to your AI assistant

API Reference

Tool API chat endpoint documentation

Getting Started

Features

Model Context Protocol

Agent Skills & Plugins

Instructing Agents

Overview

How It Works

Example Conversations

Access Methods

Stateless requests

Best Practices

Search First

Include context

Be Specific

Scope by Repo

Semantic Search

Multi-Repository

MCP Integration

API Reference

​Overview

​How It Works

​Example Conversations

​Access Methods

​Stateless requests

​Best Practices

Search First

Include context

Be Specific

Scope by Repo

​Related Resources

Semantic Search

Multi-Repository

MCP Integration

API Reference

Overview

How It Works

Example Conversations

Access Methods

Stateless requests

Best Practices

Related Resources