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AI Codebase Analyst - GitHub Repository Intelligence 🔍

Analyze any GitHub repository with AI. Get intelligent Q&A pairs, dependency analysis, README extraction, and structured data output in under 60 seconds. Perfect for developers, AI agents, and RAG pipelines.

🎯 Problem Solved

Understanding a new codebase takes hours of reading documentation, exploring files, and asking questions. AI Codebase Analyst transforms any GitHub repository into actionable intelligence instantly, saving developers time and enabling AI applications to understand codebases deeply.

Target Users:

• Developers evaluating libraries before integration
• AI engineers building RAG pipelines
• Security teams auditing dependencies
• Technical leads conducting due diligence

✨ Key Features

• 📖 README Extraction: Automatically fetches README files (README.md, README.rst, README.txt) with full content
• ❓ AI-Powered Q&A: Generates 15+ intelligent questions and answers about project purpose, installation, usage, and more
• 📦 Dependency Analysis: Extracts dependencies from package.json, requirements.txt, pyproject.toml, setup.py
• 🗂️ File Structure Mapping: Discovers repository structure up to 3 levels deep
• 🔑 Key File Extraction: Retrieves LICENSE, Dockerfile, config files, and other important documents
• 🌐 Multi-Language Support: Works with Python, JavaScript, TypeScript, Go, Rust, and 50+ languages
• ⚡ Fast Processing: Complete analysis in under 60 seconds
• 📊 Structured Output: JSON format ready for RAG pipelines and AI agents

💡 Typical Use Cases

For Developers

• Quick Onboarding: Understand new projects in seconds instead of hours
• Dependency Research: Evaluate libraries before adding to your project
• Code Review: Get context about unfamiliar codebases
• Due Diligence: Analyze open-source dependencies for security and licensing

For AI Applications

• RAG Pipelines: Feed structured repository data into vector stores
• AI Agents: Provide agents with deep repository understanding
• Documentation Generation: Auto-generate docs from code analysis
• Semantic Code Search: Enable natural language search across repositories

For Teams

• Knowledge Transfer: Bridge gaps between team members on different projects
• Security Audits: Identify dependencies and potential risks
• Technical Debt Analysis: Understand codebase complexity before adoption

⚙️ Input Parameters

Input Examples

{
"githubRepo":"QwenLM/Qwen-Agent",
"maxFiles":50
}

{
"githubRepo":"microsoft/vscode",
"maxFiles":100
}

📊 Output Format

The Actor produces structured JSON in the default dataset.

Sample Output Item

{
"repository":{
"name":"QwenLM/Qwen-Agent",
"description":"Agent framework for Qwen models",
"stars":15000,
"language":"Python",
"license":"Apache-2.0",
"url":"https://github.com/QwenLM/Qwen-Agent"
},
"readme":{
"content":"Full README content...",
"length":15121
},
"structure":["LICENSE","README.md","setup.py","qwen_agent/agent.py"],
"dependencies":["dashscope","json5","openai","pydantic"],
"pythonDependencies":["dashscope","json5","openai","pydantic","tiktoken"],
"qna":[
{
"question":"What is this project?",
"answer":"Qwen-Agent is a framework for developing LLM applications..."
},
{
"question":"How do I install it?",
"answer":"pip install -U qwen-agent[gui,rag,code_interpreter]"
}
]
}

Output Storage

• Dataset: Full analysis results accessible via Apify Console or API
• Key-Value Store: REPO_INFO key contains complete analysis as JSON

Edge Cases

• Empty README: readme.content will be null if no README found
• No Dependencies: Dependency arrays will be empty []
• Private Repos: Requires valid githubToken with appropriate permissions

🚀 Quick Start

Run via Apify Console

1. Visit AI Codebase Analyst on Apify
2. Enter repository in owner/repo format (e.g., QwenLM/Qwen-Agent)
3. Click Run - results available in under 60 seconds

Run via API

const{ ApifyClient }=require('apify-client');
const client =newApifyClient({token:'YOUR_API_TOKEN'});
const result =await client.actor('akash9078/ai-codebase-analyst').call({
githubRepo:'QwenLM/Qwen-Agent',
maxFiles:50,
});
console.log(result.data);

Run via cURL

curl-X POST \
 https://api.apify.com/v2/acts/akash9078~ai-codebase-analyst/runs?token=YOUR_API_TOKEN \
-H'Content-Type: application/json'\
-d'{"githubRepo": "QwenLM/Qwen-Agent", "maxFiles": 50}'

Run via cURL

curl-X POST \
 https://api.apify.com/v2/acts/username~ai-codebase-analyst/runs?token=YOUR_API_TOKEN \
-H'Content-Type: application/json'\
-d'{"githubRepo": "QwenLM/Qwen-Agent", "maxFiles": 50}'

💰 Pricing

This Actor uses pay-per-event pricing on the Apify platform:

• REPO_PROCESSED: One-time charge per repository analyzed
• QNA_GENERATED: Charge per Q&A pair generated (typically 15-20 pairs per run)

Check the Actor pricing page for current rates.

❓ FAQ

Q: Do you support private repositories?

A: Currently, this Actor works with public repositories only. Private repository support requires a GitHub token configured as an environment variable.

Q: What languages are supported?

A: The Actor works with any language. It has special support for Python (requirements.txt, pyproject.toml), JavaScript/TypeScript (package.json), Rust (Cargo.toml), and Go (go.mod).

Q: How many files can be analyzed?

A: Default is 50 files, configurable from 10-200 via the maxFiles parameter.

Q: What if the README is very large?

A: README content is truncated to 8000 characters for Q&A generation but the full content is stored in the output.

Q: Can I use my own LLM instead of Mistral?

A: Currently only Mistral AI is supported. Custom LLM integration is on the roadmap.

Q: How often is the data updated?

A: Data is fetched fresh from GitHub on each run. There is no caching.

Q: What happens if the repository doesn't exist?

A: The Actor will return an error with message "Resource not found by GitHub API".

🔗 Integration Examples

RAG Pipeline with LangChain

from apify_client import ApifyClient
from langchain.vectorstores import FAISS
from langchain.embeddings import OpenAIEmbeddings
# Analyze repository
client = ApifyClient("YOUR_TOKEN")
result = client.actor("akash9078/ai-codebase-analyst").call(
 githubRepo="langchain-ai/langchain"
)
# Create vector store from README
embeddings = OpenAIEmbeddings()
docsearch = FAISS.from_texts(
[result.data['readme']['content']], embeddings
)
# Query the knowledge base
docs = docsearch.similarity_search("How do I install LangChain?")

AI Agent Context

const context =`
 Repository: ${data.repository.name}
 Description: ${data.repository.description}
 Stars: ${data.repository.stars}
 README Summary: ${data.readme.content.substring(0,5000)}
 Key Q&A: ${JSON.stringify(data.qna.slice(0,5))}
`;
const response =await mistral.chat.complete({
model:'mistral-large-2512',
messages:[{role:'user',content: context + userQuestion }],
});

🏆 Why Choose AI Codebase Analyst?

🔑 Keywords

AI codebase analysis, GitHub repository intelligence, code analysis tool, AI Q&A generation, repository documentation, dependency analysis, RAG pipeline, AI agent context, codebase chat, GitHub API, Mistral AI, Apify actor, open source analysis, technical due diligence, code review assistant