See the Blast Radius
Change one function. Markar instantly shows you every file, class, and function that will break - before you write a single line.
Introducing Multi-Agent AI Engineering System
AI Agents Spot BreaksBefore You Push Code
Your codebase has secrets. Markar reveals them all.An autonomous network of AI Agents that maps every function, traces every dependency, and validates changes before they reach production.
Interactive Architecture Showcase
How Markar Agents Connect
Select an agent on the left to see the working connection from agents to code metrics, passing through our live analysis gateway.
Workspace Control Room
Agents Dashboard
Watch multi-agent workflows execute tasks, build files, and manage your complete repository in real-time.
markar-agent-dashboard.sh
LIVE PREVIEW
M
Markar.ai
Code Intelligence
Free Plan
Credits: 1
shivamkumarsingh98/markar-frontend
main
MCook Smart Ship Fast!
markar-agent console~/newchat
Please select a repository to start... Press Play to visualize agent automation.
AskAttach
Gemini 2.5 Flash (Direct)
Settings
✕
Use your own provider keys or Markar defaults. Saved keys are masked on load.
Full Privacy Control
Use Your Own API Keys
Take complete ownership of your runtime costs. Connect your credentials to use unlimited support for multi-LLMs including OpenAI, Anthropic, OpenRouter, and Gemini for free.
Unlimited execution cycles
Zero markup on model pricing
Switch models on the fly
Enterprise security masking
Advanced intelligence that
no one else has.
Markar goes beyond basic autocomplete. Our platform is engineered with deep architectural awareness and autonomous execution engines.
01
Multi-hop Engine
5-to-6 step chain queries with detective-style investigation. Each step's output dynamically decides the next logical path.
02
Blast Radius (Ripple)
Pre-emptive impact analysis. Know exactly what will break across your entire system before you even commit code.
03
Git Churn Score
Historical graph storage tracking how many times a function changed, who modified it, and exactly when.
04
Deep AST + Neo4j
Comprehensive mapping beyond simple files. We deeply map functions, classes, dependencies, and complex call graphs.
05
Autonomous Sandbox
Code generation meets isolated testing. Generate code, test in a secure isolated container, and automatically PR upon success.
06
Trace Transparency
Full visibility into agent cognition. See exactly what the agent thought, which step it took, and the reasoning behind it.
07
AutoRouter Detection
Intelligent routing that automatically detects user intent and seamlessly dispatches to the correct specialized agent.
Markar is not a linter. Not a formatter. It's your AI Software Architect.
Markar puts extra weight on system design and living documentation: it helps leaders and Staff+ engineers reason about architecture, and it helps new developers onboard faster by explaining how the codebase actually fits together. It also supports spec-driven development—keeping intent, structure, and implementation aligned—through deep codebase understanding grounded in your real dependency and call graphs.
Semantic Code Intelligence
Beyond static analysis. Markar understands what your code DOES - function call chains, API routes, service dependencies, and execution paths.
Ship with Confidence
Get an 8-step migration plan, severity scores from ISOLATED to CRITICAL, and AI-generated refactoring suggestions - automatically.
Inside Markar's Intelligence Engine
Watch how Markar turns 500 files into a living knowledge graph
> github.com/acme/repo typed...
Cloning repository... ████████ 100%
Languages detected: Python, TypeScript, Go
Functions: ... | Classes: ...
def create_user()
class AuthService
import jwt
func HandlePayment()
Step 1
Connect Your Repository
GitHub URL is typed, repo cloned, files counted, and languages detected.
941 files
Step 2
AST Parsing Begins
Line-by-line parsing identifies functions, classes, imports, and critical dependencies.
Functions: 6,872 | Classes: 1,406
Step 3
Knowledge Graph Construction
Nodes fly in and edges are drawn to build your dependency intelligence model.
9,219 nodes | 8,341 relationships
Step 4
AI Agents Analyze
Blast Radius, Security, QA, and documentation-focused agents analyze distinct regions of the graph.
Risk overlay complete
Step 5
Intelligence Delivered
Critical files, duplicate auth modules, blast radius, and maintainability summary are produced.
Maintainability score: 62/100
From Raw Code to Deep Understanding
Markar reads every line. Understands every relationship.
Your Code
line 1: const fn_0 = () => {};
line 2: const fn_1 = () => {};
line 3: const fn_2 = () => {};
line 4: const fn_3 = () => {};
line 5: const fn_4 = () => {};
line 6: const fn_5 = () => {};
line 7: const fn_6 = () => {};
line 8: const fn_7 = () => {};
line 9: const fn_8 = () => {};
line 10: const fn_9 = () => {};
line 11: const fn_10 = () => {};
line 12: const fn_11 = () => {};
line 13: const fn_12 = () => {};
line 14: const fn_13 = () => {};
line 15: const fn_14 = () => {};
line 16: const fn_15 = () => {};
Markar Parses
FunctionDef
ClassDef
Import
Call
Decorator
Knowledge Graph
0
Functions Analyzed
0
Classes Mapped
0
Relationships
0
Parse Time (x100ms)
Pre-built AI Agents & Custom AI Agents Made for Engineering Teams
A full suite of enterprise-grade agents — each one deeply aware of your codebase, knowledge graph, logs, PRs, and workflows.
INITIALIZING CORE SYSTEMS...
Supervisor Agent
Orchestrates all agents. Understands your goal, breaks it into tasks, and ensures quality execution across the entire pipeline.
ACTIVE
Blast Radius Agent
Finds everything that breaks
ACTIVE
Dependency Agent
Maps all relationships
ACTIVE
Root Cause Agent
Traces failures to source
ACTIVE
Refactoring Agent
Suggests architectural improvements
ACTIVE
Performance Agent
Finds bottlenecks and N+1 queries
ACTIVE
Security Agent
Detects vulnerabilities and exposed secrets
ACTIVE
MVP Builder Agent
Generates production-ready code from specs
ACTIVE
QA Agent
Auto-generates tests with 80%+ coverage
ACTIVE
Code Review Agent
AI-powered PR reviews
ACTIVE
Migration Agent
Safe 8-step change planning
ACTIVE
Codebase Q&A Agent
Ask anything about your code
[sys] blast_radius_agent analyzing auth_service.py... | [sys] qa_agent generating tests for UserController... | [sys] security_agent checking for exposed secrets... | [sys] docs_agent summarizing architecture for onboarding... |[sys] blast_radius_agent analyzing auth_service.py... | [sys] qa_agent generating tests for UserController... | [sys] security_agent checking for exposed secrets... | [sys] docs_agent summarizing architecture for onboarding... |[sys] blast_radius_agent analyzing auth_service.py... | [sys] qa_agent generating tests for UserController... | [sys] security_agent checking for exposed secrets... | [sys] docs_agent summarizing architecture for onboarding... |
{
"function": "create_user",
"calls": ["validate_email", "save_user_db", "send_welcome_email"],
"called_by": ["auth_router.register", "admin.bulk_create"],
"blast_radius": "CRITICAL",
"affected_files": 23
}Know Exactly Who Calls Whom
Markar traces every function call relationship in your codebase. See the complete call chain - who calls what, what breaks if you change it, and how to safely refactor.
create_user →validate_email →check_domain →DNS
/auth/login -> login_controller -> jwt_service -> db -> redis /api/users -> user_router -> UserService -> PostgreSQL
Every Entry Point. Every Route. Mapped.
FastAPI routes, Express endpoints, Next.js pages, React components, CLI commands, background workers - Markar finds and maps every entry point automatically.
Architecture Style: Modular Monolith Main Risk: Auth system duplicated across 33 files Most Complex: integrations module Maintainability Score: 62/100
Your AI Software Architect
Markar doesn't just show data - it understands your architecture. Get plain-English explanations of risks, recommendations, and a maintainability score.
PYJSTSGOJAVARUST
Python | JavaScript | TypeScript | React/TSX | Java | Go | Rust | C | C++ | C# | Ruby | PHP | Kotlin
Every Language Your Team Uses
15+ languages supported via tree-sitter AST parsing. Mixed-language repos - Python backend + React frontend - analyzed as one unified knowledge graph.
0
Lines of code analyzed this month
Designed for Engineering Teams at
From fast-moving startups to Fortune 500 enterprises
zomato
Razorpay
CRED
PhonePe
meesho
zepto
BrowserStack
freshworks
ZOHO
Postman
unacademy
Groww
NYKAA
zomato
Razorpay
CRED
PhonePe
meesho
zepto
BrowserStack
freshworks
ZOHO
Postman
unacademy
Groww
NYKAA
zomato
Razorpay
CRED
PhonePe
meesho
zepto
BrowserStack
freshworks
ZOHO
Postman
unacademy
Groww
NYKAA
zomato
Razorpay
CRED
PhonePe
meesho
zepto
BrowserStack
freshworks
ZOHO
Postman
unacademy
Groww
NYKAA
Google
Microsoft
amazon
Meta
ORACLE
salesforce
IBM
accenture
Infosys
TCS
HCLTech
Google
Microsoft
amazon
Meta
ORACLE
salesforce
IBM
accenture
Infosys
TCS
HCLTech
Google
Microsoft
amazon
Meta
ORACLE
salesforce
IBM
accenture
Infosys
TCS
HCLTech
Google
Microsoft
amazon
Meta
ORACLE
salesforce
IBM
accenture
Infosys
TCS
HCLTech
How a 50-engineer team at a Series C startup reduced production incidents by 73%
Before Markar
- • 6 hrs average debug time
- • 3 severe incidents/week
- • Unknown blast radius before push
After Markar
- • Agentic AI finds root cause in minutes
- • Full change visibility on every PR
- • Engineers ship with 100% confidence
Stop guessing. Start knowing.
Join engineering teams who ship with confidence, not hope.