An Agentic AI-powered code review system that analyzes pull requests, identifies issues, generates fixes, writes tests, executes validation workflows, and assists developers through an autonomous review pipeline.
Modern software teams spend countless hours performing repetitive code reviews, writing boilerplate tests, checking coding standards, and identifying common bugs.
Jarvis is an Agentic AI System designed to automate large portions of the software review lifecycle while keeping humans in control.
Instead of acting as a chatbot, Jarvis operates as a collection of specialized AI agents that collaborate to:
- Analyze code changes
- Understand project context
- Suggest fixes
- Generate tests
- Validate implementations
- Create review reports
- Assist developers before merge
The goal is not to replace developers but to eliminate repetitive review work and improve code quality.
Code reviews are essential but often suffer from:
- Repeated manual effort
- Missed edge cases
- Inconsistent review quality
- Slow feedback cycles
- Lack of project-wide context
Developers frequently spend more time reviewing than building.
Jarvis addresses these challenges through an autonomous multi-agent workflow.
Jarvis follows an Agentic AI architecture rather than a simple prompt-response system.
Responsible for:
- Understanding incoming pull requests
- Breaking review work into tasks
- Determining execution strategy
Responsible for:
- Generating fixes
- Refactoring code
- Creating missing implementations
Responsible for:
- Reviewing generated changes
- Identifying flaws
- Suggesting improvements
- Enforcing coding standards
Responsible for:
- Running tests
- Running static analysis
- Verifying generated code
- Producing validation reports
GitHub Pull Request
│
▼
GitHub Webhook
│
▼
Job Queue
│
▼
Go Orchestrator
│
┌────────┼────────┐
▼ ▼ ▼
Planner Coder Reviewer
│
▼
Executor
│
▼
Human Approval Layer
│
▼
Pull Request Update
Autonomous review pipeline powered by multiple specialized agents.
Uses Retrieval Augmented Generation (RAG) to understand project-specific code patterns.
Creates unit and integration tests for modified code.
Developers remain in control before changes are applied.
Stores review sessions, agent outputs, and approval decisions.
Safely validates generated code inside isolated containers.
New agents and tools can be added without modifying the entire system.
- Go
- Fiber
- PostgreSQL
- Redis
- Next.js
- TypeScript
- Tailwind CSS
- OpenAI & Gemini APIs
- Vector Embeddings
- RAG Pipeline
- Docker
- Docker Compose
- GitHub Webhooks
Jarvis/
├── cmd/
├── internal/
│ ├── orchestrator/
│ ├── agents/
│ ├── tools/
│ ├── store/
│ ├── llm/
│ └── config/
│
├── api/
├── dashboard/
├── migrations/
├── docker/
└── docs/
This project is designed to help contributors learn:
- Agentic AI Systems
- Multi-Agent Orchestration
- Retrieval Augmented Generation (RAG)
- Prompt Engineering
- Distributed System Design
- Go Backend Development
- Docker Sandboxing
- Event-Driven Architectures
- GitHub Integrations
- Project foundation
- PostgreSQL setup
- Redis queue
- Go orchestrator
- Planner Agent
- Coder Agent
- Reviewer Agent
- Executor Agent
- Retry workflows
- Agent memory
- RAG implementation
- Embedding pipeline
- Vector search
- GitHub integration
- Automated PR assistance
- Dashboard
- Advanced review intelligence
- Agent observability
- Performance optimization
We welcome contributors of all experience levels.
Ways to contribute:
- Documentation
- Backend Development
- Frontend Development
- AI Agent Design
- Prompt Engineering
- Testing
- DevOps
Please read the Contribution Guidelines before submitting a Pull Request.
Jarvis combines:
- Real-world software engineering
- Modern AI architecture
- Agent orchestration
- Distributed systems
- Open-source collaboration
making it an excellent project for developers interested in both AI and backend engineering.
MIT License
Built with ❤️ for developers who would rather spend time building software than repeatedly arguing with missing semicolons and forgotten test cases.