Cerno : Agentic Deep Research

Cerno is an open-source workspace for conducting deep, multi-step research and analysis using autonomous AI agents. Designed for developers and researchers who demand analytical transparency, Cerno exposes every reasoning step—from prompt decomposition to final synthesis—so you can observe, debug, and steer complex agentic workflows with confidence.

📚 Table of Contents

1. Highlights
2. Local-First Principles
3. Active Development & Community
4. Prerequisites
5. Docker Installation
6. CLI Reference
7. Project Structure
8. Screenshots
9. Use Cases
10. Roadmap
11. Security & Privacy
12. Contributing
13. License

---

🚀 Highlights

• Model-Agnostic Core: Effortlessly switch between premier LLMs (OpenAI, Google Gemini, Anthropic, DeepSeek) or run local models via Ollama.
• Zero-Config Setup: One CLI, one command—automatically create a virtual environment, install dependencies, and configure your workspace.
• Transparent Execution Plan: Visualize each agent task as it moves through Pending → Running → Success/Error states in real time.
• Verifiable Artifacts: Every source, webpage, and generated file (reports, code, data) is tracked and organized for easy auditing.
• Adaptive Depth: Simple queries spawn lightweight plans; complex directives trigger multi-agent, multi-tool orchestrations.
• Token & Cost Optimization: A manager-worker agent architecture balances quality and cost. Get a complete cost breakdown upon task completion.
• Local-First Ethos: Work offline, retain full control of your data, and avoid vendor lock-in. Cerno’s local-first architecture ensures your research stays where you want it: on your machine.

---

🌱 Local-First Principles

Cerno embraces a local-first philosophy:

1. Data Sovereignty: All research artifacts—notes, reports, intermediate files—live on your local drive by default.
2. Offline Capability: Core features work without internet. Use local LLMs (via Ollama) for research when connectivity is limited.
3. Privacy & Security: Sensitive prompts and outputs never leave your machine unless explicitly configured.
4. Interoperability: Write, export, and share results in standard formats (Markdown, Jupyter notebooks, JSON) without proprietary lock-in.

---

🛡️ Active Development & Community

Cerno is under active development—we’re constantly pushing new features, performance optimizations, and integrations. Your feedback is invaluable:

• Bug Reports: Found an issue? Please open an issue on GitHub with detailed steps to reproduce.
• Feature Requests: Have a great idea? Share it as an issue or discussion ticket.
• Contributions: We welcome pull requests! See our CONTRIBUTING.md for guidelines on setting up your dev environment, coding standards, and how to submit changes.

Let’s build something amazing together! 🚀

📦 Prerequisites

• Python ≥ 3.10
• Node.js ≥ 18.x & npm

---

🐳 Docker Installation

Docker is the official method of installation for Cerno Deep Research. To use Cerno, follow the steps below:

1. Clone and set up .env using the example .env.example.

2. Build and launch with Docker Compose:

   docker-compose up --build

3. Visit http://localhost:5173.

---

📁 Project Structure

├── cerno             # CLI bootstrap scripts
├── cerno_cli.py      # Click-based command definitions
├── api/              # Django backend
│   ├── core/         # Settings, wsgi, asgi
│   ├── api/          # Views, serializers, URLs
│   └── agents/       # Agent definitions & tools
├── frontend/         # React + Vite app
├── agent_outputs/    # Generated reports, code, data
├── knowledge_sources/# Ingested docs for knowledge base
├── pyproject.toml    # Dependencies & CLI entry point
└── docker-compose.yml

---

📸 Screenshots

Expand to view screenshots

---

💼 Use Cases

• Academic Research: Automate literature reviews, data extraction, and report generation.
• Market Analysis: Compile insights from news sources, financial data, and social media.
• Competitive Intelligence: Track competitor tooling and summarize key findings.
• Product Development: Prototype multi-agent workflows for user testing and iterative design.

---

📈 Roadmap

• v1.1 (Q3 2025): More integrations, advanced visualization modules, and collaborative workspaces.
• v1.2 (Q4 2025): Plugin support, permissioned sharing, and audit trails.
• Future: Community-driven integrations, mobile-first UI, and expanded local model support.

---

🔒 Security & Privacy

• Encrypted Secrets: API keys and sensitive data encrypted at rest.
• Audit Logs: Full history of agent actions and user interactions.

---

🤝 Contributing

Contributions are welcome! Fork, develop, and submit a pull request. For major features, please open an issue first to discuss design and scope.

---

📜 License

Distributed under the MIT License. See LICENSE for details.