# ROMA **Repository Path**: data_factory/deepresearch-ROMA ## Basic Information - **Project Name**: ROMA - **Description**: No description available - **Primary Language**: Unknown - **License**: MIT - **Default Branch**: main - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2025-09-11 - **Last Updated**: 2025-09-11 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README

ROMA: Recursive Open Meta-Agents

Building hierarchical high-performance multi-agent systems made easy! (Beta)

Technical Blog • Paper (Coming soon) • Build Agents for $$$

--- ## 📖 Documentation - **[🚀 Introduction](docs/INTRODUCTION.md)** - Understand the vision and architecture behind ROMA - **[📦 Setup](docs/SETUP.md)** - Detailed configuration options and environment setup - **[🤖 Agents Guide](docs/AGENTS_GUIDE.md)** - Learn how to create and customize your own agents - **[⚙️ Configuration](docs/CONFIGURATION.md)** - Detailed configuration options and environment setup - **[🗺️ Roadmap](docs/ROADMAP.md)** - See what's coming next for ROMA ## 🎯 What is ROMA?

**ROMA** is a **meta-agent framework** that uses recursive hierarchical structures to solve complex problems. By breaking down tasks into parallelizable components, ROMA enables agents to tackle sophisticated reasoning challenges while maintaining transparency that makes context-engineering and iteration straightforward. The framework offers **parallel problem solving** where agents work simultaneously on different parts of complex tasks, **transparent development** with a clear structure for easy debugging, and **proven performance** demonstrated through our search agent's strong benchmark results. We've shown the framework's effectiveness, but this is just the beginning. As an **open-source and extensible** platform, ROMA is designed for community-driven development, allowing you to build and customize agents for your specific needs while benefiting from the collective improvements of the community. ## 🏗️ How It Works **ROMA** framework processes tasks through a recursive **plan–execute loop**: ```python def solve(task): if is_atomic(task): # Step 1: Atomizer return execute(task) # Step 2: Executor else: subtasks = plan(task) # Step 2: Planner results = [] for subtask in subtasks: results.append(solve(subtask)) # Recursive call return aggregate(results) # Step 3: Aggregator # Entry point: answer = solve(initial_request) ``` 1. **Atomizer** – Decides whether a request is **atomic** (directly executable) or requires **planning**. 2. **Planner** – If planning is needed, the task is broken into smaller **subtasks**. Each subtask is fed back into the **Atomizer**, making the process recursive. 3. **Executors** – Handle atomic tasks. Executors can be **LLMs, APIs, or even other agents** — as long as they implement an `agent.execute()` interface. 4. **Aggregator** – Collects and integrates results from subtasks. Importantly, the Aggregator produces the **answer to the original parent task**, not just raw child outputs. #### 📐 Information Flow - **Top-down:** Tasks are decomposed into subtasks recursively. - **Bottom-up:** Subtask results are aggregated upwards into solutions for parent tasks. - **Left-to-right:** If a subtask depends on the output of a previous one, it waits until that subtask completes before execution. This structure makes the system flexible, recursive, and dependency-aware — capable of decomposing complex problems into smaller steps while ensuring results are integrated coherently.

Click to view the system flow diagram

```mermaid flowchart TB A[Your Request] --> B{Atomizer} B -->|Plan Needed| C[Planner] B -->|Atomic Task| D[Executor] %% Planner spawns subtasks C --> E[Subtasks] E --> G[Aggregator] %% Recursion E -.-> B %% Execution + Aggregation D --> F[Final Result] G --> F style A fill:#e1f5fe style F fill:#c8e6c9 style B fill:#fff3e0 style C fill:#ffe0b2 style D fill:#d1c4e9 style G fill:#c5cae9 ```

### 🚀 30-Second Quick Start ```bash git clone https://github.com/sentient-agi/ROMA.git cd ROMA # Run the automated setup ./setup.sh ``` Choose between: - **Docker Setup** (Recommended) - One-command setup with isolation - **Native Setup** - Direct installation for development ## 🛠️ Technical Stack - **Framework**: Built on [AgnoAgents]([https://github.com/your/agnoagents](https://github.com/agno-agi/agno)) - **Backend**: Python 3.12+ with FastAPI/Flask - **Frontend**: React + TypeScript with real-time WebSocket - **LLM Support**: Any provider via LiteLLM - **Data Persistence**: Enterprise S3 mounting with security validation - 🔒 **goofys FUSE mounting** for zero-latency file access - 🛡️ **Path injection protection** with comprehensive validation - 🔐 **AWS credentials verification** before operations - 📁 **Dynamic Docker Compose** with secure volume mounting - **Code Execution**: E2B sandboxes with unified S3 integration - **Security**: Production-grade validation and error handling - **Features**: Multi-modal, tools, MCP, hooks, caching ## 📦 Installation Options ### Quick Start (Recommended) ```bash # Main setup (choose Docker or Native) ./setup.sh # Optional: Setup E2B sandbox integration ./setup.sh --e2b # Test E2B integration ./setup.sh --test-e2b ``` ### Command Line Options ```bash ./setup.sh --docker # Run Docker setup directly ./setup.sh --docker-from-scratch # Rebuild Docker images/containers from scratch (down -v, no cache) ./setup.sh --native # Run native setup directly (macOS/Ubuntu/Debian) ./setup.sh --e2b # Setup E2B template (requires E2B_API_KEY + AWS creds) ./setup.sh --test-e2b # Test E2B template integration ./setup.sh --help # Show all available options ``` ### Manual Installation See [setup docs](docs/SETUP.md) for detailed instructions. ### 🏗️ Optional: E2B Sandbox Integration For secure code execution capabilities, optionally set up E2B sandboxes: ```bash # After main setup, configure E2B (requires E2B_API_KEY and AWS credentials in .env) ./setup.sh --e2b # Test E2B integration ./setup.sh --test-e2b ``` **E2B Features:** - 🔒 **Secure Code Execution** - Run untrusted code in isolated sandboxes - ☁️ **S3 Integration** - Automatic data sync between local and sandbox environments - 🚀 **goofys Mounting** - High-performance S3 filesystem mounting - 🔧 **AWS Credentials** - Passed securely via Docker build arguments ## 🤖 Pre-built Agents > **Note:** These agents are demonstrations built using ROMA's framework through simple vibe-prompting and minimal manual tuning. They showcase how easily you can create high-performance agents with ROMA, rather than being production-final solutions. Our mission is to empower the community to build, share, and get rewarded for creating innovative agent recipes and use-cases. ROMA comes with example agents that demonstrate the framework's capabilities: ### 🔍 General Task Solver A versatile agent powered by ChatGPT Search Preview for handling diverse tasks: - **Intelligent Search**: Leverages OpenAI's latest search capabilities for real-time information - **Flexible Planning**: Adapts task decomposition based on query complexity - **Multi-Domain**: Handles everything from technical questions to creative projects - **Quick Prototyping**: Perfect for testing ROMA's capabilities without domain-specific setup Perfect for: General research, fact-checking, exploratory analysis, quick information gathering ### 🔬 Deep Research Agent A comprehensive research system that breaks down complex research questions into manageable sub-tasks: - **Smart Task Decomposition**: Automatically splits research topics into search, analysis, and synthesis phases - **Parallel Information Gathering**: Executes multiple searches simultaneously for faster results - **Multi-Source Integration**: Combines results from web search, Wikipedia, and specialized APIs - **Intelligent Synthesis**: Aggregates findings into coherent, well-structured reports Perfect for: Academic research, market analysis, competitive intelligence, technical documentation ### 💹 Crypto Analytics Agent Specialized financial analysis agent with deep blockchain and DeFi expertise: - **Real-Time Market Data**: Integrates with Binance, CoinGecko, and DefiLlama APIs - **On-Chain Analytics**: Access to Arkham Intelligence for wallet tracking and token flows - **Technical Analysis**: Advanced charting with OHLC data and market indicators - **DeFi Metrics**: TVL tracking, yield analysis, protocol comparisons - **Secure Execution**: Runs analysis in E2B sandboxes with data persistence Perfect for: Token research, portfolio analysis, DeFi protocol evaluation, market trend analysis All three agents demonstrate ROMA's recursive architecture in action, showing how complex queries that would overwhelm single-pass systems can be elegantly decomposed and solved. They serve as templates and inspiration for building your own specialized agents. ### Your First Agent in 5 Minutes ```python ./setup.sh # Automated setup with Docker or native installation ``` Access all the pre-defined agents through the frontend on `localhost:3000` after setting up the backend on `localhost:5000`. Please checkout [Setup](./docs/SETUP.md) and the [Agents guide](./docs/AGENTS_GUIDE.md) to get started!

```python # Your first agent in 3 lines from sentientresearchagent import SentientAgent agent = SentientAgent.create() result = await agent.run("Create a podcast about AI safety") ``` ## 📊 Benchmarks We evaluate our simple implementation of a search system using ROMA, called ROMA-Search across three benchmarks: **SEAL-0**, **FRAMES**, and **SimpleQA**. Below are the performance graphs for each benchmark. ### [SEAL-0](https://huggingface.co/datasets/vtllms/sealqa) SealQA is a new challenging benchmark for evaluating Search-Augmented Language models on fact-seeking questions where web search yields conflicting, noisy, or unhelpful results. ![SEAL-0 Results](assets/seal-0-full.001.jpeg) --- ### [FRAMES](https://huggingface.co/datasets/google/frames-benchmark)

View full results

A comprehensive evaluation dataset designed to test the capabilities of Retrieval-Augmented Generation (RAG) systems across factuality, retrieval accuracy, and reasoning. ![FRAMES Results](assets/FRAMES-full.001.jpeg)

--- ### [SimpleQA](https://openai.com/index/introducing-simpleqa/)

View full results

Factuality benchmark that measures the ability for language models to answer short, fact-seeking questions. ![SimpleQA Results](assets/simpleQAFull.001.jpeg)

## ✨ Features

### 🔄 Recursive Task Decomposition Automatically breaks down complex tasks into manageable subtasks with intelligent dependency management. Runs independent sub-tasks in parallel.	### 🤖 Agent Agnostic Works with any provider (OpenAI, Anthropic, Google, local models) through unified interface, as long as it has an `agent.run()` command, then you can use it!
### 🔍 Complete Transparency Stage tracing shows exactly what happens at each step - debug and optimize with full visibility	### 🔌 Connect Any Tool Seamlessly integrate external tools and protocols with configurable intervention points. Already includes production-grade connectors such as E2B, file-read-write, and more.

## 🙏 Acknowledgments This framework would not have been possible if it wasn't for these amazing open-source contributions! - Inspired by the hierarchical planning approach described in ["Beyond Outlining: Heterogeneous Recursive Planning"](https://arxiv.org/abs/2503.08275) by Xiong et al. - [Pydantic](https://github.com/pydantic/pydantic) - Data validation using Python type annotations - [Agno]([https://github.com/agno-ai/agno](https://github.com/agno-agi/agno)) - Framework for building AI agents - [E2B](https://github.com/e2b-dev/e2b) - Cloud runtime for AI agents ## 📚 Citation If you use the ROMA repo in your research, please cite: ```bibtex @software{al_zubi_2025_17052592, author = {Al-Zubi, Salah and Nama, Baran and Kaz, Arda and Oh, Sewoong}, title = {SentientResearchAgent: A Hierarchical AI Agent Framework for Research and Analysis }, month = sep, year = 2025, publisher = {Zenodo}, version = {ROMA}, doi = {10.5281/zenodo.17052592}, url = {https://doi.org/10.5281/zenodo.17052592}, swhid = {swh:1:dir:69cd1552103e0333dd0c39fc4f53cb03196017ce ;origin=https://doi.org/10.5281/zenodo.17052591;vi sit=swh:1:snp:f50bf99634f9876adb80c027361aec9dff97 3433;anchor=swh:1:rel:afa7caa843ce1279f5b4b29b5d3d 5e3fe85edc95;path=salzubi401-ROMA-b31c382 }, } ``` ## 📄 License This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

### 🔄 Recursive Task Decomposition Automatically breaks down complex tasks into manageable subtasks with intelligent dependency management. Runs independent sub-tasks in parallel.	### 🤖 Agent Agnostic Works with any provider (OpenAI, Anthropic, Google, local models) through unified interface, as long as it has an `agent.run()` command, then you can use it!
### 🔍 Complete Transparency Stage tracing shows exactly what happens at each step - debug and optimize with full visibility	### 🔌 Connect Any Tool Seamlessly integrate external tools and protocols with configurable intervention points. Already includes production-grade connectors such as E2B, file-read-write, and more.