base on Recursive-Open-Meta-Agent v0.1 (Beta). A meta-agent framework to build high-performance multi-agent systems. <div align="center"> <div align="center"> <img src="./assets/sentient-logo-new-M.png" alt="alt text" width="60%"/> </div> <h1>ROMA: Recursive Open Meta-Agents</h1> <p align="center"> <strong>Building hierarchical high-performance multi-agent systems made easy! (Beta) </strong> </p> <a href="https://trendshift.io/repositories/14848" target="_blank"><img src="https://trendshift.io/api/badge/repositories/14848" alt="sentient-agi%2FROMA | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a> <p align="center"> <a href="https://sentient.xyz/" target="_blank" style="margin: 2px;"> <img alt="Homepage" src="https://img.shields.io/badge/Sentient-Homepage-%23EAEAEA?logo=data%3Aimage%2Fsvg%2Bxml%3Bbase64%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%2BPC9zdmc%2B&link=https%3A%2F%2Fhuggingface.co%2FSentientagi" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://github.com/sentient-agi" target="_blank" style="margin: 2px;"> <img alt="GitHub" src="https://img.shields.io/badge/Github-sentient_agi-181717?logo=github" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://huggingface.co/Sentientagi" target="_blank" style="margin: 2px;"> <img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-SentientAGI-ffc107?color=ffc107&logoColor=white" style="display: inline-block; vertical-align: middle;"/> </a> </div> <div align="center" style="line-height: 1;"> <a href="https://discord.gg/sentientfoundation" target="_blank" style="margin: 2px;"> <img alt="Discord" src="https://img.shields.io/badge/Discord-SentientAGI-7289da?logo=discord&logoColor=white&color=7289da" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://x.com/SentientAGI" target="_blank" style="margin: 2px;"> <img alt="Twitter Follow" src="https://img.shields.io/badge/-SentientAGI-grey?logo=x&link=https%3A%2F%2Fx.com%2FSentientAGI%2F" style="display: inline-block; vertical-align: middle;"/> </a> </p> <p align="center"> <a href="https://www.sentient.xyz/blog/recursive-open-meta-agent">Technical Blog</a> • <a href="docs/">Paper (Coming soon)</a> • <a href="https://www.sentient.xyz/">Build Agents for $$$</a> </p> </div> --- </div> ## 📖 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? <div align="center"> <img src="./assets/roma_run.gif" alt="alt text" width="80%"/> </div> <br> **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. <details> <summary>Click to view the system flow diagram</summary> ```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 ``` </details><br> ### 🚀 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! <div align="center"> <img src="./assets/agent_customization.png" alt="alt text" width="60%"/> </div> ```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.  --- ### [FRAMES](https://huggingface.co/datasets/google/frames-benchmark) <details> <summary>View full results</summary> A comprehensive evaluation dataset designed to test the capabilities of Retrieval-Augmented Generation (RAG) systems across factuality, retrieval accuracy, and reasoning.  </details> --- ### [SimpleQA](https://openai.com/index/introducing-simpleqa/) <details> <summary>View full results</summary> Factuality benchmark that measures the ability for language models to answer short, fact-seeking questions.  </details> ## ✨ Features <table> <tr> <td width="50%"> ### 🔄 **Recursive Task Decomposition** Automatically breaks down complex tasks into manageable subtasks with intelligent dependency management. Runs independent sub-tasks in **parallel**. </td> <td width="50%"> ### 🤖 **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! </td> </tr> <tr> <td width="50%"> ### 🔍 **Complete Transparency** Stage tracing shows exactly what happens at each step - debug and optimize with full visibility </td> <td width="50%"> ### 🔌 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. </td> </tr> </table> ## 🙏 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. ", Assign "at most 3 tags" to the expected json: {"id":"14848","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"