Sentient AI Releases ROMA: An Open-Source and AGI Focused Meta-Agent Framework for Building AI Agents with Hierarchical Task Execution
Sentient AI has launched ROMA (Recursive Open Meta-Agent), an open-source meta-agent framework for constructing high-performance multi-agent programs. ROMA constructions agentic workflows as a hierarchical, recursive process tree: dad or mum nodes break a posh objective into subtasks, cross them all the way down to little one nodes as context, and later combination their options as outcomes movement again up—making the context movement clear and absolutely traceable throughout node transitions.
Architecture: Atomize → Plan → Execute → Aggregate
ROMA defines a minimal, recursive management loop. A node first atomizes a request (atomic or not). If non-atomic, a planner decomposes it into subtasks; in any other case, an executor runs the duty through an LLM, a device/API, or perhaps a nested agent. An aggregator then merges little one outputs into the dad or mum’s reply. This determination loop repeats for every subtask, producing a dependency-aware tree that executes impartial branches in parallel and enforces left-to-right ordering when a subtask is dependent upon a earlier sibling.
Information strikes top-down as duties are damaged down and bottom-up as outcomes are aggregated. ROMA additionally permits human checkpoints at any node (e.g., to substantiate a plan or fact-check a important hop) and surfaces stage tracing—inputs/outputs per node—so builders can debug and refine prompts, instruments, and routing insurance policies with visibility into each transition. This addresses the frequent observability hole in agent frameworks.
Developer Surface and Stack
ROMA gives a setup.sh fast begin with Docker Setup (Recommended) or Native Setup, plus flags for E2B sandbox integration (--e2b, --test-e2b). The stack lists Backend: Python 3.12+ with FastAPI/Flask, Frontend: React + TypeScript with real-time WebSocket, LLM Support: any supplier through LiteLLM, and Code Execution: E2B sandboxes. Data paths help enterprise S3 mounting with goofys FUSE, path-injection checks, and safe AWS credential dealing with, protecting leaf abilities swappable whereas the meta-architecture manages the duty graph and dependencies.
In growth, you possibly can wire ROMA to closed or open LLMs, native fashions, deterministic instruments, or different brokers with out touching the meta-layer; inputs/outputs are outlined with Pydantic for structured, auditable I/O throughout runs and tracing.
Why the Recursion Matters?
ROMA constructions work as a hierarchical, recursive process tree: dad or mum nodes break a posh objective into subtasks, cross them down as context, and later combination little one options as outcomes movement again up. This recursive breakdown confines context to what every node requires, curbing immediate sprawl, whereas stage-level tracing (with structured Pydantic I/O) makes the movement clear and absolutely traceable, so failures are diagnosable slightly than black-box. Independent siblings can run in parallel and dependency edges impose sequencing, turning mannequin/immediate/device selections into managed, observable parts throughout the plan-execute-aggregate loop.
Benchmarks: ROMA Search
To validate the structure, Sentient constructed ROMA Search, an web search agent carried out on the ROMA scaffold (no domain-specific “deep analysis” heuristics claimed). On SEALQA (Seal-0)—a subset designed to emphasize multi-source reasoning—ROMA Search is reported at 45.6% accuracy, exceeding Kimi Researcher (36%) and Gemini 2.5 Pro (19.8%). The ROMA additionally studies state-of-the-art on FRAMES (multi-step reasoning) and near-SOTA on SimpleQA (factual retrieval). As with all vendor-published outcomes, deal with these as directional till independently reproduced, however they present the structure is aggressive throughout reasoning-heavy and fact-centric duties.
For further context on SEALQA, the benchmark targets search-augmented reasoning the place internet outcomes might be conflicting or noisy. Seal-0 focuses on questions that problem present programs, aligning with ROMA’s emphasis on strong decomposition and verification steps.
Where ROMA Fits?
ROMA positions itself because the spine for open-source meta-agents: it gives a hierarchical, recursive process tree wherein dad or mum nodes decompose objectives into subtasks, cross context all the way down to little one nodes (brokers/instruments), and later combination outcomes as they movement again up. The design emphasizes transparency through stage tracing and helps human-in-the-loop checkpoints, whereas its modular nodes let builders plug in any mannequin, device, or agent and exploit parallelization for impartial branches. This makes multi-step workloads—starting from monetary evaluation to inventive era—simpler to engineer with express context movement and observable execution.
Editorial Comments
ROMA isn’t one other “agent wrapper,” but it surely appears like a disciplined recursive scaffold: Atomizer → Planner → Executor → Aggregator, traced at each hop, parallel the place protected, sequential the place required. The early ROMA Search outcomes are promising and align with the framework’s objectives, however the extra necessary consequence is developer management—clear process graphs, typed interfaces, and clear context movement—so groups can iterate rapidly and confirm every stage. With Apache-2.0 licensing and an implementation that already contains FastAPI/React tooling, LiteLLM integration, and sandboxed execution paths, ROMA is a sensible base for constructing long-horizon agent programs with measurable, inspectable conduct.
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