Sentient AI Releases ROMA: An Open-Supply and AGI Centered Meta-Agent Framework for Constructing AI Brokers with Hierarchical Process Execution

Sentient AI has launched ROMA (Recursive Open Meta-Agent), an open-source meta-agent framework for constructing high-performance multi-agent programs. ROMA buildings agentic workflows as a hierarchical, recursive activity tree: mother or father nodes break a posh objective into subtasks, cross them right down to baby nodes as context, and later combination their options as outcomes circulate again up—making the context circulate clear and absolutely traceable throughout node transitions.

Structure: Atomize → Plan → Execute → Mixture

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 software/API, or perhaps a nested agent. An aggregator then merges baby outputs into the mother or father’s reply. This resolution loop repeats for every subtask, producing a dependency-aware tree that executes unbiased branches in parallel and enforces left-to-right ordering when a subtask is determined by a earlier sibling.

Data 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 verify 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 Floor and Stack

ROMA supplies a setup.sh fast begin with Docker Setup (Beneficial) 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 Help: any supplier through LiteLLM, and Code Execution: E2B sandboxes. Knowledge paths help enterprise S3 mounting with goofys FUSE, path-injection checks, and safe AWS credential dealing with, maintaining leaf expertise 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 Issues?

ROMA buildings work as a hierarchical, recursive activity tree: mother or father nodes break a posh objective into subtasks, cross them down as context, and later combination baby options as outcomes circulate 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 circulate clear and absolutely traceable, so failures are diagnosable reasonably than black-box. Unbiased siblings can run in parallel and dependency edges impose sequencing, turning mannequin/immediate/software selections into managed, observable parts inside 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 Professional (19.8%). The ROMA additionally reviews 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 extra context on SEALQA, the benchmark targets search-augmented reasoning the place net outcomes will be conflicting or noisy. Seal-0 focuses on questions that problem present programs, aligning with ROMA’s emphasis on strong decomposition and verification steps.

The place ROMA Suits?

ROMA positions itself because the spine for open-source meta-agents: it supplies a hierarchical, recursive activity tree during which mother or father nodes decompose objectives into subtasks, cross context right down to baby nodes (brokers/instruments), and later combination outcomes as they circulate 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, software, or agent and exploit parallelization for unbiased branches. This makes multi-step workloads—starting from monetary evaluation to artistic era—simpler to engineer with express context circulate and observable execution.

ROMA will not be one other “agent wrapper,” but it surely seems like a disciplined recursive scaffold: Atomizer → Planner → Executor → Aggregator, traced at each hop, parallel the place secure, 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 activity graphs, typed interfaces, and clear context circulate—so groups can iterate shortly 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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