AgentOrchestra: Hierarchical Multi-Agent Framework

Updated 18 November 2025

AgentOrchestra is a hierarchical multi-agent framework that employs a central 'conductor' planning agent and specialist sub-agents to decompose and solve complex tasks.
The framework uses a function-calling API for precise task delegation, enabling dynamic error recovery and adaptive coordination.
Empirical evaluations show state-of-the-art performance on diverse benchmarks, emphasizing its extensibility, modularity, and multimodal capabilities.

AgentOrchestra is a hierarchical multi-agent framework designed for general-purpose task solving, integrating high-level planning with modular agent collaboration. By employing a central “conductor” agent for planning and coordination, and delegating concrete subtasks to a set of specialist agents equipped with well-defined toolkits, AgentOrchestra enables robust, extensible, and multimodal solutions to complex real-world objectives. The framework has demonstrated significant advantages over monolithic and flat-agent designs, achieving state-of-the-art results on diverse benchmarks and catalyzing a line of research focused on principled orchestration in LLM-enhanced multi-agent systems (Zhang et al., 14 Jun 2025).

1. Architectural Foundation and Formalism

AgentOrchestra’s core architecture follows a strict two-tier division of labor. The system is organized around:

Central Planning Agent (“Conductor”): This agent is exclusively responsible for global reasoning, decomposing an overall user objective $T$ into an explicit sequence of sub-goals $P = \langle G_1, G_2, ..., G_n \rangle$ , and adaptively monitoring execution. Task decomposition and plan adaptation are enabled through a function-calling “PlanningTool” API.
Specialized Sub-Agents: Each sub-agent is paired with a distinct ToolSet, ModelPool, and local memory. Examples include DeepResearcherAgent (web search, recursive extraction), BrowserUseAgent (interactive browser navigation), and DeepAnalyzerAgent (multimodal file analysis and structured reasoning).

Formally, each agent is defined by the tuple

$\text{Agent} \equiv (\text{Name}, \text{ModelPool}, \text{ToolSet}, \text{Memory})$

Task decomposition, delegation, and coordination are captured as a closed-loop optimization:

$\min_{\text{plan}} \sum_{i=1}^n \left[\text{cost}(G_i) + \lambda \cdot \text{fail}(G_i)\right]$

where $\text{cost}$ and $\text{fail}$ capture execution metrics and contingency handling.

Coordination proceeds through a function-calling API shared among all agents. The PlanningAgent updates a shared state via tool-calls (create, update, mark, delete), and sub-agents poll for their assigned tasks, returning structured JSON results to the planner (Zhang et al., 14 Jun 2025).

2. Coordination, Communication, and Execution Workflow

Hierarchical planning and execution in AgentOrchestra are formalized through orchestrated sub-goal dispatch and dynamic plan adaptation. The orchestration pseudocode is:

Function Orchestrate(T):
    plan_id ← PlanningTool("create", title=T, steps=[])
    steps ← Decompose(T)                  # LLM-backed
    PlanningTool("update", plan_id, steps=steps)
    For i in 1…|steps|:
        (agent, tool_call) ← Route(steps[i])
        result ← agent.invoke(tool_call)
        if result.status == SUCCESS:
            PlanningTool("mark", plan_id, step_index=i, step_status="completed")
        else:
            new_subgoals ← Repair(result.error_logs)
            PlanningTool("update", plan_id, steps.insert(i+1, new_subgoals))
    Return CollectFinalAnswer(plan_id)

This loop enforces strict role-based communication, modular error recovery, and closed-loop monitoring. The Route and Repair routines leverage the LLM abstraction layer, dynamically utilizing the most appropriate underlying model for each sub-task, and introducing new verification or fallback goals as necessary.

Inter-agent coordination is governed by explicit tool-API schemas and type signatures. Sub-agents only accept and return structured function invocations; memory is leveraged for context continuity and error recomputation (Zhang et al., 14 Jun 2025).

3. Design Principles: Extensibility, Modularity, and Multimodality

AgentOrchestra is built to enable rapid extensibility, strong modular abstraction, and native multimodality:

Extensibility: New agent roles and tools can be introduced without system-wide retraining or rewriting core logic. The Route selection function automatically recognizes new tools through a unified registry. This plug-and-play design has been adopted in related frameworks such as Magentic-One and AGORA (Fourney et al., 7 Nov 2024, Zhang et al., 30 May 2025).
Modularity: The agent layer (reasoning policy), tool layer (function-API), and model layer (LLM backend) are strictly separated. Tools are typically sandboxed for reproducibility and safety, e.g., through Docker containers.
Multimodality: Tool APIs and DeepAnalyzerTool implementations accept and process heterogeneous modalities—text, image, audio, video, or structured data—dispatching to domain-appropriate vision or LMM pipelines automatically.

This structural separation allows for scalable growth, hot-swapping of agent modules, and seamless accommodation of complex or evolving real-world task distributions.

4. Empirical Evaluation, Performance, and Trade-offs

AgentOrchestra has been comprehensively evaluated on multiple large-scale benchmarks:

Benchmark	Task	AgentOrchestra	Best Baseline
SimpleQA	factoid QA	95.3%	93.9% (PerplexityDR)
GAIA (avg)	multimodal	82.42%	77.58% (AWorld)
HLE	cross-domain exam	25.9%	21.1% (PerplexityDR)

These results demonstrate robust gains in task success rate, especially as complexity (e.g., Level 3 GAIA tasks) increases. Hierarchical decomposition, verified execution, and explicit role allocation improve both accuracy and adaptability relative to flat or monolithic systems (Zhang et al., 14 Jun 2025).

In software engineering evaluations, AgentOrchestra’s modular planning incurs substantial coordination overhead: average trajectory lengths exceed 40 steps/task, correction rates approach 45%, and the system exhibits the highest per-task monetary cost among evaluated frameworks (e.g., \$292.01/task) (Yin et al., 2 Nov 2025). The majority of resource consumption is attributed to the central Planner Agent, highlighting a core trade-off: increased specialization and robustness at the expense of token and time efficiency. Single-agent or more loosely coordinated frameworks (e.g., OpenHands, GPTswarm) are more cost-effective for simple tasks or with minimal need for error recovery.

5. Algorithmic Relatives, Extensions, and Practical Guidelines

AgentOrchestra’s hierarchical orchestration has informed and been complemented by a suite of related approaches:

Human-in-the-loop and goal visualization: OrchVis introduces a verified, human-supervisable goal-graph representation, modular verification with machine-checkable predicates, and conflict resolution via a dual-layer GUI. OrchVis highlights the reduction in cognitive overhead achieved by hierarchically orchestrated workflows ( $O(1)$ user effort for $k$ agents) (Zhou, 28 Oct 2025).
Neural selection and dynamic assignment: MetaOrch and AMO employ learned scoring models (MLPs, meta-decision trees) for agent selection, agent/onboarding scalability, and sub-second inference, supporting settings with hundreds of candidate agents (Agrawal et al., 3 May 2025, Zhu et al., 26 Oct 2025).
Knowledge-base-aware orchestration: Dynamic, privacy-preserving task routing integrates static agent descriptions with knowledge-base-driven relevance signals, yielding 95% routing accuracy on single-label benchmark tasks (Trombino et al., 23 Sep 2025).

From a theoretical standpoint, cost-sensitive orchestration only improves performance if there are non-trivial differentials in agent competence or execution cost. Invariant agent populations see no gain from orchestration, whereas varied or regionally dominant expertise profiles maximize “appropriateness” (ratio of maximal to random assignment utility), especially under cost alignment (Bhatt et al., 17 Mar 2025).

The insight from comparative evaluations is that hierarchical specialization, though powerful for robustness and adaptation, incurs substantial communication and reflection overhead. Practical deployments must therefore weigh the cost–benefit of deep modularity, especially for large-scale or latency-sensitive settings (Yin et al., 2 Nov 2025).

6. Future Directions and Open Problems

Ongoing and future research on AgentOrchestra and its derivatives is focused on:

Adaptive and meta-learned planning: Incorporating policy-gradient or bandit feedback for online refinement of plan adaptation and agent-selection strategies.
Enhanced multimodal input: Extending orchestrator and tool APIs to natively support audio, video, and sensor data streams.
Reflection and plan summarization: Condensing multiple failed trajectories into distilled prompts to improve efficiency.
Human-centered and mixed-initiative operation: Combining dynamic autonomy with transparent verification and user-supervised planning panels as in OrchVis.
Scalability and resource optimization: Streamlining agent interaction protocols (e.g., agent merging, caching intermediate results) to control trajectory length, token usage, and runtime as agent pools and task domains increase.

The convergence of explicit plan reasoning, modular verification, and dynamic, role-driven orchestration renders AgentOrchestra a leading paradigm for general-purpose, scalable multi-agent systems in LLM-augmented environments (Zhang et al., 14 Jun 2025, Fourney et al., 7 Nov 2024, Zhou, 28 Oct 2025, Agrawal et al., 3 May 2025).