Anemoi Framework: Weather & Multi-Agent Systems

• The Anemoi framework is a dual-domain platform that integrates modular approaches for both graph-based weather prediction and semi-centralized multi-agent collaboration.
• It employs robust architectures, like encoder–processor–decoder GNNs and A2A communication protocols, to enhance forecast accuracy and agent coordination.
• Key methodologies include domain-specific boundary handling, unified training pipelines, and adaptive planning, facilitating rigorous comparative studies and operational scalability.

The Anemoi framework denotes two distinct systems in contemporary research—one in data-driven weather prediction using graph machine learning, and another in semi-centralized multi-agent systems (MAS) for collaborative task execution. Both frameworks provide modular, extensible architectures designed to address critical deficiencies in prior state-of-the-art methods, enabling rigorous, reproducible experimental setups and operational scalability. This entry surveys both instantiations of the Anemoi framework, emphasizing their design principles, core methodologies, practical applications, performance benchmarks, and operational trade-offs, as evidenced in the cited literature.

1. Definitions and Contexts of the Anemoi Framework

The Anemoi framework was introduced in two principal domains:

• Data-Driven Regional Weather Prediction: Anemoi serves as a modular platform to implement and compare limited-area models (LAM) and stretched-grid models (SGM) for regional machine learning weather prediction (MLWP) based on graph neural networks (Wijnands et al., 24 Jul 2025).
• Multi-Agent Systems (MAS): Anemoi is proposed as a semi-centralized multi-agent framework utilizing a message-passing coordination server for structured, agent-to-agent (A2A) communication and collaborative planning (Ren et al., 23 Aug 2025).

In each context, Anemoi provides a shared experimental backbone, allowing systematic evaluation of architectural choices—such as domain boundary handling in MLWP, or communication protocols in collaborative MAS—while keeping other variables (data, training configuration, evaluation) constant.

2. Architecture and Core Components

Weather Prediction (MLWP)

• Encoder–Processor–Decoder GNN: Both LAM and SGM in Anemoi are built on a GNN architecture. The encoder projects data onto a latent grid via k-nearest neighbor aggregation, the processor leverages a graph transformer with multi-head attention to enable both short- and long-range dependencies, and the decoder reconstructs outputs on the original data grid.
• Graph Construction: SGM combines a global (low-resolution) and a regional (high-resolution) domain using mesh refinement. Each node in the data grid connects to 12 nearest latent nodes in the encoder; conversely, data nodes connect to 3 closest latent nodes in the decoder.
• Boundary Domain Handling: LAM includes a separate boundary domain, updated via external forcings (e.g., reanalysis data), whereas SGM handles boundaries endogenously as part of its global domain.

Multi-Agent System

• Agent Types: Core agents include the Planner, Critique, and Answer-Finding agents, along with specialized worker agents (Web agent; Document Processing; Reasoning/Coding).
• Semi-Centralized Coordination: A central planner launches the initial plan, but all agents share information and suggest refinements during execution through direct, structured messaging.
• MCP Server Integration: The Agent-to-Agent (A2A) MCP server from Coral Protocol orchestrates thread-based, persistent communication (list_agents, create_thread, add/remove_participant, send_message, wait_for_mentions, close_thread).

3. Methodologies and Training Procedures

Weather Prediction

• Unified Training Pipeline: Both LAM and SGM are trained in two phases: first for single-step prediction (6-hour-ahead forecast from two historical steps), then via autoregressive rollout for sequential prediction up to 72 hours.
• Loss Function: Variable-weighted MSE, $L = \sum_i w_i (f_i - o_i)^2$, designed to balance prognostic variables. For SGM, loss components are split regionally/globally with tunable weighting.
• Boundary Forcing in LAM: The output for boundary domains is updated with external data each step, in contrast to SGM’s self-contained approach.

Multi-Agent System

• Task Allocation and Execution: Subtasks are mapped to agents via $\varphi: \mathcal{W} \rightarrow \mathcal{T}$; each agent contribution $r_w = w(\varphi(w))$ is collected and processed. Outputs are aggregated as $$o_i^{(t+1)} = a_i(\pi_t, \{r_w\}_{w \in \mathcal{W}})$$ and finalized via consensus $\mathcal{C}(\{v_i\})$.
• Continuous Adaptive Planning: Agents monitor task progress, flag bottlenecks, and adapt the plan dynamically.

4. Comparative Analyses and Benchmarks

Weather Prediction

Performance Metrics:

Model	Forecast Domain	Temporal Generalisability	Boundary Handling	Rollout Efficiency	Suitable Data Contexts
LAM	Regional	Lower	External forcing	Higher compute	Where global data unreliable
SGM	Global + Region	Superior	Fully internal	More efficient	Where global data available

• SGM outperforms LAM in temporal generalisability over diurnal cycles and synoptic-scale variables—e.g., for 2m temperature at untrained forecast times.
• LAM can surpass SGM for certain small-scale features when high-quality boundary data is provided.
• SGM is computationally more efficient during autoregressive rollout, attributing to the self-contained global forecast domain.
• Model scaling (e.g., increasing parameters from 62M to 246M) improves fine-scale performance for both models but can induce smoothing in synoptic patterns.

Multi-Agent System

• On the GAIA benchmark, Anemoi achieves 52.73% accuracy with a small LLM-powered planner (GPT-4.1-mini), surpassing the strongest open-source OWL baseline (43.63%) by +9.09 percentage points under identical model settings.
• The semi-centralized, A2A model enables robust performance even when the planner’s capability is reduced, by leveraging distributed, cross-agent collaboration.

5. Operational Advantages and Limitations

Weather Prediction

Advantages:

• Comprehensive modularity (shared preprocessing, graph construction, loss computation).
• Facilitates fair, controlled head-to-head comparisons of domain and boundary approaches.
• Flexible extension to operational or experimental hybrid setups (transfer learning, operational adaptation, architecture reconfiguration).

Limitations:

• LAM remains dependent on the quality of external boundary forcings; operationally, any degradation directly affects forecast performance.
• The transition from reanalysis-driven experiments to real-time, operational MLWP may require further adaptation for error robustness.

Multi-Agent System

Advantages:

• Reduces dependency on a centralized planner, distributing cognitive labor across specialized agents.
• Structured A2A communication threads eliminate redundancy and context loss endemic to prompt-concatenation pipelines.
• Enables dynamic adaptation and collaborative plan refinement.

Challenges:

• Communication latencies, especially in web agent interactions, present efficiency bottlenecks.
• Toolkit limitations observed in component support (e.g., audio/video processing).
• LLM capability remains a major determinant in overall system error rates (45.6% of errors attributed to model limitations).

6. Future Directions and Application Guidance

• Ongoing improvements to Anemoi in MLWP focus on optimizing boundary domain handling, adapting rollouts for heterogeneous data quality, and integrating operational forecasting feedback loops.
• In MAS, anticipated developments include agent specialization, enhanced multimodal processing support, and scalable asynchronous communication for real-time, large-scale task decompositions—central to the emerging "Internet of Agents" paradigm.
• For meteorological institutes, the framework supports selection between LAM and SGM based on local data availability, operational reliability of global forcings, and target forecast applications.
• Open-source distributions for MAS research (e.g., at https://github.com/Coral-Protocol/Anemoi) facilitate rapid experimentation, community-driven development, and benchmark expansion.

7. Summary and Significance

The Anemoi framework, in both the MLWP and MAS instantiations, embodies a methodological advance by providing extensible, modular platforms for controlled comparative research and operational deployment. In MLWP, it enables rigorous evaluation of regional versus global modeling strategies, impacting the design of next-generation climate and weather forecasts. In MAS, it realizes scalable agent collaboration, leveraging structured, direct message passing to deliver improved autonomous task performance, reduced context redundancy, and adaptivity in open-ended applications. The framework’s versatility and systematic design have contributed to demonstrable improvements over established baselines in accuracy, efficiency, and robustness, as detailed in contemporary research (Wijnands et al., 24 Jul 2025, Ren et al., 23 Aug 2025).