OpenApps: Modular & Composable Software

• OpenApps are open-source, modular applications with standardized interfaces that support programmable network control, interactive education, and agent benchmarking.
• They enable seamless integration in O-RAN via xApp containers, leveraging orchestration frameworks and protocols like E2AP/E2SM and A1 for real-time control.
• In education and agent testing, OpenApps offer scalable, reproducible environments using containerization, Jupyter notebooks, and YAML configurations for robust benchmarking.

OpenApps denotes a class of third-party, open, and programmable software applications or modules, with the specific definition varying by research context. In the O-RAN (Open Radio Access Network) domain, OpenApps are more commonly referred to as “xApps”—modular functions deployed in the near-real-time RAN Intelligent Controller (RIC) to enable programmable network control, resource management, and AI-driven closed-loop optimization, while in educational technology or UI-agent research, OpenApps refers to open, composable web/mobile applications that serve as a substrate for agent benchmarking or interactive instruction. The unifying principle across domains is standardized interfaces, modularity allowing composability, and an open-source implementation and deployment model that prioritizes transparency, interoperability, and vendor/resource heterogeneity.

1. System Architecture and Domains of OpenApps

Within O-RAN, OpenApps/xApps are instantiated as isolated application containers (often as Kubernetes pods) deployed in the near-RT RIC. They interact with disaggregated RAN elements (O-DU, O-CU) over standardized protocols (primarily E2AP/E2SM over E2, A1 for non-RT policy and ML model management) (Hoffmann et al., 2023, Feraudo et al., 2024, Feraudo et al., 3 Feb 2026). Deployment is supported by orchestration frameworks (e.g., OREO) and service mesh technologies (Linkerd/Istio) to facilitate service discovery, mutual authentication, and inter-app communication (Atalay et al., 2022, Mungari et al., 2024).

In educational and agent reliability benchmarking, OpenApps refers to open-source, self-contained, Python/Jupyter-based apps served via Voila (for education) (Du et al., 2022) or YAML-configurable GUI apps for UI-agent testing (Ullrich et al., 25 Nov 2025). Here, OpenApps implement a standardized API for agent interaction (BrowserGym or Python widget events), expose state and reward functions, and may be spun up in thousands of configuration variants for robust agent evaluation.

Domain	OpenApps Role	Key Interfaces/Modules
O-RAN (xApps)	RAN optimization, AI	E2AP/E2SM, A1, RMR, XRF
EdTech (OSSCAR)	Interactive learning	Jupyter, Voila, ipywidgets
UI-agent testing	Benchmarks for agents	BrowserGym, FastHTML

2. Development, Deployment, and Interoperability

xApp/OpenApp development typically follows a modular, SDK-based workflow:

• For RAN: Developers utilize frameworks (e.g., xDevSM) that abstract protocol encoding/decoding (ASN.1/E2AP/E2SM) behind Python APIs, allowing construction of control, monitoring, and ML-augmented orchestration loops with trivial code (Feraudo et al., 2024, Feraudo et al., 3 Feb 2026). Deployment is automated via Kubernetes, with CI/CD pipelines validating cross-stack compatibility (OAI, srsRAN, USRP/Foxconn hardware), and automated test scenarios for performance regression and reproducibility.
• For educational/web apps: The OSSCAR platform enables authors to fork template repositories, compose interactive notebooks with widgets, and deploy via Binder or institutional JupyterHub/Dokku for scalable and persistent delivery. Each app encapsulates a single pedagogical function, emphasizing reusability and ease of recombination (Du et al., 2022).
• For agent benchmarking: The OpenApps ecosystem defines a YAML-based configuration schema for rapid generation of thousands of app/environment variants, supporting parallel evaluation of multimodal agents under controlled environmental perturbations (Ullrich et al., 25 Nov 2025).

xDevSM exemplifies an “interoperable E2SM abstraction layer,” separating control/data logic from low-level protocol differences, thus allowing single-source xApps to execute unchanged across heterogeneous RAN stacks and hardware (Feraudo et al., 2024, Feraudo et al., 3 Feb 2026).

3. Interfaces, Security, and Orchestration

O-RAN OpenApps invoke a complex stack of interfaces for safe and efficient orchestration:

• E2AP/E2SM: Enables telemetry (e.g., KPM—Key Performance Measurements), event subscriptions, and control (RC—RAN Control) via strongly-typed messages; xDevSM provides high-level Python wrappers for subscription, report parsing, and control action emission (Feraudo et al., 3 Feb 2026, Feraudo et al., 2024).
• A1: Policy and ML model transfer between non-RT and near-RT RIC.
• XRF (xApp Repository Function): Implements zero-trust, OAuth 2.0-based authentication and fine-grained, attribute-based authorization for xApps as Kubernetes sidecars. XRF enables service discovery (O(log N) lookup) and token-based secure API invocations, with scalability confirmed at 4000+ registrations/sec and sub-50 ms latency (Atalay et al., 2022).
• OREO: Orchestrates OpenApps to maximize service coverage while minimizing resource consumption, using a multi-layer graph model. It formulates the xApp deployment and sharing problem as an NP-hard mixed-integer program, solved via Lagrangian-based heuristics (Mungari et al., 2024). Key orchestration innovation is function-level xApp sharing and complexity-factor tuning to optimize quality, resource, and latency constraints.

4. Methodologies and Performance Metrics

Applications and benchmarks built atop OpenApps/xApps use rigorous, formal methods for control and evaluation:

• Control Loops: Closed-loop xApps use KPM subscription to gather network metrics (e.g., throughput $T$, PRB usage $p$), compute KPIs (e.g., spectral efficiency $\eta = T / B$), and issue RC control actions (e.g., set_slice_prb_quota) (Feraudo et al., 3 Feb 2026).
• AI Readiness: Structured metric logs are designed to be directly compatible with ML pipelines (PyTorch, TensorFlow, Kafka). Action spaces are fully abstracted at the API layer, enabling RL-based optimization without manual protocol message handling (Feraudo et al., 3 Feb 2026, Feraudo et al., 2024).
• Agent Reliability Benchmarking: OpenApps facilitates systematic stress-testing by deploying agents across thousands of environment variants, measuring task success rates, success rate variance, and other behavior-based reliability metrics (Ullrich et al., 25 Nov 2025). Quantitative differences in agent reliability across appearance/content variants are substantial (task success fluctuations exceeding 50%).
• Orchestrator Performance: OREO’s orchestrator is benchmarked against optimal and state-of-the-art alternatives, demonstrating near-optimal service fulfillment, 20–30% resource savings, and heuristic solution times of ≪10 s for XL-scale (12×10×3) deployments (Mungari et al., 2024).

5. Practical Challenges and Best Practices

Several domain-specific challenges and accompanying best practices are identified:

• Low-level Software Complexity: Correct encoding/decoding of E2AP/E2SM, timestamping accuracy (critical for 10 ms real-time loops), and container jitter are recurring challenges. RT-kernel and container config tuning are mandatory for deterministic xApp performance (Hoffmann et al., 2023).
• Portability: Universal E2SM abstraction layers (as in xDevSM) and strict separation of protocol handling from logical code are essential for portability across RIC platforms and RAN hardware. Developers are encouraged to pin dependencies and automate test coverage across supported stacks (Feraudo et al., 2024, Feraudo et al., 3 Feb 2026).
• Security: Zero-trust patterns, sidecar-based security logic, automated certificate rotation, and attribute-based access control are mandated to isolate potentially untrusted third-party xApps (Atalay et al., 2022).
• Orchestration: Adoption of service-function decomposition and cataloging of function-to-xApp mappings with resource/latency/quality tuples allows dynamic orchestration with adaptive resource allocation (Mungari et al., 2024).
• Sustainability (EdTech): Emphasis on containerization, dependency pinning, automated testing, and robust documentation maximizes reproducibility and resilience of OpenApps for teaching (Du et al., 2022).
• Measurement Reliability (Agents): Benchmarks must expose agents to appearance/content variation, as single-instance success rates vastly overestimate real-world reliability (Ullrich et al., 25 Nov 2025).

6. Applications and Use Cases

OpenApps/xApps in O-RAN realize advanced use cases such as:

• Performance Monitoring: Live metric streaming (throughput, BLER, spectral efficiency) to external databases/Grafana dashboards for analysis or learning (Feraudo et al., 3 Feb 2026).
• Resource Allocation: Closed-loop PRB (Physical Resource Block) allocation at the slice level, governed by rule-based or ML controllers.
• Mobility Management: AI-augmented handover control via real-time subscription to KPM and RC directives, with concrete reaction times measured (<5–7 ms ACT-to-ACK) (Feraudo et al., 3 Feb 2026).
• Network Slicing: Dynamic enforcement of QoS policies and deep traffic classification via composable xApp graphs (Mungari et al., 2024).
• Education: Modular, interactive notebooks encapsulating specific course concepts (e.g., quantum eigenstates, Metropolis MC), delivered as containerized OpenApps (Du et al., 2022).
• Agent Evaluation: Comprehensive, variant-driven UI-agent benchmarking (tasks in OpenToDo, OpenCalendar, etc.), supporting reproducible, large-scale agent rollout with behavior logging (Ullrich et al., 25 Nov 2025).

7. Future Directions and Recommendations

Identified near-term priorities and recommendations include:

• Finalization of E2SM and A1-ML specifications to enable richer closed-loop ML applications and data-driven policy distribution (Hoffmann et al., 2023).
• Standardization of minimal “xApp manifest” schemas, open digital twin harnesses for repeatable benchmarking, and lightweight conflict management in RICs (Hoffmann et al., 2023).
• Continued investment in abstraction layers and container orchestration for rapid prototyping, reproducibility, and secure multi-vendor integration (Feraudo et al., 2024, Feraudo et al., 3 Feb 2026).
• Expansion of OpenApps platforms in education and agent benchmarking to other disciplinary domains and interaction paradigms, with guidelines favoring modularity, CI/CD, and universal reproducibility (Du et al., 2022, Ullrich et al., 25 Nov 2025).
• Periodic re-orchestration and dual-gap monitoring in orchestrators like OREO for optimal resource utilization under dynamic traffic and service demand (Mungari et al., 2024).

OpenApps, as a paradigm and technical reality, embody the principle of open, reusable, and composable software at the boundary between flexible automation and heterogeneous, real-world deployments, from programmable wireless networks to collaborative educational platforms and rigorous agent benchmarking environments.