Agentic AI Comparison:
LiveKit Agents vs OpenAI Swarm
vs
Introduction
This report compares OpenAI Swarm and LiveKit Agents as AI agent frameworks across autonomy, ease of use, flexibility, cost, and popularity, focusing on Swarm’s lightweight multi-agent orchestration versus LiveKit’s production-focused realtime media and agent infrastructure.
Overview
LiveKit Agents
LiveKit Agents is a production-oriented framework built around LiveKit’s realtime WebRTC platform, focused on creating agents that interact over audio, video, and data in real time. It acts as a bridge between frontends (via WebRTC) and backends such as OpenAI’s Realtime API and other models, providing orchestration, media handling, interruption management, and state coordination, with full documentation and an actively developed ecosystem.
OpenAI Swarm
OpenAI Swarm is an experimental, lightweight Python framework for building multi-agent workflows around OpenAI models, emphasizing simple function-call-based handoffs between stateless agents without heavy infrastructure or opinionated structure. It was designed more as a prototype and research vehicle than a full production platform and notably lacks built-in memory, tracing, debugging, and safety guardrails required for robust, enterprise deployments.
Metrics Comparison
autonomy
LiveKit Agents: 8
LiveKit Agents supports autonomous behavior in the context of realtime sessions by letting agents control voice, video, and interaction flows while LiveKit handles media streaming, interruption handling, and coordination with the frontend. By integrating directly with powerful models (including OpenAI’s Realtime API and GPT-series models) and providing infrastructure for continuous, event-driven operation, it can support highly autonomous conversational or voice agents in production environments, though domain-specific autonomy still depends on custom logic.
OpenAI Swarm: 6
Swarm enables multi-agent collaboration where agents can delegate and hand off tasks through LLM function calling, and its flexible structure does not impose strict task limits or a mandatory central manager, allowing agents to act somewhat independently. However, the absence of built-in memory, long-term state, and guardrails restricts its ability to support deeply autonomous, long-running, or safety-critical workflows without substantial additional engineering.
Both frameworks can orchestrate multi-step behaviors, but Swarm focuses on lightweight logical handoffs between stateless agents, limiting out-of-the-box autonomy, whereas LiveKit Agents is better suited for sustained, realtime autonomous agents that manage ongoing interactions in production voice or video applications.
ease of use
LiveKit Agents: 8
LiveKit Agents provides a well-documented framework that abstracts WebRTC, media routing, and integration with external AI services like OpenAI’s Realtime API, automatically handling tasks such as converting Realtime API audio buffers to WebRTC streams and managing interruptions. While the learning curve includes understanding LiveKit’s architecture and realtime concepts, the framework significantly simplifies building full-stack voice or video agents compared with hand-rolling similar infrastructure, and the docs and examples reduce onboarding friction.
OpenAI Swarm: 7
Swarm is intentionally minimal and simple: developers mainly define agents and handoffs via Python functions and docstrings, relying on function calling for collaboration rather than complex orchestration constructs. This simplicity makes it approachable for quick experiments, but the lack of built-in memory, debugging, tracing, and safety means developers must implement many production features themselves, which increases complexity as projects grow.
Swarm is easier for small, code-centric multi-agent experiments due to its minimalism, but LiveKit Agents offers greater practical ease of use for building real, user-facing agents by hiding complex realtime and media concerns behind higher-level APIs and documentation.
flexibility
LiveKit Agents: 7
LiveKit Agents is highly flexible within the domain of realtime, media-centric agents: it can integrate various AI backends (including multiple OpenAI services) and allows custom logic to coordinate state and behavior across participants. However, its architecture is oriented around WebRTC sessions and streaming scenarios, so it is less general-purpose for non-realtime, batch, or purely back-end multi-agent workflows compared with a lightweight orchestration library like Swarm.
OpenAI Swarm: 8
Swarm’s design is intentionally unopinionated: it does not enforce a central manager or rigid task object and instead relies on function-call-based handoffs, allowing developers to design highly customized multi-agent workflows and topologies. Its lightweight nature and focus on basic agent collaboration make it flexible for experimenting with different patterns, though the same lack of structure means developers do not get built-in patterns for complex workflows, and Swarm lacks the rich ecosystem integrations seen in more mature toolkits.
Swarm offers more general-purpose flexibility for arbitrary multi-agent orchestration patterns in code, whereas LiveKit Agents provides strong flexibility specifically for realtime, media-based applications but is more specialized to that use case.
cost
LiveKit Agents: 7
LiveKit Agents is built on top of LiveKit’s realtime infrastructure and external AI services like OpenAI’s Realtime API, meaning total cost includes both media infrastructure (bandwidth, servers, or managed LiveKit costs) and AI usage. While LiveKit’s abstractions improve development efficiency and time-to-market, realtime audio/video streaming and continuous agent sessions tend to be more resource-intensive than purely text-based or batch multi-agent workflows, making typical deployments more costly in absolute terms despite being cost-effective for their domain.
OpenAI Swarm: 8
Swarm itself is an open-source framework with no license fee and is lightweight, adding minimal computational overhead beyond the cost of underlying OpenAI API calls. Because it lacks heavy infrastructure components, developers have fine-grained control over when and how agents call models, which can enable cost-efficient designs, though they must also build their own optimizations, observability, and guardrails that might otherwise reduce waste.
Swarm’s minimal, text-centric design is generally cheaper to operate for typical workflows because it adds little infrastructure overhead beyond model usage, whereas LiveKit Agents trades higher runtime and infrastructure costs for the ability to support rich realtime audio/video interactions and more complex user experiences.
popularity
LiveKit Agents: 8
LiveKit already has an established user base for realtime communication, and LiveKit Agents builds on this platform, benefiting from existing adoption in voice and video applications as well as new interest in voice-first AI agents. The presence of official documentation, demos integrating OpenAI’s latest models, and ongoing development signals an active and growing ecosystem, positioning LiveKit Agents as a popular choice in the realtime agent space even if it is more niche than generic multi-agent libraries.
OpenAI Swarm: 6
Swarm attracted attention as a 2024 OpenAI prototype for multi-agent orchestration and is often mentioned in comparative reviews of agent frameworks, but it is generally characterized as experimental and less production-ready than emerging tools. Commentary from practitioners and analysis pieces describe Swarm more as a research project than a go-to production framework, and OpenAI’s later AgentKit announcement shifted community focus toward newer tooling rather than expanding Swarm’s ecosystem.
Swarm maintains visibility as an early OpenAI multi-agent experiment but is often seen as superseded by newer tooling and lacks strong production adoption, while LiveKit Agents is gaining traction within the realtime AI and voice/video agent community on top of LiveKit’s existing popularity.
Conclusions
OpenAI Swarm is best viewed as a lightweight, experimental framework for developers who want a minimal, code-driven way to orchestrate multi-agent workflows and explore function-call-based collaboration without committing to a large infrastructure surface. Its strengths are simplicity, low overhead, and flexible topologies, but its lack of built-in memory, tracing, and safety guardrails, combined with a more research-oriented status, make it less suitable as the core of a production system without significant custom engineering. LiveKit Agents, by contrast, is a production-focused framework designed for realtime voice and video agents that leverages LiveKit’s WebRTC infrastructure and seamlessly integrates with powerful AI backends such as OpenAI’s Realtime API and GPT models. It offers better native support for autonomy within interactive sessions, higher practical ease of use for building user-facing agents, and a growing ecosystem driven by concrete realtime use cases, albeit with higher infrastructure costs and a domain focus on media-centric applications rather than general-purpose multi-agent workflows. For most production scenarios involving live, conversational, or multimodal experiences, LiveKit Agents is likely the stronger choice, whereas Swarm remains useful for targeted experimentation with multi-agent orchestration patterns in a lightweight, open-source environment.