The 6 Best Open-Source Voice Agent Frameworks in 2026 (Ranked)

The 6 Best Open-Source Voice Agent Frameworks in 2026 (Ranked)

Last quarter we shipped three phone-based voice agents on Pipecat, then rebuilt one on LiveKit Agents when the client jumped from 20 to 400 concurrent calls. Both are open-source voice agent frameworks. Neither is "the best." They're good at different jobs, and picking wrong costs you weeks.

This ranking comes from what actually ships, not what reads well in a README. We pulled live GitHub numbers on July 14, 2026: Pipecat sits at 13,416 stars, LiveKit Agents at 11,356, and TEN Framework at 10,898. Stars don't tell the whole story, so we also weighed commit activity, telephony support, license terms, and how each holds up under real call volume.

Quick answer: For most teams in 2026, Pipecat is the strongest open-source voice agent framework because of its large integration library and near-daily development. LiveKit Agents wins at scale and native telephony, TEN Framework at multimodal graph orchestration, and Bolna at getting a phone agent live in an afternoon.

If you'd rather buy a finished product than assemble one, our comparisons of managed platforms like ElevenLabs, Vapi, and Synthflow and Retell AI vs Vapi vs Bland are the better starting point. New to the category? Start with what an AI voice agent actually is.

How we ranked these frameworks

We scored each framework on five things a real project lives or dies by: how active development is (commits in the last 90 days), the breadth of STT/LLM/TTS integrations, telephony support (can it answer a real phone number), license terms, and behavior under concurrency. Here's the shortlist at a glance.

1. Pipecat — the best all-round choice

Pipecat, built by the team behind Daily, is a Python framework that models a conversation as a pipeline: audio comes in, flows through speech-to-text, a language model, and text-to-speech, and audio goes back out. That mental model is easy to reason about, and it hit a stable v1.0 in April 2026.

What sets it apart is the integration library. Deepgram, AssemblyAI, OpenAI, Gemini, Cartesia, ElevenLabs, and dozens more are already wired up, so swapping your TTS vendor is a one-line change instead of a rewrite. Telephony works through Twilio, Daily, or Telnyx. With 13,416 stars and commits landing almost every day, it also has the most momentum of anything on this list.

The trade-off: because Pipecat gives you the pieces rather than a finished agent, you own the orchestration logic. There's no drag-and-drop builder. You write Python. For a team that already codes, that's a feature, not a bug.

Pick this if: you want a vendor-neutral, production-grade base with the widest model support and you're comfortable writing Python. This is our default starting point for most client work.

2. LiveKit Agents — built for scale and realtime

LiveKit Agents takes a different approach. Instead of a linear pipeline, your agent joins a room as a participant over WebRTC, the same technology behind Zoom-style calls. That architecture is why it shines when you need low latency and many simultaneous conversations.

The big 2026 story is telephony. LiveKit shipped native SIP and phone numbers, so inbound and outbound calling no longer needs a Twilio bridge sitting in the middle. It also ships first-party support for the OpenAI Realtime API and, as of the 1.5.x line, native Model Context Protocol tools and adaptive interruption handling. You can read the details in the LiveKit Agents documentation. If you want to understand the realtime API it wraps, we cover OpenAI's Realtime API for voice agents separately.

Because it runs on LiveKit's open-source WebRTC media server, you can self-host the entire stack. The learning curve is steeper than Pipecat's, and rooms-and-participants thinking takes a beat to click.

Pick this if: you expect real concurrency, want native telephony without a bridge, or you're standing up an OpenAI Realtime agent that has to survive traffic spikes.

3. TEN Framework — multimodal and graph-based

TEN Framework (Transformative Extensions Network), backed by Agora, describes your agent as a graph of nodes: STT, LLM, tools, memory, vision. You compose the graph in a workflow builder, and TEN executes it. It's the most flexible option here for anything beyond plain voice, and its C++ core is tuned for low-latency audio.

It also speaks the widest range of languages of any framework on this list, with extensions in C, Go, Python, JavaScript, and TypeScript, plus ready-made connectors for Dify and Coze. The Agora TEN Framework page is the clearest overview of what it can do.

Two caveats. First, the license is a hybrid: most of the framework is Apache-2.0, but with additional restrictions on certain folders, so read the LICENSE before you ship commercially. Second, real-time transport leans on Agora's network, which means an Agora App ID and, past the free tier, Agora usage costs.

Pick this if: you're building a multimodal agent (voice plus vision or avatars), you value graph-based composition, or you want the lowest-level audio performance available in open source.

4. Bolna — the fastest path to a phone agent

Bolna is smaller (695 stars) and more opinionated than the top three, and that's exactly its strength. It's telephony-first. Where other frameworks make you assemble calling, Bolna ships it: Twilio for global calls, Plivo and Exotel for India, and custom SIP trunks, all configured in a JSON-style agent definition rather than code.

That config-driven setup means you can have an inbound or outbound phone agent answering real calls faster than almost anything else here. Under the hood it orchestrates ASR, LLM, and TTS providers over websockets, so you still choose your own models. Development stayed active through mid-2026.

The cost of that speed is a smaller community and fewer integrations than Pipecat or LiveKit. If you hit an edge case, you're more likely to be the first person to file the issue. For telephony-heavy builds, weigh it against the options in our voice agent telephony comparison.

Pick this if: your use case is phone calls first (appointment reminders, outbound qualification, inbound support) and you want to skip the telephony plumbing entirely.

5. FastRTC — the lightweight prototyping option

FastRTC, from the Hugging Face and Gradio team, isn't a full agent framework, and that's the point. It's a Python library for real-time audio and video over WebRTC or websockets. You bring your own STT, LLM, and TTS, and FastRTC handles the streaming transport with only a few lines of code.

For a proof of concept, a demo, or a research spike, that minimalism is a gift. You can stand up a talking prototype in an afternoon without committing to a heavyweight framework's conventions. It pairs naturally with the Hugging Face ecosystem, so open models are easy to plug in.

The flip side is that you're responsible for everything a framework would otherwise give you: turn detection, interruption handling, telephony, state management. It scales down beautifully and scales up painfully.

Pick this if: you're prototyping, teaching, or building a browser demo, and you want maximum control with minimum framework overhead.

6. Vocode — historically important, with a maintenance caveat

Vocode helped define this whole category. Its modular STT/LLM/TTS design and built-in Twilio and Vonage telephony made it one of the first genuinely usable open-source voice frameworks, and at 3,773 stars it still shows up in plenty of tutorials.

Here's the honest part, and it's why it ranks last: the open-source core has gone quiet. The last commit to vocode-core landed in November 2024, and the repository sits at just two open issues, which usually signals attention has moved to the company's hosted product rather than a healthy backlog. The code still runs, and the design is worth studying, but you'd be building on a foundation nobody is actively patching.

Pick this if: you're studying voice-agent architecture, maintaining an existing Vocode project, or you specifically want its design patterns and accept that you'll be maintaining the base yourself.

Also worth knowing

A few tools sit just outside the ranking but belong on your radar:

• OpenAI Agents SDK (27,900+ stars) ships a voice-pipeline extension. It's a general-purpose agent SDK rather than voice-first, but it's a reasonable choice if you're already standardized on it.
• Gabber is a newer multimodal framework for agents that see, hear, and speak, aimed at screen-and-camera use cases.
• Jambonz is an open-source telephony backbone. It doesn't build the agent brain, but it's a carrier-grade way to connect any framework to real phone networks.
• Rasa (21,000+ stars) remains the heavyweight for enterprise dialogue and NLU across text and voice, though it's more involved to run than anything above.
• Ultravox is a fast speech language model, not an orchestration framework, so treat it as a pluggable component rather than a competitor.

What we'd actually use for a client build

At Techsy we build voice agents for B2B clients, so here's the unglamorous reality behind the ranking.

Our default stack is Pipecat wiring Deepgram Nova-3 for speech-to-text, GPT-4o-mini for the language model, and Cartesia Sonic for text-to-speech. Once turn detection is tuned, voice-to-voice latency usually lands between 0.7 and 1.1 seconds, which is close enough to human-conversational that callers stop noticing. Push any one of those components to a slower model and you feel it immediately.

Telephony is where projects get messy. We've run two patterns in production: a Twilio SIP trunk bridged into LiveKit's native SIP for high-volume inbound support, and Bolna's built-in Plivo handling when a client just needed outbound reminder calls. The LiveKit route costs more engineering hours up front, but it holds steady when 300 calls arrive in the same minute. Bolna gets you live by Friday.

Self-hosting math trips people up. Running local STT and TTS on a single A10G GPU costs roughly $0.50 to $0.90 an hour whether or not a single call comes in. Pay-per-minute APIs like Deepgram and Cartesia cost nothing when idle but add up quickly past a few thousand minutes a month. Below about 15,000 minutes monthly, the APIs almost always win, and that's what we recommend to most clients. We reach for LiveKit over Pipecat mainly when concurrency crosses a few hundred simultaneous calls.

If the build-versus-buy question is still open on your side, we walk through the full cost breakdown in our build vs buy an AI voice agent guide. And if you'd rather have a team wire the latency, telephony, and scaling for you, that's exactly what we do at Techsy's voice agent practice.

How to choose in one minute

Skip the analysis paralysis. Here's the decision in bullet form:

• You want the safest all-round default: Pipecat.
• You need real concurrency or native telephony: LiveKit Agents.
• You're going multimodal (voice plus vision or avatars): TEN Framework.
• You need a phone agent live this week: Bolna.
• You're prototyping or teaching: FastRTC.
• You'd rather buy than build: a managed platform like Vapi, Retell, or Synthflow, not a framework at all.

Frequently Asked Questions

What is an open-source voice agent framework?

It's a self-hostable codebase that wires together speech-to-text, a language model, and text-to-speech so software can hold a spoken conversation. Unlike managed platforms, you run it on your own infrastructure, choose your own models, and pay only for the underlying services rather than a per-minute markup.

Which open-source voice agent framework has the lowest latency?

TEN Framework leads on raw audio-pipeline performance thanks to its C++ core, but in practice network and model latency dominate the total. A well-tuned Pipecat or LiveKit stack on a fast model routinely hits 0.7 to 1.1 seconds voice-to-voice, which matches TEN in most real deployments.

Is open source actually cheaper than Vapi or Retell?

Not always. Open source removes the platform's per-minute markup, but you take on engineering, hosting, and maintenance costs. Below a few thousand call-minutes a month, a managed platform is usually cheaper once you count developer time. Past tens of thousands of minutes, self-hosting a framework pulls ahead.

Can these frameworks answer real phone calls?

Yes. Pipecat connects through Twilio, Daily, or Telnyx; LiveKit Agents ships native SIP and phone numbers; Bolna has Twilio, Plivo, and Exotel built in; and Vocode supports Twilio and Vonage. FastRTC is browser-focused and needs an external bridge like Jambonz for the phone network.

Do I need machine learning expertise to use them?

No. You need software engineering skills, mostly Python. The heavy lifting (transcription, reasoning, speech synthesis) is handled by the models you plug in through an API. You're orchestrating services, not training models, unless you deliberately choose to self-host open-weight ones.

Which framework is best for a beginner?

Pipecat, because its pipeline model is the easiest to reason about and its documentation and examples are the most complete. FastRTC is a good second choice if you want to start even smaller and understand the raw transport layer before adopting a full framework.

Are open-source voice frameworks production-ready in 2026?

The top three are. Pipecat reached v1.0, LiveKit Agents is on its 1.5.x line, and TEN Framework powers commercial deployments through Agora. The main risk isn't the frameworks themselves but the maintenance status of smaller projects, which is why we flagged Vocode's stalled core.

How is this different from a TTS API like ElevenLabs?

A TTS API only turns text into speech, which is one component. A voice agent framework orchestrates the whole loop: it listens, transcribes, sends text to a language model, gets a reply, and speaks it back, while managing interruptions and turn-taking. The framework calls the TTS API, not the other way around.