8 Best Pipecat Alternatives in 2026 (Open Source)
Pipecat — Open Source framework for voice and multimodal conversational AI. Only production-grade framework for real-time voice AI with composable pipelines — supports 17+ STT and 20+ TTS providers with ultra-low latency, unlike text-focused agent frameworks
These 8 open-source tools do the same job. They are ordered by how closely they match Pipecat, with live GitHub data so you can see which projects are actively maintained.
| Tool | GitHub stars | Stars / 30d | Last commit |
|---|---|---|---|
| Pipecat(original) | 16.1k | +833 | 2026-09-30 |
| agents | 14.4k | +1,370 | 2026-09-30 |
| Ultravox | 4.6k | +31 | 2025-12-12 |
| RealChar | 6.2k | +1 | 2024-02-03 |
| AgentScope | 32.6k | +1,846 | 2026-09-30 |
| voltagent | 10.7k | +587 | 2026-09-28 |
| Agency | 515 | +1 | 2024-12-30 |
| Cheshire Cat AI | 3.1k | +14 | 2026-07-29 |
| LangChain | 147.3k | +23,453 | 2026-09-30 |
1. agents
A framework for building realtime voice AI agents 🤖🎙️📹
What sets it apart: The leading open-source framework for realtime voice AI agents with WebRTC infrastructure, semantic turn detection, multi-agent handoff, and native telephony — vs alternatives that bolt voice onto text-first frameworks
Best for: Building production voice AI agents and assistants; Real-time conversational AI with telephony integration; Multi-agent voice workflows with handoffs
2. Ultravox
A fast multimodal LLM for real-time voice
What sets it apart: vs ASR+LLM pipelines (Whisper+GPT): direct audio-to-embedding projection eliminates ASR latency bottleneck, enabling true real-time voice understanding
Best for: Real-time voice AI agents requiring sub-100ms latency; Custom domain voice applications with proprietary audio data
3. RealChar
🎙️🤖Create, Customize and Talk to your AI Character/Companion in Realtime (All in One Codebase!). Have a natural seamless conversation with AI everywhere (mobile, web and terminal) using LLM OpenAI G
What sets it apart: vs Character.AI: fully open-source with voice cloning, multi-platform (web+iOS+phone), and pluggable LLM/TTS backends — own your AI characters
Best for: Building interactive AI character experiences with voice; Developers creating multi-platform conversational AI personas
4. AgentScope
Build and run agents you can see, understand and trust.
What sets it apart: Unlike LangGraph (stateful graph orchestration) and CrewAI (role-based crews), AgentScope uniquely combines realtime voice agents, A2A protocol, agentic RL fine-tuning, and Kubernetes-native deployment — designed for the rising capability of agentic LLMs
Best for: Teams building production multi-agent systems with realtime voice and A2A interoperability; Chinese-market developers wanting first-class DashScope/Qwen integration
5. voltagent
AI Agent Engineering Platform built on an Open Source TypeScript AI Agent Framework
What sets it apart: Full-stack TypeScript agent platform with built-in workflow engine, voice support, and observability console — more opinionated than Vercel AI SDK, more TypeScript-native than LangChain
Best for: TypeScript developers building production agent systems with observability; Multi-agent systems with workflow orchestration and voice capabilities
6. Agency
🕵️♂️ Library designed for developers eager to explore the potential of Large Language Models (LLMs) and other generative AI through a clean, effective, and Go-idiomatic approach.
What sets it apart: vs LangChainGo: Go-native design from scratch (not a Python port) — composable operations, interceptors, and multimodal support with clean Go-idiomatic architecture
Best for: Go developers wanting an idiomatic AI framework (not a Python port); Building multimodal AI applications in Go (text, image, speech); Teams preferring clean architecture with composable operations
7. Cheshire Cat AI
AI agent microservice
What sets it apart: vs LangChain/LlamaIndex: opinionated, ready-to-deploy conversational AI microservice with built-in admin panel, plugin system, and Qdrant RAG — not a framework but a complete product
Best for: Building custom AI assistants as embeddable microservices; Teams needing plugin-extensible conversational AI with admin panel
8. LangChain
The agent engineering platform
What sets it apart: vs other frameworks: Largest ecosystem with 100+ integrations, dual Python/JS support, backed by LangGraph for agent orchestration and LangSmith for production observability - the most widely adopted LLM framework
Best for: Building complex LLM applications with many integrations; Teams needing model interoperability and quick provider switching; Production AI applications requiring observability via LangSmith