Integrations and Tool Guides

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Building Bidirectional Streaming Voice Agents Using Teler with Gemini Live

Building real-time voice agents is no longer about stitching speech and text together. Modern voice systems expect natural turn-taking, fast responses, and stable conversations that work over real phone calls. That shift has pushed teams to move beyond basic request-response models toward bidirectional streaming architectures.   In this blog, we explain how to build streaming voice

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From MCP to AgentKit: How to Deploy Voice-Enabled LLM Agents with Teler

Voice interfaces are rapidly evolving from novelty features into production-grade infrastructure for AI systems. For founders and product leaders, enabling real-time voice interaction means connecting multiple moving parts – speech recognition, LLMs, synthesis, and orchestration layers. Yet, building this from scratch often leads to performance bottlenecks and integration fatigue.  This blog explores how to deploy

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How to Design Production-Grade Agent Workflows with AgentKit and Teler’s Realtime Layer

Designing AI agents is easy. Designing production-grade AI agents that can reliably talk to humans in real time is not. Most teams quickly discover the gap between “demo-ready” and “deployment-ready”: latency spikes, missing context, brittle tool-calls, inconsistent state management, and voice pipelines that collapse under real customer traffic. This is where AgentKit and Teler’s realtime

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How to Build Edge-Native Voice Agents with AgentKit, Teler, and the Realtime API

Building Edge-Native Voice Agents is reshaping how AI systems communicate – closer, faster, and smarter. As latency becomes the deciding factor for real-time intelligence, combining AgentKit, Teler, and the Realtime API offers a unified framework to bring human-like voice interactions directly to the edge. This isn’t just another AI integration – it’s about engineering precision,

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How MCP Servers Bridge AgentKit and Teler for AI Workflow Automation

In today’s AI-driven automation ecosystem, speed, context, and real-time communication define success. Yet, building voice-capable, context-aware AI workflows demands a robust bridge between logic, language, and live interaction. That’s exactly where the MCP–AgentKit–Teler integration comes in – creating a seamless automation pipeline for intelligent voice systems. This blog unpacks how MCP servers synchronize AgentKit’s automation

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