daily

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name: daily description: Documentation and capabilities reference for Daily metadata: mintlify-proj: daily version: "1.0" risk: safe source: community date_added: "2026-03-07"

When to Use

• You are building a real-time voice or multimodal AI application that uses Daily or Pipecat-style transports.
• You need guidance on low-latency audio, video, text, and AI service orchestration in one pipeline.
• You want a capability reference before choosing services, transports, or workflow patterns for an interactive agent.

Capabilities

Pipecat enables agents to build production-ready voice and multimodal AI applications with real-time processing. Agents can orchestrate complex AI service pipelines that handle audio, video, and text simultaneously while maintaining ultra-low latency (500-800ms round-trip). The framework abstracts away the complexity of coordinating multiple AI services, network transports, and audio processing, allowing agents to focus on application logic.

Key capabilities include:

• Real-time voice conversations with natural turn-taking and interruption handling
• Multimodal processing combining audio, video, images, and text
• Integration with 50+ AI services (LLMs, speech recognition, text-to-speech, vision models)
• Function calling for external API integration and tool use
• Automatic conversation context management with optional summarization
• Multiple transport options (WebRTC, WebSocket, Daily, Twilio, Telnyx, etc.)
• Production deployment across cloud platforms with built-in scaling

Skills

Pipeline Architecture & Frame Processing

Agents can construct pipelines that connect frame processors in sequence to handle real-time data flow:

pipeline = Pipeline([
    transport.input(),              # Receives user audio
    stt,                            # Speech-to-text conversion
    context_aggregator.user(),      # Collect user responses
    llm,                            # Language model processing
    tts,                            # Text-to-speech conversion
    transport.output(),             # Sends audio to user
    context_aggregator.assistant(), # Collect assistant responses
])

Agents can create custom frame processors to handle specialized logic, work with parallel pipelines for conditional processing, and manage frame types (SystemFrames for immediate processing, DataFrames for ordered queuing).

Speech Recognition & Audio Input

Agents can integrate 15+ speech-to-text providers including OpenAI, Google Cloud, Deepgram, AssemblyAI, Azure, and Whisper. Services support:

• Real-time streaming transcription via WebSocket connections
• Voice Activity Detection (VAD) for automatic speech detection
• Multiple language support (125+ languages with Google Cloud)
• Word-level confidence scores and automatic punctuation
• Configurable latency tuning for optimal performance

Text-to-Speech & Audio Output

Agents can choose from 30+ text-to-speech providers including OpenAI, Google Cloud, ElevenLabs, Cartesia, LMNT, and PlayHT. Features include:

• Real-time streaming synthesis with ultra-low latency
• Multiple voice options and speaking styles per provider
• Automatic interruption handling for natural conversations
• Audio format flexibility (WAV, PCM, MP3)
• Word-level output for precise context tracking

Language Model Integration

Agents can integrate with 20+ LLM providers including OpenAI, Anthropic, Google Gemini, Groq, Perplexity, and open-source models via Ollama. Capabilities include:

• Streaming response generation for real-time output
• Function calling (tool use) for external API integration
• Context management with automatic message history tracking
• Token usage monitoring and cost tracking
• Support for vision models and multimodal inputs

Function Calling & Tool Integration

Agents can enable LLMs to call external functions and APIs during conversations:

# Define functions using standard schema
weather_function = FunctionSchema(
    name="get_current_weather",
    description="Get the current weather in a location",
    properties={"location": {"type": "string"}},
    required=["location"]
)

# Register function handlers
async def fetch_weather(params: FunctionCallParams):
    location = params.arguments.get("location")
    weather_data = await weather_api.get_weather(location)
    await params.result_callback(weather_data)

llm.register_function("get_current_weather", fetch_weather)

Function results are automatically stored in conversation context, enabling multi-step interactions and real-time data access.

Context Management & Conversation History

Agents can manage conversation context automatically or manually:

• Automatic context aggregation from transcriptions and TTS output
• Manual context manipulation via LLMMessagesAppendFrame and LLMMessagesUpdateFrame
• Automatic context summarization for long conversations to reduce token usage
• Tool definitions and function call results s