Workflow:Elevenlabs Elevenlabs python Conversational AI Agent
| Knowledge Sources | |
|---|---|
| Domains | Conversational_AI, Voice_Agents, Real_Time_Streaming |
| Last Updated | 2026-02-15 12:00 GMT |
Overview
End-to-end process for building interactive real-time voice AI agents using ElevenAgents, enabling bidirectional audio conversations with tool-calling capabilities.
Description
This workflow covers the complete setup and execution of a real-time conversational AI agent using the ElevenLabs Conversational AI subsystem (ElevenAgents). The system establishes a bidirectional WebSocket connection for streaming audio input from a microphone and receiving synthesized speech responses. It supports client-side tool registration (both sync and async), interrupt handling, contextual updates, and event-driven callbacks for transcript updates, agent responses, and latency measurements. The conversation handler manages the full lifecycle including authentication, session initiation, message routing, and graceful shutdown.
Usage
Execute this workflow when building an interactive voice AI application where users speak to an AI agent and receive spoken responses in real time. Use cases include customer service bots, voice assistants, interactive training systems, and any application requiring natural two-way voice conversation with an LLM-powered agent.
Execution Steps
Step 1: Client and Agent Configuration
Create an ElevenLabs client instance with API key authentication. Identify the agent ID for the pre-configured conversational AI agent (created via the ElevenLabs platform). The agent defines the LLM, system prompt, voice, and available tools.
Key considerations:
- Agent must be pre-configured on the ElevenLabs platform with an agent_id
- Authentication is required (requires_auth=True) for production agents
- Optional: configure dynamic variables and conversation config overrides
Step 2: Audio Interface Setup
Initialize an audio interface that handles microphone input and speaker output. The SDK provides DefaultAudioInterface (PyAudio-based) for standard desktop applications, or a custom implementation of the AudioInterface abstract class can be provided for specialized environments.
Key considerations:
- DefaultAudioInterface requires pyaudio to be installed
- Audio format is 16-bit PCM mono at 16kHz for both input and output
- Input callback provides 250ms chunks (4000 samples at 16kHz)
- Output is buffered with a separate thread for non-blocking playback
- Custom audio interfaces must implement start, stop, output, and interrupt methods
Step 3: Client Tools Registration
Register any custom tools that the AI agent can call during the conversation. Tools are Python functions (sync or async) that receive a parameter dictionary and return a result string. The ClientTools class manages tool execution in a dedicated event loop to prevent blocking the main conversation thread.
Key considerations:
- Tools are registered by name with a handler function and async flag
- Async tools run directly in the event loop; sync tools use a thread pool executor
- Tool results are automatically sent back to the agent via the WebSocket
- Custom event loops can be provided to avoid cross-loop errors in complex applications
Step 4: Conversation Session Start
Create a Conversation (or AsyncConversation) instance and call start_session(). This establishes a WebSocket connection to the conversational AI orchestrator, performs authentication handshake (via signed URL if required), sends the initiation message with configuration overrides and dynamic variables, and begins the bidirectional audio stream.
Key considerations:
- Session establishment involves WebSocket connection, auth, and initiation message exchange
- The conversation runs in background threads (audio I/O, message handling, WebSocket receiving)
- Event callbacks can be registered for: agent response, user transcript, latency measurement, interruption
- On-prem mode is supported with a different initiation flow
Step 5: Real-time Conversation Loop
The conversation runs continuously with the following event-driven interactions: microphone audio is captured and streamed to the server; the server processes speech, runs the LLM, generates a response, and streams synthesized audio back; interruptions are detected and buffered audio is cleared; tool calls are dispatched to registered client tools; ping/pong messages maintain the connection.
Key considerations:
- The message handler routes incoming WebSocket messages by type (audio, transcript, tool_call, ping, interruption)
- Audio alignment data can be received for character-level timing information
- Contextual updates can be sent mid-conversation to modify agent context without interrupting
- User text messages can be sent programmatically alongside voice input
Step 6: Session Termination
End the conversation by calling end_session(). This closes the WebSocket connection, stops the audio interface, shuts down the client tools event loop, and cleans up all background threads. The conversation ID from the session can be used to retrieve conversation history via the API.
Key considerations:
- Graceful shutdown waits for all background threads to complete
- Client tools thread pool is shut down to release resources
- Conversation history and transcripts are available via the conversations API after session end