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chore(deps): update dependency livekit-agents to v1 - #4

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chore(deps): update dependency livekit-agents to v1#4
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renovate/livekit-agents-1.x

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ℹ️ Note

This PR body was truncated due to platform limits.

This PR contains the following updates:

Package Change Age Confidence
livekit-agents ~=0.12 → ~=1.8 age confidence

Warning

Some dependencies could not be looked up. Check the Dependency Dashboard for more information.

Release Notes

livekit/agents (livekit-agents)

v1.8.1

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Duplex Model Support

Speech models that are able to speak and listen simultaneously are now supported via the new DuplexModel class, with OpenAI's GPT-Live model as the first to be implemented.

session = AgentSession(
        llm=GPTLiveModel(
            voice="marin",
            # backend Responses model that handles reasoning and tools
            responses_options={
                "model": "gpt-5.6-luna",
                "instructions": "Use tools when current information is required.",
            },
        ),
    )

Read more about duplex models in our docs.

What's Changed

New Contributors

Full Changelog: https://github.com/livekit/agents/compare/livekit-agents@1.8.0...livekit-agents@1.8.1

v1.8.0

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OpenTelemetry Changes (#​7104)

Spans, metrics, and logs now follow the OTel GenAI semantic conventions, so
Langfuse, Datadog Agent Observability, and other GenAI-aware backends read
LiveKit traces natively. PII filtering also moved in-process.

Migration Notes

Before After
event gen_ai.system.message attribute gen_ai.system_instructions
events gen_ai.user.message, gen_ai.assistant.message, gen_ai.tool.message attribute gen_ai.input.messages
event gen_ai.choice (role/content/tool_calls) attributes gen_ai.output.messages + gen_ai.response.finish_reasons
realtime gen_ai.* on the agent_turn span on the new realtime_inference child span
realtime gen_ai.operation.name = "chat" = "generate_content"
gen_ai.provider.name = "api.openai.com", "AWS Bedrock", "google" "openai", "aws.bedrock", "gcp.gen_ai"
custom llm_node always tagged with the configured model/provider tagged only when that LLM served the call; otherwise the node span records the convention itself
PII stripped at LiveKit Cloud's collector stripped in-process; under project redaction content never reaches any exporter, Cloud included
telemetry._chat_ctx_to_otel_events(chat_ctx) telemetry.gen_ai.to_system_instructions() / to_input_messages() / to_output_messages()
telemetry.utils._redaction_enabled() telemetry.utils.redaction_enabled(span_attributes=None)

Breaking

  • Conversation content is no longer emitted as span events — gen_ai.system.message,
    gen_ai.user.message, gen_ai.assistant.message, gen_ai.tool.message and
    gen_ai.choice are gone, replaced by the attributes in the table above (JSON,
    part-based format). Dashboards and processors reading the events see nothing.
  • Realtime inference moved off agent_turn onto a new realtime_inference child span,
    so queries reading realtime token usage from agent_turn return nothing. Its
    gen_ai.operation.name also changed from chat to generate_content.
  • gen_ai.provider.name is normalized to the registry spelling. Providers outside the
    registry are unchanged. Update any group-by on the old values.
  • A custom llm_node is no longer credited with the configured model and provider
    unless that LLM actually served the request; a node calling a third-party engine is
    left unattributed and records the convention on its own span instead.
  • When the project mandates redaction, content attributes are now stripped in-process
    before every exporter, LiveKit Cloud included — previously the local pass scrubbed
    only exception details and Cloud's collector handled the rest.
  • gen_ai.usage.input_cached_tokens is emitted again on the pipeline path (reversing
    #​6852) and reasoning tokens go out under both gen_ai.usage.reasoning.output_tokens
    and gen_ai.usage.reasoning_tokens. A backend summing both spellings double-counts.
  • telemetry._chat_ctx_to_otel_events() removed; telemetry.utils._redaction_enabled()
    is now redaction_enabled(span_attributes=None).

Added

  • Request/response attributes: gen_ai.request.stream, gen_ai.output.type,
    gen_ai.conversation.id, gen_ai.response.{id,model,finish_reasons,time_to_first_chunk}.
  • Usage attributes: gen_ai.usage.cache_write.input_tokens, .reasoning.output_tokens
    and the per-modality .{text,audio,image}.* counts.
  • execute_tool spans carry gen_ai.tool.{name,type,call.id,call.arguments,call.result,description};
    turns carry gen_ai.agent.name, sessions gen_ai.workflow.name.
  • error.type on failures, set even when redaction is on.
  • Metrics gen_ai.client.token.usage, gen_ai.client.operation.duration,
    gen_ai.client.operation.time_to_first_chunk, gen_ai.invoke_agent.duration,
    gen_ai.execute_tool.duration, alongside the existing lk.agents.*.
  • OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT=0 (or
    telemetry.gen_ai.set_capture_content(False)) omits message payloads, tool
    definitions and tool arguments/results. Content capture is on by default.
  • set_tracer_provider(..., allow_pii=False) / LIVEKIT_TELEMETRY_ALLOW_PII=0 strips
    conversational content in-process before third-party exporters. PII is allowed by
    default.

What's Changed

New Contributors

Full Changelog: https://github.com/livekit/agents/compare/livekit-agents@1.7.1...livekit-agents@1.8.0

v1.7.1

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What's Changed

New Contributors

Full Changelog: https://github.com/livekit/agents/compare/livekit-agents@1.7.0...livekit-agents@1.7.1

v1.7.0: @​livekit/agents@1.7.0

Compare Source

Introducing PII Redaction Support

This release adds PII redaction support for Agent Observability. It semantically redacts detected entities from chat history and audio recordings. It also filters sensitive data from logs and traces while retaining useful diagnostic context.

To support this feature, we renamed sensitive trace attributes and log fields emitted by the framework. If your third-party observability queries depend on these names, update them when you upgrade.

Introducing Expressive Mode

Expressive mode allows voice agents to speak with natural prosody and emotion. Speech delivery is determined by emotion tags generated from the context of the conversation. Enable expressive mode in your AgentSession:

from livekit.agents import AgentSession, inference

session = AgentSession(
stt=inference.STT(model="deepgram/nova-3", language="multi"),
llm=inference.LLM(model="google/gemma-4-31b-it"),
tts=inference.TTS(model="fishaudio/s2.1-pro", voice="b347db033a6549378b48d00acb0d06cd"),
expressive=True,

# ... vad, turn_detection
)

See the documentation and the blog post for more information.

What's Changed

New Contributors

Full Changelog: https://github.com/livekit/agents/compare/livekit-agents@1.6.10...livekit-agents@1.7.0

v1.6.10

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v1.6.9: @​livekit/agents@1.6.9

Compare Source

What's Changed

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This PR was generated by Mend Renovate. View the repository job log.

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renovate Bot force-pushed the renovate/livekit-agents-1.x branch from 298dfac to 588eb6f Compare June 14, 2025 19:40
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