Packages
Python SDK for building real-time video, audio, and data applications by connecting to LiveKit servers as a participant or managing rooms via server APIs.
A framework for building realtime multimodal and voice AI agents that connect to LiveKit rooms and handle audio/video interactions with language models.
Generate access tokens and call LiveKit server APIs (room management, egress, ingress, SIP, agent dispatch, connectors) from Python backends using async/await.
Install it if you are building a backend that needs to generate tokens, manage rooms, or integrate with LiveKit's services.
Provides BlingFire tokenization bindings for the LiveKit Agents framework, enabling fast text segmentation and linguistic analysis within voice agent applications.
A LiveKit plugin that applies Ai-coustics audio enhancement to real-time audio streams, providing noise filtering and quality improvement for voice communication.
However, confirm the license terms and any associated costs with Ai-coustics before production use, and verify that the latency and model choice (QUAIL_L vs.
Anam plugin for the LiveKit Agents framework that extends voice agent capabilities with additional speech processing or model integration features.
However, verify that anam's functionality aligns with your use case before committing, as its exact role in the agent pipeline is not clearly documented in the…
Integrates Anthropic's Claude models into LiveKit's agent framework, enabling voice agents to use Claude for natural language understanding and generation in real-time conversations.
Integrates AssemblyAI speech-to-text into the LiveKit Agents framework for building real-time voice agents.
Integrates Amazon AWS AI services (Bedrock, Polly, Transcribe, Nova) into LiveKit Agents for speech-to-speech, text-to-speech, speech-to-text, and LLM capabilities.
Install it if you are building LiveKit voice agents and need AWS AI services (Bedrock, Transcribe, Polly, Nova).
Integrates Azure AI services, particularly Azure Speech, into LiveKit Agents for real-time voice and audio processing in agent applications.
Integrates Cartesia's voice AI services (speech-to-text and text-to-speech) into LiveKit Agents for real-time voice applications.
Integrates Deepgram's voice AI services (speech-to-text and text-to-speech) into LiveKit Agents for real-time audio processing in agent applications.
Install it if you are building LiveKit Agents that need Deepgram's speech-to-text or text-to-speech services.
Integrates ElevenLabs text-to-speech into the LiveKit Agents framework for building realtime voice agents that can speak with ElevenLabs' voices.
Integrates Gladia's speech-to-text API with LiveKit Agents, enabling streaming transcription with multi-language support, code-switching, interim results, and optional real-time translation.
Install it if you are building a LiveKit Agents application and need Gladia's speech-to-text capabilities; it is the intended integration point for that use case.
Integrates Google Cloud AI services (Gemini, Speech-to-Text, Text-to-Speech) with LiveKit Agents for building real-time conversational applications.
Integrates Groq's fast inference API with LiveKit Agents, enabling real-time LLM responses for voice and conversational agents.
Install it if you are already using LiveKit Agents and want to use Groq as your LLM provider, or if you are evaluating Groq for real-time voice agent workloads.
Integrates Inworld's text-to-speech and speech-to-text APIs into LiveKit voice agents, enabling real-time voice synthesis and transcription for conversational AI applications.
Applies real-time noise cancellation to inbound audio streams in LiveKit, removing background noise and optionally filtering non-primary voices before audio reaches your agent or application code.
However, clarify the license terms with LiveKit before production use, and be aware of the medium install friction from platform-specific wheels and potential…
Integrates OpenAI's Realtime, Responses, LLM, TTS, and STT APIs into LiveKit Agents, plus support for OpenAI-compatible providers like Azure OpenAI, Cerebras, Fireworks, Perplexity, and others.
Install it if you're already using LiveKit Agents and want OpenAI integration.
Integrates Rime speech recognition into LiveKit's agent framework for building real-time voice agents that can hear and respond to users.
Integrates Sarvam.ai's Indian-language voice AI services (speech-to-text, text-to-speech, and LLM chat) into LiveKit Agents for real-time conversational applications.
Provides Silero voice activity detection integration for the LiveKit Agents framework to detect when users are speaking in real-time voice agent applications.
Integrates Soniox speech-to-text and text-to-speech APIs into LiveKit Agents via WebSocket streaming, enabling real-time transcription and speech synthesis in agent applications.
Install it if you are building a LiveKit agent and want to use Soniox for speech services; it is the intended integration point for that use case.
Integrates Speechmatics speech-to-text into LiveKit Agents, enabling real-time transcription with optional speaker diarization and configurable turn detection modes.
Install it if you are building a LiveKit agent and need Speechmatics transcription with speaker diarization or flexible turn detection.
Integrates Telnyx telephony services with LiveKit Agents, enabling voice agents to make and receive phone calls through the Telnyx network.
Detects end-of-turn in voice conversations for LiveKit Agents using a language model trained for this task, replacing simpler voice activity detection with more accurate interruption prevention.
Integrates xAI's Grok LLM with LiveKit's Agent Framework to enable voice agents that can understand and respond in real-time conversations.
Provides Python protocol stubs and type definitions for LiveKit's real-time communication APIs, enabling type-safe integration with LiveKit servers for video, audio, and data features.
Automatically reloads webpages in your browser when source files change, eliminating manual refresh during development.
A Python client for Apache Livy that enables remote code execution on Spark clusters via Livy's REST interface.
Lizard analyzes code complexity and detects code clones across many programming languages, measuring cyclomatic complexity, lines of code, token count, and function parameters.
Parses LookML strings into Python objects or JSON and serializes Python objects back to LookML strings, with no external dependencies.
Install it if you need to parse or generate LookML programmatically.
Provides a typed Python client for the Llama API with support for synchronous and asynchronous requests, streaming responses, and automatic error handling.
Install it if you need to integrate Llama models into a Python application.
Python SDK for LlamaParse, an enterprise OCR and document parsing API that enables programmatic extraction and processing of structured data from documents.
SDK client for LlamaCloud services: document parsing (LlamaParse), structured data extraction (LlamaExtract), and automated document ingestion with retrieval (LlamaCloud Index).
Python bindings for llama.cpp that enable running large language models locally with CPU or GPU acceleration, offering both low-level C API access and high-level text completion APIs compatible with OpenAI, LangChain, and LlamaIndex.
However, expect high install friction due to C compilation requirements—use pre-built wheels where available to reduce setup complexity.
LlamaIndex is a data framework that connects large language models to your own data sources, enabling retrieval-augmented generation (RAG) and agentic applications through data connectors, indexing, and query interfaces.
Integrates OpenAI language models with LlamaIndex agents, enabling agents to use OpenAI's models for reasoning and decision-making within the LlamaIndex framework.
However, the aging maintenance status (411 days since last release) warrants caution—verify compatibility with your target versions of llama-index-core and…
Provides command-line interface tools for working with llama-index, a framework for building applications with large language models and retrieval-augmented generation.
LlamaIndex Core provides foundational abstractions and classes for building LLM applications, particularly retrieval-augmented generation (RAG) systems, with extensible interfaces for LLMs, vector stores, embeddings, and storage.
Install it if you're building LLM applications; skip it only if you need a minimal LLM wrapper without data integration.