LLM (large language model)

An LLM, or large language model, is the AI model that interprets user input, reasons over context, and generates the response an avatar or voice agent will speak back to the user.

How LLMs work in avatar agents

An LLM is the language model that decides what an avatar should say or do next. It reads the user's message, conversation history, instructions, retrieved context, and tool results, then generates the response.

In a real-time avatar system, the LLM sits in the middle of the loop. Speech recognition turns audio into text, the LLM or agent workflow reasons over that text, and text-to-speech plus face animation turn the answer back into a live avatar response.

A concrete example: a user asks whether an avatar API supports WebRTC. The LLM reads the relevant product context and answers in a way the avatar can speak naturally.

The LLM is important, but it is not the whole product. A good avatar also needs streaming, speech, turn-taking, grounding, tool access, and latency control around the model.

What Anam ships

Anam's Cara-4 model delivers expressive real-time avatars with around 150 ms server-side avatar-generation latency once a session is running, across 70+ languages. Builders use JavaScript and Python SDKs or integrations for LiveKit, Pipecat, ElevenLabs Agents, Agora, and VideoSDK. Bring any AI stack including OpenAI, Claude, Gemini, Mistral, Groq, Deepgram, Cartesia, or custom providers. The platform supports WebRTC delivery, SOC 2 Type II, HIPAA, zero data retention, and regional data residency. Sessions stream low-latency audio and video to browsers and native apps.

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Related terms

Prompt engineering

Prompt engineering is the practice of writing and structuring instructions, context, examples, and constraints so an LLM or avatar agent produces useful, safe, and natural responses.

Function calling

Function calling lets an LLM invoke approved tools or APIs during a conversation, so an avatar can fetch data, perform actions, or answer from live systems instead of relying on text generation alone.

RAG (retrieval augmented generation)

RAG, or retrieval augmented generation, is a pattern where an agent retrieves relevant documents or data at query time and uses that context to produce a more accurate answer.

Multimodal model

A multimodal model is an AI model that can work with more than one type of input or output, such as text, audio, images, video, or structured data in the same agent workflow.

Frequently asked questions

What does an LLM do in a real-time avatar?

The LLM interprets the user's message, reasons over context, and generates the response that the avatar will speak through text-to-speech and live animation.

Is the LLM the same as the avatar model?

No. The LLM handles language and reasoning. The avatar model handles the face, motion, rendering, and visual performance that users see in the session.

Can I bring my own LLM to an avatar API?

Often yes. Many avatar stacks let teams connect an existing LLM or agent workflow, then use the avatar layer for voice, face, lip sync, and streaming.

What makes an LLM work well for avatars?

It should produce fast first tokens, follow instructions reliably, use tools safely, stay grounded in approved context, and generate responses that sound natural when spoken aloud.

Last updated: 17th July 2026 · Reviewed quarterly.

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