> ## Documentation Index > Fetch the complete documentation index at: https://anam.ai/docs/llms.txt > Use this file to discover all available pages before exploring further. # Available LLMs > Built-in language models you can use with Anam personas Every persona needs a language model to power its conversations. Anam provides several built-in LLMs you can use without any additional setup — just reference the model's ID in your persona configuration. ## Built-in models | LLM ID | Model | Best for | | -------------------------------------- | ------------- | ------------------------------------------------------------------------------------ | | `a7cf662c-2ace-4de1-a21e-ef0fbf144bb7` | GPT OSS 120B | Recommended for most projects | | `ANAM_LLAMA_v3_3_70B_V1` | Llama 3.3 70B | Great conversationalist, very fast. Not recommended for tool calls or knowledge base | | `b4f89001-9638-4879-a9c3-02cc9f9f2004` | GPT 4.1 | Slow but capable all-rounder | ## Using a built-in LLM Set the `llmId` field in your persona configuration to the ID of the model you want to use: ```javascript theme={"system"} const personaConfig = { name: "Cara", avatarId: "30fa96d0-26c4-4e55-94a0-517025942e18", avatarModel: "cara-4", voiceId: "6bfbe25a-979d-40f3-a92b-5394170af54b", llmId: "a7cf662c-2ace-4de1-a21e-ef0fbf144bb7", systemPrompt: "You are Cara, a helpful customer service representative.", }; ``` You can also select a model from the dropdown in [Anam Lab](https://lab.anam.ai) when creating or editing a persona. ## Choosing a model For most use cases, **GPT OSS 120B** is a good starting point — it's fast, has strong reasoning, and handles [tools](/concepts/tools) reliably. Choose **Llama 3.3 70B** for the most natural conversational feel when you don't need tool calling or a knowledge base. Choose **GPT 4.1** when you need a capable all-rounder and can tolerate higher latency. Older `llmId`s previously listed here — including GPT-4.1 Mini (`0934d97d-0c3a-4f33-91b0-5e136a0ef466`), Gemini 3 Flash Preview (`27cbd128-f1e6-4b67-8ab3-9123659be08c`), Gemini 2.5 Flash (`9d8900ee-257d-4401-8817-ba9c835e9d36`), and Kimi k2 (`88190a76-3e87-4935-ab39-f4f73038815a`) — continue to work for existing configurations, but are no longer recommended for new projects. ## Using LLMs with reasoning LLMs that have reasoning enabled will produce separate `reasoning` messages in addition to the spoken text messages made by the persona. These messages contain the reasoning used by the LLM when forming the response. Currently only OpenAI spec LLMs support reasoning messages (e.g. `OpenAI`, `Azure OpenAI` and `Groq OpenAI`). For best performance we suggest using the reasoning models provided by Groq. ### How Reasoning Messages Work User: "Show me the pricing page" ```json theme={"system"} { ... "reasoning": "The user has requested to see the pricing page, I need to call the pricing page tool and respond to the user" ... } ``` The Anam SDK emits a REASONING\_HISTORY\_UPDATED event that your application can handle. Each `REASONING_HISTORY_UPDATED` event contains the full history of reasoning messages. Alternatively, you can listen for `REASONING_STREAM_EVENT_RECEIVED` which streams updates in chunks, but you will need to handle aggregating the messages yourself. ```typescript theme={"system"} import { AnamEvent, ReasoningMessage } from '@anam-ai/js-sdk'; // Option 1: Full history on each update client.addListener( AnamEvent.REASONING_HISTORY_UPDATED, (messages: ReasoningMessage[]) => { updateReasoningMessageHistory(messages); } ); ``` ```typescript theme={"system"} import { AnamEvent, ReasoningStreamEvent } from '@anam-ai/js-sdk'; // Option 2: Streaming updates (requires manual aggregation) client.addListener( AnamEvent.REASONING_STREAM_EVENT_RECEIVED, (event: ReasoningStreamEvent) => { setReasoningHistory((previousMessages) => { const lastMessage = previousMessages[previousMessages.length - 1]; // Handle streamed thoughts - append to existing message if (lastMessage && lastMessage.id === event.id) { const updatedMessages = [...previousMessages]; updatedMessages[updatedMessages.length - 1] = { ...lastMessage, content: lastMessage.content + ' ' + event.content, }; return updatedMessages; } // Handle new messages return [ ...previousMessages, { content: event.content, id: event.id, }, ]; }); } ); ``` ## Greeting behavior When using a built-in LLM, the persona greets the user with an opening message when the session starts. By default the greeting is generated from the system prompt, but you can set the exact line yourself or skip the greeting altogether. ### Set a custom first message Use `initialMessage` to set the exact line the persona speaks at the start of the session, instead of letting the LLM generate one from the system prompt: ```javascript theme={"system"} const personaConfig = { name: "Cara", avatarId: "30fa96d0-26c4-4e55-94a0-517025942e18", avatarModel: "cara-4", voiceId: "6bfbe25a-979d-40f3-a92b-5394170af54b", llmId: "0934d97d-0c3a-4f33-91b0-5e136a0ef466", systemPrompt: "You are Cara, a helpful customer service representative.", initialMessage: "Hi, I'm Cara from customer support. What can I help you with today?", }; ``` If `initialMessage` is omitted, empty, or whitespace-only, the persona falls back to a greeting generated from the system prompt. You can also set the first message from the **Prompt** tab in [Anam Lab](https://lab.anam.ai) — leave the field blank to keep the auto-generated greeting. ### Skip the greeting To have the persona wait for the user to speak first, set `skipGreeting` to `true`: ```javascript theme={"system"} const personaConfig = { name: "Cara", avatarId: "30fa96d0-26c4-4e55-94a0-517025942e18", avatarModel: "cara-4", voiceId: "6bfbe25a-979d-40f3-a92b-5394170af54b", llmId: "a7cf662c-2ace-4de1-a21e-ef0fbf144bb7", systemPrompt: "You are Cara, a helpful customer service representative.", skipGreeting: true, }; ``` This is useful when you want the user to initiate the conversation, or when the persona is responding to an event rather than starting a dialogue. When `skipGreeting` is `true`, any `initialMessage` value is ignored. ### Protect the greeting from interruptions By default, the user can interrupt the greeting as soon as the persona starts speaking. To make the greeting play through to completion, set `uninterruptibleGreeting` to `true`: ```javascript theme={"system"} const personaConfig = { name: "Cara", avatarId: "30fa96d0-26c4-4e55-94a0-517025942e18", avatarModel: "cara-4", voiceId: "6bfbe25a-979d-40f3-a92b-5394170af54b", llmId: "0934d97d-0c3a-4f33-91b0-5e136a0ef466", systemPrompt: "You are Cara, a helpful customer service representative.", uninterruptibleGreeting: true, }; ``` Use this when the greeting carries information the user needs to hear in full, such as a regulatory disclosure, a privacy notice, or instructions that frame the rest of the conversation. After the greeting finishes, normal interruption behavior resumes. `uninterruptibleGreeting` has no effect when `skipGreeting` is `true`. ## Bring your own LLM If the built-in models don't fit your needs, you can connect your own: * **[Server-side custom LLMs](/concepts/custom-llms)** — Register your model with Anam and we call it from our servers, keeping latency low. * **[Client-side custom LLMs](/examples/custom-llm)** — Handle LLM calls yourself in your client code using `CUSTOMER_CLIENT_V1` as the LLM ID. * **[LiveKit](/third-party-integrations/livekit)** — Use Anam as a face layer in your existing LiveKit agent pipeline with any LLM.