Sarvam AI

OverviewCopy Anchor Link

Sarvam AI's models for Indian languages, over an OpenAI-compatible API (sarvam-105b, plus a few hosted open models).

• SarvamLLMService: an OpenAILLMService subclass with Sarvam AI's base URL and default model.

InstallationCopy Anchor Link

bun add @pipedog/sarvam

It depends on @pipedog/openai, which is installed with it.

import { SarvamLLMService } from "@pipedog/sarvam/llm";

PrerequisitesCopy Anchor Link

• A Sarvam account and an API key from the Sarvam dashboard.
• Pass the key as apiKey. It is required, and the service never falls back to OPENAI_API_KEY. The examples read it from SARVAM_API_KEY.

SarvamLLMServiceCopy Anchor Link

ConfigurationCopy Anchor Link

The other options (organization, project, defaultHeaders, serviceTier, retryOnTimeout, retryTimeoutSecs, function calling) are OpenAILLMService's. See OpenAI.

SettingsCopy Anchor Link

Runtime-updatable settings, passed as settings: new SarvamLLMService.Settings({...}) (SarvamLLMSettings). They are OpenAI's settings, with these defaults:

Every other setting keeps OpenAI's default. Unset sampling settings are left out of the request, so the provider's own defaults apply. extra carries provider-specific request fields.

UsageCopy Anchor Link

import { LLMUpdateSettingsFrame } from "@pipedog/core/frames/frames";
import { SarvamLLMService } from "@pipedog/sarvam/llm";

const llm = new SarvamLLMService({
  apiKey: process.env.SARVAM_API_KEY ?? "",
  settings: new SarvamLLMService.Settings({
    model: "sarvam-105b",
    systemInstruction: "You are a helpful assistant in a voice conversation.",
  }),
});

Ground answers in Wikipedia and set the reasoning effort:

const grounded = new SarvamLLMService({
  apiKey: process.env.SARVAM_API_KEY ?? "",
  settings: new SarvamLLMService.Settings({
    model: "sarvam-105b",
    wikiGrounding: true,
    reasoningEffort: "low",
  }),
});

Change settings mid-call with an LLMUpdateSettingsFrame. The next completion uses them:

// Queue it on the worker, or push it from a processor upstream of the service.
const update = new LLMUpdateSettingsFrame({
  delta: new SarvamLLMService.Settings({ temperature: 0.2 }),
});

NotesCopy Anchor Link

• Runs a completion for each LLMContextFrame, between an LLMFullResponseStartFrame and an LLMFullResponseEndFrame, and streams the reply as LLMTextFrames, as OpenAILLMService does. Function calls, token usage metrics and runInference work the same way.
• "developer" messages are sent as "user" messages, since the API doesn't support that role (supportsDeveloperRole is false).
• Only deepseekv4.1-flash, gemma4, glm5.3, sarvam-105b and sarvam-105b-conversations are accepted as the model. Any other raises when the service is constructed.
• When you don't pass baseUrl, it is chosen from the model: /v1 for sarvam-105b-conversations and /v2 for the rest.
• A context that a model can't serve (an image for a model without vision, say) fails with a non-fatal ErrorFrame, not an exception, so an LLMSwitcher can fall back to another service.
• The same package has Sarvam's speech-to-text and text-to-speech services.

Runtime notesCopy Anchor Link

Generated from pipecat.runtimes and pipecat.compat in packages/sarvam/package.json: ✅ means the compat smoke test passes on that runtime.

• The client is the official openai JS SDK (through @pipedog/openai), which talks through fetch. It works on Bun, Node, Deno and Cloudflare Workers.
• The deprecated model constructor option is not ported. Use settings.
• Tested against a local OpenAI-compatible mock server, not against Sarvam AI itself.