Meta Model API

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ReqLLM connects directly to Meta's Model API at https://api.meta.ai/v1 and uses its OpenAI-compatible Responses API.

Configuration

Create a Meta Model API key and expose it as:

MODEL_API_KEY=your-api-key

ReqLLM also accepts the standard per-request api_key: option.

Basic Usage

ReqLLM.generate_text(
  "meta:muse-spark-1.1",
  "Explain OTP supervision trees in a paragraph",
  reasoning_effort: :low,
  max_tokens: 512
)

ReqLLM translates max_tokens to the Responses API max_output_tokens field.

Streaming

{:ok, response} =
  ReqLLM.stream_text(
    "meta:muse-spark-1.1",
    "Work through this problem carefully",
    reasoning_effort: :high,
    max_tokens: 1024
  )

ReqLLM.StreamResponse.tokens(response)
|> Stream.each(&IO.write/1)
|> Stream.run()

Reasoning Continuity

Meta requests default to stateless operation with:

{
  "store": false,
  "include": ["reasoning.encrypted_content"]
}

ReqLLM preserves returned encrypted reasoning items in the assistant message and replays them on later turns. This keeps reasoning context intact through tool calls without relying on server-side response storage.

Set provider_options: [store: true] to opt into server-side storage. You can also override include, although removing encrypted reasoning content prevents stateless reasoning replay.

Provider Options

Meta-specific fields belong under provider_options:

ReqLLM.generate_text(
  "meta:muse-spark-1.1",
  "Summarize the conversation",
  provider_options: [
    prompt_cache_retention: "24h",
    parallel_tool_calls: false,
    reasoning_summary: :auto
  ]
)

Supported provider options are:

  • include — additional Responses API data to return
  • max_output_tokens — Meta's native output-token limit field
  • parallel_tool_calls — allow multiple tool requests in one response
  • prompt_cache_retention — "in_memory" or "24h"
  • reasoning_summary — :auto, :concise, or :detailed
  • response_format — an OpenAI-compatible JSON schema response format
  • store — allow server-side response storage

Canonical reasoning_effort values :minimal, :low, :medium, :high, and :xhigh are sent to Meta. Muse does not accept :none, so ReqLLM maps it to :minimal. Meta does not expose a reasoning token budget; ReqLLM removes reasoning_token_budget with the configured unsupported-option policy.

Tools and Structured Output

The provider uses ReqLLM's standard tool schema and the Responses API JSON schema output format. tools, tool_choice, generate_object/4, and their streaming equivalents therefore use the same high-level APIs as other ReqLLM providers.

Live Compatibility Recording

The comprehensive provider suite can be recorded after MODEL_API_KEY is set:

mix mc "meta:muse-spark-1.1" --record

Llama Models Hosted Elsewhere

The meta provider targets Meta's direct Model API. Llama models hosted by OpenRouter, Groq, Azure, Vertex AI, Ollama, or another service should use that service's ReqLLM provider and model ID.

AWS Bedrock's native Llama payload remains supported by ReqLLM.Providers.AmazonBedrock.Meta, backed by the internal ReqLLM.Providers.Meta.Llama formatter.

Resources