Adding a new provider to ReqLLM

Rev. 2025-02 – ReqLLM 1.0.0

Developer checklist

The checklist is now split in two.
Pick ONE column depending on what the remote API looks like.

95% of new providers on the market expose a "Chat Completions" endpoint that is 1-for-1 wire-compatible with OpenAI.
For those you can use the built-in OpenAI-style encoding/decoding and skip implementing custom request/response handling.

Overview

This guide shows:

1. Minimal OpenAI-style implementation using built-in defaults (same pattern used by the Groq provider).
2. Custom encoding/decoding when the remote JSON deviates from OpenAI format.
3. Leveraging prepare_request/4 for multi-operation providers (chat, completions, embeddings, images …).

1. Provider module – minimal skeleton (OpenAI-compatible)

lib/req_llm/providers/my_openai.ex

defmodule ReqLLM.Providers.MyOpenAI do
  @moduledoc """
  MyOpenAI – fully OpenAI-compatible Chat Completions API.
  """

  @behaviour ReqLLM.Provider

  use ReqLLM.Provider.DSL,
    id: :my_openai,
    base_url: "https://api.my-openai.com/v1",
    metadata: "priv/models_dev/my_openai.json",
    default_env_key: "MY_OPENAI_API_KEY",
    # built-in OpenAI-style encoding/decoding is used automatically
    provider_schema: [
      # Only list options that **do not** exist in the OpenAI spec
      organisation_id: [type: :string, doc: "Optional tenant id"]
    ]

  import ReqLLM.Provider.Utils,
    only: [prepare_options!: 3, maybe_put: 3, maybe_put_skip: 4, ensure_parsed_body: 1]

  # ---------------------------------------------------------------------------
  # 1️⃣  prepare_request/4 – operation dispatcher
  # ---------------------------------------------------------------------------

  @impl ReqLLM.Provider
  def prepare_request(:chat, model_input, %ReqLLM.Context{} = ctx, opts) do
    with {:ok, model} <- ReqLLM.Model.from(model_input) do
      req =
        Req.new(url: "/chat/completions", method: :post, receive_timeout: 30_000)
        |> attach(model, Keyword.put(opts, :context, ctx))

      {:ok, req}
    end
  end

  # Example of a second, non-Chat operation (optional)
  def prepare_request(:embeddings, model_input, _ctx, opts) do
    with {:ok, model} <- ReqLLM.Model.from(model_input) do
      Req.new(url: "/embeddings", method: :post, receive_timeout: 30_000)
      |> attach(model, opts)
      |> then(&{:ok, &1})
    end
  end

  def prepare_request(op, _, _, _),
    do:
      {:error,
       ReqLLM.Error.Invalid.Parameter.exception(
         parameter: "operation #{inspect(op)} not supported"
       )}

  # ---------------------------------------------------------------------------
  # 2️⃣  attach/3 – validation, option handling, Req pipeline
  # ---------------------------------------------------------------------------

  @impl ReqLLM.Provider
  def attach(%Req.Request{} = request, model_input, opts \\ []) do
    %ReqLLM.Model{} = model = ReqLLM.Model.from!(model_input)

    api_key = ReqLLM.Keys.get!(model.provider, opts)

    {tools, other_opts} = Keyword.pop(opts, :tools, [])
    {provider_opts, core_opts} = Keyword.pop(other_opts, :provider_options, [])

    prepared_opts =
      model
      |> prepare_options!(__MODULE__, core_opts)
      |> Keyword.put(:tools, tools)
      |> Keyword.merge(provider_opts)

    base_url = Keyword.get(opts, :base_url, default_base_url())
    req_keys = __MODULE__.supported_provider_options() ++ [:context]

    request
    |> Req.Request.register_options(req_keys ++ [:model])
    |> Req.Request.merge_options(
      Keyword.take(prepared_opts, req_keys) ++
        [model: model.model, base_url: base_url, auth: {:bearer, api_key}]
    )
    |> ReqLLM.Step.Error.attach()
    |> Req.Request.append_request_steps(llm_encode_body: &__MODULE__.encode_body/1)
    # Streaming is now handled via ReqLLM.Streaming module
    |> Req.Request.append_response_steps(llm_decode_response: &__MODULE__.decode_response/1)
    |> ReqLLM.Step.Usage.attach(model)
  end

  # ---------------------------------------------------------------------------
  # 3️⃣  encode_body & decode_response – automatic for OpenAI-compatible APIs
  # ---------------------------------------------------------------------------

  # encode_body/1 and decode_response/1 are provided automatically by the DSL
  # using built-in OpenAI-style defaults. You do NOT need to implement them
  # unless you need provider-specific customizations or non-OpenAI wire format.

  # Usage extraction is identical to Groq / OpenAI
  @impl ReqLLM.Provider
  def extract_usage(%{"usage" => u}, _), do: {:ok, u}
  def extract_usage(_, _), do: {:error, :no_usage}
end

What you no longer need

OpenAI-compatible providers get built-in encoding/decoding automatically:

• No encode_body/1 or decode_response/1 implementations needed
• No custom context or response modules
• No protocol implementations
• No wrapper configurations

2. Non-OpenAI wire formats

If the remote JSON schema is not OpenAI-style, you can override encode_body/1 and/or decode_response/1 directly in your provider.

For partial customization, leverage the built-in helpers:

• ReqLLM.Provider.Defaults.build_openai_chat_body/1 - Complete OpenAI body with context, options, tools
• ReqLLM.Provider.Defaults.encode_context_to_openai_format/2 - Context encoding only
• ReqLLM.Provider.Defaults.decode_response_body_openai_format/2 - Response decoding
• ReqLLM.Provider.Defaults.default_decode_sse_event/2 - Streaming events

See the Google provider for an example of translating between OpenAI format and a custom wire format.

3. Multi-operation providers & prepare_request/4

prepare_request/4 may be invoked for several atoms:

• :chat – ChatCompletions
• :embeddings
• :completion (legacy)
• :images / :audio_transcription / …

You decide which are supported.
Return {:error, ...} for the others just like in the example above.

For OpenAI-style endpoints the encode/decode helpers are almost identical; only the path (/embeddings, /audio/transcriptions, …) changes. Feel free to extract a small helper like build_request_path/1.

4. Capability metadata (priv/models_dev/<provider>.json)

No change – see the Groq file for reference.

5. Capability testing

Identical process. Focus on the cheapest, deterministic model, use temperature: 0, and record fixtures with LIVE=true.

6. Best practices recap

• Prefer the OpenAI-compatible pattern with built-in defaults – fewer lines, fewer bugs.
• Move logic into attach/3; keep prepare_request/4 a thin dispatcher.
• provider_schema is only for fields outside the OpenAI spec.
• Use ReqLLM.Keys – never read System.get_env/1 directly.
• Only override encode_body/1 and decode_response/1 when necessary.
• Start small, add streaming, tools, vision, etc. incrementally.

Welcome to ReqLLM 1.0 – shipping a new provider is now a coffee-break task ☕🚀