LlamaIndex

LlamaIndex gets a governed OpenAILike LLM pointed at the Agent Fabric LLM proxy, with the chat-model flag a chat-only gateway requires already set.

What you get

• A native llama_index.llms.openai_like.OpenAILike.
• is_chat_model=True and is_function_calling_model=True set for you.
• Supported at connection_kwargs(). The client receives a static default_headers snapshot, so per-run correlation and donkey.last_call are not available (see Notes).

Install

pip install "donkey-kit[llamaindex]"

Quickstart

from donkey_kit.integrations.llamaindex import llm
 
model = llm("gpt-4o")

import OpenAI from "openai";
 
const client = new OpenAI({
  baseURL: process.env.DONKEY_LLM_PROXY_URL,   // no /v1
  apiKey: "unused",                              // required slot; proxy uses the headers below
  defaultHeaders: {
    client_id: process.env.DONKEY_LLM_PROXY_CLIENT_ID!,
    client_secret: process.env.DONKEY_LLM_PROXY_CLIENT_SECRET!,
  },
});
 
const reply = await client.chat.completions.create({
  model: "gpt-4o",
  messages: [{ role: "user", content: "Say hi in three words." }],
});
console.log(reply.choices[0].message.content);

Three ways to construct

1. Off a shared Donkey instance:

from donkey_kit import Donkey
 
async with Donkey.from_env() as donkey:
    model = donkey.llamaindex.llm("gpt-4o")

2. Module-level factory (shortest):

from donkey_kit.integrations.llamaindex import llm
 
model = llm("gpt-4o")

3. Governed kwargs, native constructor:

from donkey_kit import Donkey
from llama_index.llms.openai_like import OpenAILike
 
async with Donkey.from_env() as donkey:
    model = OpenAILike(model="gpt-4o", **donkey.llamaindex.connection_kwargs())

Manual equivalent

from llama_index.llms.openai_like import OpenAILike
 
model = OpenAILike(
    model="gpt-4o",
    api_base=...,                     # from DONKEY_LLM_PROXY_URL, no /v1 suffix
    api_key=...,
    default_headers=...,              # client_id / client_secret header pair
    is_chat_model=True,               # required — see below
    is_function_calling_model=True,
)

LlamaIndex uses api_base rather than base_url; connection_kwargs() already translates for you.

Notes

• Always set is_chat_model=True. OpenAILike defaults to is_chat_model=False, which routes requests to the completions endpoint instead of chat — and that fails against a chat-only proxy like the Omni Gateway LLM proxy. connection_kwargs() always sets it (and is_function_calling_model=True); set it yourself if you construct OpenAILike outside the adapter.
• No per-run correlation or donkey.last_call. The client receives a static default_headers snapshot, which excludes the correlation ID bound later by donkey.run(id=...), and the SDK’s httpx client is not used. No response reaches the SDK, so gateway identity, routing, and usage fields can’t be observed. When every adapter resolved on a Donkey is like this one, donkey.last_call reports status == LastCallStatus.UNAVAILABLE and available == False, and names the resolved adapters in surface. The conformance suite asserts both as documented exemptions.

See the error taxonomy for how proxy rejections surface as typed exceptions, and the verification ledger  for the current status of every constructor signature this adapter depends on.