Other clients and migration

Connect a harness

Kendr speaks the OpenAI and Anthropic wire formats, so most agents, IDEs, and SDKs connect by pointing their base URL at https://api.kendr.org and pasting one scoped key. Use model: "kendr-intelligent" for general managed routing with optimized context, kendr-intelligent-direct for general routing with original context, kendr-coder for coding-focused routing with optimized context, or kendr-coder-direct for coding-focused routing with original context. You can also use an exact or account-owned alias returned by GET /v1/models. Unknown aliases fail closed so typos cannot silently change routing or billing. Paste the key you saved at creation in place of <YOUR_KEY> below — full keys are never shown again.

export ANTHROPIC_BASE_URL=https://api.kendr.org
export ANTHROPIC_AUTH_TOKEN=<YOUR_KEY>
claude

from openai import OpenAI

client = OpenAI(base_url="https://api.kendr.org/v1", api_key="<YOUR_KEY>")
resp = client.chat.completions.create(
    model="kendr-intelligent",
    messages=[{"role": "user", "content": "Say hi in five words."}],
)
print(resp.choices[0].message.content)

curl https://api.kendr.org/v1/chat/completions \
  -H "Authorization: Bearer <YOUR_KEY>" \
  -H "Content-Type: application/json" \
  -d '{"model": "kendr-intelligent", "messages": [{"role": "user", "content": "Say hi in five words."}]}'

# Cursor -> Settings -> Models -> API keys
#   OpenAI API key:     <YOUR_KEY>
#   Override base URL:  https://api.kendr.org/v1
# Then pick any model and send a chat message.

import anthropic

client = anthropic.Anthropic(base_url="https://api.kendr.org", auth_token="<YOUR_KEY>")
msg = client.messages.create(
    model="kendr-intelligent", max_tokens=64,
    messages=[{"role": "user", "content": "Say hi in five words."}],
)
print(msg.content)

// Works with agents that accept a custom OpenAI-compatible provider
// (chat-completions API mode). Keep the key in an environment variable.
{
  "providers": {
    "kendr": {
      "baseUrl": "https://api.kendr.org/v1",
      "api": "openai-completions",
      "apiKey": "$KENDR_KEY",
      "models": [{ "id": "kendr-intelligent", "name": "Kendr Intelligent" }]
    }
  }
}

// shell
export KENDR_KEY=<YOUR_KEY>

Use OpenAI, Anthropic, or native HTTPS clients

import OpenAI from "openai";
import crypto from "node:crypto";

const client = new OpenAI({
  apiKey: process.env.KENDR_API_KEY,
  baseURL: "https://api.kendr.org/v1"
});

const response = await client.responses.create({
  model: "kendr-intelligent",
  input: "Plan a safe database migration."
}, {
  headers: { "Idempotency-Key": crypto.randomUUID() }
});

console.log(response.output_text);
console.log(response.kendr_usage);

import os
import uuid
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["KENDR_API_KEY"],
    base_url="https://api.kendr.org/v1",
)

response = client.responses.create(
    model="kendr-intelligent",
    input="Plan a safe database migration.",
    extra_headers={"Idempotency-Key": str(uuid.uuid4())},
)

print(response.output_text)

curl https://api.kendr.org/v1/messages \
  -H "Authorization: Bearer $KENDR_API_KEY" \
  -H "Idempotency-Key: messages-001" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "kendr-intelligent",
    "max_tokens": 1200,
    "messages": [{"role":"user","content":"Review this plan."}]
  }'

curl -N https://api.kendr.org/v1/chat/completions \
  -H "Authorization: Bearer $KENDR_API_KEY" \
  -H "Idempotency-Key: stream-001" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "kendr-intelligent",
    "stream": true,
    "messages": [{"role":"user","content":"Explain the rollout."}]
  }'

Migrate an OpenAI client

You do not need to replace the OpenAI Python or JavaScript package. Change the API key, base URL, and model alias; keep the rest of a supported Chat Completions or Responses call intact. Test the exact request fields you use before moving production traffic because Kendr does not claim compatibility with every OpenAI endpoint or every provider-specific extension.

from openai import OpenAI
import os

client = OpenAI(
    api_key=os.environ["KENDR_API_KEY"],
    base_url="https://api.kendr.org/v1",
)

result = client.chat.completions.create(
    model="kendr-intelligent",
    messages=[{"role": "user", "content": "Summarize this design."}],
)
print(result.choices[0].message.content)

Supported compatibility routes are GET /v1/models, POST /v1/chat/completions, and POST /v1/responses. Anthropic-format clients can use POST /v1/messages and POST /v1/messages/count_tokens. File uploads, assistants, fine-tuning, realtime sessions, and other unrelated vendor APIs are not implied by “compatible.”

Use Kendr with the Vercel AI SDK

The Vercel AI SDK has an OpenAI-compatible provider adapter. Configure it on your server with the Kendr /v1 base URL and a Kendr key. Do not expose the key through a client component or public environment variable.

npm install ai @ai-sdk/openai-compatible

import { generateText } from "ai";
import { createOpenAICompatible } from "@ai-sdk/openai-compatible";

const kendr = createOpenAICompatible({
  name: "kendr",
  apiKey: process.env.KENDR_API_KEY,
  baseURL: "https://api.kendr.org/v1",
  includeUsage: true,
});

const { text, usage } = await generateText({
  model: kendr.chatModel("kendr-intelligent"),
  prompt: "Give me a three-step rollout plan.",
});

console.log(text, usage);

Source-included helper clients

This repository includes dependency-light helper clients for JavaScript and Python. Use these from the checked-out source or your internal package registry when you want a thinner call surface around the raw HTTP routes, or call the HTTPS endpoints directly from any other runtime. A package manifest is not evidence of a public npm or PyPI release; verify a registry release independently before using an install command.

import { KendrClient } from './sdk/javascript/index.js';

const client = new KendrClient({
  apiKey: process.env.KENDR_API_KEY,
  baseUrl: 'https://api.kendr.org'
});

const response = await client.query({
  surface: 'google_search',
  query: 'best llm observability tools',
  params: { gl: 'us', hl: 'en', page: 1 }
});

console.log(response.data);

from kendr import KendrClient

client = KendrClient(
    api_key='YOUR_KENDR_API_KEY',
    base_url='https://api.kendr.org',
)

response = client.query(
    surface='google_search',
    query='best llm observability tools',
    params={'gl': 'us', 'hl': 'en', 'page': 1},
)

print(response['data'])