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Overview

a21e exposes an OpenAI-compatible /v1/chat/completions endpoint. If your application already uses the OpenAI SDK, you can switch to a21e by changing two lines — the base URL and API key.

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Setup

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_A21E_API_KEY",
    base_url="https://api.a21e.com/v1",
)

response = client.chat.completions.create(
    model="auto",
    messages=[
        {"role": "user", "content": "Explain dependency injection in Go"}
    ],
)

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: "YOUR_A21E_API_KEY",
  baseURL: "https://api.a21e.com/v1",
});

const response = await client.chat.completions.create({
  model: "auto",
  messages: [
    { role: "user", content: "Explain dependency injection in Go" },
  ],
});

curl https://api.a21e.com/v1/chat/completions \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "auto",
    "messages": [
      {"role": "user", "content": "Explain dependency injection in Go"}
    ]
  }'

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Model mapping

The model parameter maps to a21e’s tier system:
Valuea21e tierBehavior
autoAutoa21e selects the best model
economyEconomyFastest, lowest cost
standardStandardBalanced
premiumPremiumHighest quality
Any model name (e.g., gpt-4o)AutoAccepted but a21e selects the actual model

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What’s different from raw OpenAI

When you use a21e’s OpenAI-compatible endpoint, your request gets the full a21e pipeline:
  • Prompt synthesis — your message is enhanced with curated techniques
  • Memory injection — relevant memories from past sessions are applied
  • Workspace context — preferences, persona, and repo context are included
  • Quality scoring — outputs are scored for continuous improvement
The response format matches the OpenAI spec exactly, so existing integrations work without changes.

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Streaming

Streaming is supported via the standard stream: true parameter: 
stream = client.chat.completions.create(
    model="auto",
    messages=[{"role": "user", "content": "Write a Makefile for a Go project"}],
    stream=True,
)
for chunk in stream:
    print(chunk.choices[0].delta.content or "", end="")