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Guide

Use Cadreen as a Model Provider

Cadreen exposes a standard OpenAI-compatible /api/v1/cadreen/chat/completions endpoint. Point any OpenAI client at it and you get governance, memory, and intelligence.

Note
Just want model routing without Cadreen? The Model Gateway lets you route to any provider (OpenAI, Anthropic, Google, etc.) with BYOK and budgets — no governance layer required.
1

Quick start

POST/api/v1/cadreen/chat/completions
Python
from openai import OpenAI

client = OpenAI(
    base_url="https://accomplishanything.today/api/v1/cadreen",
    api_key="sk_cadreen_..."
)

response = client.chat.completions.create(
    model="cadreen",
    messages=[{"role": "user", "content": "What connectors do I have?"}]
)
print(response.choices[0].message.content)
TypeScript
import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://accomplishanything.today/api/v1/cadreen",
  apiKey: "sk_cadreen_...",
});

const response = await client.chat.completions.create({
  model: "cadreen",
  messages: [{ role: "user", content: "What connectors do I have?" }],
});
console.log(response.choices[0].message.content);
curl
curl -X POST https://accomplishanything.today/api/v1/cadreen/chat/completions \
  -H "Authorization: Bearer sk_cadreen_..." \
  -H "Content-Type: application/json" \
  -d '{
    "model": "cadreen",
    "messages": [{"role": "user", "content": "What connectors do I have?"}],
    "stream": true
  }'
2

Request fields

FieldTypeRequiredNotes
modelstringNoAccepted for compatibility, ignored internally
messagesarrayYesOpenAI message format
streamboolNoEnable streaming SSE
toolsarrayNoTool/function definitions
conversation_idstringNoTrack conversations across requests
contextobjectNoCadreen-specific context
Note
Ignored fields: temperature, top_p, max_tokens, frequency_penalty, presence_penalty, stop, n, logprobs, response_format — Cadreen controls generation internally.
3

Streaming

Set stream: true to get SSE chunks. Cadreen sends keepalive comments every 15 seconds during long operations.

Streaming example
stream = client.chat.completions.create(
    model="cadreen",
    messages=[{"role": "user", "content": "Hello!"}],
    stream=True
)
for chunk in stream:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="")
Note
Chain-of-thought (<think>...</think>) is stripped from every response — streaming and non-streaming. You never see the model's internal reasoning.
4

Tool calling

Cadreen supports hybrid tool execution. Your tools pass through. Cadreen's tools execute server-side with governance.

Python
response = client.chat.completions.create(
    model="cadreen",
    messages=[{"role": "user", "content": "Read main.go and summarize it"}],
    tools=[{
        "type": "function",
        "function": {
            "name": "read",
            "description": "Read a file",
            "parameters": {
                "type": "object",
                "properties": {
                    "path": {"type": "string", "description": "File path"}
                },
                "required": ["path"]
            }
        }
    }]
)
Note
See the Tool Calling guide for the full hybrid execution flow.
5

Response metadata

Every response includes Cadreen-specific metadata alongside the OpenAI-compatible fields.

Response
{
  "choices": [{
    "message": {
      "role": "assistant",
      "content": "You have 3 active connectors.",
      "metadata": {
        "confidence": 0.92,
        "cadreen_type": "direct",
        "classification": { "intent": "question", "complexity": "simple" },
        "assessment": { "can_do": 0.85, "assessment_quality": "complete" },
        "governance": { "decision": "auto", "approved": true }
      }
    }
  }],
  "intelligence": {
    "reasoning": { "intent": "question", "approach": "direct_answer" },
    "capability": { "can_do": 0.85 },
    "governance": { "active": true, "decision": "auto" }
  }
}
FieldDescription
metadata.confidenceClassification confidence (0-1)
metadata.cadreen_typeRouting: direct, clarify, mission, blocked
metadata.classificationIntent, complexity, confidence
metadata.assessmentcan_do, assessment_quality, gap_count
metadata.governanceDecision, approved
intelligenceFull reasoning trace (capability, governance, memory, humility)
6

What's different from OpenAI

Tool governance — Every server-side tool call evaluated against policies
Memory — 4 types of persistent memory across conversations
Intelligence traces — Full reasoning trace in every response
7

Limits

Tool calls are buffered during streaming (no incremental deltas)
n is always 1 (one completion per request)
response_format is not supported
Per-request token limits are managed internally
Note
Next: Governance Policies 101 — how to control what Cadreen is allowed to do.