New
Overview
Tzafon provides an OpenAI-compatible chat completions API. You can use the standard OpenAI SDK with Tzafon’s models by simply changing the base URL.
Use your Tzafon API key (TZAFON_API_KEY) with the OpenAI SDK—no separate authentication required.
Quick Start
from openai import OpenAI
client = OpenAI(
api_key="sk_your_tzafon_api_key",
base_url="https://api.tzafon.ai/v1"
)
response = client.chat.completions.create(
model="tzafon.sm-1",
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Hello!"}
]
)
print(response.choices[0].message.content)
Available Models
| Model | Description | Best For |
|---|
tzafon.sm-1 | Small, fast model | General tasks, quick responses |
tzafon.northstar.cua.sft | Computer-use optimized | Browser/desktop automation agents |
Use GET /v1/models to retrieve the current list of available models.
Endpoints
Chat Completions
POST https://api.tzafon.ai/v1/chat/completions
| Parameter | Type | Required | Description |
|---|
model | string | Yes | Model ID (e.g., tzafon.sm-1) |
messages | array | Yes | Conversation messages |
temperature | number | No | Sampling temperature (0-2). Default: 1 |
max_tokens | number | No | Maximum tokens to generate |
stream | boolean | No | Enable streaming responses |
stop | array | No | Stop sequences |
{
"role": "system" | "user" | "assistant",
"content": "Message text"
}
Streaming
Enable real-time token streaming for responsive UIs:
from openai import OpenAI
client = OpenAI(
api_key="sk_your_tzafon_api_key",
base_url="https://api.tzafon.ai/v1"
)
stream = client.chat.completions.create(
model="tzafon.sm-1",
messages=[{"role": "user", "content": "Write a short poem"}],
stream=True
)
for chunk in stream:
if chunk.choices[0].delta.content:
print(chunk.choices[0].delta.content, end="")
List Models
GET https://api.tzafon.ai/v1/models
from openai import OpenAI
client = OpenAI(
api_key="sk_your_tzafon_api_key",
base_url="https://api.tzafon.ai/v1"
)
models = client.models.list()
for model in models.data:
print(model.id)
Completions (Legacy)
POST https://api.tzafon.ai/v1/completions
prompt string instead of messages.
Embeddings
POST https://api.tzafon.ai/v1/embeddings
Configuration Options
Temperature Presets
| Preset | Temperature | Max Tokens | Use Case |
|---|
| Creative | 1.0 | 2048 | Creative writing, brainstorming |
| Balanced | 0.7 | 1024 | General tasks |
| Precise | 0.3 | 512 | Factual responses, code |
Example with Options
response = client.chat.completions.create(
model="tzafon.sm-1",
messages=[{"role": "user", "content": "Explain quantum computing"}],
temperature=0.7,
max_tokens=1024,
stop=["<|im_end|>", "<|end_of_text|>"]
)
Error Handling
The API returns standard HTTP status codes:
| Status | Description |
|---|
| 200 | Success |
| 400 | Bad request (invalid parameters) |
| 401 | Unauthorized (invalid API key) |
| 429 | Rate limit exceeded |
| 500+ | Server error |
from openai import OpenAI, APIError, RateLimitError, AuthenticationError
client = OpenAI(
api_key="sk_your_tzafon_api_key",
base_url="https://api.tzafon.ai/v1"
)
try:
response = client.chat.completions.create(
model="tzafon.sm-1",
messages=[{"role": "user", "content": "Hello"}]
)
except AuthenticationError:
print("Invalid API key")
except RateLimitError:
print("Rate limit exceeded - retry later")
except APIError as e:
print(f"API error: {e}")
Pricing
LLM API usage is billed per million tokens:
| Model | Input Tokens | Output Tokens |
|---|
tzafon.sm-1 | $0.20 / 1M tokens | $0.30 / 1M tokens |
tzafon.northstar.cua.sft | $0.30 / 1M tokens | $1.00 / 1M tokens |
Using with Computer Automation
Combine the LLM API with browser automation for AI-powered workflows:
from openai import OpenAI
from tzafon import Computer
# LLM client
llm = OpenAI(
api_key="sk_your_tzafon_api_key",
base_url="https://api.tzafon.ai/v1"
)
# Computer client
computer_client = Computer()
with computer_client.create(kind="browser") as computer:
computer.navigate("https://example.com")
screenshot = computer.screenshot()
# Use LLM to analyze the page
response = llm.chat.completions.create(
model="tzafon.northstar.cua.sft",
messages=[
{"role": "system", "content": "You are a web automation assistant."},
{"role": "user", "content": f"I'm on a webpage. What should I click next? Screenshot: {screenshot}"}
]
)
print(response.choices[0].message.content)
Next Steps
