knot.ai

The knot.ai library provides access to an AI client for scripts. In embedded contexts (MCP, remote, local scripts) it returns a pre-configured client connected to the server’s AI provider. For standalone use outside knot, configure knot.apiclient with AI connection details.

Functions

Function	Description
`get_default_model()`	Returns the configured default model name, or `""`
`Client()`	Get an AI client instance

Embedded Usage

    
import knot.ai as ai

client = ai.Client()

# Pass "" as model - server uses its configured default
answer = client.ask("", "What is the capital of France?")
print(answer)

Standalone Usage

Configure knot.apiclient with AI kwargs or set environment variables:

    
    
  
import knot.apiclient
import knot.ai as ai

knot.apiclient.configure(
    "https://knot.example.com", "your-token",
    ai_model="gpt-4o",
    ai_provider="openai",
    # ai_url defaults to url + "/v1"
    # ai_token defaults to token
)

client = ai.Client()
model = ai.get_default_model()  # returns "gpt-4o"

answer = client.ask(model, "What is the capital of France?")
print(answer)

Or via environment variables (read on first use):

KNOT_URL=https://knot.example.com
KNOT_TOKEN=your-token
KNOT_AI_MODEL=gpt-4o
KNOT_AI_PROVIDER=openai
KNOT_AI_URL=https://knot.example.com/v1   # optional
KNOT_AI_TOKEN=your-ai-token               # optional, defaults to KNOT_TOKEN

Function Details

get_default_model()

Returns the ai_model from knot.apiclient config. Always returns "" in embedded contexts — passing "" to any client method causes the server to use its configured default.

Returns: str - The model name, or ""

Client()

Returns an AI client instance. In embedded contexts returns the pre-configured server client. In standalone use creates a scriptling.ai client using the AI connection details from knot.apiclient.

Returns: Client - An AI client instance

Client Object Methods

The client object returned by ai.Client() has the following methods:

client.completion(model, messages, **kwargs)

Creates a chat completion.

Parameters:

model (str): Model identifier, or "" to use the server’s configured default
messages (str or list): Either a string (user message) or a list of message dicts with “role” and “content” keys
system_prompt (str, optional): System prompt to use when messages is a string
tools (list, optional): List of tool schema dicts
temperature (float, optional): Sampling temperature (0.0-2.0)
max_tokens (int, optional): Maximum tokens to generate

Returns: dict - Response containing id, choices, usage, etc.

    
    
  
# String shorthand - "" uses server default model
response = client.completion("", "What is 2+2?")

# With system prompt
response = client.completion("", "What is 2+2?", system_prompt="You are a math tutor")

# Full messages array
response = client.completion("", [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "What is Python?"}
])
print(response.choices[0].message.content)

client.ask(model, messages, **kwargs)

Quick completion that returns text directly, with thinking blocks automatically removed.

Parameters: Same as completion()

Returns: str - The response text

    
answer = client.ask("", "What is 2+2?")
print(answer)  # "4"

client.completion_stream(model, messages, **kwargs)

Creates a streaming chat completion.

Parameters: Same as completion()

Returns: ChatStream - A stream object with a next() method

    
    
  
stream = client.completion_stream("", "Count to 10")
while True:
    chunk = stream.next()
    if chunk is None:
        break
    if chunk.choices and len(chunk.choices) > 0:
        delta = chunk.choices[0].delta
        if delta.content:
            print(delta.content, end="")

client.embedding(model, input)

Creates an embedding vector for the given input text(s).

Parameters:

model (str): Model identifier
input (str or list): Input text(s) to embed

Returns: dict - Response containing data (list of embeddings), model, and usage

With Agent Framework

    
    
  
import knot.ai as ai
import scriptling.ai.agent as agent

client = ai.Client()

bot = agent.Agent(
    client=client,
    model="",
    system_prompt="You are a helpful assistant."
)

response = bot.trigger("Please greet Paul", max_iterations=5)
print(response.content)

Error Handling

    
import knot.ai as ai

try:
    client = ai.Client()
except Exception as e:
    print(f"AI not available: {e}")

Server Configuration

The AI provider is configured server-side in .knot.toml:

    
    
  
[server.chat]
enabled = true
openai_api_key = "your-api-key"
openai_base_url = "https://api.openai.com/v1"
model = "gpt-4o"
system_prompt = "You are a helpful assistant."

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