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Documentation Index

Fetch the complete documentation index at: https://docs.tracia.io/llms.txt

Use this file to discover all available pages before exploring further.

All API errors raise TraciaError with a specific error code.

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Basic Error Handling

from tracia import Tracia, TraciaError, TraciaErrorCode

client = Tracia(api_key="tr_your_api_key")

try:
    result = client.prompts.run("welcome-email", {"name": "Alice"})
    print(result.text)
except TraciaError as error:
    print(f"Error [{error.code}]: {error.message}")

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Error Codes

CodeDescription
UNAUTHORIZEDInvalid or missing API key
NOT_FOUNDThe requested resource (prompt, etc.) does not exist
CONFLICTResource already exists (e.g., duplicate slug)
MISSING_VARIABLESRequired template variables are missing
MISSING_PROVIDER_API_KEYNo provider API key provided (for run_local())
MISSING_PROVIDER_SDKRequired provider SDK (LiteLLM) not installed
UNSUPPORTED_MODELModel not recognized, use provider override
PROVIDER_ERRORError from the LLM provider (OpenAI, etc.)
INVALID_REQUESTInvalid request format
NETWORK_ERRORNetwork connectivity error
TIMEOUTRequest timed out (default 2 minute limit)
ABORTEDRequest was aborted (e.g., stream aborted)
UNKNOWNUnknown or unmapped error

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Handling Specific Errors

from tracia import Tracia, TraciaError, TraciaErrorCode

client = Tracia(api_key="tr_your_api_key")

try:
    result = client.prompts.run("welcome-email", {"name": "Alice"})
    print(result.text)
except TraciaError as error:
    if error.code == TraciaErrorCode.UNAUTHORIZED:
        print("Invalid API key")
    elif error.code == TraciaErrorCode.NOT_FOUND:
        print("Prompt does not exist")
    elif error.code == TraciaErrorCode.CONFLICT:
        print("Resource already exists")
    elif error.code == TraciaErrorCode.MISSING_VARIABLES:
        print("Missing required template variables")
    elif error.code == TraciaErrorCode.PROVIDER_ERROR:
        print(f"LLM provider error: {error.message}")
    elif error.code == TraciaErrorCode.NETWORK_ERROR:
        print(f"Network error: {error.message}")
    elif error.code == TraciaErrorCode.TIMEOUT:
        print("Request timed out")
    elif error.code == TraciaErrorCode.MISSING_PROVIDER_API_KEY:
        print("Provider API key not set")
    elif error.code == TraciaErrorCode.UNSUPPORTED_MODEL:
        print("Model not recognized")
    else:
        print(f"Unknown error: {error.message}")

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Common Error Scenarios

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Missing Variables

When running a prompt that requires variables you didn’t provide: 
# Prompt expects {{name}} and {{product}}
result = client.prompts.run("welcome-email", {
    "name": "Alice"
    # Missing "product" variable
})
# Raises TraciaError with code MISSING_VARIABLES

​
Provider Not Configured

When no LLM provider API key is configured: 
result = client.prompts.run("welcome-email", {"name": "Alice"})
# Raises TraciaError with code PROVIDER_ERROR
# Message: "No OpenAI API key configured. Add one in Settings > Providers."

​
Prompt Not Found

When requesting a prompt that doesn’t exist: 
prompt = client.prompts.get("nonexistent-prompt")
# Raises TraciaError with code NOT_FOUND

​
run_local() Errors

The run_local() method has additional error codes specific to local execution.

​
Missing Provider API Key

When no API key is found for the provider: 
result = client.run_local(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Hello!"}]
)
# Raises TraciaError with code MISSING_PROVIDER_API_KEY
# Message: "Missing API key for openai. Set the OPENAI_API_KEY environment variable
#           or provide provider_api_key in options."
Solution: Set the environment variable or provide provider_api_key: 
# Option 1: Environment variable
# OPENAI_API_KEY=sk-your-key

# Option 2: Explicit key
result = client.run_local(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Hello!"}],
    provider_api_key="sk-your-key",
)

​
Unsupported Model

When using a custom model without specifying the provider: 
result = client.run_local(
    model="my-fine-tuned-model",
    messages=[{"role": "user", "content": "Hello!"}]
)
# Raises TraciaError with code UNSUPPORTED_MODEL
Solution: Specify the provider explicitly: 
result = client.run_local(
    model="my-fine-tuned-model",
    provider="openai",
    messages=[{"role": "user", "content": "Hello!"}]
)

​
Retry Logic

For transient errors like network issues or rate limits, implement retry logic: 
import time
from tracia import Tracia, TraciaError, TraciaErrorCode

client = Tracia(api_key="tr_your_api_key")

NON_RETRYABLE = {
    TraciaErrorCode.UNAUTHORIZED,
    TraciaErrorCode.NOT_FOUND,
    TraciaErrorCode.MISSING_VARIABLES,
    TraciaErrorCode.INVALID_REQUEST,
}


def run_with_retry(slug: str, variables: dict[str, str], max_retries: int = 3):
    for attempt in range(1, max_retries + 1):
        try:
            return client.prompts.run(slug, variables)
        except TraciaError as error:
            if error.code in NON_RETRYABLE:
                raise
            if attempt < max_retries:
                delay = 2**attempt
                time.sleep(delay)
                continue
            raise