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Traceback why
your AI failed.

Find the exact prompt, retrieved doc, or tool that broke your output. No code changes.

Start tracingBook a demo

Root-Cause Visibility

Go beyond logs — see exactly what broke

Unlike generic observability platforms, Tropir doesn’t just show inputs and outputs. We map the exact flow of text across your LLM stack to reveal the prompt, tool, or doc that caused a failure — no code changes required.

Token Limit Error
Processing
Response

Pattern Recognition

Detect failure patterns before they repeat

Tropir finds recurring failure points across thousands of logs — like prompt drift, broken formats, or unreliable retrieval — and flags them early. You don’t just observe issues, you prevent them.

87%
92%
78%
64%
91%
82%

Text-Level Traceability

Follow every sentence through your LLM chain

Tropir shows you how text flows through chained LLM calls — even when outputs from one model become inputs to another. It’s not just logging — it’s true traceability across roles, providers, and generations.

We support all major AI platforms

Integrate with your existing AI infrastructure seamlessly

OpenAI logo
OpenAI
Anthropic logo
Anthropic
Gemini logo
Gemini
AWS logo
Amazon Bedrock
OpenRouter logo
OpenRouter
Perplexity AI logo
Perplexity
Vercel logo
Vercel AI SDK
Grok logo
Grok
Meta logo
Meta
Hugging Face logo
Hugging Face
And more coming soon
Quick Start

Start Using Tropir Instantly

Get Tropir running via proxy routing in just a few steps

1

Install and Configure

Install Tropir (pip install tropir) and update your API endpoint to route calls through the Tropir proxy.

2

Set Up Authentication

Create a Tropir account to get your API key and ensure your provider API keys are available as environment variables.

3

Use Header Utility (Optional)

Optionally utilize `prepare_request_headers` from `tropir.session_utils` to simplify adding Tropir headers.

openai_example.py
Import Changes
import requests
import os
+ from tropir.session_utils import prepare_request_headers
URL Change
- openai_url = "https://api.openai.com/v1/chat/completions"
+ openai_url = "https://api.tropir.com/openai/v1/chat/completions"
openai_headers = {
    "Content-Type": "application/json",
    "Authorization": f"Bearer {os.environ.get('OPENAI_API_KEY')}"
}
openai_payload = {
    "model": "gpt-4o",
    "messages": [{"role": "user", "content": "Hello"}]
}
Add Tropir Headers
+ prepare_request_headers(openai_headers)
response = requests.post(openai_url, headers=openai_headers, json=openai_payload)

Ready to get started?

Begin your LLM tracing journey today or talk to our experts about optimizing your pipelines.

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