from autoblocks.tracer import AutoblocksTracer
from openai import OpenAI
client = OpenAI()
tracer = AutoblocksTracer()
tracer.send_event("ai.request", properties=params)
completion = client.chat.completions.create(**params)
tracer.send_event("ai.response", properties=completion)
Improve your
LLM Product Accuracy
Bring your entire team together with expert-driven testing & evaluation
The collaborative testing & evaluation platform that automatically improves with feedback from users and experts.
Better tests. Better products.
Align every component of your tests more closely with reality.Curate high-quality test datasets
Keep a pulse on production with our observability tools. Use user feedback and online evaluations to identify valuable test cases.Experiment, collaboratively
Utilize our SDKs to surface any part of your pipeline into a UI, while keeping the code as the source of truth.Align evaluation metrics
Empower experts to provide detailed feedback on outputs. Use this data to align automated evaluation metrics with human preferences.Integrate easily with any codebase, any framework
Flexible SDKs- Trace events
- Test app behavior
- Manage prompts
- Manage configs
- Manage custom models
Improve accuracy with human-in-the-loop feedback
Empower subject-matter experts and non-engineer stakeholders.
Everything you need to ensure the accuracy of your LLM-based products
The collaborative testing & evaluation platform that automatically improves with feedback from users and experts.Testing & Evaluation
Turbocharge your local testing & experimentation process to always put your best foot forward.
Monitoring & Guardrails
Configure online evaluations and guardrails to ensure a safe, trustful user experience.
Debugging
Get down to the root cause of bugs and rapidly prototype solutions.
AI Product Analytics
Connect AI product state to user outcomes. Proactively uncover opportunities for improvement.
Prompt Management
Enable prompt collaboration, while ensuring your code doesn’t break.
RAG & Context Engineering
Optimize each part of your context pipeline to drive accurate and relevant outputs.
Scale with Security
Engineered to satisfy the most rigorous privacy and security requisites.
