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Ship your AI app with confidence

The all-in-one platform to monitor, debug and improve
production-ready LLM applications.

The ability to test prompt variations on production traffic without touching a line of code is magical. It feels like we’re cheating; it’s just that good!

Nishant Shukla

Sr. Director of AI

Get integrated in seconds

Use any model and monitor applications at any scale.

🚅

Other providers? See docs

node.js
python
langchain
langchainJS

1import OpenAI from "openai";
2
3const openai = new OpenAI({
4  apiKey: OPENAI_API_KEY,
5  baseURL: `https://oai.helicone.ai/v1/${HELICONE_API_KEY}/`
6});

“Probably the most impactful one-line change I've seen applied to our codebase.”

What if I don’t want Helicone to be in my critical path.

There are two ways to interface with Helicone - Proxy and Async. You can integrate with Helicone using the async integration to ensure zero propagation delay, or choose proxy for the simplest integration and access to gateway features like caching, rate limiting, API key management.

Designed for the entire LLM lifecycle

The CI workflow to take your LLM application from MVP to production, and from production to perfection.

Log

Dive deep into each trace and debug your agent with ease

Visualize your multi-step LLM interactions, log requests in real-time and pinpoint root cause of errors.

Evaluate

Prevent regression and improve quality over-time

Monitor performance in real-time and catch regressions pre-deployment with LLM-as-a-judge or custom evals

What is online and offline evaluation?

Online evaluation tests systems in real-time using live data and actual user interactions. It’s useful to capture dynamic real-world scenarios.

In contrast, offline evaluation occurs in controlled, simulated environments using previous requests or synthetic data, allowing safe and reproducible system assessment before deployment.

Experiment

Push high-quality prompt changes to production

Tune your prompts and justify your iterations with quantifiable data, not just “vibes”.

Messages	Original	Prompt 1	Prompt 2
{"role": "system", "content": "Get...	Queued...	Queued...	Queued...
{"role": "system", "content": "Get...	Queued...	Queued...	Queued...
{"role": "system", "content": "Get...	Queued...	Queued...	Queued...
{"role": "system", "content": "Get...	Queued...	Queued...	Queued...
{"role": "system", "content": "Get...	Queued...	Queued...	Queued...
{"role": "system", "content": "Get...	Queued...	Queued...	Queued...
{"role": "system", "content": "Get...	Queued...	Queued...	Queued...
{"role": "system", "content": "Get...	Queued...	Queued...	Queued...
{"role": "system", "content": "Get...	Queued...	Queued...	Queued...
{"role": "system", "content": "Get...	Queued...	Queued...	Queued...
{"role": "system", "content": "Get...	Queued...	Queued...	Queued...

LLM as a judge

Similarity

77%

LLM as a judge

Humor

81%

LLM as a judge

SQL

94%

RAG

ContextRecall

63%

Composite

StringContains

98%

Deploy

Turn complexity and abstraction to actionable insights

Unified insights across all providers to quickly detect hallucinations, abuse and performance issues.