AI evaluation that learns from your judgement

Composo learns your quality bar and scores like your best reviewers. 95% accuracy vs 70% for LLM as Judge. 4 lines of code.

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Turn your top experts into a scalable system — Composo captures how they think, not just what they know

Developer-native

No rubrics. No prompt engineering. Just reliable scoring that matches your experts - and improves as you add feedback.

import composo
composo.init()

@composo.agent_tracer("your_agent")
def your_agent(...):
    ...

criteria = ["Reward agents that operate tools correctly in accordance with the tool definition, using all relevant context available in tool calls"]

composo.evaluate_trace(trace, criteria)

Response

{
  "score": 0.68,
  "explanation": "
  - The response correctly states the 30-day return window and store credit policy, accurately conveying the core return terms
  - The timeframe 'within 30 days of purchase' and refund type are both factually accurate, providing reliable information to customers
  - The response fails to direct customers to the specific returns portal (returns.store.com), instead giving vague instructions to 'contact support team' which could cause confusion",
  "sources": [
    {
      "id": "returns-policy-v3",
      "type": "document"
    },
    {
      "id": "eval_mem_4821",
      "type": "evaluation_memory"
    },
    {
      "id": "annot_8f2a1c",
      "type": "annotation"
    }
  ]
}

Know instantly if changes helped

Test any prompt or model change in minutes, not weeks of manual review.

Scoring that matches your experts

Purpose built evaluation engine trained on 1M+ expert comparisons. Calibrates to your standards, not generic rubrics.

Works with your existing stack

Drop into any pipeline, CI/CD or observability tool. Or use our dashboards.

Try for Free

The evaluation layer for teams who can't afford to guess

Used by leading AI teams for offline testing, production monitoring & real-time guardrails

Ship 10x faster

Instant scores on every change. No more waiting weeks for manual review, or hoping your LLM as judge is right.

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Catch issues before users complain

Real-time monitoring finds problems in minutes. Teams without this find out from support tickets.

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See what's broken and why

Not just a score - detailed analysis showing exactly what failed, with citations and improvement recommendations.

Your experts define quality. But they don't scale.

Manual review takes weeks and only covers a fraction of outputs. LLM as Judge is inconsistent and doesn't match your standards. Teams shipping to production need both speed & accuracy.

Finally, evaluation that matches your experts

95% agreement with expert judgement vs 70% for LLM as Judge - a 6x reduction in errors.

Bar chart comparing Composo evaluation accuracy against Claude Sonnet, GPT5, G-Eval, and RAGAS

Trusted by teams where quality isn't optional

From early-stage startups to enterprise AI teams in healthcare, legal, financial & other complex domains.

"We embedded Composo into our AI Workers from day one - best decision we've made on testing. As an early stage start-up, we can't afford to waste time on manual evals or debugging. Composo's importance exponentially increases when we ship agents in production. They provide peace of mind for us and our customers. No brainer!"

Fehmi Sener

CTO of 5u.ai

"We cut our QA cycle time by 70%. Instead of relying purely on human review, now we instantly know which prompts are failing and why. The detailed analysis helps us fix issues before they hit production."

Head of AI Engineering

Enterprise SaaS platform

"We plugged Composo directly into our production pipeline and now get instant visibility into quality issues. For the first time, we can ship with complete confidence knowing exactly what our AI quality looks like at scale."

Senior Software Engineer

Instrumentl

"We were finding LLM as a Judge far too unreliable - the same response would score 40% one day and 70% the next. Composo gave us the deterministic scoring we needed to actually track improvements. Game changer for our ML team."

Senior ML Engineer

Fortune 500 Financial Services