LLM evaluation tooling has matured fast. There are scoring frameworks, tracing platforms, experiment trackers, and metrics libraries that would have been unthinkable two years ago. And most of them are really good at what they do.
But almost all of them share a blind spot: they're built for developers working alone. A single engineer writes the test cases, picks the metrics, runs the evaluations, and interprets the results. In practice, building a reliable LLM application involves more people than that. Domain experts understand what "correct" means in context. Product managers define the behaviors users actually care about. Compliance teams need to verify safety properties. When testing lives entirely inside a developer's notebook or CI pipeline, these perspectives get lost.
This creates two distinct categories of tooling. Developer-centric tools are excellent at scoring, tracing, and debugging. Team-centric tools add structured workflows where multiple stakeholders collaborate on what to test, how to evaluate it, and what the results mean. The best setups combine both.
Here are seven tools worth knowing, and how they fit together.
- Rhesis AI
Best for: Cross-functional teams where developers, domain experts, and product managers all need a seat at the testing table.
Full disclosure: this is us.
Rhesis AI is an open-source platform and Python SDK for testing LLM and agentic applications. Where most tools in this space focus on the scoring and tracing layers, Rhesis was built around a different question: how do you make LLM testing a team activity?
The answer starts with requirements. Describe what your application should and shouldn't do in plain language, connect your knowledge sources (docs, Notion, GitHub, Jira, Confluence via MCP), and Rhesis generates hundreds of test scenarios from those requirements, including edge cases and adversarial prompts you wouldn't write by hand.
From there, the Penelope Agent runs multi-turn conversations against your application to test context retention, role adherence, and dialogue coherence. The Polyphemus Agent combined with built-in garak integration probes for jailbreaks, prompt injection, PII leakage, and data extraction — continuous red-teaming rather than a one-off audit.
Rhesis ships with 60+ built-in metrics, including RAGAS metrics (context relevance, faithfulness), DeepEval metrics (bias, toxicity, hallucination), and garak vulnerability scans, all with reasoning explanations. Recent releases added conversation traces for single-turn and multi-turn interactions, plus conversation graph views for multi-agent workflows.
Rhesis AI platform architecture — from requirements to collaborative test review.
The collaboration layer ties it all together: reviews, tasks, comments, shared test ownership. A domain expert can flag that a generated response misunderstands industry terminology. A compliance lead can review adversarial test results. Testing stops being something one developer does in isolation.
Rhesis integrates metrics from tools like DeepEval and RAGAS directly. You don't pick Rhesis instead of those tools. You pick it alongside them, to bring structure and collaboration to a process that otherwise stays locked in a single developer's workflow. Open-source under MIT, self-hostable, works with any LLM provider.
- LangSmith
Best for: Teams in the LangChain ecosystem who need tracing, prompt debugging, and evaluation in one place.
LangSmith by LangChain is the most established name in the LLM tooling space. It combines tracing, evaluation, prompt playgrounds, and dataset management into a single platform.
End-to-end tracing of LLM calls, chains, and agents with detailed latency and token breakdowns.
Built-in evaluation with custom scoring functions and human annotation queues.
Prompt playground for iterating on prompts with side-by-side comparisons.
Dataset management for building and versioning test sets.
Hub for sharing prompts and chains across teams.
LangSmith excels at observability and debugging. When your chain produces a strange output, you can trace the issue back to a specific step and understand exactly what went wrong. It's a developer's power tool for understanding LLM behavior.
Where it pairs well with other tools: LangSmith gives you the tracing and debugging layer, and a platform like Rhesis can sit on top to provide structured test generation, adversarial probing, and a shared space where non-developers can participate in the review process.
- Braintrust
Best for: Developer teams that want a managed evaluation platform with strong logging and experiment tracking.
Braintrust positions itself as an end-to-end platform for evaluating AI products. It covers logging, scoring, dataset management, and prompt experimentation with a focus on making evaluations fast and reproducible.
Evaluation framework with support for custom scorers, LLM-as-judge, and deterministic checks.
Experiment tracking with diffs between runs, so you can see exactly what changed and how it affected scores.
Logging and tracing for production monitoring.
Dataset management with versioning and collaboration features.
Proxy for managing LLM API calls with caching and rate limiting.
Braintrust is polished and well-designed, with a good developer experience. Its sweet spot is eval and experimentation for technical teams. It assumes the developer knows what to test and writes the evaluations themselves.
Where it pairs well with other tools: Braintrust handles the evaluation and experiment tracking side well. Teams that also need test generation from requirements, or that want domain experts and product managers involved in defining and reviewing tests, can layer a collaborative testing platform on top.
- DeepEval
Best for: Developers who want a comprehensive open-source metrics library they can plug into CI/CD pipelines.
DeepEval is an open-source evaluation framework that provides 14+ research-backed metrics for assessing LLM outputs. Think of it as pytest for LLM applications: write evaluation tests, run them in your pipeline, get pass/fail results.
14+ metrics including faithfulness, answer relevancy, contextual precision/recall, hallucination, bias, toxicity, and more.
Pytest-like interface — define evaluation test cases, set thresholds, run with deepeval test run.
Conversational evaluation metrics for multi-turn interactions.
Synthetic dataset generation for building test sets.
Integration with Confident AI's platform for logging and visualization.
DeepEval is excellent at the scoring layer: defining what "good" looks like and measuring it consistently. As a framework, it gives individual developers fine-grained control over evaluation logic.
Where it pairs well with other tools: DeepEval's metrics are integrated natively in Rhesis AI. This means you can use DeepEval's scoring inside a structured, collaborative test workflow where the whole team sees results and contributes to test design — without losing the granularity of DeepEval's metrics.
- RAGAS
Best for: Teams building RAG (Retrieval-Augmented Generation) pipelines that need purpose-built evaluation metrics.
RAGAS (Retrieval Augmented Generation Assessment) has become the de facto standard for evaluating RAG systems. It provides a focused set of metrics designed specifically for the retrieval-generation pipeline.
Context relevance: does the retrieved context actually relate to the question?
Faithfulness: is the generated answer grounded in the retrieved context?
Answer relevancy: does the answer actually address the question?
Context precision and recall for evaluating retrieval quality independently from generation quality.
Lightweight, framework-agnostic, easy to integrate into existing pipelines.
RAGAS does one thing and does it well. It's a metrics toolkit for RAG evaluation, not a full platform. This makes it easy to adopt incrementally.
Where it pairs well with other tools: RAGAS metrics are available natively in Rhesis AI, so teams can combine RAG-specific scoring with conversation simulation, adversarial testing, and collaborative review workflows. Your retrieval engineer gets the metrics they need, and your domain experts get visibility into whether the answers actually make sense in context.
- Arize Phoenix
Best for: Teams that need observability and evaluation for LLM applications in production.
Arize Phoenix is an open-source observability and evaluation tool focused on tracing, debugging, and evaluating LLM applications. It sits at the intersection of monitoring and evaluation.
OpenTelemetry-based tracing for LLM calls, retrieval steps, and agent actions.
Built-in evaluation with LLM-as-judge, code-based, and annotation-based assessments.
Embeddings analysis for visualizing and understanding retrieval quality.
Experiment tracking for comparing prompt and model changes.
Works across frameworks — no lock-in to a specific LLM library.
Phoenix is particularly strong at helping you understand what's happening in production and why things go wrong. Its tracing and embeddings visualization make it a solid debugging companion.
Where it pairs well with other tools: Phoenix handles the production observability side. Teams that also need pre-production test generation, adversarial probing, and a way for non-technical stakeholders to participate in test reviews can combine it with a collaborative testing platform like Rhesis.
- Opik by Comet
Best for: Teams already in the Comet ML ecosystem or those who want experiment tracking with LLM evaluation.
Opik brings Comet's experiment tracking expertise to the LLM evaluation space. It provides tracing, evaluation, and dataset management with a focus on reproducible experiments.
End-to-end tracing for LLM calls and agent workflows.
Evaluation metrics with support for custom scoring and LLM-as-judge.
Dataset management and versioning for test sets.
Integration with Comet's broader ML experiment tracking platform.
Open-source core with a managed cloud option.
Opik benefits from Comet's maturity in ML experiment tracking. If your team already uses Comet for model training and wants a consistent experience for LLM evaluation, it's a natural fit. The metric library is less extensive than tools like DeepEval — Opik is a newer entry in the LLM evaluation space, but it's actively developing.
Where it pairs well with other tools: Opik covers experiment tracking and tracing. For teams that need structured test generation, adversarial testing, or collaborative workflows involving non-developers, it combines well with a platform that handles that side of the process.
How to choose the right tool
Most production teams end up using two or three of these tools together. Here's how to think about it.
For collaboration across multiple stakeholders — where developers, domain experts, product managers, and compliance leads all participate in testing — Rhesis AI was designed around this workflow. It integrates metrics from DeepEval and RAGAS, so you keep the scoring capabilities from standalone frameworks while adding the structure and shared ownership that makes testing sustainable across a team.
For production observability, tracing, and debugging, LangSmith and Arize Phoenix are strong dedicated choices, particularly if you're already in their respective ecosystems.
For programmable evaluation metrics in CI/CD, DeepEval gives you the most comprehensive open-source metric library. For RAG-specific evaluation, RAGAS is the standard.
For managed evaluation with experiment tracking, Braintrust and Opik offer polished workflows for comparing runs and tracking improvements over time.
The thread connecting all of this: most scoring, tracing, and evaluation tools are excellent at what they do, and they get even better when paired with a collaborative layer that brings the rest of the team into the process. That's the gap Rhesis was built to fill.
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If collaborative testing across the full lifecycle is what your team needs, give Rhesis AI a try.
by Dr. Nicolai Bohn
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