At their deepest level, LLMs are still a kind of magic. Even the developers who build them find them to be, channeling Winston Churchill, “a riddle, wrapped in a mystery, inside an enigma.”
That’s why everyone working with LLMs in their enterprise stack needs a way to peer into the dark mass of weights to help make sense of these numerical beasts.
Lately there’s been an explosion of tools that can assist. Companies are building platforms that sit in an agentic AI niche market that might be called “Evaluation and Benchmarking.” This tools track the best performing LLM or agentic options, testing their fit and watching over them as they chew through tokens.
With agentic AI still an emerging technology class, the boundaries between its nascent market niches are far from set. There are other sets of tools for tracking raw performance, an area that some call “AgentOps” or “Observability.” (See “19 AgentOps tools for monitoring AI activity, issues, and costs.”) And still more tools that focus on maintaining our faith in agent answers and on building controls to keep agents from straying, a niche that’s starting to be called “Trust and Guardrails.”
Some of the vendors operating in these spaces are starting in one category and then expanding into another. Others are diving as deeply as they can into their niche. The next year — no, let’s say the next few months — are bound to be fascinating as the tools improve and the various markets evolve and intermix.
For now, here’s a list, in alphabetical order, of some of the best options for any enterprise team that needs to evaluate agents and benchmark their performance.
Braintrust
Big projects require tools that can scale to handle the large amount of dataflows required to trace and pinpoint errors. Braintrust is built to support enterprise-size efforts to deliver meaningful answers to a large collection of users. The tool’s sales literature promises to “trace everything” in order to have the right data available when it’s time to dissect a failed response. Braintrust also delivers a helpful dashboard that aggregates all this data so large errors in latency, cost, or quality can be identified quickly. An automated set of evaluation tasks can track answers and compile useful metrics for ensuring the agent stack is answering the needs of a large set of end-users.
Pricing: A free plan comes with $10 of credits. Pro plan starts at $250 and comes with more credits and a longer retention period.
Standout feature: Loop agent tracks behavior through multiple iterations for deeper debugging power.
Best suited for: Fast-moving teams iterating on prompts and product
Confident AI
Developers who rely on DeepEval but don’t want to host the code can turn to Confident AI, a cloud-based platform for fast, simple, and seamless deployment. The system adds a sophisticated UI that includes a dashboard for tracking and archiving all tests. This collaborative environment enables teams to work swiftly together without worrying about the troubles of exchanging problematic traces or other telemetry files. This makes it easier to extend the power of tools such as DeepEval to handle the continuous tracing and testing necessary in production environments.
Pricing: A “forever free” plan offers a taste. The pay plan starts at $200 and includes features such as better automation and simulation.
Standout feature: Automated red-teaming and on-demand pen-testing helps build more secure results.
Best suited for: Enterprise teams building on established stacks that need the convenience of a collaborative environment
DeepEval
When a model finds a home in a production environment, it’s time to add unit tests that will double and triple check its behavior so the developers can iterate and the CI/CD pipeline can catch any mistakes or regressions. DeepEval delivers a set of Pytest-native Python scripts that run either locally or as part of the deployment pipeline. The tests check simple issues as well as more complicated and ephemeral problems such as hallucinations, drift, role adherence, knowledge retention, and conversation completeness. If the LLM starts to act up or turn into a toxic rogue, these tests will flag them.
Pricing: The open-source version of Confident AI’s tool is available with an Apache 2.0 license.
Standout feature: Full complement of PyTest modules watch for problems such as hallucinations or worse.
Best suited for: Teams with the depth and ability to fully embrace open-source tooling
LangSmith (from LangChain)
As agentic approaches begin to dominate, dev teams need a deep debugging tool like LangSmith, which tracks not just inputs and outputs, but all the steps an agent takes as well as the context that evolves along the way. This enables developers to pinpoint the stage or mechanism deep in the agent where latency, quality, coherence, or other agent parameters go wrong. The tool can be integrated with Python, Go, Java, or TypeScript applications or be used from a cloud-based app that offers a sophisticated UI.
Pricing: Solo accounts start for free. Paid tier ($39 per month per seat) unlocks more tracing and better support.
Standout feature: Complex agent graphs can be tracked with automated surveillance.
Best suited for: Teams invested in the Langfuse tool stack
Langfuse
Finding the best model means feeding the same prompt to the same model, a process that’s getting only more complicated as developers build out multilayered agents that break tasks into multiple steps. Langfuse is an open-source AI tracking tool from Clickhouse, a company that specializes in curating oracular tools like databases. Teams can work together through the Langfuse platform to juggle the various prompts, traces, and answers. The system nurtures an LLM evaluation loop so that teams can find the best combinations of models and agents to solve the problem at hand.
Pricing: Open-source versions offer starter support. Core version starts at $29 per month and includes more traces, longer retention, and better support.
Standout feature: Open Telemetry functionality offers modularity and flexibility.
Best suited for: Budget-focused teams with the ability to leverage open-source ecosystems
LiveBench
Developers who want to send a set of questions to an LLM and then evaluate the performance turn to LiveBench, an open-source tool kit that’s routinely used to benchmark many models during development. Answers are deliberately not graded by other LLMs but compared against hard-coded answers. The tool can be extended, but there’s no fancy GUI. The work is done with configuration text files that specify the ground truth for evaluating the result. When you’re done, you can even contribute your questions to the general open-source project so that others can use them to guide LLM development.
Pricing: Open source
Standout feature: Frequently updated benchmarks offer contamination-free evaluations of models.
Best suited for: Teams evaluating a wide range of models in search of the best performance for their applications
Maxim AI
As the workloads grow more complex and combine multiple steps through workflow graphs, tools such as Maxim AI become more useful. Maxim AI tracks results with an end-to-end tool for evaluating and simulating agents. Prompts and agents and the trajectory they take to an answer can be endlessly simulated prior to deployment and then observed through deployment. The framework-agnostic tool links datasets and data providers to give teams the best insight into how well an agent is delivering.
Pricing: Free model offers one workspace with three-day retention. Pro plan starts at $29 per person per month with longer retention period, more logs, and features such as simulations.
Standout feature: Full simulator can test a wide range of uses and users.
Best suited for: Teams focused on delivering conversational agents
MLflow
Much of the work of developing a useful agentic solution is a long slog through endless combinations and iterations. The MLflow open-source platform is designed to optimize this process and speed it up as much as possible. It is part of a larger tool collection that follows the entire lifecycle of a model from training to deployment. The later stages of development, for instance, rely on systems such as the Prompt Registry, a kind of version control that allows prompt engineers to work through various approaches and linguistic tropes. The goal of the entire process is to deliver the evaluation cycles necessary to deliver a model up to its set of targeted tasks.
Pricing: Free and open source for self-hosted. Cloud computing charges for hosted versions.
Standout feature: Full lifecycle tracking for following models and tracking their costs
Best suited for: Enterprise teams watching a collection of machine learning and AI-based algorithms
Onyx
One of the simplest ways to build a basic chat system that incorporates local retrieval-augmented generation (RAG) knowledge bases is to download Onyx, a front-end tool that’s available as either an MIT-licensed community edition or as a commercial product with a few more features useful to larger enterprises. The RAG layer guides search, and Onyx’s developers built an open-source framework for testing RAG performance. Onyx administrators can also track what users are asking and how well they like the final result.
Pricing: A free starter plan offers limited storage and one database. Pro plan starting at $49 per month offers many more traces, larger storage, and access to features such as saved workflows.
Standout feature: Real-time search for monitoring production environments at scale
Best suited for: Enterprise with larger challenges with substantial RAG integration
Promptfoo
LLMs can fail in a number of ways. Promptfoo iterates through various tests that simulate real user interactions to simulate the types of issues an LLM might face each day. Promptfoo also focuses on some of the biggest security problems and specializes in red teaming to detect any failure points that might be exposed by a malicious user. From toxic edge states to personally identifiable information (PII) leaks, the goal is to deliver tests that will expose potential jailbreaks and failures in the guardrails.
Pricing: “Free forever” means an open-source tool with community-based support. An enterprise version offers custom deployment options and better support.
Standout feature: Automated red-teaming and prompt scrutiny helps lock down implementations.
Best suited for: Security-focused teams that are constantly evaluating and re-evaluating their product’s security.
RAGAS
When RAG databases are a key part of the agentic stack, developers turn to RAGAS to stress test the deeper mathematical corners of the retrieval mechanism. The Python library offers standard and custom metrics for evaluating the performance of the RAG storage-and-retrieval mechanism at the level of vector mathematics. These measure behaviors such as faithfulness, relevance, and totality of recall. The philosophy begins with experiments to speed development but ends with fast integration with the deployment pipeline. Instead of just doing a “vibe check” on the RAG database, developers are using a more scientific approach to test and converge on better total performance.
Pricing: Fully open source under Apache 2.0 license
Standout feature: RAG focus helps teams relying on vector databases for knowledge curation.
Best suited for: Teams with a substantial reliance on RAG databases
Rhesis AI
Many of tools in this evolving market niche are designed for hard-core developers. Rhesis AI wants to bring other stakeholders into the development cycle so they can create tests and evaluate performance, too. That means domain experts, product managers, and even C-suite suits can track how the LLMs behave in conversations. Adversarial or confrontational engagements that devolve into the edge cases that bring headaches are easy to simulate repeatedly to optimize responses. The platform is designed to test all stages of development in a way that’s accessible to all stakeholders.
Pricing: Said to be “open source first” but with enterprise plans for those that need it.
Standout feature: The focus on putting humans in the loop is ideal for applications that require input from meat-based intelligence.
Best suited for: Applications requiring more collaboration with domain experts
Vellum
Anyone who needs a personal assistant can turn to Vellum to help build one that is trained on your data. Along the way, you will evaluate performance using its elaborate testing framework that tracks performance against any of the metrics and use cases you supply. Real-time dashboards track performance using metrics such as token usage costs, latency, or response quality. Multiple teams can work in parallel with version controls that allow iteration and competition. The end result is an agent that’s tuned to your needs.
Pricing: A basic free tier for experimentation. The Mighty starts at $30 per month and comes with more storage and compute credits.
Standout feature: End-to-end integration simplifies managing new development.
Best suited for: Cross-functional teams looking for a centralized solution with wide integration
Read More from This Article: Looking to avoid agentic failure? These 13 AI evaluation tools will help
Source: News