The right replacement for an AI observability and evals platform depends on which job you use it for. Production tracing, pre-release testing, user simulation and prompt management are separate jobs, and most alternatives cover only one or two of them. List what you rely on each week, then shortlist tools for that job.
Braintrust's own homepage now calls it an active observability platform for agents. It sells one product to trace agents in production, run evals and catch regressions before they reach users, with patterns found in production turned into new evals.
If you're replacing it, it helps to know that the tools below don't all do the same thing. They're in no particular order, unranked and unlinked, and Braintrust isn't one of the rows.
Active observability means three jobs in one
Tracing, evals and pattern discovery are three different jobs that Braintrust ties together. Tracing records what your agent did in production. Evals score outputs against cases you define. Discovery spots the patterns in live traffic that should become new test cases. When one product does all three, the links between them are easy to take for granted.
Leaving can therefore mean replacing three jobs, not one. Several tools on this page cover only part of that loop. Some are built for testing before release and have little to say about production. One simulates users. One pairs logs with prompt management. That's fine if you only used part of what you had, but it's a gap if you depended on the full loop and didn't notice.
Write down which of the three you'd miss if it disappeared tomorrow. That list is your shortlist filter.
Tracing, testing, simulation or prompts: pick the job
Production tracing with evals. HoneyHive helps enterprises trace, evaluate and monitor production agents across teams, and it's pitched at sensitive-data environments. LangSmith comes from the LangChain team, so it's the closest fit if your agents are built on that framework. LangWatch traces, tests, routes and governs LLM calls across a whole company, including the coding assistants engineers use. There's also an open source platform in the table that does tracing and evals from production data, if self-hosting matters to you.
Testing before release. Promptfoo tests AI apps during development and catches vulnerabilities before they ship, and its homepage now leads with AI security. Scorecard is built around testing each update before it goes out, rather than full production tracing.
Simulation. Okareo uses synthetic users to probe voice, text and headless agents for edge cases before real customers find them.
Prompt management. Lunary puts prompt management next to the logs in one small product, which suits a team that mostly needs to see what happened and tweak prompts quickly.
What each tool is built to do
HoneyHive
Best for: Enterprises running production agents across several teams who want tracing, evaluation and monitoring together, pitched at sensitive-data environments.
Langfuse
Best for: Engineers who want one open source platform to trace, evaluate and improve agents, with production data feeding the improvements.
LangSmith
Best for: LangChain users who want real-time monitoring and tracing to debug agents and find failures, from the team behind the framework.
LangWatch
Best for: AI platform teams that want to trace, test, route and govern LLM calls company-wide, including the coding assistants engineers use.
Lunary
Best for: Small teams that want observability and prompt management for LLM apps in one compact product.
Okareo
Best for: Builders of voice, text and headless agents who want synthetic users to test, evaluate and monitor edge cases on one platform (its claim).
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Promptfoo
Best for: Developers who want AI apps tested during development and vulnerabilities caught before release, with the homepage now leading on AI security.
Scorecard
Best for: Product teams that want a simple way to build and test LLM apps so each update behaves more predictably.
Eight Braintrust replacements compared
| Tool | Main job | Good for |
|---|---|---|
| HoneyHive | Agent observability and evals | Enterprises tracing agents across teams and stacks |
| Langfuse | Open source tracing and evals | An open platform built on production data |
| LangSmith | Tracing and monitoring | Agents built on the LangChain framework |
| LangWatch | Observability and evals | Governing LLM calls across a whole company |
| Lunary | Observability and prompts | Prompt management sitting next to the logs |
| Okareo | Simulation and agent testing | Synthetic users probing agents before customers do |
| Promptfoo | Testing and red teaming | Security testing before an AI app ships |
| Scorecard | Testing LLM apps | Checking each update before it reaches users |
My advice for teams replacing an evals platform
Don't try to recreate the whole loop on day one. Move the job that hurts most if it stops, usually production tracing, and keep your eval datasets exported and versioned so they can run anywhere. Then add a testing tool if the first one doesn't cover pre-release checks well.
Pairing two narrower tools is often cleaner than forcing one to do everything, as long as they share trace formats. For more context, my list of LLM evaluation tools covers testing in depth, my guide to Helicone alternatives looks at gateway-led tracing, and my roundup of Langfuse alternatives compares open source tracing options.
Make sure you're reading about the right Braintrust
One practical trap: search for the name Braintrust and much of what comes back is about the freelancer network of the same name, not the AI product. Reviews, forum threads and comparison posts get mixed up more often than you'd expect.
When you're reading opinions about the platform you're leaving, check that they mention evals, tracing or agents. If they talk about hiring, talent or contracts, they're about the other company, and they won't help you choose a replacement.
Do you make LLM evaluation or observability tools? Tool makers can ask to be considered for a featured place on this list.
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Frequently asked questions
What does active observability mean for AI agents?
It's a vendor term for combining production tracing with evals, so patterns found in live traffic feed back into new test cases instead of sitting in a separate tool.
What's the difference between LLM tracing and LLM evaluation?
Tracing records what your app or agent did in production, step by step. Evaluation scores outputs against cases you define, so you can catch regressions before release.
Is there an open source alternative to Braintrust?
Yes. Some tracing and evaluation platforms are open source and can be self-hosted. Check what the self-hosted edition includes before you plan around it.
Can I move my eval datasets to another tool?
Usually, if you export them in a plain format such as JSON or CSV. Scoring functions and custom graders often need rewriting, so allow time for that work.
Do I need a separate tool for red teaming?
Only if security testing matters for your product. Some testing tools now lead with red teaming and vulnerability checks, while tracing tools rarely cover it.
