Agent Evaluation Frameworks: Braintrust, Inspect, Langfuse, and DIY

CallMissedMay 8, 2026

·4 min readComparison

AI Agents Evaluation Comparisons Observability

The hardest question in agent engineering is not "how do I build it?" — frameworks have solved that. It is "is the new version better than the old one?" Without a credible answer, every prompt change is a vibe-check and every model bump is a coin flip. By 2026 the evaluation tooling has matured enough that DIY scripts are no longer the default, but the right tool depends on what you're optimizing for.

What an agent eval actually is

An agent eval is three things glued together:

A canonical task set — a frozen list of inputs with known-good or known-bad outcomes

Scorers — functions that judge each output (exact match, regex, LLM-as-judge, custom assertions)

A reporting layer — diffs across runs, regression detection, cost gating in CI

Anything labeled "eval framework" is some opinionated arrangement of those three.

Braintrust

Braintrust is a closed-source, end-to-end platform built around the experiment-as-data-asset model. Production traces become evaluation cases with one click; eval results show up on every pull request through CI/CD; engineers and PMs share the same workspace.

The pitch is "observability connected directly to systematic improvement." It's polished, fast to set up, and costs more than the open-source alternatives. Pricing as of 2026 starts around 1M free spans, $249/mo at Pro tier per multiple comparison sources. [Unverified] — pricing can change without notice; verify on the site.

Strong fit when you want a managed solution and don't want to think about ClickHouse or Kubernetes.

Langfuse

Langfuse is open-source under MIT, with documented self-hosting on top of ClickHouse, Redis, and S3. It is the open-source counterweight to Braintrust and the most popular self-hostable LLM observability platform.

Langfuse leans observability-first: tracing, monitoring, prompt management, and evaluation as connected building blocks. You assemble the pieces into the workflow you want. That flexibility is a strength when you need bespoke pipelines and a tax when you'd rather not run a database team.

Strong fit when self-hosting matters (compliance, data residency) or you want full control of the data layer.

Inspect AI

Inspect is the UK AI Safety Institute's open-source eval framework, designed for adversarial and capability evaluations. It's Python-first, very task-set centric, and shines when you're asking "can the model do this dangerous thing?" rather than "is my support agent better this week?"

Strong fit for safety teams, capability research, and anyone running structured benchmarks like SWE-bench, GPQA, or custom red-team task sets.

DIY

The honest answer is that most teams start with DIY and shouldn't apologize for it. A 200-line Python script that loads a YAML task set, calls your agent, runs a scorer, and writes results to Postgres covers more ground than the marketing copy on any platform suggests. You graduate to a framework when:

You have more than ~50 active eval tasks

More than two people on the team are running evals

You need PR-level regression gating in CI

You're maintaining multiple agent versions and need diff views

Below those thresholds, DIY plus tracing is fine.

Scorer choices

Independent of framework, the scorer choice is what makes or breaks the eval:

Exact / regex match — cheap, brittle, only useful for narrow tasks (does the SQL parse? does the JSON validate?)

LLM-as-judge — flexible, expensive, requires careful prompt design and a held-out validation set to avoid the judge biasing toward its own writing style

Programmatic assertions — for tool-using agents, "did the agent call search_orders with the right customer_id?" is often a stronger signal than judging the final text

Pairwise comparison — show the judge two outputs, ask which is better. More robust than absolute scoring; harder to interpret without enough samples

What good eval looks like in practice

Three things separate teams that actually improve their agents from teams that ship vibe-checked changes:

Frozen task sets. Don't let the eval drift. New tasks go in versioned suites; the old suite still runs and you watch the diff.

Cost as a first-class metric. A 2% accuracy gain that triples cost is rarely a win. Track tokens-per-task and dollar-per-task next to accuracy.

CI gating with a budget. Block merges that regress the canonical suite by more than X% or that exceed a cost ceiling. Not perfect, but stops the obvious own-goals.

When to pick which

Need	Pick
Managed, fastest to set up	Braintrust
Self-hostable, open source	Langfuse
Capability / safety benchmarks	Inspect
Under 50 tasks, one engineer	DIY + tracing

You can mix: Langfuse for tracing in production, Inspect for capability runs, a DIY harness for product-specific scorers. The frameworks are not mutually exclusive.

Frequently Asked Questions

How many eval tasks do I need to detect regressions?

[Inference] Roughly 30–50 well-chosen tasks across your top intents are enough to catch large regressions; finer-grained changes need 100–300. Quality of task selection matters more than raw count.

Should I use LLM-as-judge or programmatic scorers?

Use programmatic scorers wherever you can — they're cheaper, faster, and reproducible. Reach for LLM-as-judge for open-ended outputs where no rule captures "good," and validate the judge against human-labeled samples before trusting it.

Can I evaluate agents in production traffic?

Yes — sample a small fraction (1–5%) of production traces and run async scorers. Pair this with a canonical offline task set so you have both ecological validity and a stable regression signal.