Braintrust

AI evaluation platform API for running structured LLM evals, managing prompt versions and datasets, and scoring model outputs against golden references — designed to make eval-driven LLM development systematic.

Evaluated Mar 07, 2026 (0d ago) vcurrent
Homepage ↗ Repo ↗ AI & Machine Learning braintrust evals llm-evaluation prompt-management datasets ai-testing scoring
⚙ Agent Friendliness
58
/ 100
Can an agent use this?
🔒 Security
80
/ 100
Is it safe for agents?
⚡ Reliability
79
/ 100
Does it work consistently?

Score Breakdown

⚙ Agent Friendliness

MCP Quality
--
Documentation
82
Error Messages
78
Auth Simplicity
82
Rate Limits
65

🔒 Security

TLS Enforcement
100
Auth Strength
78
Scope Granularity
60
Dep. Hygiene
80
Secret Handling
80

HTTPS enforced. Single org-level API key with no scope granularity is a risk — key compromise exposes all projects. SOC 2 compliance for cloud. No self-hosted option currently available.

⚡ Reliability

Uptime/SLA
80
Version Stability
80
Breaking Changes
78
Error Recovery
78
AF Security Reliability

Best When

You are building LLM-powered features and need a rigorous, reproducible way to evaluate and compare prompt changes or model updates before shipping.

Avoid When

You need a lightweight observability solution with minimal setup — Braintrust's eval-centric workflow requires deliberate dataset and eval design to be useful.

Use Cases

  • Running automated evals against a dataset of golden input/output pairs to catch regressions before deploying a new prompt
  • Comparing outputs from two different models or prompt versions on the same dataset to pick the better performer
  • Storing and versioning evaluation datasets that grow over time as new failure cases are captured
  • Integrating LLM quality gates into CI/CD pipelines using the Braintrust SDK in test suites
  • Using LLM-as-judge scoring functions to evaluate free-form model responses at scale

Not For

  • Infrastructure or application performance monitoring (this is specifically for LLM output quality evaluation)
  • Real-time production tracing with sub-millisecond overhead (Helicone or Langfuse are better fits)
  • Teams without a defined evaluation methodology — eval platforms require upfront investment in dataset and scorer design

Interface

REST API
Yes
GraphQL
No
gRPC
No
MCP Server
No
SDK
Yes
Webhooks
No

Authentication

Methods: api_key
OAuth: No Scopes: No

Single API key per organization, set via BRAINTRUST_API_KEY environment variable. No fine-grained scopes — all keys have full org-level access.

Pricing

Model: freemium
Free tier: Yes
Requires CC: No

Pricing scales with logged rows (eval results, traces). Free tier sufficient for small-scale experimentation.

Agent Metadata

Pagination
cursor
Idempotent
Partial
Retry Guidance
Not documented

Known Gotchas

  • Experiment names must be unique per project — agents generating experiment names should include timestamps or UUIDs to avoid conflicts
  • Dataset rows use external_id for deduplication — agents must set this consistently to avoid duplicate test cases
  • Scorer functions run server-side but are defined client-side — versioning scorers separately from evals adds complexity
  • The SDK's Eval() function is blocking — long-running evals on large datasets will block agent execution
  • API key has full org access — treat as a high-privilege credential and avoid embedding in agent prompts or logs

Alternatives

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Scores are editorial opinions as of 2026-03-07.

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Packages Evaluated
26150
Need Evaluation
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