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Braintrust

by Braintrust Data, Inc. · Platform · also known as Braintrust Data, braintrust.dev

Hosted observability and evaluation platform for AI agents with tracing, experiments, pattern detection, a CLI, MCP access and an AI assistant (Loop) that analyzes traces and builds evals

MixedFreemiumPaidEnterpriseCloud
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Link verified Nov 2, 2026

Overview

Braintrust is a commercial platform for instrumenting, understanding and improving AI agents. Applications send traces through open-source SDKs (TypeScript and Python, Apache-2.0) or the bt CLI; the platform then supports experiments that compare prompts and models, datasets built from human annotation, and a Patterns feature that surfaces recurring problems in traces with suggested fixes. Loop, an embedded agent, can analyze data and generate evaluations on request, and an MCP server exposes Braintrust data and actions to coding assistants. The workflow is organized as Instrument, Observe, Annotate, Evaluate, Deploy and Admin.

Plans are Starter (entry tier), Pro and Enterprise, differing in retention, included processing and governance features; Enterprise adds SAML SSO, custom retention and on-premises or hosted deployment for sensitive data. The SDKs are open source but the platform is proprietary and hosted, so self-hosting is an enterprise arrangement rather than a download. Braintrust is a strong fit for teams who want evals and production traces in one place with a polished UI; those needing a free self-hosted stack typically look at Langfuse.

Editorial note on uncertainty. Self-hosting is described only as an Enterprise on-prem option, so self_hostable is left null. Starter is the entry plan; the pricing page does not label any tier as free.

Capabilities

  • Tracing of agent runs with pattern detection
  • Experiments comparing prompts and models with scorers
  • Datasets built from annotation and production data
  • Loop AI assistant for analysis and eval generation
  • `bt` CLI and MCP server for tool integration
  • Open-source TypeScript and Python SDKs

Typical workflows

  1. Instrument an agent and triage recurring failures from Patterns
  2. Run an experiment across model versions before shipping
  3. Ask Loop to draft evaluators from annotated traces

Strengths

  • Evals and observability tightly coupled
  • Open-source SDKs
  • Agent-assisted analysis built in

Limitations

  • Proprietary hosted platform
  • Self-hosting only on Enterprise
  • Retention and processing metered by plan

Prerequisites

  • Braintrust account and API key
  • TypeScript or Python application to instrument

Who it is for

  • Product teams shipping LLM features
  • AI engineers running systematic evals

Alternatives & comparison

  • Langfuse

    Open-source LLM observability and evaluation platform: tracing, LLM-as-judge and human evals, prompt management, datasets and cost dashboards, self-hostable or as Langfuse Cloud

    Open sourceOpen source
  • LangSmith

    LangChain's hosted platform for tracing, evaluating and deploying LLM applications and agents, with prompt tooling, an LLM gateway and cloud, hybrid or self-hosted options

    ProprietaryFreemium
  • promptfoo

    Open-source, local-first CLI and library for evaluating prompts, models, RAG pipelines and agents, with automated red teaming and CI integration

    MixedOpen source
  • Ragas

    Open-source Python library for evaluating LLM and RAG applications with LLM-driven and custom metrics, experiments, dataset management and result tracking

    Open sourceOpen source
  • Weights & Biases

    ML experiment tracking, sweeps, artifacts and model registry plus Weave for LLM tracing and evaluation, now sold by CoreWeave within its Forge platform with a free academic tier

    MixedFreemium
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Sources & verification

Primary sources consulted for this record. Claims are tied to the fields each source supports; the automated link checker re-verifies every URL on a schedule.

Confidence
Medium
Reviewed
Nov 1, 2026
Links
Verified · Nov 2, 2026

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