AI observability platform for tracing, evals, datasets, and production quality loops
Braintrust documents an AI observability platform at braintrust.dev where teams instrument applications to capture traces (inputs, outputs, latency, token usage, nested tool calls), analyze logs, annotate with human feedback, run experiments and scorers, and iterate on prompts before deployment. Official docs describe a workflow spanning Instrument → Observe → Annotate → Evaluate → Deploy, with auto-instrumentation for major providers (OpenAI, Anthropic, Gemini, Bedrock, Azure, and others listed in the integrations directory) and frameworks such as LangChain, LangGraph, Vercel AI SDK, and Pydantic AI. Span types documented include task, llm, function, tool, and score spans, each capturing metrics and metadata for debugging and building evaluation datasets.
Use cases
- Debug agent regressions by comparing production traces before and after prompt changes
- Build evaluation datasets from real user sessions annotated in Braintrust
- Run offline experiments with scorers before shipping model or routing updates
- Monitor token usage and latency bottlenecks across nested LLM and tool spans
- Establish a closed-loop quality process linking logs, evals, and deployments
Key features
- Tracing quickstart and auto-instrumentation guides for LLM providers and agent frameworks
- Span hierarchy with documented types (task, llm, function, tool, score) and nested execution capture
- Logs UI for filtering production traces and identifying failure patterns
- Datasets and human annotation workflows feeding offline evaluations
- Experiments, scorers, and playground tooling described in evaluate/deploy sections
Who Is It For?
- ML and AI engineers shipping LLM features to production
- Platform teams needing centralized trace storage and eval pipelines
- Product groups measuring quality beyond single-request accuracy
Frequently Asked Questions
- Is Braintrust only for offline benchmarks?
- Docs position production tracing (Observe) and offline evals (Evaluate) as complementary stages in the same workflow.
- Which frameworks are supported?
- The integrations directory lists major providers and agent frameworks; setup guides vary by stack.
- How do scorers relate to logs?
- Documentation describes score spans for online scoring in logs and offline scorers in experiments.
Related
Related
3 Indexed items
LangSmith
LangSmith is LangChain's hosted and self-hostable platform for tracing, monitoring, and improving LLM applications. Official documentation at docs.langchain.com describes instrumenting apps via environment variables, framework integrations (OpenAI, Anthropic, CrewAI, Vercel AI SDK, Pydantic AI, and others listed on the integrations page), or the LangSmith SDK so teams can inspect multi-step runs, compare prompt versions, build datasets, run offline and online evaluations, configure automations, and collect feedback queues—without assembling bespoke analytics for agent loops.
Weights & Biases (W&B)
Weights & Biases sells W&B, a cloud-hosted developer platform outlined at docs.wandb.ai where machine-learning practitioners instrument training jobs with first-party SDKs (`wandb`), stream scalars/media/system telemetry into hosted dashboards, collaborate through shared projects/workspaces, and manage hyperparameter Sweeps orchestrated according to Sweeps YAML plus controller policies described in vendor documentation rather than improvised spreadsheets. Companion guides publish patterns for versioning datasets/models through Artifacts, linking reproducible checkpoints plus evaluation payloads, emitting reports, tying runs to notebooks, integrating with prevalent PyTorch/Keras/JAX/Hugging Face/higher-level trainers, monitoring production inference where product SKUs advertise it, and upgrading team security controls—all scoped to whichever features your organization enables on wandb.ai.
Helicone
Helicone documents an AI Gateway at ai-gateway.helicone.ai that lets teams call 100+ models from OpenAI, Anthropic, Google, Groq, and other vendors through an OpenAI-compatible base URL while logging every request to the Helicone dashboard. Official quickstart guides show signing up at helicone.ai, creating API keys in the US control plane, and pointing standard OpenAI SDK clients at the gateway with automatic observability. Helicone states credits carry 0% markup versus provider list prices, support automatic fallbacks when a provider is down, and allow bringing your own provider keys instead of using Helicone-managed credentials.