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Traccia

company · India

Scores

1 product on the map — 1 open.

Traccia

Openness

5 medium confidence
5.0
license
Apache-2.0(OSI, single root LICENSE)
source
public(the whole tracing product - init/observe, automatic instrumentation, all listed exporters (Grafana Tempo, Jaeger, Zipkin, SigNoz, console, file) - ships and runs with no account)
core features withheld
no — prompt-registry fetch (load_prompt) and persisted evaluation experiments call the hosted app.traccia.ai platform, but that platform is a separate product sold beside the SDK, not a withheld part of the tracing SDK itself

Traccia's tracing, instrumentation and offline-evaluation core is a complete, self-contained Apache-2.0 SDK that exports over OTLP to any collector with no account required. Its prompt registry and persisted evaluation experiments require a hosted-platform API key with no self-hostable equivalent, but that platform is a separate product sold beside the SDK rather than a paid tier carved from it.

Adoption

1 high confidence
1.0

Adoption is measured by monthly PyPI downloads of the traccia package, the SDK itself rather than a client for the hosted platform, so its install count is the product's own usage. The repository was created in January 2026, and its GitHub stars are consistent with a very young product still building an audience.

Capability

4 medium confidence
4.0

Traccia's tracing, prompt management and evaluation-with-experiments together cover every dimension this category grades, and its governance and compliance evidence layer is a genuine addition over Langfuse. That keeps it level with Langfuse, though the prompt-management and persisted-evaluation halves depend on the hosted platform rather than shipping fully self-contained.

  • https://raw.githubusercontent.com/traccia-ai/traccia-py/main/README.md recorded 2026-09-02

    Feature list: automatic instrumentation, framework integrations, LLM-aware tracing (tokens, costs, prompts, completions, latency), guardrail detection, "AI Governance and Compliance: EU AI Act and HIPAA-oriented evidence", prompt management via `load_prompt`/`prefetch_prompts`, and offline evaluation via `evaluate()` running a task and scorers over a dataset.