Observability & traceability
German open-source AI engineering platform whose traces record LLM and non-LLM calls, retrieval, embeddings and API calls, grouped into sessions for multi-turn conversations and agent graphs. Paid tiers add audit logs, fine-grained RBAC, SSO/SCIM, retention management, client-side masking and EU, US or JP data regions.
hybrid · generally available · Research snapshot 2026-09-06
Visit the official product source ↗Observability & traceability · Evaluation & testing
Useful conversation with: AI platform lead, AI engineer, Compliance-minded engineering manager.
Show me a session trace for a multi-turn agent conversation, then show the audit log of who accessed or changed that project.
Support labels reflect the supplied research. Documentation and vendor claims are not independent product tests. “Not established” means the researcher did not find support; it does not prove a capability is absent.
Documented by provider
Traces include all LLM and non-LLM calls such as retrieval, embedding and API calls, support tracking multi-turn conversations as sessions plus user tracking, and can represent agents as graphs, with cost and latency shown in a timeline view.
Limit: Docs overview does not explicitly name tool calls or multi-agent handoffs as distinct span types.
Documented by provider
Access control is organised around users, organizations and projects with Owner, Admin, Member, Viewer and None roles, project-level roles overriding organization roles, and users only able to assign roles at or below their own.
Limit: The RBAC page does not state which plans include fine-grained project roles.
Documented by provider
Pricing documents audit logs and SCIM for Enterprise, organization- and project-level plus fine-grained RBAC, enterprise SSO with enforcement, data retention management, client-side data masking, SOC2 Type II and ISO27001 reports, a GDPR data processing agreement, and US, EU, JP or HIPAA data regions.
Limit: Pricing-page feature lists do not describe audit-log contents, immutability or retention granularity, and compliance reports are referenced rather than published.
Documented by provider
Langfuse runs managed LLM-as-a-judge evaluations on production or development traces, supports code evaluators, custom pipelines, manual labelling and human annotation queues, and dataset experiments.
Limit: Judge accuracy and calibration are not addressed.
Listing does not imply partnership, supplier status, a working DutyGraph integration, or a compliance certification.
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