Langfuse for Government
Observability and evaluations for public-sector AI, in your environment.
Build accountable AI.
Keep it under your control.
Open-source tracing and evaluations for government AI. Run it air-gapped, on-premises, or in a private cloud. Prompts, traces, and scores stay in your environment. You run the software.
- Your environment
- Open source
- Your traces
See what happened. Score what works.
You need to see how an agent reached an answer, score whether it is reliable, and change it without moving sensitive data out of your environment.
Trace every model call, tool invocation, retrieval step, and agent decision. Investigate failures with the full context of each request, session, model, prompt, latency, and cost.
Observability docs ↗Score outputs with LLM-as-a-judge, deterministic checks, human review, and user feedback. Turn production failures into datasets and regression tests before the next release.
Evaluation docs ↗Monitor quality, security scores, latency, and cost. Set thresholds and send alerts to webhooks, Slack, or GitHub Actions so you can respond before a bad run repeats.
Alerts docs ↗Run it where you already operate.
Langfuse runs behind a firewall, on a classified network, or in an approved cloud account. The product and data model stay the same.
Deploy Langfuse in a VPC, on premises, or in a fully air-gapped Kubernetes environment. Internet access is optional. Bring your own infrastructure, networking, storage, and operational controls.
Networking docs ↗The Langfuse repository is public. Tracing, evaluations, prompt management, experiments, and annotation are MIT-licensed, with no usage limits. Enterprise extensions live in marked directories and turn on with a license key.
Open-source licensing ↗Self-hosted Langfuse uses the same codebase and architecture as Langfuse Cloud. Asynchronous ingestion absorbs traffic spikes, events are persisted before processing, and background migrations reduce disruption during upgrades.
Architecture overview ↗Application traces create a detailed record of model calls and agent actions. Enterprise audit logs add immutable records of who changed what, when, and with which before-and-after state. SSO, role-based access control, SCIM, retention policies, and server-side data masking support centralized governance.
Audit logs ↗Keep data in your environment.
Application teams can debug and evaluate agents. Security teams keep telemetry, prompts, and evaluation data in the approved boundary.
For deployments with FIPS requirements, compliant Langfuse Docker images are available upon request.
Book a meeting↗Run the platform and its open-source dependencies in infrastructure you control.
Redact data in the SDK before transmission, or apply centralized ingestion masking in self-hosted Enterprise deployments.
Instrument with OpenTelemetry, or use Langfuse SDKs and integrations across models, frameworks, and languages.
Use versioned releases and deploy changes on your schedule.
Start locally. Deploy in production.
Run Langfuse locally with Docker Compose in minutes. The repository is public and the core is MIT-licensed. Move to Kubernetes or Terraform without changing the product or data model.
$ git clone https://github.com/langfuse/langfuse.git $ cd langfuse $ docker compose up
Run Langfuse in your environment.
Talk through tracing, evaluations, and self-hosting for government AI. Sensitive data stays in infrastructure you control.