Halo

Where is this feature available?

Hobby
Available
Core
Available
Pro
Available
Enterprise
Available
Self Hosted
Public Beta

Self-hosted: Halo is in public beta from version >=v4.28.0 and needs an instance-wide Langfuse AI model and a worker queue consumer. See self-hosted setup.

Halo is the Langfuse assistant for exploring your project data and taking actions in Langfuse with your approval. You can ask about traces, observations, sessions, and metrics, or ask it to create datasets, dashboards, and other resources. It brings the same Langfuse project tools available to external coding agents into the Langfuse UI.

What you can do

For work inside Langfuse, use Halo for the same investigation and resource-management tasks you would delegate to an external agent:

Goal	Example prompt
Investigate your data	"Which traces had the highest latency yesterday?"
Analyze errors at scale	"Fetch failed observations from the last 7 days and cluster the main errors."
Build a resource	"Create a dataset from last week's failed generations."
Monitor performance	"Build a dashboard of error rate and p95 latency for the last 7 days."
Navigate Langfuse	"Open the traces table filtered to today's errors."
Learn how Langfuse works	"How do I set up an evaluator?"

How it works

Halo uses the same Langfuse MCP server project tools that external agents can connect to, together with documentation search and sandboxed code execution. The in-app experience adds your current UI context (the trace, observation, session, or dashboard you are viewing, plus your name, browser language, and timezone), background execution, notifications, and approvals in Langfuse.

It can:

Query traces, observations, sessions, and metrics.
Process large observation sets with code in a sandbox.
Create or update datasets, dashboards, widgets, prompts, and score configs.
Link you directly to filtered views and relevant pages in Langfuse.

Action approvals

Halo pauses for your approval before changing data or configuration. Always approve lets a tool run without asking again in the current conversation. Halo is limited to your existing permissions in the current project. Approval does not grant additional access.

Background runs

Runs continue independently of your browser, so you can keep using Langfuse or start another conversation.

Conversation history

Halo conversations are saved per user and project so you can return to previous conversations.

Data privacy and security

Halo follows the same data handling model as other Langfuse AI features:

On Langfuse Cloud, model requests use Langfuse-managed Amazon Bedrock in your Langfuse data region. On self-hosted deployments, requests use the instance Langfuse AI model you configure.
Access is scoped to the signed-in user and current project.
AI feature tracing for product and service improvement can be enabled or disabled separately by organization admins and owners.

Limitations

Halo can misunderstand a question or return incomplete results. Verify important answers and review proposed actions before approving them.

Feedback

You can submit thumbs-up or thumbs-down feedback on Halo responses. If AI feature tracing is enabled, feedback may be used for product and service improvement. You can also share your thoughts in our feedback discussion.

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