--- title: Halo description: Use the same Langfuse project tools as external coding agents, directly from the Langfuse UI. sidebarTitle: Halo --- # Halo **Where is this feature available?** | Plan | Availability | | --- | --- | | 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](/self-hosting/configuration/halo). 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 [#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 [#how-it-works] Halo uses the same [Langfuse MCP server](/docs/api-and-data-platform/features/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 [#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 [#background-runs] Runs continue independently of your browser, so you can keep using Langfuse or start another conversation. ### Conversation history [#conversation-history] Halo conversations are saved per user and project so you can return to previous conversations. ## Data privacy and security [#data-privacy-and-security] Halo follows the same data handling model as other [Langfuse AI features](/security/ai-features): - On Langfuse Cloud, model requests use Langfuse-managed Amazon Bedrock in your [Langfuse data region](/security/data-regions). 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 [#limitations] Halo can misunderstand a question or return incomplete results. Verify important answers and review proposed actions before approving them. ## Feedback [#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](https://github.com/orgs/langfuse/discussions/14196). ## Related resources [#related-resources] - [Self-hosted Halo setup](/self-hosting/configuration/halo) - [AI Feature Security](/security/ai-features) - [Langfuse MCP Server](/docs/api-and-data-platform/features/mcp-server) - [Langfuse Agent Skill](/docs/api-and-data-platform/features/agent-skill) - [Halo announcement](/changelog/2026-06-19-langfuse-assistant-public-beta) - [Share Halo feedback](https://github.com/orgs/langfuse/discussions/14196) --- ## Agent Instructions This page is part of the [Langfuse](https://langfuse.com) documentation, published as plain Markdown for AI agents. Every page is available as Markdown by appending `.md` to its URL, or by sending an `Accept: text/markdown` header. This page: `https://langfuse.com/docs/halo.md`. ### Querying these docs If the answer is not on this page, query the documentation instead of guessing: - **Semantic search** across all Langfuse docs, returning an answer with the relevant pages and excerpts. Ask a specific, self-contained question: ```bash curl -sG "https://langfuse.com/api/search-docs" --data-urlencode "query=How do I trace a LangGraph agent?" ``` - **Index of every page**: , with per-section indexes [llms-docs.txt](https://langfuse.com/llms-docs.txt), [llms-integrations.txt](https://langfuse.com/llms-integrations.txt), and [llms-self-hosting.txt](https://langfuse.com/llms-self-hosting.txt). ### Before writing Langfuse code - **Install the [Langfuse Agent Skill](https://langfuse.com/docs/api-and-data-platform/features/agent-skill).** It encodes Langfuse's own best practices for instrumentation, prompt management, and evaluation, and materially improves results. - **Read [What does a good trace look like?](https://langfuse.com/docs/observability/best-practices.md)** before instrumenting an application. - **Verify endpoints, parameters, and response fields** against the [API reference](https://api.reference.langfuse.com) instead of inferring them from code examples. - **Use the [Langfuse CLI](https://langfuse.com/docs/api-and-data-platform/features/cli)** (`npx @langfuse/cli api `) to read or write traces, prompts, datasets, and scores from the terminal. 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