For AI agents: a documentation index is available at the root level at /llms.txt and /llms-full.txt. Append /llms.txt to any URL for a page-level index, or .md for the markdown version of any page.
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User GuideDeveloper GuidesAPI Reference
User GuideDeveloper GuidesAPI Reference
  • Getting Started
    • What is Runtype?
    • Creating your account
    • Platform keys vs. BYOK
    • Understanding the Runtype UI
    • Quickstart: Social Media Post Generator
    • Quickstart: From Agent to Chat Widget
  • Dashboard
    • What is the Dashboard?
    • Daily executions
  • Playground
    • What is the Playground?
  • Products & Surfaces
    • What are Products?
    • What are Surfaces?
    • Creating a product
    • Setting up a chat surface
    • Setting up an API surface
    • Setting up an MCP surface
    • Setting up an A2A surface
    • Setting up a Slack surface
    • Setting up a webhook surface
    • MCP authentication
    • Authenticating with product API keys
    • Embedding the chat widget (script tag)
    • Embedding the chat widget (React)
    • Surface orchestration modes
    • Product views
    • Adding capabilities to a product
    • Connecting external agents
    • How A2A works
    • Connecting to MCP clients
    • Scoping API keys to capabilities
    • Auto-generated OpenAPI spec
    • Calling your API endpoints
    • Client tokens and domain restrictions
    • AI-powered theme generation
    • Widget theming and customization
    • Product versioning and status
  • Flows
    • What are Flows?
    • Creating and editing flows
    • Flow step types overview
    • Agent and flow templates
    • Using prompt steps
    • Using transform-data steps
    • Using conditional steps
    • Using fetch-url and api-call steps
    • Using record steps (upsert/retrieve)
    • Flow variables and templates
    • Flow versioning and publishing
    • Running flows in batch
    • Handling batch failures
    • Debugging flows
  • Agents
    • What are Agents?
    • Creating and configuring agents
    • Agent tools
  • Records
    • What are Records?
    • Creating and managing records
    • Using records in flows
    • Filtering and searching records
  • Tools
    • What are Tools?
    • Built-in tools
    • Creating custom tools
    • Creating external tools
    • Runtime tools
  • Evals
    • What are Evals?
    • Running an eval
    • Interpreting eval results
  • Schedules
    • What are Schedules?
    • Automating batch processing
  • Logs
    • What are Logs?
    • Working with logs
  • Integrations
    • Connecting AI model providers
    • Slack integration
    • Google Workspace integration
    • GitHub integration
    • Linear integration
    • Weaviate (vector search)
    • Firecrawl (web scraping)
    • Exa (web search)
    • Braintrust (tracing)
  • Settings
    • What's in Settings?
    • Available AI models
    • What are Organizations?
    • Managing AI models
    • Managing API keys
    • Managing secrets
    • Billing and plans
    • Usage data
    • Team members and permissions
    • Appearance and preferences
    • Integrations (PostHog, Weaviate, Daytona)
  • Troubleshooting & FAQ
    • FAQ
    • Rate limits and usage
    • Managing Runtype with Claude
    • Agent skills
    • Flow execution failures
    • Common errors and solutions
    • Authentication issues
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On this page
  • Why logs matter
  • Accessing logs
  • What logs capture
  • Log retention
  • Next steps
Logs

What are Logs?

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Working with Logs

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Logs give you a detailed record of every Flow and Agent execution in your workspace. Each log entry captures when an execution started, what steps ran, what data flowed between them, and whether it succeeded or failed.

Why logs matter

When a Flow produces unexpected output or an Agent takes a wrong turn, logs are the first place to look. They show you exactly what happened at each step so you can pinpoint the issue without guessing.

Logs also help you understand how your Flows and Agents behave over time. You can spot patterns like consistently slow steps, recurring failures on specific inputs, or unexpected model outputs.

Accessing logs

Go to Logs in the sidebar under Manage. The log list shows recent executions with the Flow or Agent name, status, duration, and timestamp.

Click any row to open the full execution detail, which shows each step’s input, output, and timing.

What logs capture

Each log entry includes:

  • Status — whether the execution completed successfully, failed, or is still running.
  • Duration — how long the execution took from start to finish.
  • Step details — the input and output for each step in the Flow, including prompt responses, transform results, and tool calls.
  • Error information — if a step failed, the error message and which step caused the failure.
  • Metadata — the Record processed (if any), the model used, and token counts for prompt steps.

Log retention

Logs are retained based on your plan. Check your plan details in Settings → Billing for the retention period that applies to your workspace.

Next steps

  • Working with Logs — View execution details, filter and search logs, and export log data.
  • Debugging flows — Troubleshoot Flow execution issues.
  • What are Flows? — Understand the workflows that generate log entries.