Log History
Monitor your Fetch Hive interactions with log history
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Monitor your Fetch Hive interactions with log history
Last updated
Was this helpful?
Log history in Fetch Hive provides comprehensive insights into all interactions across your workspace. Whether you're debugging issues, optimizing performance, or analyzing usage patterns, the log history gives you detailed information about every operation performed within the platform.
You can access logs for the following resources:
Prompts: View all executions of your AI prompts
Endpoints: Track usage of both Prompt and Workflow endpoints
Workflows: View all executions of your Workflow steps
Datasets: Monitor dataset operations and updates
Agents: Review agent interactions and responses
Fine Tuning: Track fine-tuning job progress and results
Each log entry provides extensive information about the interaction, including:
Output: The result or response from the interaction
Inputs: Parameters and data provided to trigger the interaction
Credit Usage: Amount of credits consumed
Duration: Time taken to complete the operation
Provider: The service provider used (e.g., Exa, Google Search, OpenAI, Claude)
Owner: User who initiated the interaction
Status: Current state of the operation (e.g., Completed, Error, Running)
Timestamp: When the interaction occurred
For AI Prompt Steps or Prompts, additional information includes:
Model: The specific AI model used
Tokens: Number of tokens consumed
LLM Charge: Cost associated with the language model usage
Workflow steps may include additional custom data fields depending on the step type, providing deeper insights into the operation.
Identify failed operations and their error messages
Review input parameters that led to specific outcomes
Track the sequence of events in complex workflows
Monitor response times and resource usage
Analyze token consumption patterns
Identify opportunities for cost optimization
Log history is invaluable for:
Comparing different versions of prompts or workflows
Understanding which approaches yield better results
Fine-tuning parameters based on historical performance
Validating changes before deploying to production
Track usage patterns across different resources
Monitor credit consumption by resource type
Analyze user engagement and adoption
Navigate to the respective resource section (Prompts, Endpoints, Datasets, etc.)
Click Activity Logs in the top right of the navbar
Regular Monitoring: Review logs periodically to catch issues early
Performance Tracking: Use log data to optimize resource usage
Documentation: Reference logs when documenting successful patterns
Iteration: Use historical data to inform improvements
Cost Management: Monitor credit usage to optimize expenses
