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beam-debug-issue-tasks

Diagnose failed Beam.ai agent tasks by cross-referencing metadata with Langfuse traces.

2|1|Updated Dec 30, 2025
Search Tags:#langfuse#workflow-debugging#trace-analysis#beam-ai#task-debugging#agent-errors#failed-tasks
One-click install
npx skills add https://github.com/abdullahbeam/nexus-design-abdullah --skill beam-debug-issue-tasks-abdullahbeam
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: beam-debug-issue-tasks
Source: https://github.com/abdullahbeam/nexus-design-abdullah/tree/main/00-system/skills/beam/beam-debug-issue-tasks
Command: npx skills add https://github.com/abdullahbeam/nexus-design-abdullah --skill beam-debug-issue-tasks-abdullahbeam

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires requests, python-dotenv, and includes scripts (resource) components.

What problem does it solve?

This Skill eliminates the tedious manual work of sifting through Beam.ai task logs and Langfuse traces to identify why agent tasks failed, stopped, or errored, cutting down debugging time for engineering and agent operations teams.

Core Features & Use Cases

  • Issue Task Aggregation: Pull all tasks with error, failure, stop, timeout, or missing input statuses from Beam.ai BID or prod workspaces for any custom lookback period.
  • Langfuse Trace Analysis: Fetch corresponding execution traces to extract root causes from agent reasoning, parameter selection logic, routing decisions, and error spans.
  • Structured Debug Reports: Generate scannable Smart Brevity reports saved to your workspace's agent debug folder for documentation, team handoff, or trend analysis of recurring failure patterns.
  • Use Case: If your customer support agent is repeatedly failing on tasks due to missing input parameters, use this skill to pull all failed tasks from the last 7 days, analyze their traces to confirm the root cause, and save reports to share with your product team.

Quick Start

Use the beam-debug-issue-tasks skill to list all failed tasks for your Beam agent from the last 7 days, analyze their Langfuse traces for root causes, and save debug reports to your workspace.

Frequently Asked Questions about beam-debug-issue-tasks

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I debug failed Beam.ai agent tasks using Langfuse traces?▼

To debug failed Beam.ai agent tasks, cross-reference task metadata with Langfuse execution traces to extract root causes from agent reasoning, parameter selection logic, routing decisions, and error spans. This diagnostic approach identifies parameter mismatches, routing errors, timeout issues, and missing user input.

What is the best way to find root causes of stopped or errored Beam.ai tasks?▼

Finding root causes of stopped or errored Beam.ai tasks involves aggregating issue tasks and fetching corresponding Langfuse execution traces. Analyzing these traces reveals specific failures in agent reasoning, parameter selection, and routing decisions that caused the task to stop or error.

Can I pull failed tasks from a specific lookback period in Beam.ai workspaces?▼

Yes, you can pull all tasks with error, failure, stop, timeout, or missing input statuses from Beam.ai BID or prod workspaces for any custom lookback period. This issue task aggregation supports targeted debugging over specific timeframes to identify recurring failure patterns.

Do I need Langfuse integration enabled to diagnose Beam.ai task failures?▼

Yes, Langfuse integration must be enabled on target agents to diagnose Beam.ai task failures. The diagnostic process requires fetching Langfuse execution traces to analyze agent reasoning and error spans, alongside Beam.ai API credentials for BID and prod workspaces.

How do I generate debug reports for Beam.ai agent workflow failures?▼

You generate debug reports for Beam.ai agent workflow failures by analyzing Langfuse traces and saving structured Smart Brevity reports to your workspace's agent debug folder. These scannable reports document root causes for team handoff or trend analysis of recurring failure patterns.

What Python dependencies are required for Beam.ai task debugging with Langfuse?▼

Beam.ai task debugging with Langfuse requires the Python dependencies requests and python-dotenv. These libraries fetch and parse trace and task data by interfacing with Beam.ai API credentials and Langfuse integration to extract root causes from execution traces.

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