lindy-cost-tuning

Lindy Cost Tuning

Overview

Optimize Lindy AI costs by managing active agent count, consolidating automations, and monitoring per-agent execution frequency. Lindy uses per-agent pricing where each active agent incurs a monthly cost regardless of how often it runs.

Prerequisites

• Lindy Team or Enterprise workspace
• Admin access to agent management and billing
• Understanding of current agent portfolio

Instructions

Step 1: Audit Agent Utilization

Step 2: Consolidate Similar Agents

Step 3: Deactivate Underused Agents

Step 4: Optimize Agent Step Efficiency

Reduce per-run costs by minimizing the number of tool calls in each agent:

• Combine multiple LLM calls into a single prompt with structured output
• Cache frequently accessed data (e.g., company directory) as agent context
• Use conditional branching to skip unnecessary steps

Step 5: Monitor Monthly Spend

For detailed implementation code and configurations, load the reference guide: Read(${CLAUDE_SKILL_DIR}/references/implementation-guide.md)

Error Handling

Issue	Cause	Solution
Agent deactivated but still billed	Billing cycle overlap	Check billing date, deactivate before cycle end
Consolidated agent too complex	Too many branches	Split into 2-3 focused agents instead of 5+ single-task ones
Agent runs increasing cost	Trigger firing too frequently	Adjust trigger schedule or add deduplication
Cannot reduce below N agents	Business dependency	Document which agents are critical, optimize the rest

Examples

Basic usage: Apply lindy cost tuning to a standard project setup with default configuration options.

Advanced scenario: Customize lindy cost tuning for production environments with multiple constraints and team-specific requirements.

Output

• Configuration files or code changes applied to the project
• Validation report confirming correct implementation
• Summary of changes made and their rationale

See optimization implementation details for output format specifications.

Resources

• Official optimization documentation
• Community best practices and patterns
• Related skills in this plugin pack