lindy-performance-tuning

name	lindy-performance-tuning
description	Optimize Lindy AI agent execution speed, reliability, and cost efficiency. Use when agents are slow, consuming too many credits, or producing inconsistent results. Trigger with phrases like "lindy performance", "lindy slow", "optimize lindy", "lindy latency", "lindy speed".
allowed-tools	Read, Write, Edit
version	1.15.0
license	MIT
author	Jeremy Longshore <[e-mail]>
tags	["saas","lindy","performance"]
compatibility	Designed for Claude Code

Lindy Performance Tuning

Overview

Lindy agents execute as multi-step workflows where each step (LLM call, action execution, API call, condition evaluation) adds latency and credit cost. Optimization targets: fewer steps, smaller models, faster actions, tighter prompts.

Prerequisites

• Lindy workspace with active agents
• Access to agent Tasks tab (view step-by-step execution history)
• Understanding of agent workflow structure

Instructions

Step 1: Profile Agent Execution

In the Tasks tab, open a completed task and review:

• Total task duration: Baseline for improvement
• Per-step timing: Identify the slowest steps
• Credit consumption: Which steps cost the most
• Step count: Total actions executed per task

Common bottlenecks:

Bottleneck	Symptom	Fix
Large model on simple task	High credit cost, slow	Switch to Gemini Flash
Too many LLM steps	Long total duration	Consolidate into fewer steps
Agent Step with many skills	Unpredictable path	Reduce to 2-4 focused skills
Knowledge Base over-querying	Multiple KB searches	Increase Max Results per query
Sequential when parallel possible	Unnecessary waiting	Use loop with Max Concurrent > 1

Step 2: Right-Size Model Selection

The single biggest performance lever. Match model to task complexity:

Task	Recommended Model	Speed	Credits
Route email to category	Gemini Flash	Fast	~1
Extract fields from text	GPT-4o-mini	Fast	~2
Draft short response	Claude Sonnet	Medium	~3
Complex multi-step analysis	GPT-4 / Claude Opus	Slow	~10
Simple phone call	Gemini Flash	Fast	~20/min
Complex phone conversation	Claude Sonnet	Medium	~20/min

Rule of thumb: Start with the smallest model. Only upgrade if output quality is insufficient. Most classification and routing tasks work fine with Gemini Flash.

Step 3: Consolidate LLM Steps

Before (3 LLM calls, ~9 credits):

Step 1: Classify email (LLM)
Step 2: Extract key entities (LLM)
Step 3: Generate response (LLM)

After (1 LLM call, ~3 credits):

Step 1: Classify, extract entities, and generate response (single LLM prompt)

Consolidated prompt:

Analyze this email and return JSON with:
1. "classification": one of [billing, technical, general]
2. "entities": {customer_name, product, issue_type}
3. "draft_response": professional reply under 150 words

Email: {{email_received.body}}

Step 4: Use Deterministic Actions Where Possible

Replace AI-powered fields with Set Manually mode when values are predictable:

Field	Instead of AI Prompt	Use Set Manually
Slack channel	"Post to the support channel"	#support-triage
Email subject	"Create an appropriate subject"	[Ticket] {{email_received.subject}}
Sheet column	"Determine the right column"	Column A

Each Set Manually field saves one LLM inference (~1 credit).

Step 5: Optimize Knowledge Base Queries

• Max Results: Set to the minimum needed (default 4, max 10)

• Search Fuzziness: Keep at 100 (semantic) unless precision matching needed

• Query mode: Use AI Prompt with specific instructions:

  Search for the customer's specific product issue.
  Focus on: {{extracted_entities.product}} {{extracted_entities.issue_type}}
  

  Not: "Search for relevant information" (too vague, wastes results)

Step 6: Optimize Trigger Filters

Prevent wasted runs with precise trigger filters:

Before: Email Received (all emails) → 200 runs/day → 600 credits
After:  Email Received (label: "support" AND NOT from: "noreply@")
        → 30 runs/day → 90 credits (85% savings)

Step 7: Use Agent Steps Judiciously

Agent Steps (autonomous mode) are powerful but expensive — the agent may take unpredictable paths and use more actions than a deterministic workflow.

Use Agent Steps when: Next steps are genuinely uncertain (complex research, multi-source investigation, adaptive problem-solving)

Use deterministic actions when: Steps are predictable (classify -> route -> respond)

When using Agent Steps:

• Limit available skills to 2-4
• Set clear, measurable exit conditions
• Include a fallback exit condition to prevent infinite loops
• Monitor credit consumption of first 10 runs to establish baseline

Step 8: Loop Optimization

For batch processing, configure loops for efficiency:

• Max Concurrent: Increase for independent items (parallel execution)
• Max Cycles: Always set a cap to prevent runaway processing
• Only pass essential data as loop output (not full context)

Performance Baseline Reference

Agent Type	Expected Duration	Expected Credits
Simple router (1 LLM + 1 action)	2-5 seconds	1-2
Email triage (classify + respond)	5-15 seconds	3-5
Research agent (search + analyze)	15-60 seconds	5-15
Multi-agent pipeline	30-120 seconds	10-30
Phone call	Real-time	~20/min

Error Handling

Issue	Cause	Solution
Agent timeout	Too many sequential steps	Consolidate steps, reduce skill count
High credit burn	Large model + many steps	Downgrade model, merge LLM calls
Inconsistent output	Agent Step choosing different paths	Switch to deterministic workflow
KB search slow	Large knowledge base	Reduce fuzziness, increase specificity
Loop runs too long	High max cycles, low concurrency	Increase Max Concurrent, lower Max Cycles

Resources

• Lindy Prompt Guide
• Agent Steps
• Lindy Documentation

Next Steps

Proceed to lindy-cost-tuning for budget optimization.