Lindy Performance Tuning Skill

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

Next Steps

Proceed to lindy-cost-tuning for budget optimization.

Agent Skills: Lindy Performance Tuning

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