OpenClaw AI Performance Guide
Reduce latency, cut API costs, and scale OpenClaw agents with caching, parallel execution, token optimization, and profiling tools.
Why Performance Matters
Every OpenClaw agent runs a Reason-Act loop: the agent calls the LLM, receives a decision, executes a tool, and feeds results back for the next reasoning step. In a typical task this loop runs 5–20 times. At $0.005 per 1 000 tokens with GPT-4o, a 10-step task consuming 2 000 tokens per step costs $0.10 per run. Multiply by thousands of daily tasks and costs compound quickly. Latency multiplies too: a 2-second LLM response time × 10 steps = 20 seconds end-to-end before any tool execution overhead.
OpenClaw addresses this at four levels: response caching eliminates redundant LLM calls for identical prompts; parallel execution runs independent tool calls concurrently instead of sequentially; token optimization reduces prompt size without degrading output quality; and model selection routes straightforward sub-tasks to cheaper, faster models. Together these techniques can cut latency by 5× and reduce costs by 90% in real-world workloads.
Key Performance Metrics
| Metric | Typical Baseline | Optimized Target | Primary Lever |
|---|---|---|---|
| Task completion latency | 20–60 seconds | 4–12 seconds | Caching + Parallel execution |
| Tokens per task | 10 000–50 000 | 2 000–10 000 | Prompt compression |
| Cost per 1 000 tasks | $5–$50 | $0.50–$5 | Model selection + Caching |
| Cache hit rate | 0% | 50–80% | Response cache config |
| Parallel speedup | 1× | 2–5× | Async tool execution |
Quick-Start: Minimal Performance Config
Add these settings to your openclaw.yaml to immediately activate the most impactful performance improvements:
performance:
cache:
enabled: true # Enable in-memory response cache
ttl: 3600 # Cache TTL in seconds (1 hour)
backend: memory # Use 'redis' for distributed deployments
parallel:
enabled: true # Parallelize independent tool calls
max_workers: 4 # Concurrent tool execution threads
tokens:
max_system_prompt: 500 # Trim system prompt aggressively
context_window: 8192 # Cap context to control costs
model_routing:
default: gpt-4o-mini # Use a fast/cheap model by default
complex_tasks: gpt-4o # Route only complex reasoning upAfter applying this config, run openclaw profile run "your task" to measure the before/after improvement. See the Profiling guide for a full walkthrough.
Sections
Performance Overview
Performance model, latency breakdown, cost factors, and quick-win checklist.
Read →Benchmarks
Official benchmark results across providers, task types, and hardware configurations.
Read →Response Caching
In-memory and Redis caching, TTL configuration, cache invalidation strategies.
Read →Parallel Execution
Run multiple tool calls concurrently, async agents, and task fan-out patterns.
Read →Token Optimization
Prompt compression, context trimming, model selection, and cost monitoring.
Read →Profiling
Built-in profiler, flame graphs, slow-tool detection, and optimization workflow.
Read →Provider Benchmark Comparison
Performance varies significantly across LLM providers. The table below shows representative numbers for a standard 10-step research task (measured March 2026):
| Provider / Model | Avg Latency / Step | Cost / 1K Tokens | Best For |
|---|---|---|---|
| GPT-4o | 1.8 s | $0.005 | Complex multi-step reasoning |
| GPT-4o-mini | 0.7 s | $0.00015 | Routine tasks, cost optimization |
| Claude 3.5 Sonnet | 1.4 s | $0.003 | Long-context, code review |
| Gemini 1.5 Flash | 0.9 s | $0.00035 | High-volume, fast turnaround |
| Llama 3.1 (Ollama) | 0.4 s | $0 (local) | Dev/test, private data |
See the full Benchmarks page for hardware-specific results and multi-agent scenarios.
Quick Wins
| Technique | Typical Savings | Effort |
|---|---|---|
| Enable response caching | 50–90% on repeated calls | Low |
| Use gpt-4o-mini instead of gpt-4o | ~15× cost reduction | Low |
| Parallelize independent tool calls | 2–5× speed improvement | Medium |
| Trim system prompt to <500 tokens | 10–20% cost reduction | Low |
| Use local LLM (Ollama) for dev/test | 100% API cost reduction | Medium |