How to Run OpenClaw Agents for Under $10 a Month

The Cost Problem Nobody Warns You About

When you first set up OpenClaw, the default path is to throw Claude Sonnet or GPT-4o at every agent. It works great. Your PM agent coordinates beautifully, your writer produces solid content, your analyst crunches numbers. Then you check your API dashboard and realize you have burned through $45 in two days.

This is not a theoretical problem. In the r/OpenClaw subreddit, cost threads appear almost daily. One user shared their weekend project: they set up 5 agents, let them run on a content pipeline for 48 hours, and the final bill was $280. Another developer reported steady $15/day costs just from a 3-agent dev team running during work hours. A Mac Mini M4 user tried switching everything to Ollama and spent 20 hours debugging slow response times before giving up.

The core issue is that most guides tell you to use the best model available. They optimize for output quality and ignore cost entirely. But when you are running 5 agents that talk to each other, every message multiplies. A single task can trigger 15-20 LLM calls as agents coordinate, delegate, process, and report back. At $3 per million input tokens for Sonnet, those calls add up fast.

Strategy 1: Match the Model to the Role

This single change will cut your costs by 60-80%. Not every agent needs a frontier model. Your social media agent does not need the same reasoning power as your PM. Your data formatter does not need the same creativity as your writer.

Total for a 5-agent team: $4.35 - $8.05 per month. Compare that to running everything on Sonnet, which would cost $12-25/month for the same workload. The difference is entirely in model selection.

# Agent: Social Media Manager
# Model: gemini-2.0-flash

You are a social media manager. You repurpose blog content
into Twitter threads and LinkedIn posts.

## Rules
- Keep tweets under 280 characters
- Create 3 variants per article
- Use hooks in the first line

The model directive at the top of SOUL.md tells the OpenClaw gateway which LLM to route requests to for this agent. Each agent can use a completely different provider and model. Your PM runs on Sonnet while your social media agent runs on Gemini Flash for near-zero cost.

Strategy 2: Stagger Agent Calls Instead of Parallelizing

Parallelizing agent calls sounds efficient. Your PM delegates to 4 agents simultaneously, and they all process at once. In practice, this creates a spike of API calls that often hits rate limits, triggers retries, and wastes tokens on failed requests.

When 4 agents all fire requests within the same second, your API provider may throttle or reject some of them. The failed requests get retried automatically, sometimes multiple times. Each retry consumes tokens for the input even if the response never completes. On a bad day, retries can double your actual token usage.

Instead, structure your AGENTS.md workflow as a pipeline. The PM assigns one task at a time, waits for the response, then assigns the next. This creates a steady stream of single requests instead of bursts. Your total completion time might be slightly longer, but your cost will be significantly lower because zero tokens are wasted on retries.

## Workflow
1. @orion receives the task and assigns keyword research to @radar
2. @radar completes research and returns brief to @orion
3. @orion reviews brief and assigns writing to @echo
4. @echo completes draft and returns to @orion
5. @orion assigns social distribution to @pulse
6. @pulse creates posts and confirms to @orion
7. @orion assigns performance tracking to @metrics

## Rules
- Only one agent works on the pipeline at a time
- @orion must confirm receipt before assigning next step
- No parallel delegation unless explicitly requested

Strategy 3: Schedule Heavy Work Off-Peak

API providers experience peak traffic during US business hours, roughly 9 AM to 5 PM Pacific Time. During these windows, rate limits are tighter, latency is higher, and timeout errors are more frequent. Every timeout and retry costs tokens.

If your agents run batch work (content pipelines, weekly reports, data analysis), schedule them for early morning PT or weekends. The reduced contention means fewer failures, faster responses, and lower effective cost per task. Your agents finish faster with fewer retries, which translates directly to fewer tokens consumed.

For interactive work that needs to happen during business hours, keep it to your PM agent and one or two specialists. Batch the heavy processing for off-peak windows.

Strategy 4: Cache Context and Keep Sessions Alive

Every time an agent starts a new session, the entire system prompt and context must be sent again. For a PM agent with a detailed SOUL.md, that can be 2,000-4,000 tokens just for the setup. If your agent starts a new session for every task, you are paying for that context window repeatedly.

Keep sessions alive between tasks. The OpenClaw session system stores conversation history in ~/.openclaw/agents/[name]/sessions/sessions.json. As long as the session persists, subsequent messages only send the new content plus a compressed history, not the full context from scratch.

For long-running teams, clear sessions weekly rather than daily. A session that accumulates too much history will eventually become expensive to send, but a well-managed session that processes 20-30 tasks before clearing is significantly cheaper than starting fresh every time.

# Check session size for an agent
ls -la ~/.openclaw/agents/radar/sessions/sessions.json

# Clear sessions when they get too large (over 100KB)
rm ~/.openclaw/agents/radar/sessions/sessions.json

# Clear all agent sessions at once (weekly maintenance)
rm ~/.openclaw/agents/*/sessions/sessions.json

# Restart gateway to pick up fresh sessions
openclaw gateway restart

Strategy 5: Use HEARTBEAT.md Intervals Wisely

HEARTBEAT.md controls how often an agent checks in with the gateway for new tasks. The default interval is often too aggressive for most use cases. If your agent checks every 5 minutes but only receives a new task every 2 hours, you are paying for 24 unnecessary heartbeat calls per cycle.

Each heartbeat is an LLM call. The agent sends its current status and receives either a new task or a confirmation that nothing is pending. At $0.25 per million tokens with Haiku, a single heartbeat costs fractions of a cent. But multiply that by 5 agents checking every 5 minutes, 24 hours a day, and you get 1,440 unnecessary calls per day. That adds up to real money over a month.

# Heartbeat Configuration

## Active Hours (9 AM - 6 PM PT)
interval: 30m
mode: check-in

## Off Hours (6 PM - 9 AM PT)
interval: 2h
mode: sleep

## Weekend
interval: 4h
mode: minimal

Setting heartbeat to 30 minutes during active hours and 2 hours during off hours reduces your heartbeat calls from 1,440/day to about 50/day across 5 agents. That is a 96% reduction in polling costs.

Real Cost Breakdown: 5 Agents, One Month

Here is a realistic cost breakdown for a 5-agent content team running 5 days a week with the optimizations above applied.

That is 96% cheaper than the $280/weekend horror story from Reddit. The difference is not magic. It is model matching, sequential workflows, smart scheduling, session management, and sensible heartbeat intervals. Five straightforward changes that stack together.

What Actually Costs the Most (and How to Fix It)

Frequently Asked Questions