Run OpenClaw with Near-Zero AI API Cost: agent-cli-to-api + Cursor CLI Setup

If recurring API bills are blocking your agent automation, this setup is a practical way out.
The core idea is simple: replace OpenClaw's default model provider with agent-cli-to-api and route model calls through Cursor CLI as a custom backend.

What you get:

• OpenClaw still receives commands from your messenger (Telegram/Slack/etc.)
• Model requests go through your custom local path
• You can run a high-performance, stable bot with much lower dependency on paid external APIs

Why this architecture works

The common flow is OpenClaw -> OpenAI/Anthropic API.
This flow becomes:

OpenClaw -> Custom Provider (agent-cli-to-api) -> Cursor CLI model execution

Key benefits:

1. Cost optimization: significantly reduces external API billing dependency.
2. Operational control: local/self-managed path gives you tighter control.
3. Scalability: works naturally with OpenClaw skills and sub-agents.
4. Practical performance: very solid for code-heavy automation workflows.

Prerequisites

• macOS or Linux machine (always-on is recommended)
• Node.js 20+
• OpenClaw installed and configured
• Cursor CLI available
• Git

# version check
node -v
npm -v
git --version

1) Clone the bridge project

This bridge exposes OpenAI-compatible API endpoints, while actual model execution is handled through a CLI backend (for example, Cursor CLI).

git clone https://github.com/dev-thug/agent-cli-to-api.git
cd agent-cli-to-api

2) Install dependencies and run

# install dependencies
uv sync

For OpenClaw integration, run the gateway on port 11434.

Option A) Run directly

uv run agent-cli-to-api cursor-agent --host 127.0.0.1 --port 11434

Option B) Run in background via launchd (macOS, recommended)

scripts/install_launchd.sh --provider cursor-agent --host 127.0.0.1 --port 11434

For real usage, the launchd script is recommended because restart behavior and long-running process management are more reliable.

The reason for using 11434 is simple: it is the default custom provider port in OpenClaw, so you avoid unnecessary port mismatch issues.

When it is running correctly, you should get OpenAI-compatible endpoints similar to:

• http://127.0.0.1:11434/v1/chat/completions
• OpenAI-style request/response format

3) Point OpenClaw to your custom provider

Configure OpenClaw model settings to use your local bridge endpoint:

openclaw configure --section models

In the model selection UI, choose Custom Provider.

Set these values:

• provider/base URL: http://127.0.0.1:11434/v1
• model name: model ID exposed by the bridge (for example auto)
• api key: enter any non-empty value

Important: if the API key field is empty, OpenClaw throws an error. Even when real auth is not required, provide a dummy value such as dummy-key.

Now OpenClaw receives model responses through your custom provider path.

4) Validate Cursor CLI integration (critical)

agent-cli-to-api calls Cursor CLI as the actual AI backend.
OpenClaw sees a normal API, but real inference runs through Cursor CLI.

Validation flow:

1. Test bridge endpoint directly
2. Run the same prompt from OpenClaw
3. Verify responses are mapped correctly

Start with small prompts:

• "Summarize files in current directory"
• "List 3 possible causes from this error log"
• "Draft a git commit message for these changes"

5) Connect Telegram for real usage

openclaw configure --section channels
openclaw gateway start

Then from Telegram you can trigger:

• refactoring requests
• log analysis
• deployment checklist generation
• blog draft generation

All handled via your custom provider route.

Recommended production setup

For long-running stability:

• use a process manager (pm2 or systemd)
• add health checks for bridge and gateway
• split logs (OpenClaw / bridge / channel)
• set automatic restart policy

# Example: run bridge with pm2
npm i -g pm2
pm2 start "npm run start" --name agent-cli-to-api
pm2 save

Common troubleshooting

1) OpenClaw is up but no response

• verify bridge port is listening
• check model base URL typo
• verify network binding (127.0.0.1 vs 0.0.0.0)

2) Responses are slow

• lower output-heavy params
• limit concurrent sub-agent count
• shorten prompts and summarize before sending

3) Output schema is broken

• recheck OpenAI-compatible response mapping
• confirm stream on/off consistency

FAQ

Q1) Is this truly free?

It depends on your environment. But this setup is highly effective for reducing dependency on paid external APIs.

Q2) Can beginners follow this?

Yes. Follow this order: clone -> run bridge -> connect OpenClaw model -> connect channel.

Q3) Is it stable in production?

With process manager + health checks + log separation, production stability improves significantly.

Closing

The OpenClaw + agent-cli-to-api + Cursor CLI stack is one of the most practical options if you want continuous AI automation without heavy API billing pressure.
Set it up once, then operate your personal AI workflow directly from messenger commands.