Monitoring Open Interpreter with Vigilmon

Open Interpreter is the open-source local implementation of ChatGPT's Code Interpreter. It lets any LLM — local via Ollama or remote via OpenAI/Anthropic — write and execute Python, JavaScript, and shell commands directly on your machine. When you expose it via --server mode (port 10001), it becomes a programmable code-execution API that workflows, agents, and scripts can call. Vigilmon gives you continuous visibility into whether that API is reachable, whether the LLM backend is responding, and whether the sandbox environment stays healthy.

What You'll Set Up

• HTTP availability monitor for the Open Interpreter server (port 10001)
• LLM provider API connectivity check (OpenAI, Anthropic, or Ollama)
• Local Ollama / LM Studio backend port monitor (if using a local model)
• SSL certificate expiry alerts for reverse-proxy deployments
• Alerting on server unavailability with low-noise thresholds

Prerequisites

• Open Interpreter installed (pip install open-interpreter)
• Server mode running: interpreter --server (listens on port 10001 by default)
• A configured LLM backend (OpenAI API key, Anthropic API key, or local Ollama)
• A free Vigilmon account

Step 1: Monitor the Open Interpreter HTTP Server

When Open Interpreter runs with --server, it exposes a REST API at http://localhost:10001. This is the primary endpoint your integrations call for code-execution requests. If it's down, every automated workflow that depends on it breaks silently.

1. Log in to vigilmon.online and click Add Monitor.
2. Set Type to HTTP / HTTPS.
3. Enter the server URL. If Open Interpreter is on a remote host behind a reverse proxy:

   https://your-host.example.com/
   

   If you're monitoring a local or LAN instance directly:

   http://192.168.1.x:10001/
   

4. Set Check interval to 1 minute.
5. Set Expected HTTP status to 200.
6. Click Save.

Open Interpreter's server returns HTTP 200 on the root path when running. A non-200 or connection refused means the process has crashed or failed to start.

Tip: If your environment blocks direct port access, place nginx or Caddy in front of Open Interpreter and monitor the proxy URL instead. This also lets you add TLS for secure remote access.

Step 2: Check LLM Backend Connectivity

Open Interpreter delegates all language model calls to a configured backend. If that backend becomes unreachable — API key revoked, service outage, or local model crash — the server will accept connections but fail every execution request. Monitor the backend endpoint directly.

OpenAI or Anthropic (remote API)

Add an HTTP monitor to the provider's health or base URL:

• OpenAI: https://api.openai.com/
• Anthropic: https://api.anthropic.com/

1. Click Add Monitor → HTTP / HTTPS.
2. Enter the provider base URL above.
3. Set Expected HTTP status to 200 (OpenAI) or 307/200 (Anthropic — check what their root redirects to).
4. Set Check interval to 5 minutes.
5. Click Save.

This tells you whether the upstream API is reachable from your server. It does not verify your API key — for that, use a heartbeat sent from a scheduled probe script (see Step 3).

Local Ollama Backend

If Open Interpreter is configured to use Ollama (LLM_MODEL=ollama/...), Ollama must be running on its default port:

1. Click Add Monitor → HTTP / HTTPS.
2. Enter: http://localhost:11434/ (or the LAN IP if on a separate host).
3. Set Expected HTTP status to 200.
4. Set Check interval to 1 minute.
5. Click Save.

Ollama serves a simple JSON response on its root path when healthy. If it crashes or is OOM-killed after loading a large model, this monitor alerts within one minute.

LM Studio Backend

LM Studio exposes an OpenAI-compatible API on port 1234 by default:

1. Click Add Monitor → HTTP / HTTPS.
2. Enter: http://localhost:1234/v1/models.
3. Set Expected HTTP status to 200.
4. Set Check interval to 2 minutes.
5. Click Save.

Step 3: Heartbeat Monitor for API Key Validity

A connectivity check to api.openai.com tells you the network is up, but not that your API key is valid and has quota remaining. Use a Vigilmon cron heartbeat driven by a lightweight probe script that runs a real (cheap) API call.

1. In Vigilmon, click Add Monitor → Cron Heartbeat.
2. Set Expected ping interval to 60 minutes.
3. Copy the heartbeat URL (e.g. https://vigilmon.online/heartbeat/abc123).

Create a probe script check_llm.sh:

#!/bin/bash
# Probe the configured LLM with a minimal request
RESPONSE=$(curl -s -o /dev/null -w "%{http_code}" \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"gpt-4o-mini","messages":[{"role":"user","content":"ping"}],"max_tokens":1}' \
  https://api.openai.com/v1/chat/completions)

if [ "$RESPONSE" = "200" ]; then
  curl -s "https://vigilmon.online/heartbeat/abc123"
else
  echo "LLM probe failed: HTTP $RESPONSE"
fi

Schedule it hourly with cron:

0 * * * * /path/to/check_llm.sh

If the API key expires or quota runs out, the heartbeat stops pinging and Vigilmon alerts after one missed interval.

Step 4: Monitor the Code Execution Sandbox

Open Interpreter executes code directly in your local Python, Node.js, and shell environment. The sandbox health depends on the Python interpreter, Node.js runtime, and shell being available and functional. A failed system update or misconfigured PATH can silently break code execution even while the HTTP server stays up.

Add a heartbeat that runs a real execution probe:

1. In Vigilmon, click Add Monitor → Cron Heartbeat.
2. Set Expected ping interval to 10 minutes.
3. Copy the heartbeat URL.

Create check_sandbox.sh:

#!/bin/bash
# Verify Python and shell execution work
python3 -c "print('ok')" > /dev/null 2>&1 || exit 1
node -e "process.exit(0)" > /dev/null 2>&1 || exit 1
# Send heartbeat only if both pass
curl -s "https://vigilmon.online/heartbeat/YOUR_SANDBOX_HEARTBEAT_ID"

Schedule every 10 minutes:

*/10 * * * * /path/to/check_sandbox.sh

This confirms the execution environment Open Interpreter relies on is functional, catching issues like missing runtimes, broken virtualenvs, or permissions errors before they affect real jobs.

Step 5: Monitor File System Access

Open Interpreter's code execution requires read/write access to the working directory. A permissions change or full disk will cause execution failures with no obvious server-side error.

Add another heartbeat probe:

#!/bin/bash
WORKDIR="${INTERPRETER_WORKDIR:-$HOME}"
# Check write access
TESTFILE="$WORKDIR/.vigilmon_fs_check"
echo "ok" > "$TESTFILE" && rm "$TESTFILE" || exit 1
# Check available disk (alert if <500MB free)
AVAIL=$(df -m "$WORKDIR" | awk 'NR==2{print $4}')
[ "$AVAIL" -gt 500 ] || exit 1
curl -s "https://vigilmon.online/heartbeat/YOUR_FS_HEARTBEAT_ID"

Schedule every 15 minutes. If disk fills or permissions break, the heartbeat stops and Vigilmon alerts.

Step 6: SSL Certificate Monitoring (Reverse Proxy)

If Open Interpreter is exposed behind nginx, Caddy, or Traefik with a TLS certificate, monitor the certificate expiry alongside uptime.

1. Open the HTTP monitor created in Step 1.
2. Enable Monitor SSL certificate.
3. Set Alert when certificate expires in less than 21 days.
4. Click Save.

This covers both Let's Encrypt auto-renewal failures and manually managed certificates that quietly expire.

Step 7: Configure Alert Channels

1. Go to Alert Channels in Vigilmon and add Slack, email, Discord, or a webhook.
2. On the Open Interpreter server monitor: set Consecutive failures before alert to 2 — a single missed probe may be a transient hiccup, not a real crash.
3. On the Ollama / LM Studio backend monitor: set Consecutive failures to 1 — local inference servers tend to crash hard, not flap.
4. On heartbeat monitors: the default single-missed-interval alert is appropriate — a dead heartbeat is always significant.

Example Slack Alert Setup

1. Create a Slack incoming webhook in your workspace.
2. In Vigilmon → Alert Channels → Add → Slack.
3. Paste the webhook URL.
4. Assign it to all monitors created in this tutorial.

Summary

| Monitor | Type | URL / Target | Interval | |---------|------|-------------|----------| | Open Interpreter server | HTTP | http://host:10001/ | 1 min | | OpenAI API reachability | HTTP | https://api.openai.com/ | 5 min | | Ollama backend | HTTP | http://localhost:11434/ | 1 min | | LLM API key validity | Heartbeat | probe script → Vigilmon | 60 min | | Code execution sandbox | Heartbeat | probe script → Vigilmon | 10 min | | File system access | Heartbeat | probe script → Vigilmon | 15 min | | SSL certificate | SSL (on HTTP monitor) | reverse proxy domain | — |

Open Interpreter is powerful precisely because it can run arbitrary code on demand — which makes its availability critical for any automated workflow that depends on it. With Vigilmon watching the server, the LLM backend, the sandbox runtime, and the file system, you'll know about failures within minutes rather than discovering them when a workflow silently produces no output.

Get started for free at vigilmon.online.