OpenClaw Production Checklist

Why You Need a Production Strategy

Running an autonomous agent like OpenClaw on your laptop is fun. Running it multiple/multiple on a public server is a liability if not done correctly.

In a production environment, your agent needs to handle network interruptions, safeguard API keys, and resist prompt injection attacks, all while maintaining state across restarts. The "it works on my machine" approach falls apart when you need high availability and strict data isolation.

According to CrowdStrike, improperly configured AI agents have led to critical vulnerabilities like CVE-2026-25253, allowing remote code execution on host machines. Security isn't just a checkbox; it's the foundation of your deployment.

When you move to production, you are no longer just running a script; you are managing an independent system that can execute code, spend money via APIs, and modify files. This checklist connects a prototype and a hardened, production-ready agent infrastructure.

Helpful references: Fast.io Workspaces, Fast.io Collaboration, and Fast.io AI.

What to check before scaling openclaw production checklist

Don't starve your agent. Reliable autonomous operations require specific compute resources, especially if you're running local models. A common mistake is under-provisioning memory, leading to crashes when the agent's context window fills up.

Production Hardware Baseline:

• CPU: Minimum multiple vCPUs for smooth Node.js event loop handling and concurrent tool execution. Agents are often I/O bound waiting for LLM responses, but tool execution (like parsing large files or running code) requires burstable CPU power.
• RAM: multiple minimum system memory. OpenClaw's context management can spike memory usage during long tasks. If you run a local vector store alongside the agent, multiple is safer.
• Storage: NVMe SSDs are needed. Vector database lookups and log writing can bottleneck on standard HDDs. Aim for at least multiple of disk space to handle growing log files and Docker image layers.
• GPU (Optional but Recommended): If running local LLMs via Ollama, Milvus recommends at least 24GB of VRAM for reliable performance with 32B+ parameter models. For simpler multiple models, multiple VRAM may work, but production reliability favors headroom.

Operating Environment:

• Node.js: Ensure you are running Node.js v22 (LTS) or later. Older versions may miss critical security patches or performance optimizations for asynchronous operations.
• Containerization: Always use Docker. It provides a clean, reproducible environment and an essential layer of isolation from the host OS. Managing dependencies via npm install on a bare metal server leads to "dependency hell" and security drifts.

2. Security Hardening

An autonomous agent with shell access is a high-value target. Lock it down. The principle of least privilege is your best friend here.

Network Isolation:

• Bind Interfaces: Never bind OpenClaw to multiple.0.0.multiple on a public server. Use 127.0.0.multiple and a reverse proxy (Nginx/Caddy) with SSL/TLS. This ensures that only traffic passing through your secured gateway reaches the agent.
• Firewall Rules: Deny all incoming traffic by default. Allow only necessary outbound ports (e.g., multiple for API calls). If your agent doesn't need to SSH into other servers, block port multiple outbound.
• VPN: Use a private mesh network like Tailscale for administrative access instead of exposing SSH or control panels to the public internet. This effectively hides your management interface from the public web.

User Permissions:

• Non-Root User: Never run the OpenClaw process as root. Create a dedicated openclaw user with limited filesystem access. If an attacker compromises the agent, they should be trapped in a low-privilege user account.
• Cap Drop: When using Docker, run with --cap-drop ALL to strip the container of privileged capabilities. This prevents container escape attacks where a process breaks out of Docker to access the host kernel.

Secret Management:

• No Hardcoded Keys: Inject API keys via environment variables or a secrets manager (like HashiCorp Vault or AWS Secrets Manager). Never commit .env files to git.
• Scope Your Tokens: Use restricted API keys. For example, give your AWS credentials access only to the specific S3 bucket the agent needs, not full Admin access.
• Key Rotation: Implement a policy to rotate API keys regularly (e.g., every multiple days) to minimize the blast radius of a potential leak.

3. Application Configuration

Configure OpenClaw to be resilient and predictable. A well-configured agent recovers from failures automatically and respects operational boundaries.

State Management:

• Persistent Storage: Mount a Docker volume for the agent's database (PostgreSQL/SQLite). Ephemeral containers mean your agent forgets everything on restart, its training, its history, and its ongoing tasks.
• Redis: Use Redis for session caching to reduce latency and API costs. Redis helps manage the "short-term memory" of the agent, keeping active conversation context hot and accessible.

Tool & Skill Governance:

• Whitelist Strategy: Explicitly whitelist allowed skills. Disable "shell execute" or "browser" skills unless strictly necessary for the agent's role. If an agent is only supposed to write code, it shouldn't have access to your email marketing tool.
• Human-in-the-Loop: Configure sensitive actions (like sending emails, deleting files, or spending money) to require human approval via the messaging interface. This "circuit breaker" pattern prevents runaway agents from causing damage.

LLM Settings:

• Context Window: Use models with at least a 64k token context window to handle complex, multi-step instructions without losing the thread. Small context windows lead to "amnesiac" agents that forget the goal halfway through a task.
• Rate Limits: Implement rate limiting on the agent's output to prevent cost runaways if the agent gets stuck in a loop. A simple "max multiple requests per hour" rule can save you thousands of dollars.

4. Solving the Memory Problem with Fast.io

Production agents need a "long-term memory" that is cheaper than a vector database and easier to manage than raw S3 buckets. This is where Fast.io fits into your architecture.

Instead of building complex RAG (Retrieval-Augmented Generation) pipelines from scratch, you can treat a Fast.io workspace as your agent's extended brain.

The Fast.io Advantage for OpenClaw:

• Zero-Config RAG: Enable "Intelligence Mode" on a workspace, and Fast.io automatically indexes every file (PDFs, code, docs) for semantic search. You don't need to chunk files, generate embeddings, or manage a Pinecone instance.
• Native Integration: Install the official skill with a single command: clawhub install dbalve/fast-io. This gives your agent immediate, authenticated access to your storage without complex OAuth dances.
• Shared Workspaces: Your agent works in the same folders as your human team. It can upload a report that you can immediately view, comment on, and approve. No more "where did the agent put that file?" confusion.
• multiple MCP Tools: Access a large toolkit for file operations, from reading logs to generating charts, without writing custom tool definitions.

Free Tier for Agents: Fast.io offers a specialized free tier for agent accounts: multiple of storage, multiple monthly credits, and no credit card required. It's built to be the default storage layer for autonomous systems.

5. Observability & Maintenance

You can't fix what you can't see. Production means knowing your agent's health at a glance.

Logging Strategy:

• Structured Logs: Output logs in JSON format for easy ingestion into tools like Datadog or ELK Stack. Text logs are hard to query; structured logs let you filter by "error_type" or "agent_id" instantly.
• Audit Trails: Log every tool execution and external API call. This helps you debug "why did the agent do that?" scenarios. You need to be able to reconstruct the chain of thought that led to an action.

Monitoring Metrics:

• Token Usage: Track tokens per minute to detect loops or attacks. A sudden spike in token usage often indicates an agent stuck in a retry loop or an active prompt injection attack.
• Error Rates: Set alerts for API timeout spikes or 5xx errors from LLM providers. If OpenAI goes down, your agent should pause gracefully, not crash.
• Cost Tracking: Set hard daily budget caps at the LLM provider level (OpenAI/Anthropic) to prevent billing shocks.

Update Policy:

• Pin Versions: Pin your OpenClaw version and skill versions in package.json or Dockerfile. Auto-updates can break production workflows. Treat agent updates like server updates: test them in staging first.
• Security Patches: Subscribe to OpenClaw release notifications to apply security patches immediately. The AI security landscape moves fast; you need to stay ahead of new vulnerabilities.

6. Common Pitfalls to Avoid

Even with a checklist, things can go wrong. Here are the most common mistakes we see in production deployments:

The "Root" Trap: Running Docker containers as root is the most common security failing. It effectively gives the container root access to your host machine if a vulnerability is exploited. Always specify a USER in your Dockerfile.

The "Infinite Loop" Bill: Agents can get stuck in loops, repeating the same wrong action endlessly. Without rate limits and budget caps, this can cost thousands of dollars overnight. Always implement circuit breakers.

The "Silent Failure": Agents often fail silently, hallucinating a success message while actually doing nothing. Implement "verification steps" where the agent must prove it completed a task (e.g., by reading back the file it just wrote) before marking it as done.