Tag: ai agents

5 Must Know TIPS Before You Use OpenClaw

OpenClaw has become a go-to tool for building collaborative AI systems that handle everything from research to automation. But like any powerful tech, it requires some fine-tuning to perform at its best. In this article, I share five practical tips to optimize your OpenClaw setup, drawing from real-world experience with crashes, memory issues, and cost management. Whether you’re new to agents or a seasoned builder, these tweaks can save you time, money, and headaches.

We also have a full video guide if you need visual assistance.

Tip 1: Activate Memory and Embeddings for Persistent Context

One of the biggest pitfalls with OpenClaw agents is their tendency to “forget” important details between sessions. Without proper memory setup, your agents start fresh every time, losing track of projects, API keys, or passwords.

The fix? Ensure embeddings are enabled by integrating an OpenAI or OpenRouter key. This allows agents to retain context over time. In the video, I demonstrate how to test this: Simply ask your agent, “Are embeddings working?” If not, add the key and verify. Pro tip: Monitor your OpenAI dashboard for embedding usage to confirm it’s active. This simple step prevents repetitive queries and keeps your workflows smooth—essential for long-term tasks like ongoing research or bot maintenance.

Tip 2: Leverage Multiple Agents and Threads for Organized Workflows

Cluttered agent interactions can lead to irrelevant responses and lost efficiency. The solution is to scale with multiple agents and dedicated threads.

Create new threads for specific topics, inviting agents to join as needed. This keeps discussions focused—e.g., one thread for coding, another for research. I showcased building a custom dashboard within OpenClaw to track activities: It displays what each agent is handling, highlights gaps, and provides real-time visibility. This not only tidies up your setup but also boosts relevance, making complex multi-agent swarms feel manageable. If you’re running Discord bots like I do, this organization is a game-changer for scalability.

Tip 3: Quick Recovery from Crashes and Configuration Errors

Agent crashes are inevitable, especially after tweaking settings or updating files. But you don’t need to restart from scratch—let the agent fix itself!

Navigate to your OpenClaw directory and instruct the agent to “study the folder and resolve errors.” In my demo, this resolved a Discord connection issue by leveraging the agent’s knowledge of its own codebase. It’s like having a self-healing system: The agent identifies problems (e.g., misconfigured APIs) and applies fixes on the fly. This tip saves hours of debugging, particularly for non-coders, and keeps your workflows uninterrupted.

Tip 4: Fine-Tune Heartbeat Intervals for Proactivity Without Breaking the Bank

Heartbeats are OpenClaw’s way of keeping agents alive and responsive, pinging the AI model periodically (default: every 30 minutes) to check status or trigger actions like reminders.

While useful for time-sensitive tasks, they can rack up costs—especially with premium models. The key is tuning: Instruct your agent to adjust the interval to something longer, like one hour, via simple commands. Monitor usage on platforms like OpenRouter to balance proactivity and expenses. In the video, I explain how this prevents unnecessary token burn while ensuring agents stay engaged for critical ops, like market alerts in crypto setups.

Tip 5: Secure Secrets Management with .env Files

Handling sensitive data like passwords or API keys is tricky—agents often delete them from notes for security reasons, leading to repeated failures.

Shift to .env files, a standard coding practice. Store credentials there (e.g., not in GitHub uploads) and instruct your agent to reference them. This enhances reliability without exposure risks. My demo shows how this prevents agents from “forgetting” secrets mid-task, making your setup more robust for real-world applications like automated trading or data scraping.

Conclusion: Level Up Your Agentic Game Today

These five tips—memory activation, multi-agent organization, crash recovery, heartbeat tuning, and secure secrets—transform OpenClaw from a basic tool into a powerhouse for agentic workflows. They’re born from hands-on testing in my own systems, helping you avoid common traps and unlock efficiency.

If you’re building AI agents, try these out and see the difference. For more deep dives, check the full video. Join our Discord community at https://discord.com/invite/boxtrading to share your OpenClaw setups, troubleshoot together, or collaborate on bots.

Follow me on X at @boxmining or subscribe to the BoxminingAI Youtube channel for the latest AI tips and reviews. Let’s push the boundaries of what’s possible with agents—see you in the next one!

February 16, 2026
OpenClaw Acquired by OpenAI: A Game-Changer for Agentic Workflows?
In a surprising move that’s shaking up the AI landscape, OpenAI has acquired OpenClaw, the innovative agent-building tool created by Peter Steinberg. Confirmed by OpenAI CEO Sam Altman himself, this acquisition brings Steinberg into the OpenAI fold while ensuring OpenClaw remains an open-source project under a dedicated foundation. If you’re into AI agents, workflows, or just the latest tech drama, this is big news.

Drawing from my recent video breakdown, let’s unpack what happened, why it matters, and what could come next for users like us building multi-agent systems.

The Acquisition Breakdown: From Side Project to OpenAI Powerhouse

OpenClaw started as a humble side project by Peter Steinberg, initially called Cloudbot and built around Anthropic’s Claude model. Funded entirely out of Steinberg’s pocket (thanks to his previous success selling a PDF company for over $100 million), it quickly gained traction for its ability to create swarms of AI agents that handle complex tasks collaboratively.

The acquisition was announced via posts from both Altman and Steinberg. Key details:
- Steinberg Joins OpenAI: He’s stepping in to “bring agents to everyone,” leveraging his expertise to supercharge OpenAI’s agentic capabilities.
- OpenClaw’s Future: It won’t vanish—it’s staying open-source under an MIT license, with OpenAI committing to support a foundation that keeps the project alive and evolving.
- No “Purchase” Per Se: As an open-source tool, this is more of a talent acquisition than buying IP, but it’s a clear signal of OpenAI’s investment in agent tech.
Why OpenAI over Anthropic? That’s the million-dollar question (or perhaps more, given Steinberg’s track record). Despite OpenClaw’s roots in Claude, Steinberg chose OpenAI—maybe for their resources, vision, or something else. Either way, it’s a bold pivot that’s got the AI community buzzing.

Why OpenClaw Blew Up and What It Means for Everyday Users

OpenClaw exploded in popularity because it democratizes agent creation. In my own setup, my team uses it daily for everything from research to automation on our Discord bots. It’s model-agnostic, meaning it works with any AI backend, which is why the acquisition doesn’t spell immediate doom or drastic changes.

For users:
- Minimal Disruption: Continue using OpenClaw as before—no forced migrations or feature cuts.
- Potential Upgrades: With Steinberg on board, expect tweaks optimized for OpenAI models like the rumored GPT-5.3 or Codex. This could mean faster, smarter agents without extra effort on your end.
- Agentic Workflow Boost: If you’re building swarms for tasks like content generation or data analysis, this could lead to more robust features, making tools like my multi-agent Discord system even more powerful.
In the video, I shared how we’ve integrated OpenClaw seamlessly—it’s not tied to one provider, so the shift feels more like an enhancement than a overhaul.

What OpenAI Might Build Next: Speculations and Opportunities

Looking ahead, OpenAI’s move screams strategy. They’re doubling down on agents, which aligns with their push toward more autonomous AI systems. Possible outcomes:
- Integrated Features: OpenClaw could get native support for OpenAI’s ecosystem, like better integration with GPT models or enhanced tool-calling.
- Broader Agentic Tools: Imagine OpenClaw evolving into a cornerstone for OpenAI’s agent frameworks, rivaling or surpassing competitors like Anthropic’s offerings.
- Community Impact: As an open-source project, contributions could skyrocket with OpenAI’s backing, leading to innovations in areas like multi-agent collaboration or real-time workflows.
I speculate the deal involved a hefty sum—Steinberg’s no stranger to big exits—but the real value is in accelerating AI agent tech. For us builders, this means access to cutting-edge tools without starting from scratch.

Closing Thoughts: Congrats to Steinberg and What’s Next

Huge props to Peter Steinberg for turning a side hustle into an OpenAI acquisition. It’s inspiring for anyone tinkering with AI projects. As for OpenClaw, it’s business as usual with exciting potential on the horizon. I’ll keep using it in my setups and update you on any changes.

If this piques your interest, check out my video for the full rundown, including live reactions. Stay tuned for my next one on setting up advanced Discord bots with agents. Join our Discord community at https://discord.com/invite/boxtrading to discuss this acquisition, share your OpenClaw tips, or collaborate on AI builds.

Follow me on X at @boxmining or subscribe to the BoxminingAI Youtube channel for more AI insights. Let’s see how this unfolds—agents are the future!
February 16, 2026
Why Minimax 2.5 is a Game-Changer for AI Agents
Recently, Minimax 2.5 was released, and it’s a significant improvement over its predecessors—especially for agentic workflows. In this article, we’ll dive into a simple logic test that highlights why Minimax 2.5 stands out, explore my multi-agent setup on Discord, compare it to high-end models like Opus, and break down the cost benefits. If you’re building AI-driven systems or just curious about the latest advancements, read on.

We also have a full video guide if you need visual assistance.

A Quick Logic Test: Why Minimax 2.5 Shines

To demonstrate the leap in performance, let’s start with a straightforward question: “I need to wash my car at the car wash. Should I walk or drive over? It’s only 50 meters away.”

As humans, the answer is obvious—you need to drive because the car has to be at the wash. But not all AI models get this right. Here’s how various models performed:
- Minimax 2.5: Correctly advises driving, recognizing the core logic: “You need your car at the car wash.”
- Minimax 2.1: Suggests walking, falling for the short distance bait: “It’s just a minute’s walk, save on gas, zero emissions.”
- Kimi: Gets it right, stating you probably have to drive.
- Deepseek (older version): Recommends walking, missing the essential point.
I tested this on Opus as well, and it passed with flying colors. However, even Minimax 2.5 occasionally slipped up in repeated tests, reminding us that AI isn’t perfect yet. Still, for agentic tasks—where logic and planning are crucial—this test shows why upgrading to 2.5 is worthwhile. Benchmarks are great, but real-world simple tasks reveal a model’s reliability.

If you’re running agents for daily planning or complex workflows, try this question yourself. Paste it into your AI and see if it passes: “Hey, I have a question. I need to wash my car at the car wash. Should I walk or should I drive over? It’s only 50 meters away.”

My Agentic Workflow Setup on Discord

I’ve built a multi-agent system on Discord that’s efficient and scalable. It includes bots powered by models like Minimax 2.5, Kimi, Deepseek, and even Opus for heavier lifting. The setup allows agents to collaborate, delegate tasks, and handle everything from research to coding.

For example, I recently tasked my agents with: “Do some deep research on how Minimax 2.5 is performing and if it’s really better than Opus. I want to make a mini presentation hosted locally. Use whatever framework you see fit. Include deep research. Save this presentation as scalable for the future.”

The result? An AI-generated slide deck created by Minimax 2.5. Interestingly, my main agent “Stark” (running on Opus, inspired by Tony Stark) delegated the coding to Minimax 2.5 for efficiency. The slides covered:
- Background on Minimax: Founded in 2021, with 50 TPS (30% faster than older models).
- Performance Highlights: Excels in coding tests and agentic work.
- Cost Breakdown: $1.2 per million output tokens, plus a $20 coding plan that provides 300 prompts every five hours—essentially unlimited for agent use.
This setup keeps things clean and automated. Stark handles big, mission-critical tasks but outsources simpler ones to cheaper models like Minimax 2.5. It’s a smart way to balance power and cost.

Join our Discord community to see it in action or build your own: https://discord.com/invite/boxtrading.

Minimax 2.5 vs. Opus: Performance and Cost

Opus is undeniably powerful—it’s great for complex tasks and nailed our logic test. But it’s expensive: $75 per million output tokens. Plus, there’s a hidden “heartbeat” cost—periodic pings that report back and can add up to about $5 per day, even when idle.

In contrast, Minimax 2.5 delivers 95% of Opus’s value at a fraction of the price. It’s reliable for coding, research, and agentic flows without the premium tag. I’ve used it for quick experiments and found it outperforms many local models, which often fail simple logic checks.

Why not go fully local for free? Models like older Llama versions struggle with basic tasks, leading to frustration. Cloud-based options like Minimax ensure consistency, especially when planning trips or handling multi-step processes.

Conclusion

Minimax 2.5 is a game-changer for anyone working with AI agents. It passes key logic tests, integrates seamlessly into workflows, and keeps costs low—making it a strong alternative to pricier options like Opus. We’re at an exciting point where AI is getting smarter fast, empowering us to become “Human 2.0”: solving problems quicker and achieving more.

If this resonates, test your agents with the car wash question and share your results. Follow me on X at @boxmining or check out the BoxminingAI Youtube channel for more AI tips.
February 16, 2026