Connect Agent Builder to 8,000+ tools
The Core: In this tutorial, you will learn how to connect OpenAI's Agent Builder to over 8,000 apps using Zapier MCP, enabling you to build powerful automations like creating Google Forms directly through AI agents.
Step-by-step:
Go to platform.openai.com/agent-builder, click Create, and configure your agent with instructions like: "You are a helpful assistant that helps me create a Google Form to gather feedback on our weekly workshops." Then select MCP Server → Third-Party Servers → Zapier
- Visit mcp.zapier.com/mcpservers, click "New MCP Server," choose OpenAI as the client, name your server, and add apps needed (like Google Forms)
- Copy your OpenAI Secret API Key from Zapier MCP's Connect section and paste it into Agent Builder's connection field, then click Connect and select "No Approval Required"
- Verify your OpenAI organization, then click Preview and test with: "Create a Google Form with three questions to gather feedback on our weekly university workshops." Once confirmed working, click Publish and name your automation
Pro tip: Experiment with different Zapier tools to expand your automation capabilities. Each new integration adds potential for custom workflows and more advanced tasks.
The Core: In this tutorial, you will learn how to connect OpenAI's Agent Builder to over 8,000 apps using Zapier MCP, enabling you to build powerful automations like creating Google Forms directly through AI agents.
Step-by-step:
Go to platform.openai.com/agent-builder, click Create, and configure your agent with instructions like: "You are a helpful assistant that helps me create a Google Form to gather feedback on our weekly workshops." Then select MCP Server → Third-Party Servers → Zapier
- Visit mcp.zapier.com/mcpservers, click "New MCP Server," choose OpenAI as the client, name your server, and add apps needed (like Google Forms)
- Copy your OpenAI Secret API Key from Zapier MCP's Connect section and paste it into Agent Builder's connection field, then click Connect and select "No Approval Required"
- Verify your OpenAI organization, then click Preview and test with: "Create a Google Form with three questions to gather feedback on our weekly university workshops." Once confirmed working, click Publish and name your automation
Pro tip: Experiment with different Zapier tools to expand your automation capabilities. Each new integration adds potential for custom workflows and more advanced tasks.
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Start your journey :)
🚨 A developer built an AI engine that simulates thousands of people to predict the future.
It's called MiroFish.
You upload a news article, policy draft, or financial report and it spins up a full parallel digital world with thousands of AI agents that have independent personalities, long-term memory, and behavior logic.
Then you watch society evolve and get a full prediction report.
100% Opensource.
Link - https://github.com/666ghj/MiroFish
It's called MiroFish.
You upload a news article, policy draft, or financial report and it spins up a full parallel digital world with thousands of AI agents that have independent personalities, long-term memory, and behavior logic.
Then you watch society evolve and get a full prediction report.
100% Opensource.
Link - https://github.com/666ghj/MiroFish
OpenClaw launched Jan 2026 and went from 9k → 247k GitHub stars in days.
Here's every alternative, heaviest to lightest 🧵
OpenClaw (TypeScript)
github.com/openclaw/openclaw
The one that started it all. Went from 9k → 247k GitHub stars overnight. Does everything — memory, tools, scheduling, 20+ messaging apps. The problem? It's massive, slow, and once caused a $400k accidental wire transfer.
> 430,000 lines of TypeScript
> Uses 1.5 GB RAM
> CVSS 8.8 security exploit
100% Open Source.
Here's every alternative, heaviest to lightest 🧵
1. Nanobot (Python)
github.com/HKUDS/nanobot
OpenClaw has 430,000 lines of code. This has 4,000. Built by Hong Kong University researchers so you can actually read and understand every line yourself.
> ~100 MB RAM
> Telegram, Discord, WhatsApp, Gmail
> Full memory + tools included
100% Open Source.
github.com/HKUDS/nanobot
OpenClaw has 430,000 lines of code. This has 4,000. Built by Hong Kong University researchers so you can actually read and understand every line yourself.
> ~100 MB RAM
> Telegram, Discord, WhatsApp, Gmail
> Full memory + tools included
100% Open Source.
2. NanoClaw (TypeScript)
github.com/qwibitai/nanoclaw
OpenClaw once caused a $400k accidental wire transfer. NanoClaw was built directly in response — every agent runs in its own locked container. It physically cannot touch your files without permission.
> Docker/microVM isolation per agent
> WhatsApp, Telegram, Slack, Discord
> Now officially partnered with Docker
100% Open Source.
github.com/qwibitai/nanoclaw
OpenClaw once caused a $400k accidental wire transfer. NanoClaw was built directly in response — every agent runs in its own locked container. It physically cannot touch your files without permission.
> Docker/microVM isolation per agent
> WhatsApp, Telegram, Slack, Discord
> Now officially partnered with Docker
100% Open Source.
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3. ZeroClaw (Rust)
github.com/zeroclaw-labs/zeroclaw
ZeroClaw is an open-source agent runtime written entirely in Rust. It lets you run fully autonomous AI agents on $10 hardware 🤯
- Uses less than 5MB of RAM.
- Fully swappable models, tools, memory, and channels.
- Connects to 15+ platforms without renting a cloud server.
- Keeps all your data completely private on the device.
100% Open Source.
github.com/zeroclaw-labs/zeroclaw
ZeroClaw is an open-source agent runtime written entirely in Rust. It lets you run fully autonomous AI agents on $10 hardware 🤯
- Uses less than 5MB of RAM.
- Fully swappable models, tools, memory, and channels.
- Connects to 15+ platforms without renting a cloud server.
- Keeps all your data completely private on the device.
100% Open Source.
4. PicoClaw (Go)
github.com/sipeed/picoclaw
Chinese engineers just made OpenClaw 99% lighter!
PicoClaw is an Agent written in Go that runs on a $10 Linux board.
Compared to OpenClaw, PicoClaw:
- needs 10 MB of RAM instead of 1 GB
- has 400x faster startup
- boots in 1s on a 0.6GHz single core
100% open-source!
github.com/sipeed/picoclaw
Chinese engineers just made OpenClaw 99% lighter!
PicoClaw is an Agent written in Go that runs on a $10 Linux board.
Compared to OpenClaw, PicoClaw:
- needs 10 MB of RAM instead of 1 GB
- has 400x faster startup
- boots in 1s on a 0.6GHz single core
100% open-source!
5. NullClaw (Zig)
github.com/nullclaw/nullclaw
NullClaw is a fully autonomous AI infrastructure written in Zig. While everyone else needs a $600 mac mini.. this runs on a $5 board.
- Binary is 678 KB.
- Uses ~1 MB of RAM.
- Boots in under 2 milliseconds.
100% Open Source.
github.com/nullclaw/nullclaw
NullClaw is a fully autonomous AI infrastructure written in Zig. While everyone else needs a $600 mac mini.. this runs on a $5 board.
- Binary is 678 KB.
- Uses ~1 MB of RAM.
- Boots in under 2 milliseconds.
100% Open Source.
6. IronClaw (Rust)
github.com/near/ironclaw
Built for banks, hospitals, and regulated industries. Every tool runs in a locked WebAssembly sandbox. The AI model never sees your actual passwords or API keys they're stored in an encrypted vault inside a Trusted Execution Environment.
> Zero plaintext credentials
> WASM sandboxed tools
> Built by NEAR AI
100% Open Source.
github.com/near/ironclaw
Built for banks, hospitals, and regulated industries. Every tool runs in a locked WebAssembly sandbox. The AI model never sees your actual passwords or API keys they're stored in an encrypted vault inside a Trusted Execution Environment.
> Zero plaintext credentials
> WASM sandboxed tools
> Built by NEAR AI
100% Open Source.
7. TinyClaw
github.com/machinae/tinyclaw
Not a personal assistant. A team of AI agents. One agent writes code, another reviews it, another deploys it. They hand work off to each other automatically you just describe the goal.
> Multi-agent chain execution
> Each agent runs in its own isolated workspace
> Zero manual handoffs
100% Open Source.
github.com/machinae/tinyclaw
Not a personal assistant. A team of AI agents. One agent writes code, another reviews it, another deploys it. They hand work off to each other automatically you just describe the goal.
> Multi-agent chain execution
> Each agent runs in its own isolated workspace
> Zero manual handoffs
100% Open Source.