pomclaw

pomclaw / pomclaw

Public

Enterprise AI Agent Platform: Distributed memory storage + SSH sandbox execution, serve unlimited agents with minimal cloud infrastructure

openclaw postgresql
13
0
89% credibility
Found Apr 19, 2026 at 13 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Go
AI Summary

PomClaw is a platform for deploying and managing numerous AI agents on shared remote workspaces with centralized memory storage to reduce infrastructure costs dramatically.

How It Works

1
📰 Discover PomClaw

You learn about PomClaw, a clever way to run lots of smart AI helpers sharing just a few computers instead of one each.

2
📥 Bring it home

You grab the program onto your computer with an easy one-click installer that sets everything up quickly.

3
💾 Set up shared memory

You connect a central storage spot where all your AI helpers keep their memories, chats, and notes safe and organized.

4
🔗 Link work areas

You connect remote computers as safe workspaces where agents can run tasks without interfering with each other.

5
🚀 Launch the hub

You start the main control center, and it springs to life, ready to manage all your agents from one spot.

6
🤖 Create your first helper

You make your first AI agent, name it, pick its smarts, and watch it join the team.

🎉 Scale and save

Now you run hundreds of AI helpers on a handful of computers, saving tons of money while they work efficiently together.

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AI-Generated Review

What is pomclaw?

PomClaw is a Go-based enterprise agent platform for running unlimited AI agents on shared infrastructure, using distributed PostgreSQL or Oracle storage for memories, conversations, and state, plus SSH sandboxes for secure execution. It solves the pain of one VM per agent by pooling compute nodes and databases, enabling multi-tenant isolation for thousands of agents at 90% lower cost. Users get a central gateway API on port 18790 for creating agents via curl, real-time logs, and vector search over agent data.

Why is it gaining traction?

It beats traditional setups with centralized enterprise agent orchestration—no linear scaling costs, just add SSH nodes as needed—and built-in RBAC, audit logs, and observability dashboard. The quick-start Docker Compose spins up Postgres and gateway in minutes, with env vars for easy config. Developers hook on the enterprise agentic architecture that reuses existing Linux servers without VM overhead.

Who should use this?

Enterprise devs scaling agentic AI platforms for customer support bots or RPA workflows, where multi-tenant isolation matters. Ops teams managing enterprise agent apps on on-prem Oracle/Postgres, ditching per-agent VMs for shared sandboxes. Data engineers building distributed analysis agents with persistent memory across organizations.

Verdict

Early maturity with 11 stars and 0.9% credibility score means prototype it via Makefile or Docker, not prime-time prod—docs are thorough, tests cover basics. Solid for enterprise agent frameworks if you need cost-efficient scaling; contribute to push it forward.

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