Lessons learned building a local-first multi-agent AI runtime

[Poi5eN]

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Product Feedback

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Title: Lessons learned building a local-first multi-agent AI runtime

I've spent the last month building an open-source multi-agent runtime that coordinates specialized AI agents with shared memory and tool access.

Some things surprised me during development:

• Memory management became harder than agent orchestration.
• Context growth was often a bigger bottleneck than model latency.
• Tool execution debugging required much better visibility than traditional chat applications.
• Parallel agent execution improved responsiveness, but result merging became a challenge.
• Running everything locally with Ollama simplified privacy concerns but introduced hardware variability.

For those who have built agent systems, what architectural decisions ended up mattering most in production?

I'm particularly interested in experiences around:

• Shared memory/context management
• Multi-agent coordination
• Tool execution tracing
• Local-first deployments

One of the greatest features of Nexus is that you can direcly use it and access it through the slack and telegram bots but just connecting with it. Also it comes with an outstanding openclaw orchestration that connects with nexus brain and sync with slack.

Try it out Nexus at: https://saarlabs.in/
And star the repo and clone the project at: https://github.com/Poi5eN/Nexus/
I'd love to compare notes with others working on similar systems.

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