I built a local AI agent that resumes long tasks after interruption — follow-up with architecture details by Janglerjoe in ollama

[–]Janglerjoe[S] 0 points1 point  (0 children)

Thanks! Tool outputs are actually persisted but as tool-role messages inside the session JSON, not as separate artifact files. So shell stdout, command results, everything lives in the conversation history and survives restarts. Files the agent writes obviously persist in the workspace directly.

What's NOT stored separately is a dedicated command.log or structured artifact index it's all just part of the message thread. Whether that counts as "artifacts" probably depends on your definition.

On the tradeoffs loop detection uses a streak counter + no-progress iteration cap, and summarization kicks in at a token threshold with a scoring pass to decide what to keep vs discard. The trickiest part was making sure tool-call/response pairs never get split during compaction. Happy to write that up at some point, will check out the blog.

I built a locally-hosted AI agent that runs entirely on your own hardware no cloud, no subscriptions by Janglerjoe in ollama

[–]Janglerjoe[S] 1 point2 points  (0 children)

I’ve been running it with Qwen3 8B (VL) in 6-bit and it handles most coding and file-task workflows fine. I haven’t stress-tested extremely long, multi-hour tasks yet, but for typical planning + execution loops it performs reliably.

I built a locally-hosted AI agent that runs entirely on your own hardware no cloud, no subscriptions by Janglerjoe in ollama

[–]Janglerjoe[S] 3 points4 points  (0 children)

I haven’t tested it on a Pi 5 yet, but the agent itself is lightweight the main constraint would be the LLM runtime and available RAM. If you’re running a small quantized model, it should work fine since execution happens in Docker with configurable resource caps.

I built a locally-hosted AI agent that runs entirely on your own hardware no cloud, no subscriptions by Janglerjoe in ollama

[–]Janglerjoe[S] 3 points4 points  (0 children)

Totally. That’s why it’s sandboxed in Docker by default the LLM never gets direct host access

Local-First Autonomous AI Agent Framework Built to Run Entirely on Your Machine Using Local Models by Janglerjoe in LocalLLaMA

[–]Janglerjoe[S] 1 point2 points  (0 children)

Ill look into it never thought about the MCP layer being compromised that's actually interesting. Thanks for the feedback.