Offline Autonomous AI Engineer: Phase 1–2 Complete — Local LLM + Memory + Eval Loop (Architecture Inside)

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:waving_hand: What I’m Building

I’m developing a fully offline, memory-retaining autonomous AI engineer. It’s designed to take user intent, retain task history, generate/refactor code, and evolve independently — no API calls, no cloud dependencies.

This isn’t a co-pilot — it’s an engineer that thinks back.

:brain: What’s Built So Far

  • Local LLM inference (Mistral-based, fast + cheap)
  • Full command interface
  • Memory layer (session + indexed context)
  • Output interpreter
  • Plugin scaffold (Phase 2 now live)
  • Improvement loop UI (task queue, log summarization, retries)

:magnifying_glass_tilted_left: Why This is Different

  • Fully modular + explainable
  • Memory is a real system, not context stuffing
  • Architecture-first, not prompt-first
  • Soon expanding into hybrid (local + cloud-enhanced modes)

:camera_with_flash: Screenshot

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:link: Full article with diagrams:

https://medium.com/@bradkinnard/im-building-an-autonomous-ai-engineer-and-it-s-already-thinking-back-d2a05034c603

:rocket: Feedback I’m Looking For:

  • Offline vector memory strategies
  • Best practice for task evaluators + retry loops
  • Anyone doing similar agentic orchestration locally?

Tags:
offline-llm, memory-layer, agent-architecture, open-source-llm, mistral, dev-tools

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Cool project. The offline-first approach is the right call for something like this.

One thing I’d think about early is how you handle memory conflicts as the task history grows. Once you have hundreds of retained facts, you’ll start getting contradictions (especially if the agent revises its own decisions). If you just append everything, retrieval quality degrades fast.

What worked for me was batching new facts against related existing ones and letting the LLM decide per-fact whether to add, update, delete, or skip. One call instead of N, and the memory stays clean over time.

Also curious how you’re scoring relevance during retrieval. Pure vector similarity, or do you weight by recency/importance too? For an autonomous agent that runs long sessions, recency weighting makes a big difference since older task context can drown out recent decisions.

Built a memory library focused on exactly these problems: GitHub - remete618/widemem-ai: Next-gen AI memory layer with importance scoring, temporal decay, hierarchical memory, and YMYL prioritization · GitHub – fully local with Ollama, might be useful as a component.

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