I don’t pay for Perplexity or ChatGPT after combining my local LLMs with Perplexica and SearXNG
It's a neat way to avoid outdated responses to my queries
The article explores the emergence of private agentic operating systems (OS), which are local-first stacks where autonomous agents manage tasks like file manipulation and workflow execution using private, locally-hosted large language models (LLMs). This represents a shift from passive APIs to active agents. Key areas covered include the architecture of a local agentic OS, lessons from Eliza and Hister, and the planning problem.
The architecture consists of five core components: the LLM layer (the brain), memory layer (the state), tool/action layer (the hands), planner (the executive), and guardrail layer (the conscience). Local deployment offers privacy, latency, and deterministic execution advantages over cloud-based AGI.
Eliza's modular design and Ecosystem Model of treating LLMs as one component in a larger ecosystem of providers and adapters informs the architecture of private OS. Memory is crucial for context retention, and a knowledge graph can augment semantic memory. Hister demonstrates the need for tool use beyond prompting, employing the ReAct pattern of reasoning and acting.
However, unrestricted tool access poses significant risks, highlighting the Planning Problem—the difficulty LLMs have in breaking down complex goals into logical steps. The article concludes by emphasizing the importance of these lessons in building robust private agentic OS.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
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