What Is a Local-First Agent OS?
A local-first agent OS is the shared memory, routing, handoff, and guardrail layer around Claude, Codex, OpenCode, Ollama, and other AI tools.
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A local-first agent OS is the operating layer that keeps AI work coherent across multiple agents while keeping the canonical memory and control plane on your machine. It is not just a chatbot, a model router, or a note app. It is the glue that lets Claude, Codex, Claude Code, OpenCode, Hermes, OpenClaw, Ollama, qmd, and Obsidian work as one system.
The point is simple: when one AI tool hits a limit, gets expensive, loses context, or is the wrong fit for the task, the work should keep moving.
The short definition
| Layer | What it does |
|---|---|
| Memory | Keeps notes, handoffs, daily logs, project state, and retrieval in one source of truth. |
| Routing | Decides which work belongs in premium, local/free, browser/tool, or human-review lanes. |
| Guardrails | Blocks risky paid calls, public/private leaks, unsafe side effects, and stale-memory changes. |
| Benchmarks | Tests whether memory, qmd, skills, routing, and handoffs degraded after a change. |
| Telemetry | Records which harness, model, cost lane, files, and outcomes were involved. |
That is what Fuck Big Tech is building as an open-source local-first Agent OS.
What it is not
A local-first agent OS is not a replacement for every AI tool.
It wraps the tools people already use:
- Claude and Codex for high-judgment work
- Claude Code and OpenCode for coding workflows
- Ollama and other local runtimes for routine local tasks
- qmd and Obsidian for retrieval and source-of-truth memory
- Browser/tool harnesses for UI checks, screenshots, and automations
The category error is comparing it to one model. The better comparison is an operating layer around the models.
Why this matters now
AI users are hitting two problems at once.
First, premium model usage is getting expensive and rate-limited. More work is moving through Claude, Codex, and frontier models, but not every task needs that lane.
Second, people are switching between harnesses. A developer might plan in Claude, implement in Codex, test in OpenCode, ask Ollama to summarize logs, and keep project memory in Obsidian. Without a shared OS layer, each switch leaks context.
The local-first rule
Local-first means:
- canonical memory starts local
- retrieval points back to source files
- routing decisions are recorded
- private notes stay private
- premium models are used deliberately
It does not mean pretending local models are always better. Some work still deserves frontier models. The OS should make that decision explicit instead of letting every task drift into the expensive lane.
What to install first
Start with the smallest useful loop:
fuckbigtech init --with-memory
fuckbigtech doctor
fuckbigtech memory-test quick
fuckbigtech route "summarize today's handoff"If that works, you have the first version of the system: shared memory, health checks, and routing around your agent workflow.
Quick Answers
Is a local-first agent OS the same thing as a local model?
No. A local model runs inference. A local-first agent OS coordinates memory, handoffs, routing, guardrails, tests, and local/free delegation around multiple agents and models.
Does local-first mean every task must run locally?
No. Local-first means the default memory and control plane stay local, while high-risk or high-judgment tasks can still route to approved premium models.
Why does shared memory matter across AI agents?
Shared memory prevents every harness switch from becoming a restart. Claude, Codex, OpenCode, and local models should read the same canonical context instead of each building a separate silo.