Linzumi

Quick Verdict

Linzumi is a chat-based control plane for AI coding teams. People start and supervise work in a shared thread; coding agents use approved directories on the user’s computer, execute commands and change code, then send commands, diffs, tests, screenshots, previews, and replay evidence back to the thread. The product is explicitly in beta and currently promotes an Apple Silicon macOS app, web sign-up, and Codex workflows. Linzumi is not a new model. Its value is visibility, steering, and review around agents working in real local environments.

It fits teams that already use coding agents but struggle with opaque terminal sessions and remote intervention. “Local execution” does not mean “fully local”: commands run on the user’s machine, team threads and remote control use Linzumi’s service, and inference follows the connected agent or provider using the user’s credentials. Compare Cursor for a mature AI IDE, Codex or Claude Code for direct terminal use, and LangGraph for open-source custom orchestration.

Best For

• Engineering teams using real local repositories with AI coding agents.
• Leads who want to observe and redirect agent runs before merge.
• Developers who need to respond to blocked agents away from a workstation.
• Organizations prepared to govern directories, credentials, model accounts, and audit evidence.
• Not ideal for fully offline environments, teams requiring mature public policies, or buyers needing a stable cross-platform enterprise product today.

Key Features

• Shared control threads: People and agents work in one conversation with mid-run redirection.
• Local execution: Agents operate in explicitly approved directories on user-controlled hardware.
• Run evidence: The product presents commands, files, diffs, tests, screenshots, previews, and replay in the thread.
• Remote supervision: Review progress and answer blocking questions from web or mobile without direct SSH.
• Session guardrails: Linzumi says directories are approved explicitly, sensitive capabilities require people, and forwarded ports and permissions expire with the session.
• Local CLI: The public @linzumi/cli package requires Node.js 20+ and connects local agents to the service.
• Active delivery: npm listed 1.0.152 as latest on July 21, 2026, with a same-day release, indicating rapid active development.

Use Cases

• Supervising long coding runs while away from the computer.
• Reviewing implementation, commands, and test evidence inside a team thread.
• Steering several agents while controlling concurrency and branch conflicts.
• Testing code against the real local environment rather than a vendor VM.
• Evaluating replay and exports as review evidence without assuming they already satisfy compliance requirements.

Pricing

Flat pricing avoids per-seat math, but “unlimited agent-hours” should be checked for fair-use terms, model charges, and local compute. The user’s own agent account or API key usually pays inference; the Company fee should not be read as unlimited model usage.

Licensing is component-specific. npm identifies @linzumi/cli as MIT. That does not make Linzumi’s hosted chat, website, team data, or commercial services MIT-licensed, open source, or self-hostable.

Pros

• Combines real local development environments with team-visible supervision.
• Commands, diffs, tests, and replay allow earlier correction than a final PR alone.
• Free Personal and flat Company pricing make a pilot easy to understand.
• The CLI is MIT-licensed and was actively published at the review date.
• Product design emphasizes directory approval and human gates for sensitive capabilities.

Cons

• Still beta, with current packaged desktop support centered on Apple Silicon macOS.
• C3, meeting transcription, cited Q&A, truth reconciliation, and generated specs are explicitly roadmap items.
• Local execution exposes real repositories, processes, and credentials to real mistakes.
• Public service terms, privacy, retention, and model-training policies were not readily available at standard website paths during review.
• MIT covers the CLI package, not the hosted control plane; enterprise claims need contractual evidence.

Alternatives

FAQ

What is available now versus on the roadmap?

Local agents, team threads, run records, remote steering, and the macOS beta form the current product. C3, meeting transcription, cited knowledge answers, contradiction reconciliation, and context-to-spec are marked as roadmap features.

Does code really run locally?

Linzumi says agents run on user-controlled hardware inside approved directories. Threads, remote control, and evidence still interact with the Linzumi service, so local runtime and cloud control must be assessed separately.

Which models does Linzumi use?

Linzumi is not a model provider. The current site promotes Codex, while the CLI package includes Codex and Anthropic SDK dependencies. Check the active client for supported agents and billing; a dependency does not prove a complete supported workflow.

Is Linzumi open source?

The public npm CLI is labeled MIT. The hosted team-chat service does not become open source through that package. Self-hosting and redistribution depend on the exact component and enterprise agreement.

Does the free plan include model usage?

The site says agents run on your machine with your own keys, so provider accounts and inference costs generally remain the user’s responsibility.

Can it process sensitive company code?

Do not assume so before reviewing service terms, privacy policy, DPA, retention and deletion, subprocessors, training policy, and enterprise security evidence. Begin with a sanitized repository and map the data flow.

Bottom Line

Linzumi addresses a genuine tradeoff: local agents have environment fidelity but often lack team visibility and remote supervision. Shared threads, run evidence, and session guardrails connect those sides, and active CLI releases show a working product rather than a static concept.

Beta status, roadmap labeling, and policy gaps must remain part of the decision. Draw three boundaries before adoption: code and commands execute locally, team context enters Linzumi’s cloud service, and inference belongs to an external model provider. Move from a personal pilot to company repositories only when data, permissions, cost, retention, and deletion are clear at all three layers.