The Best GUIs, TUIs, and CLIs for Running AI Agents Locally
Local AI agents do not fail only because the model is weak. They fail because the operator surface is wrong for the job.
A browser chat window is fine for asking a model to summarize a note. It is not enough when an agent needs to edit files, call tools, run shell commands, keep state, and hand work to another process. At that point, the interface becomes part of the system architecture.
I split local agent interfaces into two groups:
- GUIs for shared, document-heavy, or non-technical workflows
- TUIs and CLIs for code, automation, and tight feedback loops
That split matters more than brand choice. A polished GUI can make a weak workflow feel safe while hiding what the agent is doing. A terminal interface can feel rough while giving you the exact control surface you need to debug real work.
This is the guide I wish I had when I started testing local agent stacks. Not a ranking by hype. Best means best for the workflow, not best in a vacuum.
What I mean by local
Local does not always mean the model weights run on your laptop.
For this post, local means you control the runtime surface: the app, the workspace, the files, the logs, and the tool permissions. The model can be Ollama on a desktop GPU, a local llama.cpp server, an OpenAI-compatible endpoint, or a hosted API behind a self-hosted interface. That is local control, not always local inference.
That distinction is important. Most useful agent systems are hybrid. I often run retrieval and workflow state locally while sending hard reasoning calls to a frontier model. The point is not purity. The point is ownership of the operating loop.
If the interface controls your files, shells out to tools, stores memory, or routes work between agents, it belongs in this conversation.
The short version
If I had to pick fast:
- Open WebUI is the best general self-hosted GUI for local model experimentation and shared chat workflows.
- LibreChat is the best ChatGPT-style team interface when you need accounts, agents, provider switching, and admin control.
- AnythingLLM is the easiest GUI for document chat and light agent workflows on a single machine.
- Jan is the cleanest desktop-first local model app when the priority is offline chat, not deep agent orchestration.
- Goose is the best mixed desktop plus CLI agent when you want one tool that spans coding, research, and general automation.
- OpenCode is the strongest terminal-native coding agent if you want a modern TUI and an open source stack.
- Aider is still the terminal tool I trust for deliberate code edits because it treats the Git repo as the center of the workflow.
None of these replaces system design. They are control surfaces. Pick the one that matches the work.
Before picking a tool, I check six criteria:
- local control over files, models, and runtime
- permission boundaries for tools and shell access
- logs, diffs, or transcripts I can audit later
- provider support without locking the workflow to one model
- team and admin controls when more than one person uses it
- fit for the actual work: code, documents, research, or long-running automation
Open WebUI: best general local AI GUI
Open WebUI is the default recommendation when someone asks for a self-hosted AI interface and does not yet know what their workflow will become.
It supports Ollama and OpenAI-compatible APIs, has a clean web interface, and can run as a self-hosted app. The GitHub repository is large and active. In mid-May 2026, it had roughly 137,000 stars, which is not a quality guarantee, but it does show community gravity.
Where it works well:
- trying local models quickly
- giving a small team a familiar chat surface
- connecting Ollama, LM Studio, OpenRouter, Groq, or other compatible endpoints
- adding lightweight tools and functions
- running a private internal assistant without building a frontend
The advantage is breadth. Open WebUI gives you enough pieces to build a useful local AI workspace: model switching, user management, RAG features, tool support, and a browser UI that normal people can use.
The tradeoff is that breadth can become softness. If you are building a production agent that edits a repo, runs tests, and needs exact state handoffs, I would not start here. I would use Open WebUI for chat, exploration, and shared access, then move execution into a terminal agent or a background worker.
Use Open WebUI when the interface needs to be approachable. Do not use it as an excuse to hide operational details from yourself.
LibreChat: best self-hosted ChatGPT-style agent GUI
LibreChat is the better choice when the problem looks less like "I want a local model UI" and more like "I want a controlled ChatGPT replacement for a team."
It supports many providers, agents, MCP, OpenAPI actions, code interpreter workflows, user accounts, presets, and admin configuration. The repository had roughly 37,000 stars in mid-May 2026, and the project has the shape of a serious self-hosted product rather than a weekend wrapper.
Where it works well:
- multi-user internal AI portals
- teams that need provider switching
- organizations that want one UI for OpenAI, Anthropic, Google, local APIs, and OpenRouter
- agent workflows with permissions and reusable presets
- replacing a loose pile of personal chat accounts
LibreChat is less exciting than a raw terminal agent, but that is the point. It gives you a governed surface. If you are letting multiple people use models and tools, the boring admin features matter.
The tradeoff is setup and scope. LibreChat has more moving parts than a desktop app. You should expect configuration work. If you are a solo operator trying to run a local Llama model against a few notes, it is probably more infrastructure than you need.
Use LibreChat when the interface is for a team. Use a TUI when the interface is for an operator.
AnythingLLM: best document-first local GUI
AnythingLLM is the fastest route from "I have files" to "I can ask questions over those files."
It ships as a desktop app for Mac, Windows, and Linux, and it also has server deployment options. It supports document workspaces, model provider configuration, and agent features without forcing you to assemble each component yourself. In mid-May 2026, the repository had roughly 60,000 stars.
Where it works well:
- local document chat
- personal knowledge bases
- small business knowledge assistants
- quick internal tools for non-technical users
- lightweight agent tasks attached to a workspace
I like AnythingLLM for the same reason I like a good notebook: it reduces friction. You can get to a useful loop before you have designed a full system.
The limit is the same as most document-first tools. If the workflow becomes more than retrieval plus light actions, you will end up wanting a clearer execution layer. Document chat is not the same thing as an agent operating system.
Use AnythingLLM when the knowledge base is the center. Move beyond it when the work becomes stateful execution.
Jan: best desktop-first local model app
Jan is the app I point to when someone wants a local ChatGPT replacement that feels like a real desktop product.
It focuses on downloading and running models with local control and privacy. The project had roughly 42,500 stars in mid-May 2026. It is not trying to be a universal workflow engine, and that restraint is useful.
Where it works well:
- offline chat
- testing local models without a server stack
- giving non-technical users a desktop AI app
- privacy-sensitive personal workflows
- quick evaluation of model quality
Jan is not the strongest agent interface in this list. That is not a criticism. A clean local chat app has a place. Sometimes I want to test whether a model can think through a prompt before I wire it into a heavier agent loop.
Use Jan when the first requirement is local model access. Do not confuse that with local agent orchestration.
Goose: best cross-surface local agent
Goose is the most interesting bridge between GUI and TUI. It offers a desktop app, CLI, and API for a general-purpose local agent. The project has moved into the Agentic AI Foundation at the Linux Foundation, and the README positions it as an agent for code, research, writing, automation, and data analysis.
In mid-May 2026, the Goose repo, now under aaif-goose/goose after the Block redirect, showed roughly 45,000 stars. More important than the star count: Goose has the right shape. It treats the local machine as the workspace, supports many providers, and connects to extensions through MCP.
Where it works well:
- mixed technical and non-technical agent work
- desktop users who also need a CLI
- local automation beyond code edits
- MCP-based tool extension
- teams standardizing on one agent runtime
The strength is continuity. A desktop app is good for visibility. A CLI is good for speed. An API is good for embedding. Goose puts those surfaces near each other.
The tradeoff is maturity under pressure. General-purpose agents have to do a lot of things well. I would still isolate risky actions, review file edits, and keep final publication or deployment behind approval until the workflow earns trust.
Use Goose when you want one agent that can live across surfaces. Keep your boundaries explicit.
OpenCode: best modern TUI coding agent
OpenCode is the terminal-native coding agent I would test first if I were starting a new open source coding workflow today.
It is open source, written in TypeScript, and built around a modern TUI. The sst/opencode project showed roughly 160,000 stars in mid-May 2026. That number is inflated by the current coding-agent wave, but the direction is clear: terminal coding agents have become the serious operator surface.
Where it works well:
- repo-aware coding sessions
- fast file edits and test loops
- developers who live in the terminal
- local project workflows with remote model calls
- replacing browser chat for implementation work
The reason TUIs win for code is simple. Code work already has a control plane: Git, files, tests, logs, package managers, and shell commands. A terminal agent can sit inside that plane instead of pretending the browser is the workspace.
OpenCode's best feature is not that it looks good. It is that it keeps the work near the repo.
The tradeoff is operator skill. A TUI gives you more visibility, but it also assumes you know what you are looking at. Non-technical users will bounce off it. That is fine. They are not the target.
Use OpenCode when the work is code and the operator is technical.
Aider: best deliberate Git-centered code agent
Aider has been around long enough to earn trust. It is AI pair programming in the terminal, but the deeper point is that it treats Git as the source of truth.
In mid-May 2026, Aider showed roughly 45,000 stars on GitHub. Its README badges also reported large install and token-use numbers, but badges drift. The important signal is the workflow: Aider is not a toy.
Where it works well:
- editing existing codebases
- reviewing diffs before committing
- small to medium implementation tasks
- pairing with a model while keeping human control
- using Git history as the audit trail
I trust Aider most when I know what change I want and need a tight edit loop. It is less glamorous than an autonomous coding agent, but that is part of the appeal. The workflow is explicit: pick files, ask for the change, inspect the diff, run tests, commit.
That structure prevents a common agent failure: vague autonomy. The agent cannot quietly wander through the repo if you keep the file set and diff review tight.
Use Aider when correctness matters more than spectacle.
How to choose
The cleanest decision rule is this:
If the work is chat or shared knowledge, start with a GUI. If the work touches files, shells, tests, deploys, or long-running state, start with a TUI or CLI.
A more practical map:
- Solo local model testing: Jan or Open WebUI
- Personal document assistant: AnythingLLM
- Team AI portal: LibreChat
- General local agent with desktop and CLI surfaces: Goose
- Coding in an active repo: OpenCode for a TUI, Aider for a Git-centered CLI
- Production multi-agent workflow: a task runner, a durable state store, and a narrow interface per role
That last category is where most UI comparisons break down. A production agent setup is not one app. It is a system: interface, runtime, memory, tools, logs, review gates, and escalation rules.
I wrote about that in AI Agent Workflows That Actually Ship and Why AI Agents Break in Production. The interface choice matters after the workflow has a shape.
My current bias
I do not want one interface for all agents.
I want the interface to match the failure mode.
For research and knowledge work, I want a GUI because reading, comparing, and sharing are visual tasks. For code, I want a TUI because the terminal already holds the repo, tests, logs, and deployment commands. For scheduled or delegated work, I want no interface at all. I want a background worker that writes structured handoffs and stops when it hits a human decision.
That is the pattern I keep returning to:
- GUI for visibility
- TUI for control
- worker for autonomy
- human gate for irreversible actions
The mistake is trying to make one app carry all four jobs.
Final recommendation
Start boring.
Set up Open WebUI if you need a self-hosted AI workspace. Install AnythingLLM if your main problem is local documents. Install Jan if you want a clean offline desktop chat app. Set up LibreChat if you are building for a team. Install Goose if you want a general agent that spans desktop and terminal. Install OpenCode for TUI coding, or Aider for deliberate Git-centered CLI edits.
Then judge the tool by operational questions, not screenshots:
- Can I see what the agent did?
- Can I restrict what it can touch?
- Can I recover state after a failed run?
- Can I inspect diffs before changes become permanent?
- Can I hand work to another process without losing context?
- Can I cap provider spend or see usage by user, workspace, or agent?
The best local AI agent interface is not the prettiest one. It is the one that keeps the agent close to the work while making failure easy to see.
That is the interface worth running.
Read next: AI Agent Workflows That Actually Ship and Why AI Agents Break in Production.