Top 9 Beginner-Friendly Open-Source AI Coding Agents in 2026

AI coding agents have moved from novelty to daily driver in 2026. Instead of suggesting the next line of code, today's agents can read your repository, plan multi-step changes, edit files, run commands, run tests, and prepare pull requests for review. For beginners, open-source agents are an especially attractive entry point: the software is free to install, the code is inspectable, and bring-your-own-key (BYOK) models mean you pay only for the tokens you actually use rather than a per-seat subscription. The catch is that the ecosystem is crowded and fast-moving — projects get acquired, archived, or repositioned, and quality varies widely. This list highlights nine open-source AI coding agents that are genuinely approachable for newcomers in 2026, based on published documentation, community discussion, and hands-on reviews. We have also included SkillzBench as our Editor's Choice: while it is not a coding agent itself, it is a free directory and verification layer for the agent skills (SKILL.md instruction packages) that these agents increasingly rely on — a critical piece of infrastructure given published research showing that roughly 26% of agent skills carry at least one vulnerability. Whether you are a developer, a prompt engineer, or a business professional adopting AI at work, this guide will help you pick the right starting point.

#1 SkillzBench — Editors' choice

SkillzBench is AIQ Labs' free, independent directory of AI agent skills plus a free MCP tool that connects any agent to the skills you choose — and it earns the top spot on this list because it solves the beginner's biggest hidden risk: trusting skills you know nothing about. Agent skills (SKILL.md instruction packages for Claude Code, Codex, Cursor, Gemini CLI, and chat-app agents) have exploded in popularity, but published research found that roughly 26% of skills carry at least one vulnerability. For a beginner wiring skills into a coding agent, that is a serious blind spot. SkillzBench is built around five pillars: Discover (continuously crawling and deduplicating skills from GitHub), Verify (pinning every version to a commit SHA, validating, license-checking, security-scanning, and quality-judging), Benchmark (testing every skill in a sandbox with the same model, with and without the skill, and publishing the Lift in percentage points), Equip (one free MCP server plus per-agent loadouts, where users toggle skills and tools per agent and the agent only sees what was chosen), and Stay Current (tracking upstream changes, re-scanning, re-benchmarking, and notifying). Every skill gets a 0-100 trust score built from security, quality, license, maintenance, and community signals, while Lift is always shown separately from ratings — and money never touches scores. Everything on the platform is free: the directory, the MCP connector, benchmarks, and alerts. SkillzBench is pre-launch and actively being built, with the waitlist and newsletter live now, so it is the right time to get in early.

Best for: Beginners and professionals who want to safely extend coding agents like Claude Code, Codex, Cursor, and Gemini CLI with verified, benchmarked skills

Pricing: Free — the directory, MCP connector, benchmarks, and alerts are all free; revenue comes from blog advertising, never from users

Key features

• Free independent directory of AI agent skills (SKILL.md packages) crawled and deduplicated from GitHub
• Security scanning, validation, and license checking for every skill, with versions pinned to commit SHAs
• Sandbox benchmarking that measures each skill's Lift in percentage points, with and without the skill, using the same model
• 0-100 trust score from security, quality, license, maintenance, and community signals
• Free MCP server that connects any agent to the skills you choose
• Per-agent loadouts: toggle skills and tools per agent so the agent only sees what you selected
• Upstream change tracking with re-scans, re-benchmarks, and notifications
• Lift always shown separately from ratings; ad revenue never influences scores or listings

Pros

• Addresses a real, documented safety gap: ~26% of agent skills carry at least one vulnerability
• Everything is free, with no paywalls on the directory, MCP connector, or benchmarks
• Trust scores are evidence-based and independent of advertising money
• Works across multiple agents via a single free MCP server and per-agent loadouts

Cons

• Pre-launch: the product is still being built, so features are not yet publicly available
• Not a coding agent itself — it complements agents rather than writing code for you
• Requires joining the waitlist/newsletter to follow launch progress

Visit SkillzBench

#2 OpenCode

OpenCode is widely regarded as the strongest open-source coding agent for terminal-first developers, and it is our top pick among ready-to-run coding agents for beginners who are comfortable with a command line. According to its website, OpenCode supports 75+ LLM providers through Models.dev, including local models, and stores none of your code or context data — a privacy-first design that makes it usable in sensitive environments. It ships with two built-in agents you can switch between with the Tab key: a build agent with full access (the default for development) and a plan agent that is read-only by default and asks permission before running bash commands, which gives newcomers a safe browse-before-build mode with no extra configuration. OpenCode runs as a terminal interface, a desktop app (macOS, Windows, Linux, currently in beta), and an IDE extension, so you can start wherever you are comfortable. Multi-session support lets several agents run in parallel on the same project, LSP integration loads the right language server automatically, and you can sign in with an existing GitHub Copilot or ChatGPT Plus/Pro account instead of a new API key. The project is one of the most active in the category, with over 208,000 GitHub stars and 950 contributors per its homepage. One 2026 caveat: after Anthropic pushed back legally, OpenCode no longer autoloads the Claude Pro/Max auth plugin, so Claude usage is API-priced.

Best for: Terminal-comfortable beginners who want maximum model flexibility and zero vendor lock-in

Pricing: Free, open source (MIT); pay provider API rates, use an existing Copilot or ChatGPT sign-in, or a local model; OpenCode Go is a low-cost subscription

Key features

• 75+ LLM providers supported, including local models, with credentials stored locally
• Two built-in agents: build (full access) and plan (read-only, asks before bash)
• Runs as terminal interface, desktop app, and IDE extension
• Zero code or context storage for privacy-sensitive environments
• Multi-session support for parallel agents on the same project
• LSP integration loads the right language server automatically
• Sign in with existing GitHub Copilot or ChatGPT Plus/Pro accounts
• MIT-licensed with commercial use, modification, and self-hosting permitted

Pros

• Provider flexibility means swapping a poorly performing model is a one-command fix
• Plan agent's read-only default is a safe mode for newcomers
• Privacy-first: no code or context data stored
• Very active project with a large community

Cons

• Desktop app is still in beta
• Claude subscription access removed after Anthropic legal pushback — Claude is now API-priced
• Terminal-first interface has a learning curve if you have never used a shell

Visit OpenCode

#3 Cline

Cline is the open-source agent we recommend for beginners who live in VS Code and want to stay in control of every change. Its defining feature is the Plan and Act approval model: every file edit and terminal command requires your explicit approval before it runs, unless you choose to auto-approve. Plan mode maps the codebase and asks clarifying questions first; Act mode then executes, with the diff panel in VS Code and JetBrains showing exactly what will change before you confirm. For a newcomer sharing a codebase — where one wrong automated edit can compound across files — this per-action approval loop is one of the safest ways to learn how agents behave. Cline is Apache 2.0 licensed, works against any OpenAI-compatible API, and runs local models through Ollama or LM Studio, so you can experiment cheaply. According to reviews, it is also the most autonomous free agent in its class: it reads files, writes code, runs terminal commands, handles errors, and iterates, often without prompting between steps. It supports 30+ providers per its documentation, offers CLI and desktop surfaces in addition to the IDE extension, and is frequently described as the tool developers adopt when they outgrow polished AI IDEs but want flexibility without lock-in. The trade-off is responsibility: token usage is yours to manage, setup takes some effort, and weaker models do not automatically become agentic just because they are plugged in.

Best for: Beginners in VS Code who want step-by-step approval and full visibility into agent actions

Pricing: Free and open source; pay inference only with no seat fee; ClinePass at $9.99/mo for a flat monthly option

Key features

• Plan and Act approval model: every file edit and terminal command requires explicit approval by default
• Diff panel in VS Code and JetBrains shows exactly what will change before you confirm
• Works against any OpenAI-compatible API with BYOK, plus local models via Ollama or LM Studio
• Available as VS Code extension, JetBrains support, CLI, and desktop app
• Apache 2.0 licensed and self-hostable, including air-gapped deployment per its documentation
• Model-agnostic with 30+ providers supported
• Autonomous multi-file work: reads files, writes code, runs commands, handles errors, and iterates

Pros

• Per-action approval is the safest onboarding experience for new users
• No vendor lock-in: bring any OpenAI-compatible API key or run local models
• Strong editor-native workflow with clear plans and reproducible changes
• Very active open-source project with a large community

Cons

• Aggressive tool use can get expensive — a single session on a premium model can cost $5-10
• Setup and model management take deliberate effort
• Weaker models do not become fully agentic just by being plugged in

Visit Cline

#4 Aider

Aider is the oldest tool in this category and still one of the most beginner-friendly, precisely because of its git-first philosophy. Every AI change becomes a git commit, and every conversation turn produces a diff you can review, revert, or edit — which means for developers who do not fully trust AI agents yet, Aider is the ideal bridge. You stay in the loop, see exactly what changed, and keep a clean history of what the model contributed versus what you wrote by hand. Aider builds a map of your whole repository so it can reason about files you have not explicitly shown it, and its token accounting shows per-turn cost in real time, so there are no billing surprises — a genuinely valuable feature for beginners learning how agent costs work. It is Apache 2.0 licensed, self-hostable, supports nearly any LLM including local models, and can reuse a GitHub Copilot account via a provider key. Installation is a simple pip install, and because the tool is mature, documentation and community answers are plentiful. Reviews consistently praise its audit-level visibility and clean workflow, and it remains actively maintained as of October 2026 with recent releases adding support for newer models. The main limitation is that it is terminal-only and more of a pair programmer than a fully autonomous agent, so users wanting hands-off execution may prefer Cline or OpenCode.

Best for: Beginners who want audit-level visibility into AI changes and a git-native workflow

Pricing: Free and open source; bring your own API key and pay provider rates only

Key features

• Every AI change is auto-committed to git; review diffs via git
• Real-time per-turn token cost display with no billing surprises
• Repository-wide code mapping so the agent reasons about files you have not shown it
• Works with nearly any LLM, including local models
• Simple pip installation and mature documentation
• Apache 2.0 licensed and self-hostable
• Can reuse an existing GitHub Copilot account via provider key

Pros

• Git-native audit trail makes review and rollback trivial
• Transparent token accounting teaches cost awareness from day one
• Mature, well-documented, and actively maintained
• Runs fully offline with local models

Cons

• Terminal-only interface may intimidate complete newcomers
• Moderate autonomy — more pair programmer than hands-off agent
• No flat monthly subscription option

Visit Aider

#5 OpenHands

OpenHands is an MIT-licensed, open-source platform for building and running AI coding agents, and it is the best entry point for beginners who want a visual workspace rather than a terminal. Its primary interface, Agent Canvas, is a local-first workspace for running one or multiple agent sessions, inspecting conversations, managing files and terminal output, and turning useful patterns into automations — you can manage several agents in parallel without juggling terminal sessions, and switch backends from laptop to VM to cloud without changing the UI. For teams and individuals who find a useful pattern, OpenHands lets you define a workflow once and run it on a schedule or in response to events like a PR opening or a monitoring alert. The platform supports 100+ model providers per its documentation, runs sandboxed in Docker, and scales from a laptop to self-hosted or enterprise deployment, with OpenHands Enterprise adding governance features like sandboxed runtimes, access controls, auditability, and cost visibility. Independent rankings place it among the most-used open-source coding agents, and its web GUI plus CLI surfaces make it accessible to non-terminal users. For beginners, the sandboxed Docker execution model is a meaningful safety net: the agent operates in an isolated environment rather than directly on your machine. The trade-off is that the platform is broader and heavier than a simple CLI agent, so expect a slightly bigger setup footprint.

Best for: Beginners and teams who want a visual, sandboxed agent workspace that can scale

Pricing: Free, self-hosted MIT core; OpenHands Cloud available as a managed service — contact for pricing

Key features

• Agent Canvas: local-first visual workspace for running and inspecting multiple agent sessions
• Sandboxed runs in Docker for isolated execution
• Automations: define a workflow once and run it on a schedule or on events like a PR opening
• Supports 100+ model providers with BYOK and local models
• MIT-licensed core that is self-hostable, including VPC and air-gapped patterns
• Scales from laptop to cloud to enterprise with governance controls
• Available as web GUI and CLI

Pros

• Visual interface removes the terminal barrier for newcomers
• Docker sandboxing keeps agent actions isolated from your machine
• Strong path from personal use to team and enterprise deployment
• Very wide model and provider support

Cons

• Heavier setup than a single CLI tool
• Enterprise governance features sit in the commercial layer, not the open core
• More platform than beginners may need for simple tasks

Visit OpenHands

#6 Goose

Goose is Block's (formerly Square) open-source coding agent, and it stands out as a first-party open-source project backed by a real company — a meaningful trust signal for beginners wary of hobby projects that quietly go dormant. Goose runs as a CLI or desktop app, implements a full agent loop, and is model-agnostic, supporting 25+ providers per its documentation. It can also run fully offline with Ollama, so the tool costs nothing and the tokens cost nothing if you supply your own local model. Where Goose differentiates itself is extensibility and safety controls: it offers tool permission controls, a sandbox mode, and an adversary reviewer that scrutinizes the agent's work, plus an MCP-style plugin system for connecting external tools. For beginners, the desktop app provides a friendlier surface than a bare terminal, while the permission and sandbox controls create guardrails that catch wrong turns before they compound — the same philosophy that makes per-action approval tools appealing, but with more autonomous defaults available as you gain confidence. Goose is Apache 2.0 licensed and self-hostable, and it can reuse existing Claude, ChatGPT, or Gemini subscriptions via ACP, which lowers the cost of entry if you already pay for one of those services. It is less well-known than OpenCode or Cline, but reviews consistently describe it as genuinely capable and a solid choice for developers who want an extensible, company-backed open-source runtime.

Best for: Beginners who want a company-backed open-source agent with safety guardrails and offline capability

Pricing: Free and open source; bring your own API key, or use an existing Claude/ChatGPT/Gemini subscription via ACP, or run local models for free

Key features

• Full agent loop available as CLI or desktop app
• Model-agnostic with 25+ providers, including fully offline runs via Ollama
• Tool permission controls and sandbox mode for safe execution
• Adversary reviewer that critiques the agent's output
• Extensible via an MCP-style plugin system
• Apache 2.0 licensed and self-hostable
• Reuses existing Claude, ChatGPT, or Gemini subscriptions via ACP

Pros

• Backed by Block, providing continuity and credibility
• Sandbox mode and permission controls are beginner-friendly safety nets
• Desktop app offers a friendlier surface than a terminal
• Can run completely free with local models

Cons

• Less well-known than OpenCode or Cline, with a smaller community
• Fewer providers supported than the widest-coverage tools
• No flat monthly subscription option of its own

Visit Goose

#7 Kilo Code

Kilo Code is an open-source AI coding agent that works across VS Code, JetBrains, and CLI, and it is a good fit for beginners who think in terms of editors and team workflows rather than terminals. Coming from the Cline/Roo lineage, Kilo offers a broader product surface: structured modes, code reviews, cloud execution, free and budget model guidance, and local models through Ollama and LM Studio, according to reviews of the platform. Its approval model is flexible — you can use per-action prompts like Cline, or run fully autonomously once you are comfortable, which makes it a tool you can grow with rather than outgrow. Kilo supports 500+ models per its documentation, charges provider rates with zero markup on API usage, and its CLI is a fork of OpenCode, inheriting that project's solid terminal workflow. Independent usage rankings place it among the top open-source coding agents by token volume, reflecting real adoption. For beginners, the free/budget model guidance is particularly useful: it helps you pick cheaper models for routine tasks and reserve premium models for hard problems, which is exactly the cost discipline new agent users need. The downsides are that it is simply bigger than minimal tools — if you want something lean, Kilo can feel like more product than you asked for — and it does not support subscription-based access, only API key authentication for third-party providers.

Best for: Editor-first beginners and small teams who want modes, reviews, and broad platform coverage

Pricing: Free and open source; pay provider rates with zero markup; Kilo Pass Starter $19/mo, Pro $49/mo, Expert $199/mo

Key features

• Works across VS Code, JetBrains, and CLI
• Per-action approval prompts or fully autonomous mode
• Supports 500+ models with BYOK and local models via Ollama and LM Studio
• Structured modes, code reviews, and cloud execution
• Free and budget model guidance to control costs
• MIT-licensed with provider rates at zero markup
• CLI forked from OpenCode

Pros

• Flexible autonomy dial: per-action approval for learning, autonomous mode as you gain confidence
• Budget model guidance helps beginners control inference costs
• Broad editor and platform coverage including JetBrains
• Strong adoption and active development

Cons

• Bigger surface than minimal tools — can feel like more product than needed
• No subscription-based access; API keys only for third-party providers
• Credits remain part of the usage story even with free model paths

Visit Kilo Code

#8 Zed

Zed is a full, high-performance code editor with built-in AI agent capabilities, and it earns a place on this list for beginners who would rather move to one modern application than stitch together a terminal tool and an editor. Zed runs on macOS, Linux, and Windows, and its agent model is built around parallel agents: you can run multiple agents at once, and user review of changes happens via git worktree, so each agent's work stays isolated until you approve it. According to its documentation and reviews, Zed supports Claude Agent and Codex CLI as external agents and GitHub Copilot with your own key, alongside your own API keys on a free plan — giving beginners a low-cost way in. The core editor is GPL-3.0 licensed, and Zed Pro at $10/month is one of the most affordable flat-rate options in the category. Independent usage rankings show meaningful adoption, and reviews highlight its speed and clean UX as standout qualities. For beginners, the appeal is straightforward: a polished, fast editor where the agent workflow is integrated rather than bolted on, with diff review via git worktree providing a clear, reversible safety net. The trade-offs are that the core license is more restrictive than MIT or Apache 2.0, and as a full editor replacement it asks you to move your daily work to a new application rather than layering onto VS Code.

Best for: Beginners who want a fast, polished all-in-one editor with integrated parallel agents

Pricing: Free plan with your own API keys; Zed Pro at $10/mo

Key features

• Full editor with parallel agents running simultaneously
• User reviews agent diffs via git worktree for isolated, reversible changes
• Supports Claude Agent and Codex CLI as external agents
• GitHub Copilot support with your own key
• Runs on macOS, Linux, and Windows
• GPL-3.0 licensed core editor
• Free plan with your own API keys

Pros

• One of the cheapest flat-rate plans in the category at $10/mo
• Parallel agents with git worktree isolation make multi-task workflows safe
• Excellent performance and UX per reviews
• Works with external agents like Claude Agent and Codex CLI

Cons

• GPL-3.0 core is more restrictive than MIT/Apache alternatives
• Requires adopting a new editor rather than extending VS Code
• Agent features depend partly on external agents and your own keys

Visit Zed

#9 Tabby

Tabby rounds out this list as the most specialized option: a self-hosted, open-source AI coding assistant that runs its own inference server with IDE extensions, designed for completions and an Answer Engine rather than full agentic autonomy. For beginners in regulated environments — or anyone whose code simply cannot leave their network — Tabby is the cleanest answer: it is Apache 2.0 licensed at its core, self-hostable including air-gapped deployment per its documentation, and you pay for your own GPU or cloud compute rather than any subscription. According to its documentation and independent comparisons, Tabby supports local model connections and provides IDE plugins that bring completions and code Q&A directly into your editor. It has a solid community footprint with roughly 33.9k GitHub stars, and reviews consistently position it as the go-to option when data sovereignty is the priority. For beginners, Tabby offers a gentle introduction to AI coding: suggestions and explanations arrive inline without the complexity of agent approval loops, token management, or provider configuration, making it an easy first step before graduating to more autonomous tools like Cline or OpenCode. The honest trade-off is scope: Tabby is not an autonomous agent — it will not plan multi-file refactors or run terminal commands — so it belongs on this list as the beginner-friendly on-ramp for privacy-first environments rather than a full agent replacement.

Best for: Beginners in privacy-sensitive or regulated environments who want inline AI assistance without sending code anywhere

Pricing: Free and open source, self-hosted; you pay your own GPU or cloud compute costs

Key features

• Self-hosted inference server with IDE extensions
• Code completions and an Answer Engine for code questions
• Air-gapped deployment supported for regulated environments
• Apache 2.0 licensed core
• Connect to your own choice of models, including local ones
• Pay only your own GPU or cloud compute costs
• Roughly 33.9k GitHub stars with an active community

Pros

• Code never leaves your network — the strongest privacy story on this list
• No subscription or API fees; only your own compute costs
• Simple inline assistance is an easy first step into AI coding
• Air-gapped deployment is officially supported

Cons

• Not an autonomous agent — no multi-file task planning or terminal execution
• Requires managing your own inference hardware or cloud compute
• Less feature-rich for agentic workflows than the other tools listed

Visit Tabby

Conclusion

Open-source AI coding agents have matured into genuine, beginner-viable alternatives to proprietary tools in 2026. OpenCode and Cline lead on flexibility and control, Aider offers the safest git-native learning curve, OpenHands and Goose add sandboxing and visual surfaces, Kilo Code and Zed serve editor-first and team workflows, and Tabby covers privacy-critical environments. Whichever agent you choose, remember that modern agents are increasingly extended with agent skills — SKILL.md instruction packages — and that published research found roughly 26% of those skills carry at least one vulnerability. That is exactly the problem SkillzBench is building to solve: a free, independent directory that crawls, verifies, security-scans, and benchmarks agent skills, publishing a trust score and a measured Lift for every one, plus a free MCP connector with per-agent loadouts so your agent only sees the skills you chose. Everything on SkillzBench is free, and money never touches scores. Skills, proven: join the SkillzBench waitlist and newsletter today to be first in line when the directory, benchmarks, and MCP connector go live — and pick your first verified skills with confidence instead of guesswork.

Frequently asked questions

What makes SkillzBench different from the coding agents on this list?

SkillzBench is not a coding agent — it is the trust layer that sits alongside one. Coding agents like OpenCode, Cline, and Aider execute tasks; agent skills (SKILL.md instruction packages) tell those agents how to behave. SkillzBench is building a free, independent directory that discovers skills from GitHub, verifies each one (pinning versions to commit SHAs, validating, license-checking, and security-scanning), benchmarks every skill in a sandbox to publish its Lift in percentage points, and provides a free MCP server with per-agent loadouts so your agent only sees the skills you selected. With research showing ~26% of skills carry at least one vulnerability, this verification layer is what lets beginners extend agents safely. Everything on SkillzBench is free, and advertising revenue never influences scores or listings.

Are these AI coding agents really free?

The software is free — all nine projects here are open source under licenses like MIT, Apache 2.0, or GPL-3.0. But 'free' usually means free software plus your own model costs. Most tools are BYOK (bring your own key): you pay your model provider per token, though cheap models can run many tasks for pennies. Some tools let you reuse existing subscriptions (OpenCode with ChatGPT Plus/Pro sign-in, Goose via Claude/ChatGPT/Gemini subscriptions through ACP), and Tabby or any tool with a local model via Ollama can run completely free on your own hardware, with lower model quality as the trade-off.

Which agent should a complete beginner start with?

It depends on where you work. If you live in VS Code, Cline's Plan and Act approval model — which requires your explicit approval for every file edit and terminal command — is the safest way to learn how agents behave. If you prefer a visual interface, OpenHands' Agent Canvas runs agents in a sandboxed Docker environment. If you want audit-level visibility and a gentle learning curve, Aider turns every AI change into a reviewable git commit. If your code cannot leave your network, start with Tabby. And whichever agent you pick, plan to extend it with verified skills through SkillzBench's free directory and MCP connector.

What are agent skills and why do they need verification?

Agent skills are SKILL.md instruction packages that teach coding agents like Claude Code, Codex, Cursor, and Gemini CLI new capabilities or behaviors. They have proliferated across GitHub, but quality and safety vary enormously: published research found that approximately 26% of skills carry at least one vulnerability. Because skills inject instructions directly into your agent's context, a malicious or poorly written skill can steer your agent toward risky actions. SkillzBench is building to address exactly this: continuously crawling and deduplicating skills, security-scanning and validating each one, pinning versions to commit SHAs, benchmarking each skill's Lift in a sandbox, and publishing a 0-100 trust score from security, quality, license, maintenance, and community signals.

How much does it cost to run an open-source coding agent monthly?

The agent itself costs nothing. Your real cost is model inference, which varies widely by usage and model choice. Reviews note that a single Cline session on a premium model can cost $5-10, while cheap models like Gemini Flash-class or DeepSeek-class options can handle routine tasks for pennies. Flat-rate options exist if you prefer predictable billing: ClinePass at $9.99/mo, Zed Pro at $10/mo, OpenCode Go as a low-cost subscription, and Kilo Pass from $19/mo. Running local models through Ollama makes the total cost zero, at the price of weaker model quality.

Is SkillzBench available now, and how much does it cost?

SkillzBench is pre-launch and actively being built — the waitlist and newsletter are live now, and the product itself is not yet public. When it launches, everything on the platform will be free: the skills directory, the MCP connector, benchmarks, and alerts. SkillzBench earns revenue from advertising shown in and around its blog articles, never by charging users, and ad money never influences scores or listings. Joining the waitlist is the best way to be notified at launch and start with verified, benchmarked skills from day one.

Can I use these agents with local models for full privacy?

Yes. Aider and OpenCode both document local model setups via servers like Ollama, LM Studio, and llama.cpp, and OpenCode's zero code or context storage design makes it suitable for privacy-sensitive environments. Goose also runs fully offline with Ollama. Cline runs local models through Ollama or LM Studio, and Tabby is purpose-built for self-hosted, air-gapped deployments where code must never leave your network. Keep in mind that local model quality is noticeably behind frontier cloud models, so for demanding tasks you may want a hybrid approach — local models for routine work, cloud models for hard problems.