Open Source Kilo Alternatives

OpenCode is an open source AI coding agent built for developers who want full control over their tools and data. It runs in the terminal, as a desktop app (available in beta on macOS, Windows, and Linux), and as an IDE extension, so it fits into existing workflows without forcing a specific environment.

Model flexibility is a core part of the design. It connects to 75+ LLM providers through Models.dev, including local models, and supports Claude, GPT, Gemini, and others. Developers with existing GitHub Copilot or ChatGPT Plus/Pro subscriptions can log in directly and use those accounts without paying for another service.

LSP (Language Server Protocol) support is built in, meaning OpenCode automatically loads the appropriate language servers for the LLM context. This gives the agent a more accurate understanding of your codebase rather than treating it as plain text. You can also run multiple agents in parallel on the same project, which is useful when working across separate features or debugging threads simultaneously.

Session sharing lets you generate a link to any coding session. That makes it easier to hand off context to a colleague or revisit a debugging thread later.

Privacy is handled by design: OpenCode does not store your code or context data. That makes it usable in environments where sending source code to third-party servers is a concern. Tools like Cline and Continue take similar approaches to local-first AI coding, but OpenCode's combination of desktop, terminal, and IDE support in one package is relatively uncommon.

The project has over 160,000 GitHub stars, 900 contributors, and is used by roughly 7.5 million developers monthly.

Pi is a terminal coding agent built around a single principle: the harness should adapt to you, not the other way around. Unlike AI coding agents that ship with fixed opinions about plan modes, sub-agents, and permission flows, Pi keeps the core deliberately small and exposes everything through a TypeScript extension system.

The extension model is the real differentiator. You can add tools, commands, keyboard shortcuts, events, custom editors, status bars, and overlays. Bundle those into a package and share it via npm or git. Third-party extensions already exist, including one that turns Pi into a drawing canvas inside the terminal. If you want a feature Pi doesn't have, you ask Pi to build it, hit /reload, and keep going.

Key capabilities:

• 15+ providers including Anthropic, OpenAI, Google, Azure, Bedrock, Mistral, Groq, Cerebras, xAI, Ollama, OpenRouter, and more. Switch models mid-session with /model or cycle favorites with Ctrl+P.
• Tree-structured history so sessions branch rather than scroll. Navigate any prior point with /tree, export to HTML, or upload to a GitHub gist for a shareable URL.
• Context engineering via AGENTS.md (project instructions), SYSTEM.md (custom system prompts), skills (on-demand capability packages), prompt templates, and fully customizable compaction that summarizes older messages before hitting the context limit.
• Steering while running. Press Enter to interrupt the current run with a steering message, or Alt+Enter to queue a follow-up once it finishes.
• Four modes: interactive TUI, print/JSON for scripting, RPC over stdin/stdout for non-Node integrations, and an SDK for embedding Pi in your own apps.

Pi is token-efficient by design. Its system prompt is minimal, and skills use progressive disclosure so you're not burning tokens on capabilities you haven't loaded. Features like Aider or Cline bake more in by default; Pi bets that a smaller, extensible core is more useful to developers who want control over their tooling.

Licensed under MIT and self-hostable.

OpenHands is an AI agent platform built for software teams that need more than code suggestions. Instead of autocompleting lines in an editor, it runs autonomous agents that plan, execute, and ship changes across entire codebases. Think: open a GitHub issue, an agent investigates, writes the fix, runs tests, and opens a pull request for review.

It's model-agnostic by design. You can point it at any LLM, swap models as needs change, and integrate it into existing CI/CD pipelines without rearchitecting your workflow. For teams already using self-hosted developer infrastructure, it fits naturally into that setup.

Key capabilities include:

• Vulnerability remediation: Scans repositories, patches security issues, and opens reviewable PRs automatically
• PR review automation: Reviews pull requests for quality, security, and best practices
• Legacy migration: Migrates COBOL systems to Java with testing and validation built in
• Incident triage: Investigates production errors, traces root causes, and posts actionable debugging summaries
• Test coverage expansion: Generates and maintains tests for new features to catch regressions before they ship
• Parallel execution: Runs thousands of agent tasks simultaneously, not just one at a time

The platform runs inside isolated Docker or Kubernetes environments. Your code stays in your environment, on-prem or private cloud, with full auditability over every agent action and artifact. That matters for teams with strict compliance requirements.

A Large Codebase SDK handles dependency mapping across complex systems, letting multiple agents work in parallel without creating conflicts. This makes it practical for large legacy codebases that most AI tools struggle with.

Teams can interact with OpenHands through a web UI, CLI, or SDK. It integrates directly with GitHub, GitLab, Slack, and standard ticketing tools, so agents can be triggered from wherever work already happens. Developers building their own AI-powered tooling can embed the SDK into custom workflows.

OpenHands has accumulated over 75,000 GitHub stars and an active contributor community. The open-source foundation means full visibility into how agents behave, which is a meaningful difference from closed-source alternatives.

Cline is an open-source AI coding agent that works inside VS Code and the command line. It reads and edits files across a project, runs terminal commands, reacts to their output in real time, and handles long-running processes like dev servers or test suites.

The Plan/Act mode split lets you discuss a strategy with the agent before it touches any code, then switch to Act mode when you're ready to execute. Every step is approvable, or you can enable auto-approve for unattended runs.

Model choice is unrestricted. Claude, GPT, Gemini, local Ollama or LM Studio instances, and any OpenAI-compatible endpoint all work. You bring your own key or your own weights.

For teams, Cline supports multi-agent setups where a coordinator delegates to specialist agents with their own tools and context windows. It also connects to Slack, Discord, Telegram, and Linear, and runs headlessly inside GitHub Actions or GitLab pipelines. Tools like Roo Code and OpenHands take a similar multi-agent approach if you want to compare.

.clinerules files let you ship coding standards, architecture guidelines, and deployment conventions alongside your repo so the agent follows project-specific rules consistently.

Extensibility comes through the SDK and MCP server support, which lets you register custom tools and connect to databases, APIs, or infrastructure. The project is Apache 2.0 licensed with 250+ contributors.

Open Interpreter brings the capabilities of a coding agent to your local machine, without requiring a subscription to a proprietary API. It lets language models write code, run it, browse the web, manage files, and interact with your operating system directly. Think of it as a terminal-based assistant that can actually do things, not just suggest them.

It's built specifically to work well with open-weight and local models, making it a practical choice for anyone who wants to avoid sending code or sensitive data to external servers. You can connect it to models running through Ollama or LocalAI, or point it at hosted providers for more capable models when needed.

Key capabilities include:

• Code execution across Python, JavaScript, Shell, and other languages, run directly in your environment
• File and system access so the agent can read, write, move, and modify files as part of completing tasks
• Web browsing to research, fetch data, or interact with online content
• Multi-model support with easy switching between local and remote LLMs
• Interactive chat interface that keeps context across a session, so you can refine tasks conversationally
• Computer control for more advanced automation tasks

Compared to cloud-only AI coding agent orchestrators like Codex, Open Interpreter puts you in control of which model runs and where. That matters for privacy, cost, and offline use. It also means performance depends heavily on the model you choose. Pair it with a strong open-weight model and it handles real tasks; use a weaker one and you'll feel the limits quickly.

It suits developers, researchers, and power users who want a capable local agent and are comfortable picking and configuring their own models.

Goose is a general-purpose AI agent that runs natively on your machine. It's not narrowly focused on coding: you can use it for research, writing, data analysis, browser automation, or any multi-step task you'd otherwise piece together manually. It ships as a desktop app for macOS, Linux, and Windows, a full CLI for terminal-first workflows, and an API for embedding it in other tools.

The LLM support is broad. Goose works with 15+ providers, including Anthropic, OpenAI, Google, Ollama, LocalAI, OpenRouter, Azure, and Bedrock. You can use API keys or connect through existing Claude, ChatGPT, or Gemini subscriptions, so you're not locked into a new billing relationship just to try it.

Extensibility is central to how goose works:

• 70+ MCP extensions connect it to databases, APIs, browsers, GitHub, Google Drive, and more via the open Model Context Protocol standard
• MCP Apps let extensions render interactive UIs inside the desktop app, including buttons, forms, and visualizations
• Recipes let you capture repeatable workflows as portable YAML configs, shareable with a team or runnable in CI
• Subagents can be spawned to handle tasks in parallel, keeping the main conversation focused
• Security controls include prompt injection detection, tool permission controls, and a sandbox mode

Goose also implements the Agent Client Protocol, which means it works as an ACP server you can connect from editors like Zed, JetBrains, or VS Code. It can use Codex and similar agents as providers through the same standard.

The project sits under the Agentic AI Foundation at the Linux Foundation, which keeps it vendor-neutral and community-governed. Built in Rust for performance, it has attracted 500+ contributors and 45k+ GitHub stars. If you want a self-hostable, provider-agnostic agent that works across interfaces and integrates deeply with the MCP ecosystem, goose covers a lot of ground.

Continue adds automated quality control to your pull request workflow by running AI checks you define directly in your repo. You write the checks as Markdown files, commit them alongside your code, and Continue enforces them on every PR as native GitHub status checks. When code misses the mark, it surfaces suggested fixes inline.

The core idea is specificity. Unlike generic AI code reviewers that surface unsolicited opinions or broad style feedback, Continue only enforces what you've explicitly told it to catch. That means no surprise flags, no noise, and no drift from your actual standards.

Key capabilities:

• Source-controlled checks written in Markdown, versioned with your codebase so standards evolve alongside the code
• Native GitHub status checks that integrate directly into your existing PR workflow without separate dashboards
• Suggested fixes delivered when a check fails, reducing back-and-forth between author and reviewer
• Consistent enforcement across every PR, regardless of who wrote the code or how fast the team is shipping

It's built for engineering teams that have already defined what good looks like and want that enforced mechanically, not left to whoever has bandwidth for review. The checks cover areas like security, code reuse, and custom standards your team sets.

Tools like CodeRabbit or Qodo take a broader approach to AI review. Continue's value is the opposite: narrow, deliberate, and fully under your control. Your standards, your checks, your call on what gets enforced.

Grok Build is a terminal coding agent built for developers who want serious AI assistance without leaving the command line. It handles the full development cycle: planning, writing code, running tests, committing to git, and deploying. Any language, any codebase.

The tool's Plan Mode sets it apart from simpler autocomplete tools. Before touching a single file, it proposes a structured plan you can approve, comment on line by line, or rewrite entirely. Every approved change appears as a clean diff. Nothing gets written until you say so.

For large tasks, subagents run in parallel, each with its own context window and optionally its own git worktree. Ask it to find a latency regression and it can simultaneously explore your checkout flow, infrastructure, shared libraries, and pricing engine at once.

Key capabilities:

• Skills: reusable slash commands you build from any session using /skillify, auto-invoked when a task matches
• Plugins: bundle skills, agents, hooks, and MCP servers into a single install, shareable via marketplace or a self-hosted git repo
• MCP servers: connect to Cline-style external tools like Linear, Sentry, Grafana, and Postgres
• Hooks: run scripts automatically on file edits or tool calls
• AGENTS.md: set per-directory conventions the agent follows consistently
• Memory: decisions and context persist across sessions
• Headless mode: scriptable in CI/CD pipelines
• Sandboxed execution: run untrusted code in isolation
• Web search: look up docs and packages without switching context

When a task is ambiguous, Grok Build asks targeted multiple-choice questions before starting, so it picks the right framework, schema, or design direction upfront rather than guessing. The fullscreen terminal UI supports mouse input and keyboard-first navigation.

It's free to try, and unlike Grok in a browser chat interface, this is purpose-built for working directly inside your existing development environment.

SWE-agent enables language models like GPT-4o or Claude Sonnet 3.7 to autonomously use tools to solve GitHub issues, fix bugs, and implement new features. Built by researchers from Princeton and Stanford Universities, it achieves state-of-the-art performance on SWE-bench among open-source projects.

Key Features:

• 🏆 SOTA Performance: Leading results on SWE-bench benchmarks
• 🔧 Fully Configurable: Single YAML file controls all agent behavior
• 🎯 Research-Ready: Simple, hackable design for experimentation
• 🚀 Free-flowing Agency: Maximizes language model autonomy

The platform provides comprehensive documentation including installation guides, tutorials, and API references. Whether you're looking to automate code fixes, resolve complex GitHub issues, or conduct AI research, SWE-agent offers a robust foundation with proven academic backing and real-world performance.

Zero is a terminal coding agent built for developers who want full control over their AI tooling. It reads your repo, edits files, runs commands, and stores every session locally on your disk. No cloud sync, no telemetry, no vendor lock-in on the model side.

The model choice is genuinely open. Zero connects to 25+ providers including OpenAI, Anthropic, Gemini, Mistral, Groq, DeepSeek, Bedrock, Vertex AI, and local options like Ollama and LM Studio. You can switch providers mid-session with /model, or point it at any OpenAI- or Anthropic-compatible endpoint.

Permissions are explicit and visible. Every side effect (file writes, shell commands, network access) is gated, with OS-level sandboxing. Autonomy is an opt-in, not the default. That makes it practical to run in CI as well as interactively.

Key capabilities:

• Headless mode for one-shot or scripted runs with JSON and stream-JSON output, plus reliable exit codes for pipelines
• MCP support in both directions: consume MCP servers over stdio, HTTP, or SSE, or expose Zero itself as an MCP server
• Spec-first runs to draft and review a plan before any file changes
• Specialist subagents to fan work out to focused sub-sessions you can inspect live
• Isolated git worktrees for risky changes you can discard if they don't pan out
• Session history that's searchable, resumable, and forkable from the command line
• Skills and plugins for encoding team conventions in markdown and extending behavior with hooks
• Scheduled agents for recurring jobs like nightly triage or dependency sweeps
• GitHub Action for PR review and CI jobs using the same agent

Sessions live in ~/.config/zero/ and API keys are stored in an encrypted local store. Unlike some other terminal agents, Zero ships as a single Go binary that runs on macOS, Linux, and Windows (x64 and arm64). Twelve contrast-audited themes are included.

It's MIT licensed. You pay your model provider, or nothing at all with a local model.

Roomote is a self-hosted, source-available AI coding agent built for engineering teams that want autonomous coding work without handing their code, prompts, or data to a vendor's black box. It connects to the tools your team already uses and returns reviewable pull requests through your normal review workflow.

Unlike IDE-based tools like Cline or local agents, Roomote runs in the cloud on your own infrastructure. You assign it work from Slack, an issue tracker, GitHub, or the web. It investigates, plans, and ships changes, then opens a PR your team can read end to end. You keep full control of what merges.

What it can do:

• Full lifecycle tasks. Repo-backed answers, scoped fixes, database migrations, bug investigations, and reviewable PRs. Not just code suggestions.
• Self-review loops. Before marking work done, it runs the actual app, reviews its own changes, and attaches previews or screenshots as proof.
• Live preview URLs. Reviewers get a running instance to inspect, not just a diff.
• Parallel tasks. Multiple teammates can run unlimited tasks simultaneously, including live multi-user sessions on the same issue.
• Model-agnostic. Bring Claude, GPT, or any open-weight model. Swap providers without changing your workflow.
• Broad integrations. Connects to Jira, Linear, Sentry, Grafana, Supabase, BigQuery, Snowflake, Figma, Notion, PostHog, Vercel, and more.

Roomate uses OpenCode as its underlying harness, which keeps it token-efficient compared to first-party vendor agents.

Every prompt, decision path, and line of the agent is in the repository. You can read it, fork it, and adapt it for your team's needs. The license lets you self-host, modify, and redistribute it for internal use, education, and professional services. Competing commercial products built on top of it are the main restriction.

It's a practical fit for engineering leaders who want model choice and deployment control, and for solo developers who want a cloud agent they own and can run cheaply on open-weight models. Non-engineering teammates can also assign work through familiar interfaces without any local setup.