Eight open source AI coding assistants worth using in 2026, organized by category. GitHub stars, supported models, IDE compatibility, and a clear decision guide at the end. No fluff.
All Open Source AI Coding Assistants Compared
| Tool | Type | GitHub Stars | License | IDE Support | Model Support |
|---|---|---|---|---|---|
| Cline | IDE extension | 58.8k | Apache 2.0 | VS Code | Any API + Ollama |
| OpenCode | Terminal agent | 95k+ | MIT | Terminal / VS Code ext | 75+ providers + Ollama |
| Aider | Terminal agent | 41.8k | Apache 2.0 | Terminal (any editor) | Any API + Ollama |
| Continue.dev | IDE extension | 31.8k | Apache 2.0 | VS Code + JetBrains | Any API + Ollama |
| Tabby | Self-hosted server | 32.2k | Apache 2.0 | VS Code, JetBrains, Vim, Emacs | Self-hosted models |
| Goose (Block) | Terminal + desktop agent | 32.7k | Apache 2.0 | Terminal / desktop app | Any LLM + MCP |
| Roo Code | IDE extension | 22.6k | Apache 2.0 | VS Code | Any API + Ollama |
| Kilo Code | IDE extension | 1.5M users | Apache 2.0 | VS Code + JetBrains + CLI | 500+ models |
| Use Case | Best Pick | Why |
|---|---|---|
| Terminal + git workflow | Aider | Auto-commits, 100+ languages, git-native |
| VS Code power user | Cline | 58.8k stars, browser automation, MCP, BYOM |
| VS Code + JetBrains | Continue.dev | Best multi-IDE support, inline autocomplete + chat |
| Fully offline / air-gapped | Tabby + Ollama | Self-hosted server, no cloud calls, GPU inference |
| Max model choice | Kilo Code | 500+ models, VS Code + JetBrains + CLI |
| Multi-mode workflows | Roo Code | Custom AI personas per task, scoped permissions |
| Autonomous terminal agent | OpenCode | 95k stars, plan-first, approval-based execution |
| Extensible + MCP | Goose (Block) | MCP-first, built by Block, desktop + CLI |
CLI Agents
Terminal-native tools that run in your shell, read your file system directly, and compose with unix tools. No editor required. All support local models via Ollama.
1. Aider
Stars: 41.8k. License: Apache 2.0. Language: Python. Weekly volume: 15 billion tokens processed.
Aider maps your entire codebase before writing a single line. Every change is a git commit with a sensible message. If you want to undo, you run git revert. There is no special undo button because the git log is the undo system.
Supports 100+ programming languages and any LLM: Claude, GPT, DeepSeek, Gemini, local Ollama models. The codebase map lets it understand cross-file relationships without loading everything into context, which keeps token costs low on large repos.
Aider quickstart
# Install
pip install aider-chat
# Start with Claude (recommended)
aider --model claude-sonnet-4-5
# Or with local Ollama model
aider --model ollama/qwen2.5-coder:32b
# Aider maps your repo, opens a REPL
# Every accepted change is auto-committed to gitBest for: Developers who live in the terminal and want AI assistance that respects their existing git workflow. The 15 billion tokens per week of community usage proves this is production-grade. Compare options in our Aider vs Cursor breakdown or the Aider vs Cline comparison.
2. OpenCode
Stars: 95k+. License: MIT. Interface: Terminal TUI, desktop app, VS Code extension.
OpenCode grew to 95k GitHub stars faster than any other open source coding agent. The core differentiator is breadth: 75+ LLM providers, a plan-first execution model where you approve before the agent acts, and a native terminal UI built on Bubble Tea.
Unlike Aider (which defaults to automatic commits), OpenCode shows you a plan and waits for approval before executing. Better for exploratory sessions where you want to understand what the agent intends before it touches your files.
OpenCode Zen
OpenCode ships a curated model roster called Zen: a set of models the team has tested and benchmarked specifically for coding agents. If you do not want to evaluate 75+ providers yourself, Zen gives you a short list of what actually performs well.
Best for: Developers who want the widest provider choice in a terminal agent and prefer explicit approval before the agent acts. Also useful as a starting point for teams building custom agent workflows, since the codebase is clean MIT-licensed TypeScript.
3. Goose (Block)
Stars: 32.7k. License: Apache 2.0. Developer: Block (formerly Square). Interface: CLI + desktop app.
Goose is an extensible agent from Block, the company behind Cash App and Square. The architecture is MCP-first: every capability is a Goose Extension, and any MCP server plugs straight in. It can build projects from scratch, write and execute code, debug failures, and call external APIs without you intervening.
Unlike Aider or OpenCode which focus purely on code, Goose is designed to operate across the full developer environment: browsing the web, querying databases, running tests, reading documentation. The multi-model configuration lets you assign different models to different task types.
Best for: Developers who want an MCP-first agent that handles more than just file edits. The Block backing gives it long-term maintenance credibility. Desktop app makes it accessible to developers who prefer GUI over pure terminal.
IDE Extensions
Extensions that live inside your editor. Visual diffs, inline completions, chat panels, and approval workflows without leaving VS Code or JetBrains. All support BYOM (Bring Your Own Model) with zero markup.
4. Cline
Stars: 58.8k. License: Apache 2.0. Installs: 5M+ VS Code. Enterprise: Samsung Device eXperience division rollout.
Cline is the most-starred open source VS Code extension for AI coding. It operates as an autonomous agent: it can create and edit files, run terminal commands, launch and interact with browsers (via computer vision), and call MCP servers. Every file change and terminal command requires your approval, which gives you a clear audit trail.
The dual Plan/Act modes matter in practice. In Plan mode, Cline reads your codebase and proposes a solution. In Act mode, it executes with checkpoints at each step. You can roll back to any checkpoint if the agent goes off track.
Cline model support
Cline works with OpenRouter, Anthropic, OpenAI, Google Gemini, AWS Bedrock, Azure, GCP Vertex, Cerebras, Groq, and any OpenAI-compatible API. For local inference, it connects to Ollama and LM Studio. You pay your provider directly at list price.
Best for: VS Code developers who want the strongest open source agent with visual diffs, MCP integration, and browser automation. The approval-before-action model makes it suitable for production codebases where you want oversight. See our Cline vs Cursor comparison and Cline alternatives roundup.
5. Continue.dev
Stars: 31.8k. License: Apache 2.0. IDEs: VS Code + JetBrains. Free tier: Full-featured solo plan.
Continue.dev is the only major open source assistant with first-class JetBrains support alongside VS Code. Three core modes: Chat (ask anything without leaving the editor), Autocomplete (tab-completion as you type), and Edit (select code and describe the change you want).
The team added AI-powered code reviews enforced in CI. You define check behavior in .continue/checks/ markdown files, and the open source cn CLI runs those checks on every pull request as a GitHub status check. This brings structured AI review into the merge workflow.
Best for: Developers who split time between VS Code and JetBrains. Continue.dev is the only open source tool that covers both without requiring separate setup. The CI-integrated code review feature is unique among open source tools. Full comparison in our Cline vs Continue page.
6. Roo Code
Stars: 22.6k. License: Apache 2.0. Users: 1M+. Forks: 2.9k.
Roo Code started as a Cline fork (originally Roo Cline) and diverged into a distinct product. The key addition is Custom Modes: you define specialized AI personas with scoped tool permissions. A "Security Reviewer" mode can read but not write. A "Test Writer" mode only touches test files. An "Architect" mode only discusses, never executes.
Roo Code Inc. raised funding, achieved SOC 2 Type 2 compliance, and launched team cloud features. The latest release (v3.51) added GPT-5.4 and GPT-5.3 support. The core extension remains Apache 2.0 and BYOM.
Best for: Teams that want structured role-based agent workflows. The Custom Modes system prevents agents from doing more than their role allows, which is useful for security-sensitive codebases. Compare options in our Roo Code vs Cline and Roo Code vs Cursor comparisons.
7. Kilo Code
Users: 1.5M+ (self-reported). License: Apache 2.0. Funding: $8M seed. IDEs: VS Code, JetBrains, CLI.
Kilo Code runs on 500+ AI models and supports VS Code, JetBrains, and CLI in one package. Four workflow modes (Architect for planning, Coder for implementation, Debugger for fixes, Orchestrator for coordinating subtasks) give it the widest mode coverage of any open source extension.
The Kilo Pass credits model ($19/month) gives you access to all 500+ models at provider rates with no markup. The extension itself is always free with BYOM. Browser automation and MCP support are included.
Best for: Developers who want the widest model coverage in an IDE extension, or who switch between VS Code and JetBrains. The Orchestrator mode is the closest open source equivalent to Devin's multi-step task handling. See our Kilo Code vs Cline and Kilo Code vs Claude Code comparisons.
Self-Hosted Code Completion Servers
For teams where code cannot leave the building. These run on your own hardware and expose an API that IDE plugins call. Zero cloud dependency once deployed.
8. Tabby
Stars: 32.2k. License: Apache 2.0. Language: Rust (server) + TypeScript (plugins). Enterprise: SSO, audit logs, team analytics.
Tabby is the only open source code completion server designed for enterprise on-premises deployment. It runs on your own hardware, supports GPU and CPU inference, and ships with IDE plugins for VS Code, JetBrains, Vim, Emacs, and other editors. Your code never leaves your infrastructure.
The Rust server is fast enough for real-time completions on commodity hardware. A single NVIDIA A10G handles 20-30 concurrent developers comfortably. The enterprise tier adds SSO (LDAP/OAuth), per-user analytics, team dashboards, and API access control.
Tabby server quickstart
# Install via Docker (GPU)
docker run -it --gpus all -p 8080:8080 -v $HOME/.tabby:/data tabbyml/tabby serve --model TabbyML/DeepseekCoder-6.7B-instruct --device cuda
# Install via Docker (CPU-only)
docker run -it -p 8080:8080 -v $HOME/.tabby:/data tabbyml/tabby serve --model TabbyML/StarCoder-1B --device cpu
# VS Code plugin connects to http://localhost:8080Best for: Enterprises and regulated industries where code cannot touch external APIs. Financial services, defense contractors, healthcare, and other compliance-heavy environments use Tabby because it gives them GitHub Copilot-quality completions with zero data leaving their network.
Local Inference with Ollama
Ollama is not a coding assistant itself. It is a local model server that any BYOM tool can call. Combined with Continue.dev or Aider, it creates a fully offline coding assistant with no API costs and no data leaving your machine.
Fully offline coding assistant: Ollama + Continue.dev
# Step 1: Install Ollama (macOS/Linux)
curl -fsSL https://ollama.ai/install.sh | sh
# Step 2: Pull a coding model
ollama pull qwen2.5-coder:7b # fast, ~4GB
ollama pull qwen2.5-coder:32b # best quality, ~20GB
# Step 3: Install Continue.dev extension in VS Code
# (marketplace ID: Continue.continue)
# Step 4: Add to Continue config (~/.continue/config.json)
{
"models": [{
"title": "Qwen2.5-Coder 7B",
"provider": "ollama",
"model": "qwen2.5-coder:7b"
}],
"tabAutocompleteModel": {
"title": "Autocomplete",
"provider": "ollama",
"model": "qwen2.5-coder:7b"
}
}Hardware requirements
7B models: 8GB VRAM minimum, 16GB RAM for CPU inference. Completions at 80-100 tok/s on an RTX 4090.
32B models: 24GB VRAM (two RTX 3090s work), 64GB RAM for CPU. Slower but noticeably better output quality.
CPU-only: Works on any machine with 16GB RAM using quantized 7B models. Expect 8-15 tok/s, which is usable for chat but too slow for real-time autocomplete.
Open Source Coding Models
The underlying models you run locally or through self-hosted Tabby. Sorted by capability.
| Model | Parameters | Best Score | Notes |
|---|---|---|---|
| DeepSeek Coder V2 | 236B (21B active) | Leads HumanEval | MoE architecture, needs 40GB+ VRAM for full model |
| Qwen2.5-Coder 32B | 32B | Beats GPT-5.4 on SWE-bench | Best quality-to-size tradeoff, runs on ~24GB VRAM |
| Qwen2.5-Coder 7B | 7B | Strong HumanEval | Best for real-time autocomplete, 8GB VRAM |
| StarCoder2-15B | 15B | 66.2% MBPP | Matches CodeLlama-34B, 600+ languages, BigCode consortium |
| CodeLlama 34B | 34B | 65.4% MBPP | Meta, mature toolchain, broad community support |
| StarCoder2-3B | 3B | Fast inference | Edge devices, single-user completion servers |
For real-time autocomplete
Qwen2.5-Coder 7B via Ollama. Runs at 80-100 tok/s on an RTX 4090. Fast enough that suggestions appear as you type. Use this as your tab-completion model.
For chat and generation
Qwen2.5-Coder 32B if you have the VRAM, DeepSeek Coder V2 if you have a multi-GPU setup. Both beat GPT-5.4 on SWE-bench and handle complex cross-file reasoning.
For self-hosted servers (Tabby)
StarCoder2-15B is the community standard for Tabby deployments: good balance of quality and resource use, broad language support, and a clean Apache 2.0 license.
Qwen2.5-Coder benchmark context
Alibaba's Qwen2.5-Coder 32B Instruct became notable for matching GPT-5.4 on HumanEval and outperforming it on SWE-bench Verified, a real-world agentic coding benchmark. Both Qwen2.5-Coder and DeepSeek Coder V2 landed in the top tier of open coding models in late 2024 and held that position through 2026. For most local use cases, Qwen2.5-Coder is the default recommendation because the 7B and 32B variants cover both autocomplete and chat in a single model family.
When to Use What
Five questions narrow the field:
Terminal or IDE?
Aider, OpenCode, and Goose live in your shell and compose with unix tools. Cline, Continue.dev, Roo Code, and Kilo Code live in your editor. Pick based on where you already work. Both categories support the same underlying models.
Does code need to stay on-prem?
Tabby is the answer. Deploy on your hardware, point IDE plugins at your server, and no code touches any external API. Every other tool in this list sends prompts to cloud APIs unless you configure Ollama.
Git-native workflow?
Aider. Every change is a commit. Every session is reviewable as a branch. If you think in git, no other tool matches how naturally Aider integrates. You can also use git revert instead of a special undo button.
VS Code only or multi-IDE?
Cline and Roo Code are VS Code-only. Continue.dev covers VS Code and JetBrains. Kilo Code covers VS Code, JetBrains, and CLI. If you split time across editors, Continue.dev or Kilo Code avoids the setup mismatch.
How much model control do you want?
Kilo Code supports 500+ models and lets you route different tasks to different models. Aider and OpenCode both handle local Ollama models cleanly. Tabby requires you to manage the model server yourself but gives you complete isolation.
| Dimension | Open Source (Cline/Aider) | Paid (Copilot/Cursor) |
|---|---|---|
| Cost | $0 extension + API at cost | $10-$20+/month flat |
| Model choice | Any provider or local | Locked to vendor's model roster |
| Code privacy | Local models: stays on machine | Sent to cloud (with NDAs) |
| Setup time | 5-30 minutes | 2-5 minutes |
| Autocomplete quality | Cloud: competitive. Local: slightly behind | Best-in-class for inline completions |
| Agent capability | Cline/Aider: full agent | Cursor/Copilot: full agent |
| Enterprise features | Tabby: SSO, audit logs | Built-in |
The practical conclusion: open source tools have closed the capability gap on agent workflows. Where they still trail is inline autocomplete quality and setup simplicity. If you care about model freedom, cost transparency, or code privacy, open source wins on all three. If you want the fastest setup and best inline suggestions, Copilot or Cursor still have an edge.
The Apply Layer
Every tool in this list faces the same technical problem: after the LLM generates an edit, something has to merge it into your existing file. Diffs break when context shifts. Search-and-replace misses when code moves. Full rewrites waste tokens and introduce regressions.
Morph's Fast Apply model handles this step at over 10,500 tokens per second with a deterministic merge. If you are building a custom agent or extending any open source tool, the apply step is the reliability bottleneck Morph solves.
Morph Fast Apply API (OpenAI-compatible)
import { OpenAI } from 'openai';
const morph = new OpenAI({
apiKey: process.env.MORPH_API_KEY,
baseURL: 'https://api.morphllm.com/v1'
});
const result = await morph.chat.completions.create({
model: 'morph-v3-fast',
messages: [{
role: 'user',
content: `<instruction>Add error handling to all API calls</instruction>
<code>${originalFile}</code>
<update>${llmEditSnippet}</update>`
}],
stream: true
});Frequently Asked Questions
What is the best open source AI coding assistant in 2026?
For IDE users: Cline (58.8k GitHub stars, 5M VS Code installs, browser automation, MCP). For terminal users: Aider (41.8k stars, git-native, 15B tokens/week processed). For multi-IDE: Continue.dev (VS Code + JetBrains). For self-hosted: Tabby (enterprise SSO, GPU inference, code never leaves your server).
Can I run an AI coding assistant completely offline?
Yes. Install Ollama, pull qwen2.5-coder:7b for autocomplete or qwen2.5-coder:32b for chat, then install Continue.dev and point it at localhost:11434. The whole pipeline runs locally in under 10 minutes. Aider and OpenCode also support Ollama as a provider for fully offline CLI use.
What is the difference between Cline and Aider?
Aider is terminal-native and git-first. Every accepted change becomes a git commit. It maps your codebase before editing and supports 100+ languages. Cline is a VS Code extension with visual diffs, browser automation, a human-in-the-loop approval system, and MCP integration. Aider suits terminal developers who want git-integrated workflows. Cline suits developers who want visual context and fine-grained approval. See the Aider vs Cline comparison for details.
Is Roo Code a fork of Cline?
Yes. Roo Code started as a fork (originally Roo Cline) and diverged significantly. The key addition is Custom Modes: specialized AI personas with scoped tool permissions. A Security Reviewer mode can only read. A Test Writer mode only touches test files. Both are Apache 2.0 and BYOM. Roo Code has 22.6k stars and 1M+ users.
What open source model should I use for local coding?
Qwen2.5-Coder 7B for real-time autocomplete (8GB VRAM, 80-100 tok/s on RTX 4090). Qwen2.5-Coder 32B for chat and complex generation (24GB VRAM, outperforms GPT-5.4 on SWE-bench Verified). DeepSeek Coder V2 if you have a multi-GPU setup and want maximum capability.
How does Tabby differ from Cline or Continue.dev?
Tabby is a model inference server you deploy on your own hardware. IDE plugins connect to your Tabby server rather than to a cloud API. Cline and Continue.dev are IDE extensions (clients) that call cloud model APIs by default. Tabby is the right choice when code cannot leave your infrastructure. See the comparison in the table above.
What is BYOM and which open source tools support it?
BYOM (Bring Your Own Model) means you supply your own API key from any LLM provider and pay that provider at list price. No subscription, no markup. Cline, Aider, Continue.dev, OpenCode, Roo Code, Kilo Code, and Goose all support BYOM. You can also point any of them at Ollama for local inference at zero API cost.
Does Qwen2.5-Coder beat GPT-5.4 on coding benchmarks?
Qwen2.5-Coder 32B Instruct matches GPT-5.4 on HumanEval and outperforms it on SWE-bench Verified according to Alibaba's benchmark results. DeepSeek Coder V2 leads HumanEval among all open source models. Both landed in the top tier of coding models in late 2024 and maintained that position through 2026.
Build on Reliable Infrastructure
Open source coding agents need a reliable apply layer. Morph's Fast Apply model merges LLM edits deterministically at 10,500+ tokens per second. Drop-in OpenAI-compatible API.