Top 9 Cursor Alternatives in 2025: Best Open Source AI Dev Tools for Developers

AI-powered coding assistants have become essential tools in modern software development. Tools like Cursor have led the way by offering developers AI-driven features such as automated workflows, code generation, and smart reviews - all powered by large language models (LLMs). But as the ecosystem matures, many developers and teams are now seeking alternatives that offer greater control, flexibility, and privacy.

In 2025, the most in-demand features for AI coding assistants include:

• Data privacy and offline/local usage
• Custom model integration, including open-weight and self-hosted LLMs
• Flexible workflows that adapt to existing developer setups
• Transparent pricing and freedom from vendor lock-in

This has led to the rise of powerful open-source alternatives to Cursor. These tools prioritize developer autonomy and enterprise-grade compliance, while often being model-agnostic and highly customizable. One notable example is Cline, a conversational coding agent that works directly in your terminal, supports local models, and provides real-time code editing with security in mind.

Below, we highlight 10 of the top open-source Cursor alternatives for 2025. Each tool reflects the new priorities shaping the future of developer productivity: trust, transparency, and tailored control.

Privacy, Control, and Innovation: Setting a New Standard for AI Coding Tools

Many legacy AI coding assistants operate in cloud-only environments, sending your code to external servers - raising serious concerns around IP leakage, compliance violations, and loss of control. As source code increasingly becomes a company’s core asset, developers and enterprises alike are demanding higher guarantees around privacy, auditability, and customizability.

In response, open source AI coding agents are emerging that prioritize:

• Local-first execution to avoid external code exposure
• "Bring your own key" (BYOK) or bring-your-own-model flexibility
• Direct integration with open-source or self-hosted LLMs
• Transparent logging and diff-based action history
• Full user approval before any AI-driven code change is applied

These tools offer much more than autocomplete - they deliver trustworthy automation under your control.

How to Find the Best and Cheapest Open Source Cursor Alternatives in 2025

The following table provides a high-level comparison of 10 top open-source AI coding assistants, with a focus on the priorities driving developer adoption in 2025:

• Data Privacy: Local-first execution, offline mode, or BYO model support to maintain full control over source code
• Model Support: Compatibility with open models like LLaMA 3, Mixtral, GPT-NeoX, and others via OpenAI-compatible APIs or local runtimes
• Extensibility: Support for custom workflows, CLI/GUI interfaces, plugin systems, and IDE integrations
• Workflow Transparency: Reviewable actions, diff previews, full logging, and approval gating before code changes

This matrix brings together both design philosophy and technical capabilities to help developers evaluate trusted, autonomous AI agents tailored for modern coding workflows.

Note: Features are simplified and may depend on exact deployment/configuration. Always check each project’s repository and documentation for up-to-date capabilities and security practices.

Cline: A Top Local-First Cursor Alternative

Looking for a top local-first Cursor alternative? Cline stands out for its emphasis on developer autonomy, auditability, and enterprise-grade privacy. It represents a clear shift from opaque, cloud-only copilots to tools built for transparency, customizable AI workflows, and full-stack automation - giving developers complete control over how AI interacts with their codebase.

Key Features:

• Direct client-side operation: Cline runs entirely on your machine. Your source code and application context stay secure within your environment - ensuring full control over proprietary data and intellectual property.
• Model-agnostic flexibility: Choose any model that suits your needs - from OpenAI to Claude, Mixtral, or local LLaMA via OpenRouter, Ollama, or custom APIs. This removes vendor lock-in and supports flexible, cost-efficient scaling.
• Advanced workflow automation: Go beyond autocomplete - Cline analyzes entire codebases, generates new files, modifies existing ones, monitors errors, issues commands, debugs, and even interacts directly with running web apps. It behaves more like a coding agent than a passive suggester.
• Diff-first approval: Every change is surfaced as a clear, reviewable diff. Developers can inspect, approve, or reject suggestions before anything is committed - ensuring visibility and compliance.
• Open, extensible architecture: Cline supports custom workflow creation via its automation protocol (MCP), letting teams define intelligent, context-aware behaviors and integrations.

Organizations struggling to make GitHub Copilot or Cursor compliant often turn to Cline because it supports:

• Strict governance and security policies
• Regulatory and audit alignment
• Predictable, usage-based budgeting (no SaaS lock-in)

Its open-core foundation, transparent behavior, and BYO-model flexibility make Cline one of the top open source Cursor alternatives for developers and teams prioritizing both innovation and control.

Other Leading Alternatives

While Cline is earning its place at the forefront, several other open-source tools are making a strong case for themselves. Depending on your requirements and workflows, the following options might suit your team:

Continue

Continue has made waves with its lightweight, open source VS Code integration and flexible support for both OpenAI and local LLM deployments. Its bias toward privacy, local control, and low-friction setup lowers onboarding barriers. While its workflow automation is not as expansive as Cline’s, Continue is a solid fit for teams seeking code completion and conversational AI coding without losing control over data.

Key Features:

• Open-source AI coding assistant for VS Code and JetBrains
• Supports multiple LLM providers and local models
• Context-aware code completion and chat
• Inline code editing and refactoring
• Privacy-focused: runs locally

Tabby

Tabby targets organizations prioritizing on-premises installations, especially for regulated industries. It hosts its own model (or can integrate third-party LLMs) and supports standard IDEs. While its automation depth is less than Cline’s, Tabby’s focus on code completion, secure environments, and open governance make it a favorite for organizations that cannot use public cloud LLM APIs.

Key features:

• Open-source AI coding assistant designed for self-hosting
• Supports multiple programming languages and major IDEs
• Real-time, context-aware code completion and suggestions
• Integrates with your private codebase for personalized results
• Privacy-first: all data stays within your infrastructure
• Scalable for individual developers and enterprise teams

OpenCopilot

OpenCopilot is an open-source AI assistant designed to act as a co-pilot for web applications and internal tools. Unlike traditional autocomplete tools, it allows developers to plug AI into the logic layer of their applications through a visual or API-based interface. It’s lightweight, easily embeddable, and ideal for teams looking to automate routine actions inside dashboards, CRMs, or developer-facing tools.

Key Features:

• Plug-and-play AI assistant for internal tools
• Supports reasoning over app state and user queries
• Can be embedded into UIs or used through APIs
• BYO-model support with OpenAI, Claude, and local LLMs
• Client-side execution and self-hosting possible

OpenDevin

OpenDevin goes all-in on agent-driven programming, automating complex multi-step tasks and integrating closely with web-based interfaces and editors. It aims to shift from “autocomplete” to “co-developer,” though its large architecture and web-first focus may be overkill for those seeking simplicity.

Key Features:

• Open-source autonomous AI software engineer
• Capable of multi-step reasoning, planning, and code execution
• Can analyze, edit, and debug entire codebases
• Integrates with VS Code and other popular development tools
• Supports both cloud-based and local LLMs
• Designed for transparency, extensibility, and privacy

CodeGeeX

CodeGeeX is a multilingual AI code generation model and toolkit developed by the Chinese Academy of Sciences. It is designed to assist with intelligent code completion across languages like Python, Java, C++, and more. While it doesn’t support deep automation like full AI agents, it’s popular among developers looking for a lightweight, free alternative to Copilot in VS Code.

Key Features:

• Supports over 20 programming languages
• Based on a large-scale transformer model trained on public code
• Autocomplete, function generation, and inline suggestions
• Extensions available for VS Code and JetBrains IDEs
• Ideal for multilingual development workflows
• Free to use with BYO hosting or via public endpoints

Knoas

Knoas is an emerging open-source AI assistant tailored for individual developers and small teams. It emphasizes fast onboarding, flexible integration, and modular deployment - making it suitable for startups or internal dev environments. While still maturing, it offers strong potential as a bridge between scripting and full agent workflows.

Key Features:

• Modular architecture for different dev tasks
• Plugin-style extensibility for commands or actions
• Web UI and browser extension for usability
• Built-in support for multiple LLMs (OpenAI, Claude, more)
• Can be run locally or behind firewalls
• Still evolving with limited workflow transparency features

Windsurf

Windsurf is a proprietary autocomplete and code intelligence platform offering free access to high-performance AI models. It supports a wide range of languages and IDEs, with real-time code suggestions and docstring generation. Though not fully open source, it has become popular for its responsiveness, performance, and zero-cost tier.

Key Features:

• Fast, real-time autocomplete for 70+ languages
• Docstring and comment generation features
• Integrates with VS Code, JetBrains, Jupyter, and more
• Cloud-based (data goes to their servers)
• Free to use with proprietary backend
• Limited transparency or local control for sensitive code

Smol Developer

Smol Developer, built around agent-based programming, takes automation a step further. It coordinates multiple AI agents to generate, refactor, and debug projects, making it valuable for ambitious engineering teams and research contexts.

Key Features:

• Lightweight, open-source autonomous AI coding agent
• Focuses on building and shipping small, functional apps
• Minimal setup and resource requirements
• Executes multi-step coding tasks with simple prompts
• Easily extensible for custom workflows
• Ideal for rapid prototyping and solo developers

What Developers Are Saying

Reports from engineering teams experimenting with these tools highlight a few recurring themes:

• Productivity is real: Developers report saving hours each week as AI assistants take over boilerplate generation, error tracing, code refactoring, and cross-file scaffolding.
• Context and memory matter: Assistants that can "see" and retain the entire codebase (not just the last few lines) are more helpful, accurate, and reliable.
• Privacy and compliance are non-negotiable: Especially in regulated industries, many teams won’t touch tools that transmit code to third-party clouds without full control.
• Cost transparency beats subscriptions: While compute/token costs can grow with long-context agents, most developers prefer pay-as-you-go or BYO-model setups over expensive SaaS subscriptions.
• Trust requires transparency: Tools that offer diff-based approvals, logging, and traceability are more easily adopted into collaborative codebases.

Choosing Your AI Coding Assistant

Selecting the right tool depends on your technical stack and organizational needs. Ask yourself:

• Cloud vs. On-Prem: Do your policies allow cloud-based tools, or is local execution required?
• Editor/CI Integration: Do you need deep support for specific IDEs or build systems?
• Compliance & Auditability: Are diff-approvals and action logging required for team adoption?
• Model Flexibility: Will you want to switch between LLM providers (e.g. OpenAI, Mistral, Claude)?
• Workflow Autonomy: Do you want a co-pilot, or a fully autonomous code agent?

Open Source: Driving the Next Wave

Open source is accelerating innovation in AI coding tools:

• Developers are sharing workflows, improving agent reasoning, and extending editor support faster than closed vendors.
• The ability to review, fork, and control your AI stack is empowering teams - from solo developers to Fortune 500s.
• Features like monorepo-scale context, diff-first approvals, and model-agnostic backends are quickly becoming baseline expectations.

This ecosystem is forcing even commercial vendors to rethink their roadmaps as open alternatives raise the bar.

Adapting for Tomorrow

AI coding is no longer a novelty - it’s infrastructure.

With tools like Cline and others, developers finally have full control over how AI is embedded in their workflows. Whether your goal is to boost productivity, reduce risk, or ensure compliance, open-source coding agents offer:

• Local-first execution
• Transparent and reviewable AI actions
• Seamless model interoperability
• Open, customizable automation pipelines

The future of AI-assisted development is open, secure, and developer-first.

FAQs (Frequently Asked Questions)

What is the best open source alternative to Cursor?

There’s no single best tool - it depends on your needs. However, Cline stands out for its local-first design, model-agnostic flexibility, and full diff-based review system. Continue, OpenDevin, and Tabby are also strong contenders for specific use cases like IDE integration, agent workflows, or enterprise readiness.

I'm looking for Cursor alternatives without usage limits - what should I try?

If you're hitting usage caps or token limits with commercial tools like Cursor or Copilot, open source tools like Cline and Continue are great options. These tools let you bring your own model and infrastructure, meaning there are no imposed limits - just what your system can support.

What’s the cost of using these tools?

Most are free to use under open-source licenses. However, your model usage (e.g. GPT-4, Claude, or hosted LLaMA) may incur token or compute costs. Tools like Cline follow a BYO-model approach, letting you control infrastructure and budget more precisely.

Do these tools support real-time collaboration or team workflows?

Some do. Cline, OpenDevin, and Windsurf (to an extent) support workflows across shared repositories. However, full multi-user agent collaboration is still an emerging space - many open tools are solo-first but increasingly extensible for team adoption.

What models can I use with these tools?

Most of these tools support OpenAI-compatible APIs, which means you can plug in models like LLaMA 3, Mixtral, Claude, GPT-NeoX, Deepseek Coder, and others. Some tools like Cline and OpenDevin also support custom backends via protocols like MCP (Model Context Protocol).

Can these AI coding assistants run entirely offline?

Yes - many of them support offline or on-premises use. Tools like Cline can be configured to work with locally hosted models (e.g., via Ollama, LMDeploy, or vLLM), keeping all source code within your environment.

Can I switch between different LLMs?

Yes - many tools are model-agnostic and allow you to swap in different providers with just a config change. This gives you flexibility to experiment with open-weight models or premium APIs without vendor lock-in.

How are these tools different from autocomplete plugins?

While many provide autocomplete, the best open-source AI agents (like Cline and OpenDevin) go much further. They can read and reason across entire codebases, generate new files, refactor intelligently, debug apps, and even interact with running processes - acting as full agents, not just predictors.