Cline vs Continue: Which AI Coding Tool Should You Use?

US monthly search volume for the phrase "cline vs continue" sits at about 140 queries, according to DataForSEO keyword data pulled for this article. That demand is real, yet thin comparison posts still frame both products as interchangeable "AI in the editor" plugins. They are not. Cline is an approval-gated autonomous coding agent. Continue is an open-source IDE coding assistant focused on chat, autocomplete, and customizable model workflows.

Official Cline documentation describes a tool that reads files, writes code, runs terminal commands, uses a browser, and builds features through conversation, with every action requiring your explicit approval unless you turn on auto-approve. Surfaces include a VS Code extension, JetBrains plugin, CLI (npm i -g cline), a Kanban board for parallel agents, and an SDK (@cline/sdk) for embedding the same agent core. Official Continue documentation positions Continue as a pioneering open-source coding agent available as a CLI, VS Code extension, and JetBrains plugin, with Chat, Plan, and Agent modes plus a mature autocomplete path.

One more structural fact matters for long-term choice. Continue's docs state that the continuedev/continue repository is no longer actively maintained and is read-only, after shipping a final v2 release of the VS Code extension, CLI, and JetBrains plugin. Cline remains under active product and enterprise development, with multi-surface packaging and org-level controls documented on cline.bot and docs.cline.bot. If you are choosing a primary coding agent now, maintenance trajectory belongs in the decision, not only feature checklists.

The practical distinction is workload shape. Continue is strong when you want tab completions, chat against the open repo, and a configurable model stack for inline assistance. Cline is strong when you want a multi-step agent loop that plans, edits across files, runs commands, and extends through MCP, with human gates on writes and execution. Many teams run both patterns: a completion-first assistant for drafting, and an agent for migrations, multi-file refactors, and scripted runs.

Use this table when you need a fast answer. Details and caveats follow in later sections.

Quotable definition: Cline is an approval-gated autonomous coding agent; Continue is an open-source IDE coding assistant focused on chat, autocomplete, and customizable model workflows.

Neither row wins by default. If your daily pain is "I want better completions and private local chat," Continue's model-role design is still a clear fit for personal setups. If your pain is "I need an agent that can plan, edit a tree of files, run tests, and live in CI or a board of tasks," Cline's surface area matches that workflow more closely.

Both products separate thinking from writing, but the agent contract is not the same.

Cline Plan and Act

Cline's Plan mode can read the codebase, search, and discuss strategy without modifying files or running commands. Act mode keeps planning context and then writes files, runs shell commands, and executes the strategy. Official docs recommend Plan first for non-trivial work, and note that you can assign different models to Plan and Act (for example a stronger reasoning model for planning and a faster model for implementation). For large work, /deep-planning runs a longer exploration pass before implementation. Checkpoints before Act mode give you a rollback path when an agent run goes sideways.

The approval gate is central to Cline's product story. Default behavior is human-in-the-loop: file edits and commands wait for you. Auto-approve exists for trusted loops, but it is an opt-in trade of speed for less review. That design matches agent risk: once a model can run package installs and multi-file edits, silent execution is a security decision, not a convenience toggle.

Continue Chat, Plan, and Agent

Continue documents three IDE modes with different tool sets. Chat includes no tools. Plan mode exposes only read-only tools (read file, list directory, grep/glob search, repo map, web fetch, and related inspection tools). Agent mode adds write and execute tools: create file, edit file, run terminal command, create rule blocks, and MCP tools. Tool calls go through a handshake: the model proposes a tool, the user grants permission unless the tool policy is Automatic, Continue executes the tool, then returns the result to the model.

Continue also implements "system message tools," converting tools into XML-style instructions so Agent mode can work with models that lack strong native tool APIs. That helps local and open models participate in agent flows. Still, Continue docs are explicit that Agent mode depends on models that can follow tool instructions well, and some local models advertise tool support they do not actually deliver.

What this means in practice

If you ask either tool to "explain this service," Plan-style modes are enough. If you ask either tool to "migrate this package across the monorepo and fix the build," you are in agent territory. Cline's product identity is that loop with multi-surface packaging (IDE, CLI headless, Kanban parallelism, SDK embeds). Continue can run Agent mode inside the IDE and has a CLI, but its long-standing strengths remain autocomplete and highly configurable chat/edit stacks rather than org-scale agent runtime packaging.

Approval fatigue is real on both sides. Long agent tasks produce many tool calls. Reviewing each diff and command is the safe path. Automatic policies or broad auto-approve settings speed work and increase the chance a bad shell command or dependency change lands before you notice. For shared codebases, treat auto-approve as an exception for narrow, well-scoped tasks, not the default for production branches.

Keep Cline and Continue output in one shared workspace

Give agents MCP access to org-owned files with version history, Intelligence Mode search, and human handoff. Start a 14-day free trial on Starter, Business, or Growth.

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Thin comparison posts often stop at "both are open source and support lots of models." The interesting differences show up in MCP wiring, local inference design, and how teams govern usage.

MCP: both speak the protocol, with different packaging

Cline's MCP docs cover local stdio servers and remote servers over Streamable HTTP (recommended) or legacy SSE. IDE users manage servers from the Cline panel; CLI users run cline mcp or inspect config with cline config mcp. Config examples show command/args for local servers and url plus headers for remote endpoints, with autoApprove lists for individual tools. Security guidance is direct: only install servers you trust, keep secrets in environment variables, limit auto-approve, and review tool calls.

Continue's MCP docs place servers under mcpServers in config and in .continue/mcpServers/ workspace blocks. Supported transports include stdio, SSE, and streamable-http. MCP is available in Agent mode. Continue can also pick up JSON MCP configs familiar from other tools when dropped into .continue/mcpServers/. Secrets can be injected via ${{ secrets.* }} style references.

For shared workspaces and file operations outside the local disk, an MCP server on a cloud workspace is often the missing piece. Local disk, object storage such as S3, or consumer drives like Google Drive can hold artifacts, but they rarely give agents and humans the same permissioned, versioned, searchable surface. Fast.io exposes consolidated MCP tools over Streamable HTTP at /mcp and legacy SSE at /sse, so agents that already speak MCP can read and write project files in an org-owned workspace with per-file version history and an append-only audit log. See storage for agents for setup paths, endpoints, and the current MCP skill surface.

Example remote MCP shape for Cline (replace the URL and token with values from your Fast.io workspace setup):

{
  "mcpServers": {
    "fastio": {
      "type": "streamableHttp",
      "url": "https://YOUR_MCP_HOST/mcp",
      "headers": {
        "Authorization": "Bearer YOUR_TOKEN"
      },
      "disabled": false,
      "autoApprove": []
    }
  }
}

Keep autoApprove empty until you trust specific tools. For Continue Agent mode, the same remote idea maps to a streamable-http server block in MCP config rather than a different protocol.

Local models: Continue is role-native; Cline is BYOK-local

Both support Ollama and privacy-sensitive offline work. The configuration styles differ.

Continue documents model roles: chat, edit, apply, autocomplete, embed, rerank, and agent-related planning. You can run a small coder model for tab complete (docs recommend qwen2.5-coder:1.5b via Ollama) and a larger model for chat or agent work. Autodetect can list local Ollama models. Agent mode needs tool-capable models; Continue's system message tools expand which models can participate. Hardware notes in Continue's Ollama guide start at 8GB RAM minimum with 16GB or more recommended, and scale memory with model size (roughly 4GB for the smallest coder models, about 8GB for 7B-class models, and about 32GB for large 32B-class models).

Cline documents local runtimes under BYOK: Ollama and LM Studio need no API key if the runtime is running. Cloud BYOK covers Anthropic, OpenAI, Gemini, Bedrock, Azure, Vertex, DeepSeek, OpenRouter, and OpenAI-compatible endpoints. Cline also offers a managed usage-billing path and ClinePass, listed in official docs at nine dollars and ninety-nine cents monthly, for selected open coding models with higher rate limits. Local privacy is available, but the product's default onboarding paths include hosted and subscription options alongside pure local setups.

Which is better for local models? For pure local day-to-day coding with separate autocomplete and chat models, Continue's role-based YAML is still one of the clearest open setups. For local models driving a multi-step agent that also needs CLI, CI, and board-style parallelism, Cline's agent surfaces matter more than autocomplete ergonomics. Many people use a hybrid: local models for private chat or light edits, cloud frontier models for hard agent runs.

Team governance: where org control lives

Cline Enterprise documentation describes client-side execution (code stays in your environment), bring-your-own inference, centralized membership and roles (Member/Admin/Owner), model and tool controls, remote configuration, usage tracking, and OpenTelemetry export to stacks such as Datadog, Grafana, and Splunk. That is a governance story aimed at platform teams rolling an agent out to many developers without scattering personal keys.

Continue remains excellent for individual and small-team config-as-code: share config.yaml, MCP blocks, and rules in the repo. It does not present the same enterprise control plane in official docs as Cline Enterprise. After the final v2 maintenance note, treat Continue as a stable open foundation you own operationally, not as a vendor-managed agent platform with expanding org features.

Governance also applies to outputs. Agent diffs land in git if you commit them. Build logs live in CI. Specs and handoff notes often scatter across chat, local folders, and personal drives. Shared alternatives include S3 buckets, Google Drive folders, or a team NAS. An intelligent workspace such as Fast.io adds org ownership, granular permissions, version history, hybrid search once Intelligence Mode is enabled, and agent access through MCP so humans and agents see the same files. Plans start with a 14-day free trial (credit card required) on Starter at $29/mo, Business at $99/mo, or Growth at $299/mo. Creating an account is free, but real org work runs on a paid subscription after trial.

Choosing Cline or Continue only solves the in-editor half of the problem. Agent work produces patches, generated docs, test artifacts, screenshots, and decision notes. If those stay only on one laptop, the next person (or the next agent) cannot resume cleanly.

Common patterns:

• Git alone: Best for source. Weak for large binaries, durable client shares, and semantic search over mixed document sets.
• Object storage (S3 and similar): Fine for bulk blobs. You still build your own indexing, permissions UX, and agent tooling.
• Consumer cloud drives: Easy human sharing. Awkward for agent auth, audit trails, and concurrent multi-agent writes.
• Intelligent workspaces: Shared org-owned storage with search, permissions, and MCP so agents and humans use one system of record.

Fast.io is built for that last pattern. Enable Intelligence Mode so files are indexed for semantic search with citations. Use Metadata Views when you need structured fields extracted from PDFs, images, or spreadsheets into a live table. Agents can create workspaces, write deliverables, and transfer ownership to a human while keeping admin where appropriate. Webhooks support reactive flows when files change. Collaborative Notes let people and agents co-edit specs in real time. Pair that with Cline or Continue over MCP rather than treating storage as a bolt-on dump folder. For agent onboarding and MCP setup paths, start at storage for agents and product AI.

A concrete loop that works well with Cline:

1. Plan the change in Cline Plan mode against a checked-out repo.
2. Act mode implements under approval, with checkpoints enabled.
3. Persist non-git artifacts (design notes, exported reports, media) into a Fast.io workspace via MCP.
4. Human reviews diffs in git and narrative context in the workspace.
5. Transfer ownership when a contractor agent built the workspace and a staff engineer takes over.

Continue users can run the same storage pattern from Agent mode once MCP is configured, especially for doc generation and file packaging tasks. The IDE assistant still shines for autocomplete while you manually finish edges the agent left incomplete.

Use this decision path instead of treating the comparison as a popularity contest.

Choose Cline when:

• You want a default-on approval gate for file writes and shell commands.
• You need more than one surface: IDE plus CLI automation, parallel Kanban agents, or an SDK-embedded agent.
• You care about enterprise governance (central roles, model/tool policy, remote config, telemetry export).
• Your tasks are multi-step: refactors, migrations, test-fix loops, headless CI agents.
• You want active product development and documented Plan/Act deep-planning workflows.

Choose Continue when:

• Your priority is open, YAML-configured chat and autocomplete with clear model roles.
• You run Ollama (or similar) and want a small local model for tab complete plus a larger model for chat.
• You are comfortable owning a final v2 open-source stack without expecting rapid upstream feature velocity.
• Agent mode is occasional, and most of your day is completions, edits, and Q&A on the open repo.
• You prefer config-as-code in the repo over a vendor enterprise control plane.

Use both when:

• Continue (or any completion plugin) drafts line-level code while Cline owns multi-file agent tasks.
• Local models handle private autocomplete; cloud models power heavy Cline Act sessions.
• Shared deliverables land in a workspace both humans and agents can reach over MCP.

Cost reality check: the extension binaries are free and open source on both sides (Apache license lineage). Money shows up as inference. Cline offers usage-billing, ClinePass (nine dollars and ninety-nine cents monthly) for selected models, or pure BYOK. Continue is BYOK-style for whatever providers you put in config. Heavy agent loops burn more tokens than tab complete. Budget for tokens, not just licenses.

Maintenance reality check: Continue's official docs mark the main repo as no longer actively maintained after the final v2 release. That does not make Continue unusable today. It does change risk if you need new IDE APIs, ongoing security response velocity, or a vendor roadmap. Cline documents ongoing surfaces (CLI, Kanban, SDK, enterprise) and remains the safer primary agent bet for teams standardizing now.

Bottom line for cline vs continue: pick Cline for approval-gated agent execution and multi-surface automation; pick Continue for open, local-friendly IDE assistance and autocomplete. Wire either (or both) to a shared intelligent workspace when the work must outlive one developer's session.