AgentLoop vs Cline: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of AgentLoop and Cline — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
AgentLoop
Edward Yi
AgentLoop turns a single ChatGPT plan into unattended Codex worker + independent-critic cycles that build against your local rubric until the work passes.
Key features
- Fresh Worker Per Cycle: Each build cycle spawns a clean Codex worker with fresh context so long-running loops do not accumulate stale state or memory drift.
- Independent Critic Process: A separate fresh process grades every result against your rubric so passing tests never become permission to stop looking.
- Rubric in GUIDELINES.md: Definition-of-done lives as plain Markdown in your repo and is read on every cycle, so standards persist while prompts do not.
- Evidence Carried in Files: Worker output, critic verdicts, and fixes are written to project files so the next cycle inherits the actual state of the work.
- Bounded Goal + Cycle Budget: You cap the loop with a goal.md and cycle budget so unattended runs stop at a predictable ceiling.
- MCP Status Interface: Ask ChatGPT for status through MCP so you can monitor cycles, verdicts, transcripts, and cost without opening the dashboard.
- Local-first Install: git clone the pinned v1.1.0 release and run node src/daemon.js — no npm install, no hosted workspace, MIT licensed.
Best for
- Shipping a bounded feature: Add a CSV export across UI, API, and regression suite while the critic enforces end-to-end behavior and edge cases.
- Migration work: Run an unattended migration where fresh workers apply the change and the critic verifies each step against a rubric.
- Hardening pass: Give AgentLoop a hardening goal so it iterates on defects the existing test suite misses, like malformed input handling.
- Product polish loop: Point AgentLoop at a polish goal with clear acceptance criteria and let it converge to VERDICT: PASS.
- Unattended overnight runs: Kick off a long loop, monitor cycle verdicts, and cancel from the dashboard or via MCP when the receipt looks right.
- Enforcing team standards: Codify team engineering standards in GUIDELINES.md so every worker builds against the same definition of done.
Cline
Cline Bot Inc
Open-source coding agent runtime that runs in your IDE, your terminal or embedded via SDK, works with any model, and asks approval on every step.
Key features
- One runtime, three surfaces: The same agent runs as a VS Code extension, a terminal CLI, or embedded in your own product through the SDK
- Model agnostic: Works with Claude, GPT, Gemini, local Ollama or LM Studio models and any OpenAI-compatible endpoint, using your own key or weights
- Plan then Act: Align on a strategy in Plan mode before execution, then approve each step in Act mode or flip on auto-approve
- Multi-file edits with undo: Coordinated changes across a project with linter-aware fixes, diffs, checkpoints and one-click undo on every step
- Live terminal execution: Runs bash commands and reacts to output as it appears, handling dev servers, test runs and deploys
- Rules and Skills: Ship .clinerules with the repo so the agent follows your coding standards, architecture and deployment conventions
- Multi-agent teams: Coordinator agents delegate to specialists with their own tools and context, and can run on cron for recurring automation
- MCP and integrations: Register MCP servers and custom tools, chat from Slack, Discord, Telegram or Linear, and run headlessly in GitHub Actions or GitLab
Best for
- A developer onboarding to an unfamiliar codebase and asking the agent how files, dependencies and behaviour fit together
- Refactoring across a large repository while keeping imports, types and behaviour consistent
- Running recurring maintenance — dependency bumps, lint sweeps, scheduled checks — from cron or a CI pipeline
- A team that must keep code on self-hosted or local models for compliance reasons, pointing the agent at its own endpoint
- Embedding an agent loop inside an internal developer platform via the SDK instead of building one from scratch
- Encoding team conventions in .clinerules so every engineer's agent produces consistent, review-ready changes
- Triggering a coding task from Slack or Linear and having the agent open the resulting change