AgentLoop vs Grass: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of AgentLoop and Grass — 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.
Grass
Grass
VM-first compute platform that gives coding agents a dedicated, always-ready virtual machine for running and testing code without local setup.
Key features
- Dedicated VM Allocation: Provides each coding agent with a dedicated virtual machine that is pre-provisioned and kept ready to execute code, eliminating per-run provisioning delays and local resource use.
- Zero-Configuration Runtime: Removes developer setup and configuration by supplying preconfigured runtimes so agents can run, test, and iterate on code immediately.
- Agent Integrations: Works natively with agent runtimes such as Claude Code and OpenCode to allow LLM-based agents to connect directly to the VM environment for code execution and debugging.
- Free Trial Hours: Offers an initial free allocation (10 hours) so teams can evaluate the platform and run early experiments without payment.
- Remote Execution & Isolation: Executes agent workloads inside isolated VMs to protect developer machines from heavy compute, long-running processes, or accidental resource exhaustion.
- Warm VM Availability: Keeps VM instances ready-to-use to reduce cold-start latency for interactive agent-driven coding sessions.
- Provisioned, dedicated VM per coding agent that stays ready to run tasks
- No local setup or configuration required
- Compatibility stated with Claude Code and OpenCode agent platforms
- Managed compute to avoid using developer laptop resources
- Free initial allocation (10 hours) to start
Best for
- Agent-driven Code Testing: Run language-model-based coding agents to generate, compile, and run test suites in a safe remote VM without installing dependencies locally.
- Offloading Heavy Builds and Tests: Execute CPU- or memory-intensive compilation and test jobs in remote VMs to avoid overloading developer laptops or CI runners.
- Interactive Agent Pair-Programming: Connect Claude Code or OpenCode agents to a persistent VM for fast, iterative coding and debugging sessions with immediate execution feedback.
- Automated Repair and Refactoring: Allow agents to run refactoring scripts or automated repair tools on real runtime environments and verify results in-isolation.
- Prototyping and Experimentation: Quickly spin up agent-backed development environments to prototype integrations or reproduce bugs using a predictable, preconfigured VM.
- Running autonomous coding agents that need persistent compute
- Offloading heavy or long-running code execution from developer machines
- Integrating external code-focused LLM agents (e.g., Claude Code, OpenCode) with dedicated runtime environments
- Quick experimentation with agents using the free trial hours before committing to paid plans