AgentLoop vs Lamatic 3.0: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of AgentLoop and Lamatic 3.0 — 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.
Lamatic 3.0
Lamatic AI
Platform to build, deploy, and optimize agentic apps using pre-built workflows, a visual builder, and serverless infrastructure.
Key features
- Pre-built Workflows: Ready-made agentic workflow components that accelerate building domain-specific apps and reduce time-to-first-deploy by providing common patterns and connectors.
- Visual Builder: A drag-and-drop visual interface to design and compose agent behaviors and orchestration without heavy engineering, enabling faster iteration and collaboration with non-developers.
- Serverless Infrastructure: Managed, serverless hosting for agentic apps that abstracts infrastructure management, enabling automatic scaling and simplified deployment.
- Domain Expertise Capture: Tools and templates to encode domain knowledge into reliable agent behaviors and workflows, helping teams turn subject-matter expertise into production agents.
- Rapid Launch Templates: Opinionated templates and starter apps that allow teams to launch agentic applications up to 10x faster compared to building from scratch.
- Operational Optimization: Built-in capabilities to monitor, refine, and optimize deployed agents to improve reliability and performance over time.
- Visual workflow and app builder for designing agentic logic
- Pre-built agentic workflows to accelerate development
- Serverless infrastructure for deployment and scaling
- Tools for launching and iterating agentic applications quickly
- Focus on domain-expert-driven app creation
Best for
- Converting internal subject-matter expertise into an automated agent that answers employee questions or triages requests.
- Building customer-facing virtual assistants using pre-built workflows and visual composition to reduce support load.
- Deploying domain-specific automation for sales or operations that integrates with existing systems and scales via serverless hosting.
- Rapidly prototyping and launching proof-of-concept agentic apps for new business workflows using starter templates.
- Iterating on deployed agents with monitoring and optimization tools to improve accuracy and reliability in production.
- Convert domain expertise into deployed agentic applications
- Rapidly prototype and launch autonomous workflows
- Deploy serverless agent-based services without managing infrastructure
- Automate business processes using pre-built agent workflows