AgentLoop vs Prava: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of AgentLoop and Prava — 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.
Prava
Prava
Payments API that enables AI agents to make secure, PCI-compliant autonomous purchases with built-in financial guardrails.
Key features
- PCI-Compliant Card & Wallet Access: Provides infrastructure to enable card and wallet payments that adhere to PCI compliance standards so agents can transact securely.
- Built-in Financial Guardrails: Offers configurable controls (limits, restrictions, and policies) that prevent unsafe or unauthorized autonomous spending by AI agents.
- Agentic Commerce API: Exposes endpoints designed for AI agents to request, authorize, and execute purchases programmatically, enabling end-to-end agent-driven transactions.
- Fast Integration: Advertises a minimal-install integration flow ("Integrate in 4 lines") to help developers add agent payment capabilities quickly.
- Regional Support: Focused operational support for transactions across the United States and Southeast Asia, enabling geo-aware flows and merchant coverage.
- Developer Documentation & GitHub Presence: Public docs and repository presence (Prava-Payments GitHub) to help developers implement and test integrations.
- Payments API for autonomous agent purchases with PCI-compliant card and wallet access
- Built-in financial guardrails and regional support targeting US and Southeast Asia
- Quick integration claim ("Integrate in 4 lines") and public docs
- Prava SDK (Controls API) for digital labor and looped agent workflows driven by screenshots
- Pretrained models: prava-af-medium (general automation) and prava-quick-click (fast/simple automation)
- Standardized action types: left_click, type, key, scroll, wait, stop
- Client examples and integration helpers for Playwright, PyAutoGUI, TypeScript, and Python
- API key based authentication and example-driven documentation in GitHub repos
Best for
- Autonomous Shopping Agents: Allowing an AI shopping assistant to select items and complete purchases on behalf of a user while enforcing spending limits and merchant restrictions.
- Virtual Assistant Bookings: Enabling virtual assistants to book travel, event tickets, or subscriptions by executing payments directly with stored card or wallet access.
- SaaS Platforms Delegating Payments: Letting SaaS apps delegate low-risk payments to automated workflows (billing third-party services or procuring software licenses) under guardrails.
- Marketplace Agent Checkout: Allowing agent-driven checkout flows in marketplaces where agents finalize orders and handle payment authorization and receipts.
- Regional Commerce Services: Supporting businesses operating in the US and Southeast Asia to enable agents to transact in region-specific merchant and regulatory contexts.
- Enable AI agents to complete purchases autonomously within apps and web stores
- Integrate payments into agent-driven commerce workflows with guardrails and wallet support
- Automate repetitive GUI tasks and end-to-end digital workflows using screenshot-driven agent loops
- Build agent assistants that propose actions, execute them (via Playwright/PyAutoGUI), and iterate
- Rapid prototyping of automation flows using provided SDK models and example code