AI Developer Tools

Mirrors

Mirrors turns your production traces into an isolated copy of your agent's environment so you can test changes safely.

What is Mirrors?

Mirrors is a tool that creates deterministic, isolated copies of production environments for AI agents, allowing developers to test changes, reproduce bugs, and catch regressions without affecting live systems.

Mirrors vs Similar AI Tools

Pricing ModelFree, Custom PricingFreeFreeFree
Free Credits
Key Features
  • Ingest traces from production
  • Build an isolated, runnable mirror with seeded database and bound tools
  • Run and evaluate agents against deterministic worlds
  • Seven breakable boxes covering OWASP Agentic Top-10 vulnerabilities
  • Three guided simulations for cascading failures, human-agent trust, and rogue agents
  • Network-isolated Docker containers for safe execution
  • Code-to-runtime reasoning across cloud, Git, and Kubernetes
  • Action-gate enforces read-only policy on every API call
  • Sandboxed JavaScript execution for concurrent research
  • Append-only, SHA-256-addressed event history through Jaybase
  • AES-256-GCM encryption for stored node payloads
  • Unified RBAC for ledger, notes, snapshots, and audit reads
Pros
  • Deterministic world seeding for 100% reproducible testing
  • Safe testing of risky flows without touching production
  • Covers full OWASP Agentic Top-10 in a realistic manner
  • Docker isolation prevents accidental damage
  • Read-only by construction prevents accidental writes
  • Evidence-backed verification cross-checks every finding
  • Opinionated and secure accounting CLI with immutable audit trail
  • Designed for AI agent integration with JSON output
Cons
  • Requires production traces to build mirrors
  • Limited to agents that can be connected via MCP or API
  • Requires Docker and technical setup
  • Not for production use; only for lab environments
  • Requires an LLM API key, incurring token costs
  • Limited to read-only operations, cannot remediate
  • Pre-1.0, limited feature set
  • No native QuickBooks import (agents must normalize data)
Best For
  • Teams developing and deploying AI agents
  • Quality assurance and regression testing for agent workflows
  • Security researchers focusing on AI agent vulnerabilities
  • Developers building MCP-based applications
  • Security engineers
  • DevOps teams
  • Small teams needing secure, auditable accounting with AI agent support
  • Developers integrating automated bookkeeping workflows

How to use Mirrors?

  1. 1Connect Mirrors to your agent via browser authentication. Then run in your terminal: claude mcp add --transport http mirrors https://api.runmirrors.com/mcp. Then run /mcp → mirrors → Authenticate via browser.

Mirrors Key Features

  • Ingest traces from production
  • Build an isolated, runnable mirror with seeded database and bound tools
  • Run and evaluate agents against deterministic worlds
  • Deterministic seeding for byte-identical replay
  • On-demand sandboxes with scale-to-zero
  • Versioned API and workspace keys for programmatic access

Mirrors Use Cases

  • Catch regressions before shipping agent changes
  • Reproduce bugs on demand with the same seed
  • Test risky flows like refunds, deletes, and sends safely
  • Know if a change is better with coverage and accuracy scores

Mirrors Pricing & Free Credits

Mirrors currently operates on a Free, Custom Pricing model.

Free TierFree Credits

Free

$0/mo

60 sandbox minutes per month, unlimited mirrors, deterministic seeding, in-app playground, community support

Paid Plans

Custom

Contact for pricing

Unlimited on-demand sandboxes, public /v1 API + keys, SSO, eval suites, fidelity reports, priority support & onboarding

Free

$0/mo

60 sandbox minutes per month, unlimited mirrors, deterministic seeding, in-app playground, community support

Custom

Contact for pricing

Unlimited on-demand sandboxes, public /v1 API + keys, SSO, eval suites, fidelity reports, priority support & onboarding

Mirrors Pros & Cons

Pros

  • Deterministic world seeding for 100% reproducible testing
  • Safe testing of risky flows without touching production
  • Easy setup via browser authentication and MCP

Cons

  • Requires production traces to build mirrors
  • Limited to agents that can be connected via MCP or API

What is Mirrors best for?

  • Teams developing and deploying AI agents
  • Quality assurance and regression testing for agent workflows

Mirrors FAQ

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