getmytwin.io

What It Is

GetMyTwin positions itself as “Structural observability for AI systems.” It is not a traditional log monitoring tool; instead, users paste or upload an AI architecture configuration, and it automatically generates a living visual twin along with a Fragility Index, Resilience score, SPOF and Bridge detection, system health, cost paths, and improvement recommendations. It is suitable for analyzing systems such as RAG, customer support AI, multi-agent setups, LangGraph, CrewAI, OpenAI Agents, and MCP configurations.

Core Capabilities

Its structural analysis focuses on redundancy and connectivity: tri=0 means the two endpoints of an edge have no shared neighbors; the lower the FI, the better; SPOF refers to nodes whose removal increases FI by more than 15 points; and a Bridge is an edge whose removal would disconnect the graph. The interface supports hovering to inspect cognitive-load / isolation-risk, clicking nodes for knockout simulation, and using Chaos Mode for random failure simulations. From an economics perspective, Cost Overlay can display a tokens / dollars heatmap, estimate per-call and daily/monthly costs using tokens_per_call, cost_per_call_usd, latency, and requests_per_day, and identify token hotspots and expensive loops.

Integrations and Ease of Use

Usability is strong: users can directly paste a configuration or upload .yaml, .json, .py, or .txt files, with automatic detection in around 500ms. It supports formats including GetMyTwin YAML, LangGraph, CrewAI, OpenAI Agents, MCP Config, JSON, and Python. One caveat is that LangGraph Python support is regex-only and does not execute code, so reconstruction is limited for dynamic graph construction or complex runtime logic. Non-native formats also do not include cost data and will fall back to default estimates.

Pricing and Privacy

The main content does not disclose pricing, free quotas, payment methods, or enterprise support. On privacy, the FAQ says YAML analysis exists only in process memory; it may persist across refreshes while the server is running, but is cleared on each deployment. The only persistent data is the User / VerificationToken authentication tables. There is no team or sharing layer, and the main persistence vector is the session id in the URL. This explanation is relatively transparent, but information on enterprise-grade data residency, auditing, and permission management is insufficient.

Pros, Cons, and Who It’s For

Its strengths are a clear metrics system and the ability to visualize single points of failure, missing redundancy, and costly paths in AI architectures, making it useful for pre-launch reviews and before/after comparisons during refactoring. Limitations include cost analysis that depends on static configuration and default values, undirected analysis of structural edges, path enumeration capped at 50, and a maximum file size of 500KB. It is better suited to AI application architects, Agent/RAG engineers, and technical leads. If you need production-grade tracing, prompt evaluation, or LLM call logs, it can be paired with tools such as LangSmith, Langfuse, and Phoenix. The main content does not provide information on access from China or payment options, so real-world availability is unknown.