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Graph2agent; Mermaid diagrams, explained for agents

Details

External ID
49250014
Source
HN
Company
—
Product
Graph2agent; Mermaid diagrams, explained for agents
Website domain
github.io
Launched
Aug. 10, 2026
Cohort
—
Upvotes
7
Upvotes percentile
0.40793010752688175
Tags
—
Fetched at
Sept. 10, 2026, 5:32 a.m.
Updated at
Sept. 10, 2026, 5:32 a.m.

Description

During the implementation of a huge high performance service. In order to keep context small (mainly for humans) I kept the specs into mermaid diagrams. When communicating with humans; diagrams were easy to follow and to remember. But when I asked the agent to implement what's in the diagram, most of the times it failed.So I came into conclusion that agents are good into writing mermaid diagrams but they are not good into reading them.I built graph2agent in order to deterministically (without inference :) ) convert mermaid diagrams into digestible rich text for agents. examples: https://github.com/graph2agent/examples/blob/main/examples/m...This gave us 50% error reduction for any class of diagrams and 80% error reduction for sequence diagrams specifically. Also Input tokens increased on avg by 8% (which is expected) but Reasoning tokens dropped by almost 50%.You can use it either with MCP so agents can call it with any mermaid diagram, and also can put it in pre-commit jobs and run it on every PR so all diagrams are agent ready!I hope you like it! Let me know your thoughts!

Enrichment

Theme
browser automation and scraping for AI
Vertical
Horizontal
Function
Agent / copilot
Audience
Developer
AI stance
AI-native
Project type
Commercial product
Normalized one-liner
explain mermaid diagrams to ai agents
Manually corrected
False

Could you build this?

Yes It is an AST parser for Mermaid flowchart syntax that deterministically generates human- and agent-readable text representations describing topology and nodes.

Discussion

1 comment analyzed.

Competitors

Other products that read as similar to this one — 77 launches clear the similarity bar, closest 8 shown.

Attention rank: #44 of 78 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 280 days after the earliest competitor.

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Same idea, different domain

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