Aisop – Define AI agent workflows as Mermaid or JSON flow graphs
AISOP is an open protocol designed for defining structured AI programs using either Mermaid or JSON flow graphs. It allows for the creation of multi-step AI workflows with features like branching, parallel execution, and error handling. The protocol is machine-readable and optimized for large language model comprehension, making it versatile for various applications.
- ▪AISOP supports dual flow formats, allowing both Mermaid strings and JSON flow objects to coexist in the same program.
- ▪The protocol includes over 14 control flow patterns, enabling complex workflows with features like error handling and sub-task support.
- ▪AISOP files are plain JSON, making them editable in any text editor and versionable in git.
Hacker News (AI / LLM) files mainly under ai. We currently carry 3,266 of its stories.
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Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | GitHub |
| Canonical URL | https://github.com/AIXP-Labs/AISOP |
| Publication time | Fri, 05 Jun 2026 02:28:02 +0000 |
| Retrieval time | 2026-06-05T03:21:10.232Z |
| Last seen | 2026-06-05T03:21:10.232Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | hImZV5XIG2iW |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
AISOP — AI Standard Operating Protocol 中文版 README An open protocol for defining structured AI programs using Mermaid or JSON flow graphs. AISOP enables defining multi-step AI programs with visual (Mermaid) or structural (JSON) control flow — branching, parallel execution, sub-tasks, and error handling — all in a single portable JSON format. Why AISOP? Approach Definition Portable Machine-readable Token-efficient Natural language prompts Free text Yes No No Python / YAML workflows Code / config No Partially No Visual builders (Dify, etc.) Proprietary No No No AISOP Mermaid + JSON Yes Yes Yes AISOP files are plain JSON with flow graphs (Mermaid or JSON) — readable by any language, editable in any editor, versionable in git, and optimized for LLM comprehension.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.