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WFGY 5.0 Polaris Protocol is now moving through a staged functional rollout.
WFGY 5.0 has grown into multiple public layers: evidence packages, portable protocols, reproducibility materials, runtime structures, and deeper engine components.
Instead of making everyone wait for one giant release, useful parts will now be released in batches.

The earlier Cite First Verification release was a small WFGY Easter Egg during the schedule adjustment. The first public portable protocol component is now available: Polaris Goal Compiler.

Open the CFV Easter Egg
Open Polaris Goal Compiler
Preview WFGY 5.0 Polaris Protocol
Join the WFGY Discord

WFGY, led by WFGY 5.0 Polaris Protocol

We build WFGY, an open-source reasoning, debugging, and protocol ecosystem for AI systems.
The current flagship public route is WFGY 5.0 Polaris Protocol, now being released through a staged functional rollout.

One lineage, multiple public entry points, one growing ecosystem under the MIT License.

Quick navigation

  • ⭐️ WFGY 5.0 Polaris Protocol : flagship public route for staged WFGY 5.0 release.
  • ⭐️ Polaris Goal Compiler : first public portable protocol component for human-AI execution workflows.
  • ⭐️ Polaris Experiments : public evidence packages with raw outputs, parsed outputs, verdicts, audits, token records, and hashes.
  • ⭐️ AI Troubleshooting Atlas : practical entry for broken RAG, agent, and AI workflows.
  • ⭐️ Global Debug Card : image-first triage for a single failing run.
  • ⭐️ Ecosystem Map : canonical map of how the public WFGY system fits together.
  • ⭐️ WFGY 3.0 : frontier reasoning and long-horizon evaluation surface.

Trusted open-source support

Supported through selected open-source programs, sponsored plans, and infrastructure credits.

WFGY Trust Wall

Public adoption and public support are not the same thing.
This support surface sits alongside the adoption and ecosystem proof pages below.

WFGY Twin Flame


What WFGY 5.0 is doing now

WFGY 5.0 Polaris Protocol is not being released as one single drop.
It is being opened batch by batch so each layer can be inspected, tested, and used properly.

The current WFGY 5.0 public route includes:

  • public evidence first : experiment packages, raw outputs, parsed outputs, verdicts, audits, token records, and hashes
  • portable protocol components : practical protocol files users can download, paste, and test
  • reproducibility materials later : future Colab and reproduction workflows
  • deeper engine layers later : runtime structures and mathematical spine materials released step by step

Currently public WFGY 5.0 materials:


First portable protocol component

Polaris Goal Compiler is the first public protocol component released under the WFGY 5.0 Polaris line.

Polaris Goal Compiler is a portable human-AI execution protocol for assistants, agents, and skill-based workflows.

It helps AI systems avoid treating raw natural language as if it were already an executable task.

It focuses on:

  • compiling user goals before construction
  • separating truth work from expression work
  • exposing the active task
  • exposing blocked downstream work
  • preventing premature completion claims
  • verifying before unlock
  • making long AI workflows easier to inspect

Start here:


Who is WFGY for

WFGY is for people who need structured debugging and serious reasoning, not just another prompt recipe.

  • RAG and agent teams : pipelines run, infra looks healthy, but answers are still wrong or unstable.
  • Infra and platform owners : you need a way to inspect and route failures across models, tenants, or deployments.
  • Researchers and evaluation teams : you study reasoning, robustness, safety, or stress tests and want concrete observables.
  • Founders, PMs, and domain experts : you carry difficult AI workflows and want a more structured system for treating them.
  • AI builders and workflow designers : you want portable protocol components that make long AI tasks easier to inspect and harder to fake.

If you do not fit neatly into any of the above, start with Polaris Goal Compiler, the AI Troubleshooting Atlas, or the Global Debug Card.

Entry points


Why WFGY looks like an ecosystem

WFGY is not a single page, a single chart, or a single claim.

The public system is easiest to read as:

  • one version lineage : WFGY 1.0 → WFGY 2.0 → WFGY 3.0 → WFGY 4.0 → WFGY 5.0 Polaris Protocol
  • one flagship public route : WFGY 5.0 Polaris Protocol
  • one staged release model : evidence first, portable protocols next, deeper engine layers later
  • one strong practical wedge : AI Troubleshooting Atlas, Problem Map, Global Debug Card, and Global Fix Map
  • one public proof and collaboration layer : Adopters, Case Evidence, Recognition Map, Evidence Timeline, Work with WFGY, and Support

The goal is simple: make reasoning failures more visible, reproducible, inspectable, and fixable.


Public proof and ecosystem integration

  • ⭐️ Adopters : shortest public adoption summary.
  • ⭐️ Case Evidence : what those integrations imply in real systems.
  • ⭐️ Recognition Map : broader ecosystem record of integrations, citations, curated lists, and public mentions.
  • ⭐️ Evidence Timeline : historical timeline of how WFGY became public, usable, and externally legible.

Most current public references point first to the WFGY ProblemMap / 16-problem failure checklist line.
A smaller but growing set also uses WFGY 3.0 as a long-horizon, TXT-based reasoning and evaluation surface.
WFGY 5.0 Polaris Protocol is now becoming the new flagship public route through staged release.

This does not mean every project uses the full WFGY ecosystem.
In many cases, WFGY appears first as a practical diagnostic layer for RAG and agent pipelines.

Representative integrations
Project Stars Segment How it uses WFGY ProblemMap Proof (PR / doc)
LlamaIndex GitHub Repo stars Mainstream RAG infra Integrates the WFGY 16-problem RAG failure checklist into its official RAG troubleshooting docs as a structured failure mode reference. PR #20760
RAGFlow GitHub Repo stars Mainstream RAG engine Introduced a RAG failure modes checklist guide to the RAGFlow documentation via PR, adapted from the WFGY 16-problem failure map for step-by-step RAG pipeline diagnostics. PR #13204
FlashRAG (RUC NLPIR Lab) GitHub Repo stars Academic lab / RAG research toolkit Adapts the WFGY ProblemMap as a structured RAG failure checklist in its documentation. The 16-mode taxonomy is cited to support reproducible debugging and systematic failure-mode reasoning for RAG experiments. PR #224
DeepAgent (RUC NLPIR Lab) GitHub Repo stars Academic lab / agent research Adds a multi-tool agent failure modes troubleshooting note inspired by WFGY-style debugging concepts for diagnosing tool selection loops, tool misuse, and multi-tool workflow failures in agent pipelines. PR #15
ToolUniverse (Harvard MIMS Lab) GitHub Repo stars Academic lab / tools Provides a WFGY_triage_llm_rag_failure tool that wraps the 16 mode map for incident triage. PR #75
Rankify (University of Innsbruck) GitHub Repo stars Academic lab / system Uses the 16 failure patterns in RAG and re-ranking troubleshooting docs. PR #76
Multimodal RAG Survey (QCRI LLM Lab) GitHub Repo stars Academic lab / survey Cites WFGY as a practical diagnostic resource for multimodal RAG. PR #4
LightAgent GitHub Repo stars Agent framework Incorporates WFGY ProblemMap concepts into its documentation via a Multi-agent troubleshooting (failure map) section, providing a structured symptom → failure-mode → debugging checklist for diagnosing role drift, cross-agent memory issues, and coordination failures in multi-agent systems. PR #24

Work with or support WFGY

If you maintain an AI system, research project, or infra platform and want to explore collaboration around WFGY, start here:

You can also:

  • open an issue describing your current system and failure modes
  • reference the matching WFGY ProblemMap number if you already know it
  • reach out via Discord for exploratory discussion

We are especially interested in:

  • RAG or agent teams testing WFGY diagnostics in production-like settings
  • research groups designing stress tests or observables on the atlas line
  • platform owners exposing WFGY-style diagnostics to their users
  • AI builders testing portable protocol components such as Polaris Goal Compiler

The long-term goal is simple: make reasoning and debugging layers a normal, visible part of AI systems.


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