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retoor af080fe68e Strict goal verification, approval hardening, retrieval eval, work limits, sandbox caps (1.38.0)

- Approval: re-prompt unknown answers (max 3), shell-only affirmative
  guidance recovery; destructive actions stay strict
- GoalVerifier subagent with strict JSON verdicts, bounded follow-up
  rounds, nonzero exit plus open turn on unverified one-shots
- Retrieval eval (Hit@K/MRR/nDCG/Context Precision), FTS cascade with
  title weighting, query/read event logging, --eval-retrieval CLI
- Supervisor caps (8 parallel, 64 records) and scheduler hardening
  (4 fires/tick, per-schedule lease, stated misfire policy)
- Sandbox box resource trio: 2g/2.0/256 at create, best-effort
  update for existing boxes, rm -f recovery pointer
- Research archive: 12 subjects, 667 sources, 27 conclusions;
  suites 979 + 16 green

2026-10-18 12:13:24 +02:00

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GraphRAG README

Metadata

Field	Value
Institute	Molodetz Institute — Research & Retrieval Division
Subject	prompt-context-scaling
Document type	Source Dossier
Tier	1
Source title	GraphRAG README
Source URL	https://raw.githubusercontent.com/microsoft/graphrag/main/README.md
Source publisher / author	Microsoft
Source type	repository
Date researched	2026-10-18
Researched by	tai (agent), model: Muse Spark, on behalf of retoor
Research method	direct page fetch
Confidence	High
Status	Active

Summary

Microsoft's GraphRAG repository, the reference implementation of graph-based retrieval, now carries a maintenance-mode warning (no new PRs or features) and a cost warning on indexing. It describes itself as a research demonstration for extracting structured data from unstructured text with LLMs, not a supported offering.

Key findings

"This project is largely in maintenance mode, and won't be accepting new PRs or implementing new features", because "the capabilities of frontier models have changed dramatically" since the July 2024 release.
"Warning: GraphRAG indexing can be an expensive operation, please read all of the documentation to understand the process and costs involved, and start small."
"The provided code serves as a demonstration and is not an officially supported Microsoft offering."
The pipeline "is designed to extract meaningful, structured data from unstructured text using the power of LLMs."

Pros / strengths

First-party statement of the project's status and cost profile; settles the adopt-or-not question from the vendor's own mouth.
The maintenance rationale (frontier models changed the tradeoff) is itself evidence about where the field moved.

Cons / limitations / caveats

Says nothing about the technique's merits, only the implementation's status; the paper dossier carries the method claims.
A research code drop was never the right adoption target anyway; serious adopters reimplement the pipeline.

Relevance to tai

Decisive against adopting full GraphRAG extraction: expensive LLM indexing, a maintenance-mode reference, and tai's content is lookup-shaped (commands, logs, errors) where the technique underperforms flat retrieval (see the interview and practice cards). tai's existing tag/edge graph plus one-hop expand keeps the cheap half of the idea without the extraction pipeline.

Changelog

Date	Change
2026-10-18	Initial dossier created.

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