hyperresearch-pi

Deep research harness for pi — tier-adaptive 16-step pipeline with adversarial audit, source provenance, and a persistent searchable vault. Wraps the hyperresearch Python CLI.

Packages

Package details

extensionskill

Install hyperresearch-pi from npm and Pi will load the resources declared by the package manifest.

npm report

$ pi install npm:hyperresearch-pi

Package: hyperresearch-pi
Version: 0.1.6
Published: Jul 11, 2026
Downloads: 1,231/mo · 67/wk
Author: gongsunyuan
License: MIT
Types: extension, skill
Size: 337 KB
Dependencies: 0 dependencies · 5 peers

Pi manifest JSON

{
  "extensions": [
    "./extensions"
  ],
  "skills": [
    "./skills"
  ]
}

Security note

Pi packages can execute code and influence agent behavior. Review the source before installing third-party packages.

README

hyperresearch-pi

Deep research harness for pi — 16-step adversarial pipeline with persistent searchable vault.

Ports hyperresearch to a native pi extension.

Install

Prerequisite

The 16-step pipeline hard-depends on the subagent tool (13 of 17 steps spawn subagents). pi-subagents is a separate npm package, not built into pi — install it first:

pi install npm:pi-subagents

Install

pi install npm:hyperresearch-pi

The extension auto-installs the hyperresearch Python CLI on first launch (requires Python 3.11–3.13). If auto-install fails, run pip install hyperresearch manually, or set HYPERRESEARCH_BIN to the executable path.

Verify: run /hr — it shows vault status when ready.

Uninstall

/hr-uninstall                          # clean up deployed agent files in the project
pi remove hyperresearch-pi             # unregister the package

Vault data (research/, .hyperresearch/) and model config are not deleted — remove manually if desired.

Commands

Command	Description
`/hyperresearch <question>`	Run the 16-step research pipeline
`/hr`	Show vault status
`/hr-search <query>`	Quick vault search
`/hr-uninstall`	Remove deployed agent files (run before `pi remove`)

Usage

/hyperresearch your research question

The pipeline auto-classifies into a tier: light (~30 min) or full (~1.5–2.5 hours), decided by step 1.

Model configuration

Config file

~/.pi/agent/hyperresearch-models.json (auto-created from a template on first run; /reload picks up edits):

{
  "tiers": {
    "opus": {
      "model": "opus",
      "fallbackModels": ["sonnet"]
    },
    "sonnet": {
      "model": "sonnet",
      "fallbackModels": []
    }
  },
  "agents": {
    "hyperresearch-fetcher": {
      "_note": "fetcher runs in high parallelism, use a cheap model",
      "model": "sonnet",
      "fallbackModels": []
    }
  }
}

The model values in the template are placeholders (opus/sonnet). Replace them with real IDs from pi --list-models (format provider/model-id) before actual use.

Model ID format

provider/model-id — i.e. the first/second column of pi --list-models. Example: zai-glm/glm-5.2, not glm-5.2.

Priority

High → low: subagent tool's model param (per-spawn, temporary) > agents.<name> (persistent, per-agent override) > tiers.<tier> (persistent, tier default)

Fallback

When the primary model fails on a retryable error (no API key, rate limit, 502/503, timeout), pi-subagents auto-tries each fallbackModels entry in order. If the whole chain is unavailable, the spawn hard-fails — pass a model param at spawn time to use a different model.

Note

.pi/agents/hyperresearch/*.md are auto-generated deploy artifacts — don't hand-edit them, /reload overwrites. To force regeneration, delete that directory and /reload.

License

MIT