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Summary

A new Hermes Agent plugin for BackSearch provides point-in-time web search and fetch capabilities, allowing AI agents to access archived web content as of specific dates for tasks like forecasting backtests and reproducible benchmarks.

Made a stand-alone Hermes Agent plugin for BackSearch - a wayback machine-like SaaS for agents - adds two tools, and requires their API key. Let me know if you like it https://github.com/NousResearch/hermes-plugin-backsearch…
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Made a stand-alone Hermes Agent plugin for BackSearch - a wayback machine-like SaaS for agents - adds two tools, and requires their API key. Let me know if you like it https://github.com/NousResearch/hermes-plugin-backsearch…


NousResearch/hermes-plugin-backsearch

Source: https://github.com/NousResearch/hermes-plugin-backsearch

hermes-plugin-backsearch

BackSearch — search the web as it was

Point-in-time web search & fetch for Hermes Agent, backed by BackSearch by General Reasoning.

BackSearch is a frozen news archive: every request carries an as_of date, search returns only documents crawled on or before it, and fetch returns the article text as archived at that time. Same query + same as_of = same results, forever. Built for forecasting backtests, quant research loops, RL environments, and reproducible benchmarks — any task where evidence after a cutoff date must not leak in.

The plugin registers two model tools, both gated on OPENREWARD_API_KEY — with no key configured they never reach the model schema, so the tool footprint is zero:

ToolWhat it does
backsearchHybrid search over the frozen corpus as of a date (query, as_of, optional k, allowed_domains/blocked_domains)
backfetchFetch a page’s extracted text from the latest capture on or before the cutoff (url, as_of, optional prompt for a focused summary)

Install

# 1. Clone into the Hermes plugins dir
git clone https://github.com/NousResearch/hermes-plugin-backsearch.git ~/.hermes/plugins/backsearch

# 2. Enable
hermes plugins enable backsearch

# 3. API key — BackSearch bills against an OpenReward prepaid balance.
#    Get a key (or_...) at https://openreward.ai/
echo 'OPENREWARD_API_KEY=or_...' >> ~/.hermes/.env

New sessions pick the tools up automatically once the key is present.

Semantics worth knowing

  • as_of gates on crawl_date, not the article’s self-reported publish date. A page first archived after the cutoff is never returned, even if it claims an earlier publish date — that’s what guarantees no post-cutoff leakage into a backtest.
  • Archive window: the current preview archive covers news domains, December 2025 – July 2026. An as_of outside the window returns an empty hit list (not an error).
  • Billing: per successful request. A fetch with no capture on or before the cutoff returns a soft 404 and costs nothing; an exhausted OpenReward balance returns 402 with an actionable message.
  • Fetch text is capped at 15K chars; pass a prompt to get a focused summary of a long article instead of the full text.
  • OPENREWARD_SEARCH_URL overrides the base URL (https://search.openreward.ai) for testing/self-routing.

Tests

# No network, no key needed — run from a hermes-agent checkout's venv so
# tools/ imports resolve (set HERMES_AGENT_REPO if not ~/.hermes/hermes-agent):
python -m pytest tests/ -q

Attribution

Originally built as hermes-agent PR #71207; extracted to this standalone repo per the hermes-agent policy that third-party service integrations ship as plugins rather than core code.

License

MIT

General Reasoning (@GenReasoning): 🔍 Introducing BackSearch.

LLMs are increasingly asked to predict the future, but a good backtest requires a snapshot of the internet at a point in time.

BackSearch allows LLMs to search the web as it was on a particular date. It’s great for:

🔮 Forecasting and prediction

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