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$ show Cyrene963/hermes-patches

[account ↗] [repo ↗] APPLICATION ★ 202 25 ● active (last update 2 months ago)
reviewed by: deepseek/deepseek-v4-flash

This repository is a community patch overlay for Nous Research's Hermes Agent, adding a durable 'Memory OS': a PostgreSQL-backed URI-addressed memory graph (core://, writer://, system:// domains) with row-level security multi-tenancy, vector semantic recall via a hindsight embeddings service, and a fail-closed write pipeline that classifies candidate memories, verifies them by readback before promotion, routes uncertain writes to human review, and tracks corrections in a regression-replay ledger. It ships a standalone FastAPI + React memory review WebUI, watchdog/scorecard/canary scripts, systemd units, and CI gates including patch verification and a secret-scraping privacy guard. The dominant character is application: an agent-runtime extension integrated with the Hermes CLI, Telegram gateway, and cron scheduler, with tools registered through a central tool registry and policy modules (confidence calibration, privacy inference, relationship boundaries, completion fidelity) returning frozen dataclass verdicts that gate tool execution. Security discipline is unusually strong — writes are readback-verified, secrets are redacted/hashed before persistence, LLM failures block writes rather than proceeding, memory deletion uses HMAC-signed short-TTL grants with nonce replay protection, and roughly 100 test files exercise gates, CLI behavior, gateway gating, plugins, and operational scripts. The main structural weaknesses are two parallel memory-graph backend implementations (embedded agent/memory_graph vs standalone-memory-graph-webui/backend) with duplicate ORM models that may diverge, many hardcoded ~/.hermes profile paths and deployment-specific service names in scripts/tests, and an explicit patch-overlay design that must be re-applied as upstream Hermes evolves. Some data-research flavored utilities exist (trajectory_compressor.py for fine-tuning corpus compression, precision/recall eval harnesses, CJK-aware search tokenization), but they serve the application rather than defining it. For an AI agent looking for memory infrastructure patterns — readback-verified writes, fail-closed policy verdicts, RLS namespace isolation, HMAC-granted deletes — this repo is a strong reference; for direct reuse, treat the standalone WebUI and embedded backend as candidates for consolidation, and expect to parameterize path assumptions.

RATING7.6/10
QUALITY7.6/10
MAINTENANCEactive
DRIFTmedium
CONTRIBUTORS3
VELOCITY0.38
OPEN ISSUES8
CLOSED ISSUES1
LAST COMMIT2 months ago
Files by Type (count)
Python
259
Other (.patch)
40
JSX
25
Other (.woff2)
24
Markdown
15
YAML
11
Shell
8
SQL
5
JSON
5
JavaScript
5
Other (.service)
4
Git Ignore
2
Files by Type (size)
Python
6MB
Other (.jpg)
3MB
Other (.patch)
2MB
JavaScript
1MB
Other (.woff2)
1MB
JSX
241KB
Other (.webp)
180KB
Markdown
142KB
JSON
114KB
CSS
105KB
Shell
85KB
SQL
18KB

The LLM identified 5 files worth highlighting for reuse:

Standalone fire.CLI utility that compresses ShareGPT-style JSONL agent trajectories to fit fine-tuning token budgets while preserving first/last turns, replacing compressed middle regions with a single human-role [CONTEXT SUMMARY] message, and emitting per-trajectory and aggregate compression metrics. Zero coupling to the Hermes runtime — extractable into any LLM trajectory preprocessing pipeline.

file: trajectory_compressor.py ↗ | tags: #llm-data-curation #compression #cli-tool

Contains StreamingContextScrubber, a stateful chunk-safe state machine that strips <memory-context> spans from streaming LLM output even when spans split across chunks or escape sequences, without buffering the whole stream. The class is self-contained and can be lifted into any streaming LLM response pipeline needing redaction or context fencing.

file: agent/memory_manager.py ↗ | tags: #streaming-redaction #state-machine #chunk-safe

Provider-agnostic embeddings factory with auto-dimension detection and backend support for local, OpenAI, Cohere, Google, LiteLLM, and TEI endpoints. Isolated from agent internals; reusable in any retrieval pipeline that needs swappable embedding backends behind a single interface.

file: site-packages/hindsight_api/engine/embeddings.py ↗ | tags: #embeddings #provider-abstraction #factory-pattern

Large runtime provider resolver covering a broad LLM provider matrix (OpenAI, Anthropic, OpenRouter, xAI, Gemini OAuth, Bedrock, Azure Entra, custom/local endpoints) with host-gated env-var fallbacks, lookalike-host protection, and per-provider API-mode auto-detection. The _host_derived_api_key and _detect_api_mode_for_url helpers are portable security patterns for any multi-provider LLM client or gateway.

file: hermes_cli/runtime_provider.py ↗ | tags: #provider-resolution #credential-security #api-mode-detection

Implements time-limited HMAC-signed delete grants with nonce replay protection and ContextVar turn binding, so memory deletions are authorized per-turn without persistent permission state, plus a lifecycle manager for archiving stale leaves and cascading rollback. A non-obvious security pattern reusable wherever privileged single-use operations need short-lived delegated authority.

file: agent/memory_lifecycle.py ↗ | tags: #hmac-grants #nonce-replay-protection #secure-delete
Last analyzed: 2026-10-26 01:25:02.291646
Next check: 2026-11-02 01:25:02.293281
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