Every AI coding agent on your machine keeps its own private memory. Memory Bridge reads them all — the conversations you've already had — and gives Hermes one searchable index across every one of them.
curl -fsSL antharmaya.com/memory-bridge/install.sh | bash9
Agents auto-discovered
0
API keys required
<5ms
Per prefetch()
100%
Local index
Claude Code knows your architecture. Codex remembers your preferences. Gemini has the project plan. But each one starts every session from zero — and Hermes, your always-on agent, can't see any of it.
Other memory tools start recording the day you install them. Memory Bridge reads the conversations already sitting on your disk — every session you've had with Claude Code, Codex, Gemini and the rest — and brings them in on day one.
Deep extraction runs through ctx.llm — Hermes' own configured model. There's no second key to manage, no separate provider to wire up. Point Hermes at a local model and it stays fully offline.
Scanning, rules-based extraction and the SQLite index are 100% local. Memory Bridge makes zero network calls of its own. The only data that ever moves is the LLM pass — and only to the model you already chose.
FTS5 trigram search with a fuzzy fallback. Search “clouflare” and it still surfaces the Cloudflare decision. Sub-millisecond, even across 100K+ entries, with no vector database to run.
Every architecture call, every trade-off, every "we'll go with X" gets extracted, attributed to the framework that shaped it, and tracked through to its outcome.
You make a call — a framework, an architecture, a trade-off — across any of your agents.
Memory Bridge extracts the decision, attributes the framework that shaped it, and files it with its rationale and confidence.
Later, mark how it actually turned out. Your agents stop repeating decisions that already failed.
$ hermes memory-bridge decisions --unverifiedAuto-discovered on your machine — no hooks, no setup
Auto-discovers every agent history on disk — 9 agents, no hooks, no instrumentation.
Rules-based extraction is free and instant; an optional ctx.llm pass adds deep semantic facts.
SQLite + FTS5 trigram. Sub-millisecond, typo-tolerant, zero external services.
Hermes queries the index every turn via prefetch(). Relevant memory is injected automatically.
histories → scanner → extractor → SQLite + FTS5 → Hermes, every turn
No external services. No API keys. No telemetry. Python 3.11+ and Hermes 0.16+.
curl -fsSL https://antharmaya.com/memory-bridge/install.sh | bashClones into ~/.hermes/plugins/memory/, installs deps, runs the first scan.
git clone https://github.com/antharmaya/mem-bridge.git \
~/.hermes/plugins/memory/antharmaya-bridgeMemory providers live in ~/.hermes/plugins/memory/<name>/ — the Hermes discovery convention.
hermes memory setupSelect "antharmaya-bridge", or set memory.provider in ~/.hermes/config.yaml.
MIT licensed · Built by Antharmaya Labs · Made in India