nigrty
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@nigrtyremember that I prefer dark mode in all my apps"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
RecallLattice
A local-first permanent memory lattice for AI agents. RecallLattice combines SQLite, FTS5, ranked fuzzy recall, typed association graphs, spaced review, and a compact MCP interface without embeddings or a hosted database. Existing v3 installations retain the nigrty runtime/module names for complete compatibility.
Your database stays on your machine. The repository never needs it.
Why RecallLattice
Smarter recall: multi-keyword, stemming, prefix, typo-tolerant, tag-aware ranking.
Connected memory: new facts auto-link to related facts; recall can add one-hop graph context.
Low-token MCP: compact JSON emits each fact once and retrieves full text only on request.
Durable: WAL-mode SQLite, FTS synchronization triggers, integrity checks, and simple backups.
No vector stack: the core uses Python's standard library plus SQLite FTS5.
Full ownership: one portable database file and a localhost-only optional dashboard.
Backwards compatible: upgrades existing nigrty v3 databases in place without changing memory IDs.
Related MCP server: Synapto
Architecture
Hermes / any MCP client
│ stdio
▼
nigrty_mcp.py ───── compact ranked context
│
▼
nigrty.py ─────── dedup · scoring · decay · graph
│
▼
SQLite + FTS5 ───── memories · links · activity · XP
▲
│
FastAPI dashboard (optional, 127.0.0.1:8799)Recall ranking
nigrty scores matches using:
Exact phrase relevance.
Exact token and stem matches.
Prefix and typo-tolerant similarity.
Tag matches and query coverage.
Pin status, importance, and memory strength.
Optional one-hop graph neighbors after direct results.
This keeps retrieval deterministic and inspectable while catching queries such as Qran reel vidio for a memory containing Quran Reels ... video.
Install
git clone https://github.com/Adam-ZS/RecallLattice.git
cd RecallLattice
./scripts/install.shThe installer:
preserves an existing
~/.nigrty/nigrty.db;backs up existing source files;
creates a persistent
~/.nigrty/.venv;installs MCP and dashboard dependencies;
installs and starts the localhost dashboard service.
Open the dashboard at http://127.0.0.1:8799.
Hermes Agent integration
Add this MCP server to ~/.hermes/config.yaml through Hermes configuration tooling:
mcp_servers:
nigrty:
command: /home/YOU/.nigrty/.venv/bin/python
args:
- /home/YOU/.nigrty/nigrty_mcp.py
enabled: trueFor an existing nigrty entry, update its interpreter and enable it:
hermes config set mcp_servers.nigrty.command "$HOME/.nigrty/.venv/bin/python"
hermes config set mcp_servers.nigrty.enabled trueStart a new Hermes session after changing MCP configuration.
MCP tools
Tool | Purpose |
| Store, categorize, tag, deduplicate, and auto-link a fact |
| Ranked compact recall with optional graph context |
| Retrieve one full memory and its links |
| Delete one memory |
| Control critical-memory priority |
| Strengthen a memory through spaced review |
| Add a typed graph edge |
| Inspect memory and graph health |
| List memories needing review |
| Export graph nodes and edges |
| Summarize activity |
| Export memory records |
| Preview stale low-value records before deletion |
Compact versus full recall
Compact recall is the default:
{"query":"project memory","limit":8,"include_related":true,"max_chars":320}Use "full": true only when the complete matching records are necessary. This progressive-disclosure pattern avoids paying for timestamps and bookkeeping on ordinary recalls.
Python API
import nigrty
saved = nigrty.store(
"The project uses SQLite FTS5 for local ranked retrieval",
category="project",
importance=8,
)
matches = nigrty.recall("SQLite ranked project", limit=5)
print(matches[0]["content"])Use a custom database path without changing source:
export NIGRTY_DB="$HOME/my-brain/memory.db"Dashboard API
curl http://127.0.0.1:8799/api/stats
curl --get --data-urlencode 'q=ranked memory' http://127.0.0.1:8799/api/search
curl -X POST http://127.0.0.1:8799/api/store \
-d 'content=One durable fact' -d 'importance=8'The REST write endpoints use form encoding. The dashboard has no authentication and binds to localhost by default.
Backups and rollback
cp ~/.nigrty/nigrty.db ~/.nigrty/nigrty.db.backup-$(date +%Y%m%d)
./scripts/rollback-local.sh /path/to/pre-upgrade-backupNo migration deletes memories. v3.1 adds FTS maintenance triggers and an internal schema version, then rebuilds the search index from the canonical memories table.
Testing
python -m unittest discover -s tests -v
python -m py_compile nigrty.py nigrty_protocol.py nigrty_mcp.py server.pyThe test suite covers deduplication, auto-linking, multi-keyword ranking, typo tolerance, tag filtering, access metadata, FTS store/update/delete synchronization, and compact output.
Privacy
Never commit nigrty.db, exports, backups, or .env files. The included .gitignore blocks these patterns, but inspect staged files before every push.
License
MIT — see LICENSE.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
No tool schema history has been recorded yet.
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