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., "@coherra audit my memory and fix any issues"
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.
An AI agent's memory gets messy. Coherra keeps it clean.
After months of use, any long-running agent accumulates contradictions (the same fact stored twice with different values), duplicates (the same value under different key names), and stale entries (facts that were true six months ago but almost certainly aren't now). Coherra audits the entire memory on demand, scores its health, surfaces every issue with a clear explanation, and repairs them — logging every single action permanently inside Sibyl Memory itself. No external database. No separate service. Everything lives inside Sibyl.
┌─────────────────────────────────────────────────────────┐
│ Agent / IDE │
│ (Claude Code, Codex CLI, Cursor, …) │
└───────────────────────┬─────────────────────────────────┘
│ MCP (stdio)
┌─────────────▼──────────────┐
│ Coherra MCP Server │
│ coherra_remember │ ← structured writes
│ coherra_recall │ ← single entity read
│ coherra_list │ ← browse memory
│ coherra_audit │ ← run full scan
│ coherra_repair │ ← fix one issue
│ coherra_repair_safe │ ← batch safe fixes
└─────────────┬──────────────┘
│ sibyl-memory-client SDK
┌─────────────▼──────────────┐
│ Sibyl Memory │
│ WARM tier — entities │ ← facts, preferences, people
│ HOT tier — state │ ← coherra:last_audit, coherra:config
│ COLD tier — journal │ ← coherra_scan, coherra_repair events
└─────────────────────────────┘
~/.sibyl-memory/memory.dbCoherra sits between the agent and Sibyl Memory. It adds a schema layer (versioned, confidence-tagged bodies), an audit engine (three detectors: contradiction, duplicate, staleness), and a repair engine that logs every action to Sibyl's own append-only journal — so the audit trail is itself part of the memory being managed.
Related MCP server: WorldOracle
# 1. Clone and install
git clone <repo>
cd coherra
pip install -e .
# 2. Make sure Sibyl Memory is initialised
# (run once if you haven't already)
sibyl init
# 3. Load the demo dataset
python -m coherra.seed --demo
# 4. Run your first audit
coherra scan
# 5. Browse the issues
coherra issues
# 6. Fix safe issues automatically
coherra fix --all
# 7. Resolve contradictions interactively
coherra fix <issue-id>
# 8. Confirm clean memory
coherra scanCommand | What it does |
| Run a full audit — contradiction, duplicate, staleness checks |
| One-line summary of the last scan result |
| List every flagged issue, grouped by severity |
| Fix one issue; contradictions get an interactive prompt |
| Auto-apply all safe repairs (duplicates + stale) |
Add to your .mcp.json or Claude Code settings:
{
"mcpServers": {
"coherra": { "command": "coherra-mcp" }
}
}Tools exposed: coherra_remember, coherra_recall, coherra_list,
coherra_audit, coherra_repair, coherra_repair_safe.
Most memory systems only answer the question "what do I know?" Coherra answers "what do I know that's wrong?" — and fixes it, permanently, with a complete audit trail stored inside the same memory it's cleaning.
Built for the Sibyl Labs Memory Hackathon · August 2026.
Requires: sibyl-memory-client >= 0.5.0, mcp >= 1.0.0.
This server cannot be installed
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
Shared long-term memory vault for AI agents with 20 MCP tools.
Bitcoin-anchored, tamper-evident audit log for AI agents — record, disclose and verify actions.
Collective memory for AI agents. One agent solves a bug — every agent gets the fix instantly.
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/Steve2009729/coherra'
If you have feedback or need assistance with the MCP directory API, please join our Discord server