Config is the same across clients — only the file and path differ.
{
"mcpServers": {
"agentmem": {
"args": [
"--db",
"./memory.db",
"--project",
"myproject",
"serve"
],
"type": "stdio",
"command": "agentmem"
}
}
}Are you the author?
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Shared memory for Claude Code, Cursor, and Codex that knows what's still true. Save sessions, catch stale and conflicting rules, and stop your agent from repeating old mistakes.
Run this in your terminal to verify the server starts. Then let us know if it worked — your result helps other developers.
uvx 'quilmem' 2>&1 | head -1 && echo "✓ Server started successfully"
After testing, let us know if it worked:
Five weighted categories — click any category to see the underlying evidence.
No known CVEs.
Checked quilmem against OSV.dev.
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Shared memory for Claude Code, Cursor, and Codex that knows what's still true. Save sessions, catch stale and conflicting rules, and stop your agent from repeating old mistakes.
Your AI coding assistant forgets everything between sessions. It repeats old mistakes. It can't tell current rules from outdated ones. Context compresses and recovery is painful.
Most memory tools solve storage. agentmem solves trust.
pip install quilmem[mcp]
agentmem init --tool claude --project myapp
That's it. Restart your editor. Your agent now has 13 memory tools. Run memory_health to confirm.
Python-only?
pip install quilmemworks without the MCP extra. See the Python API below.
from agentmem import Memory
mem = Memory()
# Store typed memories
mem.add(type="bug", title="loudnorm undoes SFX levels",
content="Never apply loudnorm to final mix. It re-normalizes everything.",
status="validated")
mem.add(type="decision", title="Use per-line atempo",
content="Bake speed into per-line TTS. No global pass.",
status="active")
# Something you're not sure about yet
hypothesis = mem.add(type="decision", title="Maybe try 2-second gaps before CTA",
content="Hypothesis from last session. Needs testing.",
status="hypothesis")
# Search — validated and active memories rank highest.
# Deprecated and superseded memories are excluded automatically.
results = mem.search("audio mixing")
# Context-budgeted recall — fits the best memories into your token limit
context = mem.recall("building a narration track", max_tokens=2000)
# Lifecycle — promote what's proven, deprecate what's not
mem.promote(hypothesis.id) # hypothesis -> active -> validated
mem.deprecate(hypothesis.id, reason="Disproven by data")
# Supersede: replace an outdated memory with a newer one
replacement = mem.add(type="decision", title="Use 1-second gaps before CTA",
content="Confirmed by A/B test.", status="active")
mem.supersede(hypothesis.id, replacement.id) # old points to replacement
# Health check — is your memory system trustworthy?
from agentmem import health_check
report = health_check(mem._conn)
# Health: 85/100 | Conflicts: 0 | Stale: 2 | Validated: 14
Other memory tools store things. agentmem knows what's still true.
| Mem0 | Letta | Mengram | agentmem | |
|---|---|---|---|---|
| Memory storage | Yes | Yes | Yes | Yes |
| Full-text search | Vector | Agent-driven | Knowledge graph | FTS5 |
| Memory lifecycle states | No | Partial | No | hypothesis -> active -> validated -> deprecated -> superseded |
| Conflict detection | No | No | Partial | Built-in |
| Staleness detection | No | No | No | Built-in |
| Health scoring | No | No | No | Built-in |
| Provenance tracking | No | No | No | source_path + source_hash |
| Trust-ranked recall | No | No | No | Validated > active > hypothesis |
| Human-readable source files | No | No | No | Canonical markdown |
| Local-first, zero infrastructure | No | Self-host option | Self-host option | Yes, always |
| MCP server | Separate | Separate | Yes | Built-in |
The core idea: every memory has a status that tracks how much you should trust it.
hypothesis New observation. Not yet confir
... [View full README on GitHub](https://github.com/Thezenmonster/agentmem#readme)