Connect agentmem to your coding agent in 2 minutes. Pick your tool.
pip install quilmem[mcp]
Verify it works:
agentmem --help
If that prints the command list, you’re good.
Claude Code reads MCP config from ~/.claude/settings.json (global) or .claude/settings.json (project-level).
claude mcp add agentmem -- agentmem --db ./memory.db --project myproject serve
That’s it. Restart Claude Code. The 13 agentmem tools should appear.
Add this to ~/.claude/settings.json (global) or .claude/settings.json in your project root:
{
"mcpServers": {
"agentmem": {
"command": "agentmem",
"args": ["--db", "./memory.db", "--project", "myproject", "serve"],
"type": "stdio"
}
}
}
Replace ./memory.db with wherever you want the database. Replace myproject with your project name (used for scoping if you run multiple projects on one DB).
Use the full Python path:
{
"mcpServers": {
"agentmem": {
"command": "python",
"args": ["-m", "agentmem", "--db", "./memory.db", "--project", "myproject", "serve"],
"type": "stdio"
}
}
}
On Windows, you may need the full Python path:
{
"mcpServers": {
"agentmem": {
"command": "C:/Python313/python.exe",
"args": ["-m", "agentmem", "--db", "C:/path/to/memory.db", "--project", "myproject", "serve"],
"type": "stdio"
}
}
}
In Claude Code, the agentmem tools should show up. Ask Claude to run memory_health to confirm the connection.
Cursor reads MCP config from ~/.cursor/mcp.json (global) or .cursor/mcp.json (project root).
Create or edit the file:
{
"mcpServers": {
"agentmem": {
"command": "agentmem",
"args": ["--db", "./memory.db", "--project", "myproject", "serve"]
}
}
}
If agentmem isn’t on PATH, use the full Python path:
{
"mcpServers": {
"agentmem": {
"command": "python",
"args": ["-m", "agentmem", "--db", "./memory.db", "--project", "myproject", "serve"]
}
}
}
Open the command palette (Ctrl+Shift+P / Cmd+Shift+P), type “MCP”, and check that agentmem appears as a connected server. You can also type “Open MCP Settings” to see the config.
Codex reads MCP config from ~/.codex/config.toml (global) or .codex/config.toml (project root, trusted projects only).
Add this to the config file:
[mcp_servers.agentmem]
command = "agentmem"
args = ["--db", "./memory.db", "--project", "myproject", "serve"]
If agentmem isn’t on PATH:
[mcp_servers.agentmem]
command = "python"
args = ["-m", "agentmem", "--db", "./memory.db", "--project", "myproject", "serve"]
Run codex and ask it to use the memory_health tool. If it returns a health score, you’re connected.
Windsurf uses the same MCP config format as Cursor. Create ~/.windsurf/mcp.json or .windsurf/mcp.json in your project:
{
"mcpServers": {
"agentmem": {
"command": "agentmem",
"args": ["--db", "./memory.db", "--project", "myproject", "serve"]
}
}
}
Once connected, your agent has 13 tools:
| Tool | What it does |
|---|---|
add_memory |
Store a typed memory (bug, decision, setting, procedure, context, feedback) |
search_memory |
Full-text search with filters |
recall_memory |
Context-budgeted retrieval — fits best memories into a token limit |
update_memory |
Update a memory by ID |
delete_memory |
Delete a memory by ID |
list_memories |
List memories with optional type filter |
save_session |
Save current work state before conversation ends |
load_session |
Restore state at the start of a new session |
promote_memory |
Advance trust: hypothesis -> active -> validated |
deprecate_memory |
Mark as no longer true (excluded from recall) |
supersede_memory |
Replace old memory with new one |
memory_health |
Score the memory system 0-100 |
memory_conflicts |
Find contradictions between active memories |
Copy-paste a ready-made instruction block into your CLAUDE.md, .cursorrules, or AGENTS.md:
Agent Instructions - Session protocol, trust hierarchy, when to search vs add.
This is the difference between “memory is installed” and “memory is actually used.”
Start every session by having your agent call load_session. This restores where the last session left off.
End every session with save_session. Capture what’s in progress, what’s blocked, and what was decided.
Promote memories that prove correct. A validated memory ranks higher than active in recall. A hypothesis ranks lowest. This is how your agent learns what to trust.
Run health checks periodically. memory_health tells you if contradictions, stale rules, or orphaned references are degrading trust.
Use project scoping if you work on multiple codebases. Each project gets isolated memory with --project projectname.
Tools don’t appear after config change Restart your editor. MCP servers are loaded at startup.
“Command not found” errors
Use the full path to Python and -m agentmem instead of the agentmem command directly.
Windows path issues
Use forward slashes in JSON config: C:/Users/you/memory.db, not backslashes.
Server starts but no tools appear
Check that you have the MCP extra installed: pip install quilmem[mcp]. The base package doesn’t include the MCP dependency.
Multiple agents sharing one database
Use different --project values. Each project’s memories are isolated in search and recall, but stored in the same file.