agentmem — Governed Memory for Coding Agents
Description: Your coding agent stops forgetting, stops repeating mistakes, and knows what's still true. Governed memory with trust, provenance, and conflict detection.

# Governed memory for
coding agents

Your agent should know what is true, what changed, and what to ignore.

pip install quilmem
View on GitHub MCP Setup (2 min) Python Quickstart

## The problem with AI memory

Your coding assistant forgets everything between sessions.
Most memory tools solve storage. None of them solve trust.

🔄

### Repeats old mistakes

Fixed a bug last week? Your agent doesn't remember. Same error, same fix, every session.

💥

### Contradicts itself

Old rule says "always do X." New rule says "never do X." The agent sees both and picks randomly.

💭

### Context collapse

Long conversation compresses. Your agent loses the project map, the decisions, the preferences.

❓

### No source of truth

Is that memory from yesterday or last month? Is it still valid? Nobody knows. Not even the agent.

## Memory with governance

Every memory has a status. Your agent knows what to trust.

hypothesis → active → validated
deprecated • superseded — excluded from recall

### Trust-ranked recall

Validated memories rank above hypotheses. Deprecated ones are excluded entirely.

### Conflict detection

Finds contradictions between active memories. Separates real conflicts from duplicates.

### Staleness detection

Flags memories when source files change, go missing, or haven't been updated.

### Provenance tracking

Every memory knows where it came from: source file, section, content hash.

### Health scoring

Scores your memory system 0-100. Know exactly how trustworthy your agent's knowledge is.

### Local-first

No cloud, no API keys, no vector DB. Just SQLite. Your data stays on your machine.

## See it work

    # Store memories with trust levels
    mem.add(type="decision", title="Use per-track volume",
            content="Never apply loudnorm to voice.",
            status="validated")

    # Recall only returns what's trustworthy
    context = mem.recall("audio processing", max_tokens=2000)

    # Find contradictions
    conflicts = detect_conflicts(mem._conn)
    # !! "Always apply loudnorm" vs "Never apply loudnorm"

    # Check system health
    report = health_check(mem._conn)
    # Health: 85/100 | Conflicts: 1 | Stale: 0

    # Lifecycle management
    rule = mem.add(type="decision", title="Batch writes",
            content="Hypothesis: may reduce latency.",
            status="hypothesis")
    mem.promote(rule.id)                     # hypothesis -> active -> validated
    mem.deprecate(rule.id, reason="Disproven")
    mem.supersede(old.id, new.id)            # old points to replacement

## Battle-tested in production

65+
Videos produced
330+
Governed memories
0
Repeated bugs
66
Tests passing

## How it compares

| Mem0 | Letta | Mengram | agentmem
--- | --- | --- | --- | ---
Memory lifecycle states | No | Partial | No | 5 states
Conflict detection | No | No | Partial | Built-in
Staleness detection | No | No | No | Built-in
Provenance tracking | No | No | No | source + hash
Trust-ranked recall | No | No | No | Status-weighted
Health scoring | No | No | No | 0-100 score
Local-first | Cloud | Self-host | Yes | Always
MCP server | Separate | Separate | Yes | 13 tools

## Something break? Tell us.

Every activation path has a way to report where it broke.
Run `agentmem debug-info` to capture your system state, then open an issue.

Setup problem MCP not connecting Bug report Docs confusion Feature request

Or see Troubleshooting for common fixes.

## Get early access

We're building a dashboard for conflicts, stale memory, health history, and team memory.
Join the beta. Takes 10 seconds.

Email What tool do you use?
Biggest memory pain right now? Interested in a paid dashboard later?
Yes Maybe No

No spam. Just beta access and updates.

### You're in.

We'll email you when the dashboard beta opens.
In the meantime: set up the MCP server and start using governed memory now.