pip install quilmem[mcp]
agentmem init --tool claude --project myapp

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# Deprecated and superseded memories are excluded automatically.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# Supersede: replace an outdated memory with a newer one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