Introduction

Every developer knows this:          AMFS does the same for agent memory:

  repo                                 agent brain
  ├── main branch                      ├── main (what the agent knows)
  ├── feature branch                   ├── experiment branch (isolated changes)
  ├── pull request                     ├── pull request (review before merge)
  ├── code review                      ├── diff (what changed in the branch)
  ├── merge                            ├── merge (accept changes into main)
  ├── git log                          ├── timeline (every operation logged)
  └── git revert                       └── rollback (restore to any point)

from amfs import AgentMemory

mem = AgentMemory(agent_id="review-agent")

mem.write(
    "checkout-service",
    "risk-race-condition",
    "Race condition in order processing under concurrent load",
    confidence=0.85,
)

entry = mem.read("checkout-service", "risk-race-condition")
# → confidence: 0.85, written_by: review-agent

1. Identify yourself         →  amfs_set_identity("checkout-agent", "Fixing retry logic")
2. Get a briefing            →  amfs_briefing(entity_path="checkout-service")
3. Search for specifics      →  amfs_search("checkout-service")
4. Read relevant entries     →  amfs_read("checkout-service", "retry-pattern")
5. Do the work               →  (agent performs its task)
6. Write findings            →  amfs_write("checkout-service", "new-pattern", ...)
7. Record outcomes           →  amfs_commit_outcome("DEP-287", "success")
8. Next agent starts at #2   →  Knowledge compounds across agents and sessions