agent-memory-ledger 0.1.0

An append-only, auditable ledger for LLM agent memory.

Agent Memory Ledger

An append-only, tamper-evident ledger for LLM agent memory. When an agent makes a bad call, find out what it believed — and why.

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The problem

An agent wrote user_employer: "Acme Corp" on January 1.

On April 5 it read that memory and sent an email to the user at Acme.

The user left Acme in March. The agent was never told.

Your memory backend shows user_employer = "Acme Corp". That is all it shows. There is no record of when the agent learned it, where it came from, whether it was ever revisited, or whether the agent even read it before it acted. The bug is undebuggable, because there is nothing to debug against.

What the ledger gives you

  2. WAS ANY OF IT STALE?
──────────────────────────────────────────────────────────────────

     ok     user_seniority    43 days old at read
  ⚠  STALE  user_employer     94 days old at read

  Two beliefs, read in the same instant. One is fine -- seniority
  was refreshed in February. The other had not been touched since
  January, and the world had moved on without telling the agent.

Two memories, consulted at the same moment. One is fine. One is three months stale and about to cause an incident. The ledger tells them apart.

Run it yourself:

python examples/stale_belief_incident.py

Quick start

pip install agent-memory-ledger
aml --db demo.db --agent demo-agent demo
aml --db demo.db --agent demo-agent incident 2026-04-05T10:05:00+00:00

The third command answers the question you actually have at 2am — why did the agent believe that?

Incident window: up to 2026-04-05T10:05:00

  WHAT IT READ
      ok   user_seniority         senior engineer        43d old at read
           why: drafting outreach email -- need seniority
    STALE  user_employer          Acme Corp              94d old at read
           why: drafting outreach email -- need current employer

  WHAT IT BELIEVED
    user_employer          = Acme Corp
    user_seniority         = senior engineer

  Chain verified: 3 operations.

The agent read user_employer one minute before it sent the email. The value was 94 days old. It was written once, in January, from a single onboarding message, and never checked again — the user changed jobs in March and nobody told the agent.

A memory backend can only tell you the value is Acme Corp. The ledger tells you when the agent read it, how stale it was at that instant, where it came from, and — via the hash chain — that nobody edited the record afterward to make the incident look better than it was.

The full scenario, with provenance and belief reconstruction, is in examples/stale_belief_incident.py. See ARCHITECTURE.md for how it works.

API

Call	Answers
write(key, value, provenance=)	Record a belief, and why
read(key, provenance=)	Consult a belief — and log that you did
as_of(when)	What did the agent believe at that instant?
history(key)	Every change to this belief, with provenance
reads_before(when)	What did the agent consult before it acted?
stale_reads(when, older_than=)	Which of those were already out of date?
verify()	Has the record been tampered with?
EntityResolver.resolve(agent, key)	Are Acme Corp and ACME the same company?

How it works

• Append-only. Nothing is ever updated or deleted. A delete records a tombstone — the fact that the agent forgot something is itself a fact worth keeping.
• Hash-chained. Every operation is SHA256(prev_hash + payload). Edit a value, delete a row, or rewrite a provenance string, and verify() fails. The record is tamper-evident.
• Bitemporal. Time-travel is a single indexed query, not a replay from genesis.
• Reads are separate. Reads are 10–100× write volume and don't change belief, so they live outside the chain — but each one points at the exact ledger row the agent saw.
• Merges are beliefs too. When the entity resolver decides Acme and ACME are the same company, that decision goes in the ledger with its confidence and method. Why the agent thinks two things are the same is itself auditable.

See ARCHITECTURE.md for the design in depth.

Status

v0.x — early, but real. 81 tests passing. Not yet on PyPI.

Install from source:

git clone https://github.com/nomarin-ui/agent-memory-ledger
cd agent-memory-ledger
pip install -e ".[dev]"
pytest

Roadmap

• LangChain integration — wrap your existing memory, change nothing else
• CLI for time-travel queries
• Postgres adapter
• PyPI release (v0.1.0)

Feedback

If you run agents in production and have ever asked "why did it think that?" — I would like to hear from you. Open an issue.

License

MIT