Heartwood Memory

Heartwood Memory is a governed memory store for AI agents: provenance-signed audit, policy-gated recall, tenant isolation, and a per-subject key-destruction proof on erasure.

License at a glance. Heartwood Memory 0.2.0 and later is source-available under the Business Source License 1.1 (BSL 1.1), not an OSI "open source" license. Non-production use is free at any size. Small Organizations—fewer than 100 employees and independent contractors and less than $1M in prior-tax-year revenue, as adjusted from 2019 under the license—may also use it in production at no charge. Each version converts automatically to the Apache License 2.0 four years after release. Versions 0.1.0–0.1.2 were MIT-licensed and remain MIT-licensed permanently.

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Governed, source-auditable memory for AI agents, embedded beside your existing systems of record.

Heartwood is a cryptographic trust root for agent memory: every memory is signed, recall runs under policy before ranking, the audit log is hash-chained and tamper-evident, and erasure emits a falsifiable per-subject key-destruction receipt. The package ships as an embedded Python library with governed adapter surfaces that run on your infrastructure.

Honest boundary. Heartwood is managed-key: the server decrypts to serve recall. The receipts below are source-auditable today. Deletion is a per-subject key-destruction workflow, not an instantaneous deletion guarantee. See Key custody and erasure.

Install

python -m pip install "heartwood-memory[recall,mcp]"

5-minute quickstart

Remember a governed memory, recall it under policy, and emit a key-destruction receipt:

from heartwood import Heartwood, Policy, Principal, prove_crypto_erase_path

# 1. Open an embedded, tenant-scoped store.
db = Heartwood(path="./heartwood.db", tenant="tenant:acme")

# 2. Remember. The record is signed and written to a hash-chained audit log.
db.remember(
    "Customer 42 is on the Enterprise plan.",
    subject="customer:42",
    created_by="agent:support",
    policy=Policy(classification="internal"),
)

# 3. Recall. Policy gates the candidate set before ranking.
principal = Principal(
    id="agent:support",
    tenant="tenant:acme",
    roles=("support",),
    clearance="internal",
)
out = db.recall(
    "what plan is customer 42 on?",
    principal=principal,
    filters={"subject": "customer:42"},
    k=5,
)

for hit in out["results"]:
    print(hit["content"], hit["provenance"]["signature_valid"])

# 4. Forget. This crypto-shreds the per-subject key and purges derived artifacts.
receipt = db.forget(
    "customer:42",
    mode="hard",
    actor="agent:support",
    reason="right-to-erasure request",
)
db.close()

proof = prove_crypto_erase_path(
    "./heartwood.db",
    tenant="tenant:acme",
    root_present=False,
).to_dict()
print(receipt["key_shredded"], proof["content_unrecoverable"])

Keep local artifacts out of Git. This repository's .gitignore does not propagate into downstream repositories. If you run these examples in another checkout, add equivalent ignores there for local Heartwood databases and sidecars, token/config files, root-local JSONL inputs, generated *-report.json files, and .venv/; alternatively, keep sensitive runtime state under an ignored .heartwood/ directory. Keep deliberate fixtures in non-root paths so they remain reviewable.

Want governed memory for an MCP-capable agent instead of a library? See the governed MCP quickstart and the Codex local-stdio quickstart. Write and erase verbs are not exposed by default; operators opt in by naming them explicitly.

What you get - five receipts

Governance you can inspect and re-run at the record level:

Key docs

• MCP quickstart
• Codex local-stdio quickstart
• Onboarding guide
• Python API reference
• Key custody and erasure
• Multi-agent identity
• Postgres and SQLite migration guide
• Full public documentation map

Run the console script after installation:

heartwood --help

License

From version 0.2.0, Heartwood Memory is source-available under the Business Source License 1.1 (BSL 1.1) — not an OSI "open source" license. You may read the source, run it locally, develop against it, evaluate it, and self-host it for non-production use at no charge. Small organizations (fewer than 100 people and less than $1M annual revenue) may also run it in production at no charge. Larger organizations need a commercial license for production use. Each version converts automatically to the Apache License 2.0 four years after its release.

Versions 0.1.0–0.1.2 are MIT-licensed and remain so permanently. See NOTICE for details. Commercial support, managed key custody, and hosted services are available separately.

Current Bias

Prove boring trust before building ambitious cognition:

• provenance
• typed memory routing
• policy-aware recall
• temporal state
• deletion completeness
• generated-memory faithfulness
• repeatable evals

The cognitive database vision should be earned by evidence from these loops.