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Governed, provenance-first agent memory for AI systems.

Project description

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.

Website · Compare Heartwood · FAQ · PyPI

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]"

Development checks

From a source checkout, use Python 3.11 and install the declared development dependencies before running the local quality gate:

python3.11 -m venv .venv
source .venv/bin/activate
python -m pip install -e ".[dev]"
bash scripts/check.sh

scripts/check.sh runs Ruff and the full pytest suite. The optional Hermes Agent contract suite reports as skipped unless its separate integration dependency is installed. To install the same gate as a pre-commit hook without overwriting another hook, run bash scripts/install-hooks.sh.

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:

Receipt What it does Boundary today
Signed provenance Every memory is signed; the signature and content hash are re-verified at read and surfaced on each result. Re-verified and surfaced, not yet enforced as a hard read failure.
Tamper-evident audit Hash-chained append-only log; verify_chain() detects an in-place edit or dropped row. Catches in-place tampering; tail-truncation needs an external anchor.
Policy before ranking Recall is restricted to cleared records before ranking; denied records are not scored, returned, or counted. Source-auditable; multi-tenant-at-scale validation is still in progress.
Key-destruction receipt forget(mode="hard") destroys the per-subject key and purges derived artifacts. Shows key destruction, not byte-level content deletion.
Faithfulness + egress gate Generated memories fail closed unless they pass a faithfulness check; rejected egress requests block the external-model call. Explicit override stores a downweighted, review-only copy.

Key docs

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.

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