Heartwood Memory — governed memory for AI agents

io.github.jermayne36Published by GitHub account jermayne36v0.2.9Updated Oct 25, 2026

Heartwood Memory — governed AI agent memory; signed provenance. BUSL-1.1; not OSI open source.

The publisher's own server: Memory.fit

Overview

AI-generated overview

A local, governed memory store for AI agents that signs every memory, gates recall by policy, keeps a tamper-evident audit log and issues key-destruction erasu

What you can do
Store, recall and forget agent memories in a local SQLite database, with tenant scoping and per-subject erasure receipts (README quickstart). Verify signed provenance on each recalled record and check the hash-chained audit log for edits or dropped rows. Apply policy and clearance before ranking so uncleared records are not scored or returned. Gate generated memories behind a faithfulness check and block rejected external-model egress. Write and erase verbs are not exposed by default; operators opt in by naming them in the tool allowlist.
When to use it
Use it when an assistant needs persistent memory that must be auditable and policy-controlled, for example support agents recalling customer facts under clearance rules or workflows that need a provable erasure step. It suits teams willing to run an embedded local store rather than a hosted memory service.
Requirements
Runs as a local stdio process, installed from PyPI (heartwood-memory) and launched with uvx; Python 3.11 is used for development checks. Requires HEARTWOOD_DB_PATH pointing at a local SQLite database; HEARTWOOD_TENANT isolates memories and HEARTWOOD_MCP_ALLOWED_TOOLS sets the tool allowlist, read-only by default. No authentication is declared.
Before you install
The server holds the keys and decrypts to serve recall, so memory contents are readable by the process. Erasure is a per-subject key-destruction workflow, not an instantaneous deletion guarantee, and does not prove byte-level deletion. Write and erase verbs are off unless explicitly allowlisted. Keep local databases, sidecars and token or config files out of Git. Licensed under BSL 1.1, not OSI open source, with free production use only for small organizations.

Installation

In SourceWeft

  1. Open Heartwood Memory — governed memory for AI agents in the dashboard and add it to a workspace.
  2. Enable the server for the chats that should use its tools.

Desktop only via STDIO. STDIO servers start a local process, so they need the SourceWeft desktop host.

Other MCP clients

Follow the launch instructions in the repository.

README

Heartwood Memory — governed memory for AI agents

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

bash
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:

bash
python3.11 -m venv .venvsource .venv/bin/activatepython -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.

Re-run the public trust suite

The public trust-receipts benchmark lives in the source repository rather than the installed wheel. Starting from a clean clone, run:

bash
git clone https://github.com/jermayne36/heartwood-memory.gitcd heartwood-memorypython3.11 -m venv .venvsource .venv/bin/activatepython -m pip install --quiet -e ".[dev]"python bench/run_benchmark.py --out .heartwood/trust-receipt.json

On Windows PowerShell, replace the activation line with .\.venv\Scripts\Activate.ps1.

The command exits non-zero if an executable contract or positive-control case fails, or if the benchmark's existing claim-anchor scan finds a violation. Its one-line terminal summary reports the live case counts; the JSON file contains the per-probe results and the separately published boundary cases.

5-minute quickstart

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

python
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:

ReceiptWhat it doesBoundary today
Signed provenanceEvery memory is signed; the signature and content hash are re-verified at read and surfaced on each result.Default OFF surfaces verification state; opt-in FILTER drops failed records and ENFORCE fails before returning results. The signed scope does not cover authorization metadata.
Tamper-evident auditHash-chained append-only log; verify_chain() detects an in-place edit or dropped row.While the external AnchorSink and pinned verification root remain outside the attacker boundary, rollback at or below the latest anchor is detected; post-anchor rows remain an explicit open window.
Policy before rankingRecall is restricted to cleared records before ranking; denied records are not scored or returned, and neither the results nor the signed receipt carries a count of them.Source-auditable under the committed single-trust-domain pre-seed posture; multi-tenant deployment is not claimed.
Key-destruction receiptforget(mode="hard") destroys the per-subject key and purges derived artifacts.Reports per-subject key destruction and purge counts; it does not prove byte-level content deletion.
Faithfulness + egress gateGenerated memories fail closed unless they pass a faithfulness check; rejected egress requests block the external-model call.Unaccepted faithfulness results are blocked by default; store_unaccepted=True stores a generated_needs_review proposal, which typed ranking downweights.

Replacing a memory

From 0.2.10, when a new memory names the memory it replaces, or a file is edited or deleted through the memory tool (MCP memory or MemoryToolBackend), Heartwood's default recall stops returning the old text, including after a restart. Earlier versions of an edited file stay reachable only when history is asked for explicitly.

Limit. Heartwood does not detect replacements by itself: if a new memory is written without naming what it replaces, both stay current and the old one can still be recalled.

To name it, pass the old memory's id when you write the new one: db.remember(..., supersedes=old_id). History stays reachable with filters={"include_review_states": ["superseded"]} until forget() erases its subject. A memory-tool file written by an older release keeps its earlier versions in default recall until its next edit, rename or delete; see "Existing data" in the 0.2.10 changelog. Details: Recall visibility and retirement.

Key docs

Run the console script after installation:

bash
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.

Source: README.md at commit c0a4ace

Tools

0
SourceWeft has not started this package in its sandbox yet, so its tools are not known.

Version history

3
  1. v0.2.9LatestOct 3, 2026
  2. v0.2.8Oct 2, 2026
  3. v0.2.7Sep 16, 2026

Score

40% of the applicable checks met

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  • Auth declared10 pts

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  • Tools listed15 pts

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  • DescriptionRequired15 pts
  • Logo10 pts
  • Documentation15 pts
  • License10 pts

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