homestead-memory

Stop renting your mind.

A tamper-evident record of what your AI agent actually did, in a local file, that someone who does not trust you can verify.

Agents fail quietly. The run reports success, the tool returns 200, and the thing you asked for did not happen. There is usually no record to contradict it. This keeps one, and every entry is hash-chained to the one before it, so see it catch a forged record, live:

pip install homestead-memory      # Python 3.10+, macOS / Linux / Windows

hsm watch --demo
#      0  14:02:11  Bash    npm test
#      1  14:02:19  Read    src/api/billing.py
#      2  14:02:24  Edit    src/api/billing.py
#
# ② now someone edits record 1…
#   !! chain break at index 1: hash_mismatch
#      record content does not match its own hash (edited in place)
#   exit 1  ·  gate it in CI like a test

Got No matching distribution found? macOS still ships Python 3.9 as its built-in python3, and this needs 3.10+. Nothing is wrong with the package. Either use a newer Python, or skip installing entirely: uvx --from homestead-memory hsm watch --demo (uv fetches a suitable Python for you.)

Record your own agent

hsm hook --install     # prints a Claude Code hook; you paste it, nothing is edited for you
hsm watch              # what your agent did, in order

Every entry is hash-chained to the one before it, so editing, deleting, or reordering any record breaks every hash after it. hsm watch reports the break at the exact index and exits non-zero. Sign it and a wholly rebuilt chain is caught too.

Both phases are recorded, which is what makes it evidence rather than a log. The hook captures the decision before a tool runs and the outcome after. A record of outcomes alone shows what happened; it cannot show that anything was authorised first. As the IETF Agent Audit Trail draft puts it, "a denial that is only logged after execution provides no evidence that the denial was enforced".

hsm export --format aat     # the same ledger as draft-sharif-agent-audit-trail-01

That draft maps explicitly to EU AI Act Article 12, and this emits it alongside the native format rather than replacing it, so nothing already on disk changes. Where we differ we say so: it specifies ECDSA P-256, which the AAT export uses, while the native EvidencePack stays Ed25519 so its verifier can remain standard-library only and a recipient needs nothing installed.

What it costs you: about 68ms per tool call (median; 92ms p95, measured on an M3 Pro with a 4KB tool response). The hook runs as a fresh process on every call your agent makes, so on a 100-call session that is roughly 7 seconds spread across the run. Almost all of it is Python interpreter and import startup rather than the recording itself. If that is too much for your loop, do not install the hook: the number is here so you can decide before you find out.

This is a file, not a platform. Agent observability tools are far richer than this and they want a deployment: the self-hosted ones document a production floor of several services and roughly 16 GB of RAM. This is pip install, one hook line, and a JSONL file on your disk. Different job. If you need dashboards, evals, and span analytics, use one of those. If you want a record you can grep and prove, use this.

Secret-shaped values are redacted and payloads truncated to a 200-character head, with a SHA-256 of the full original kept so the evidence survives redaction. That is a mitigation, not a guarantee: no pattern list is complete.

hsm export --evidence  # a pack anyone can verify with no install at all

The pack carries the records, the signature, the public key, an integrity report, and a standard-library verifier a third party can read in full and run. It states what it does NOT prove, including that a signature only establishes origin if you already know which key to expect.

And the memory it reads is yours too

The same tool owns the memory your agent reads and writes: plain markdown you can read, git diff, and walk away with. It catches its own rot, tampering, and poisoning with mechanical checks rather than an LLM judge, scores it 0 to 100, and exits non-zero so it gates CI and cron like a test suite.

npm install -g @tobilu/qmd@2.1.0  # optional hybrid retrieval runtime

hsm verify --demo
# ① a clean vault           ✅  MEMORY INTACT — 100/100
# ② rot is planted…         🔴  ROT DETECTED —   0/100
#    🔴 [self_contradiction] the note argues with itself about its own status
#    🔴 [uncited_claim]      a distilled claim has no source citation
#    🔴 [dangling_citation]  a cited source no longer exists
#    ⚠️  [broken_link]        a reference points at a note that isn't there

That scoring is RotBench, published as an open spec so the number is reproducible rather than self-reported.

Capture is Claude Code only right now. The MCP integration below works anywhere MCP does; the hooks that record every tool call use Claude Code's PreToolUse and PostToolUse. Cursor and Codex need their own mechanisms and those are not built yet.

Quickstart (60 seconds)

hsm init   ./my-vault          # scaffold or adopt any markdown folder
hsm ingest ./my-vault          # index it (hybrid BM25+vector via qmd, optional)
hsm ask    "what did I decide about X?"
hsm ask    "what did I decide about X?" ./my-vault --budget 1200 --json
hsm search "what did I decide?" ./my-vault --retrieval balanced --json
hsm qmd start                  # persistent loopback runtime; no shared global index
hsm verify ./my-vault          # the integrity gate — the whole point
hsm distill ./my-vault         # optional: build the cited, verifiable fact layer
hsm history <note> --as-of 2026-06-01   # what was true THEN (temporal layer)
hsm serve                      # local HTTP API (auth'd, loopback-only)

Python agents can use the SDK directly:

from homestead_memory import connect

memory = connect("~/my-vault", agent="my-agent")
memory.remember("user", "city", "Berlin")
memory.ask("what city is the user in?")

The local HTTP API is documented in docs/openapi.yaml.

Retrieval profiles

Homestead keeps qmd in dedicated cache and config directories. It never runs maintenance against qmd's global index. Every structured result reports engine, retrieval_mode, degraded, reason, elapsed_ms, and index_age_seconds.

The route is persistent qmd MCP, then the dedicated qmd CLI, then a read-only direct scan. Run hsm qmd doctor, hsm qmd refresh, and hsm qmd status to inspect the runtime without touching any other qmd collection.

Refresh is explicit and incremental. It writes an atomic checkpoint beneath .hsm/refresh-state.json, refuses foreign or unhealthy QMD runtimes, emits a live heartbeat while QMD works, and commits the vault fingerprint only after embedding reaches zero pending vectors. Reads never trigger an implicit refresh; if QMD is unavailable, retrieval falls back to a read-only scan and reports the degraded engine and reason.

For a Linux/systemd reference deployment, see deploy/reference/.

Memory under the router

Routers can swap the served model while homestead-memory keeps the same vault underneath. The model name is just the runtime argument; provenance is stamped as name@model in the agent field when a write happens.

from homestead_memory import connect
from homestead_memory.adapters.openai_compat import MemoryChat

memory = connect("~/my-vault")

def remember_reply(response, memory, agent):
    memory.remember(
        "conversation",
        "last_reply",
        response.choices[0].message.content,
        source="chat",
        agent=agent,
    )

chat = MemoryChat(openai_compatible_client, memory, remember_fn=remember_reply)
chat.create(model="claude-sonnet-4.7", messages=[{"role": "user", "content": "brief me"}])
chat.create(model="glm-4.7", messages=[{"role": "user", "content": "continue"}])

memory.history("conversation")  # agents include assistant@claude-sonnet-4.7 and assistant@glm-4.7

LiteLLM can use the same pattern with a pre-call injection helper and a success logger:

from homestead_memory import connect
from homestead_memory.adapters.litellm_memory import MemoryLogger, inject_memory

memory = connect("~/my-vault")
messages = inject_memory([{"role": "user", "content": "brief me"}], memory)

# LiteLLM callback registration style depends on your app setup.
logger = MemoryLogger(memory, agent_name="assistant")

MCP already sits above harness-level routers. In a claude-code-router-style setup that swaps the backend model, homestead-memory keeps working with zero config because memory is external to the model. history() and verify then attribute every recorded fact to the exact name@model that wrote it.

Integrations

Adapters target the public framework interfaces listed here as of the current releases and may need version bumps as those APIs evolve. Core remains stdlib-only; install only the extra for the framework you use.

Universal tools work with any orchestrator that can register callables or JSON-schema function tools:

from homestead_memory import connect
from homestead_memory.adapters.tools import recall_tool, remember_tool, tool_specs, verify_tool

memory = connect("~/my-vault", agent="my-agent")
tools = [remember_tool(memory), recall_tool(memory), verify_tool(memory)]
specs = tool_specs(memory)  # name, description, parameters

LangGraph BaseStore (targets langgraph>=0.2):

from homestead_memory import connect
from homestead_memory.adapters.langgraph_store import HomesteadStore

store = HomesteadStore(connect("~/my-vault", agent="langgraph"))
graph = builder.compile(checkpointer=checkpointer, store=store)

CrewAI storage/memory (targets crewai>=0.70, storage-style save/search/reset):

from homestead_memory import connect
from homestead_memory.adapters.crewai_memory import HomesteadCrewAIStorage

storage = HomesteadCrewAIStorage(connect("~/my-vault", agent="crewai"))
storage.save("Researcher found the supplier shortlist", metadata={"task": "supplier_shortlist"})

AutoGen autogen_core Memory protocol (targets autogen-core>=0.4):

from autogen_core.memory import MemoryContent, MemoryMimeType
from homestead_memory import connect
from homestead_memory.adapters.autogen_memory import HomesteadAutoGenMemory

memory = HomesteadAutoGenMemory(connect("~/my-vault", agent="autogen"))
await memory.add(MemoryContent(content="Use metric units", mime_type=MemoryMimeType.TEXT))

OpenAI Agents SDK Session protocol or function tools (targets openai-agents>=0.0.1):

from homestead_memory import connect
from homestead_memory.adapters.openai_agents import HomesteadSession, function_tools

memory = connect("~/my-vault", agent="openai-agents")
session = HomesteadSession(memory, session_id="user-123")
agent_tools = function_tools(memory)

Claude Code / Desktop / Cursor (MCP: memory tools, not action capture):

claude mcp add homestead-memory -- hsm mcp ~/my-vault
# tools: memory_ask · memory_search · memory_verify · memory_history ·
#        memory_ingest · memory_distill

Why this exists

"Runs on your device" is table stakes now — every memory tool stores locally. Nobody verifies. Memory rots quietly: a note contradicts itself, an extracted "fact" loses its source, a body drifts past its own changelog, the current value gets shadowed by a stale one. You find out weeks later, when your agent confidently tells you something that stopped being true in March.

And rot is only the passive failure. Memory also gets tampered with (a fact edited after it was written) and poisoned (untrusted input injects a "memory" that was never true — a named 2026 attack class). homestead-memory catches all three mechanically: sign the vault and any edited byte breaks the signature; a distilled claim must cite a source that resolves or it's dropped. Recall benchmarks measure whether the model remembers; RotBench measures whether the memory can be trusted — see benchmarks/ROTBENCH.md.

homestead-memory is built around three commitments:

1. Markdown-primary. The human-readable files ARE the memory. Indexes and projections are derived and disposable. You can leave any time — it's your folder. Import/export Google's Open Knowledge Format (hsm export --format okf) plus Mem0/Zep: we're OKF, but signed and verifiable.
2. Verification over trust. Integrity is a number (RotBench, 0–100), computed by mechanical checks — no LLM judging its own homework. See benchmarks/ROTBENCH.md.
3. Auditable extraction. The optional distilled layer (docs/DISTILL_SPEC.md) extracts entity facts with verbatim quotes, checked in code — a claim either cites a real source or it's dropped. Contradictions append a changelog line (update current_crm: "Salesforce" -> "HubSpot" (source: chat-042.md)) — never a silent overwrite. Extraction you can audit is extraction you can trust.

The two camps (where this sits)

Honest numbers (LongMemEval)

Measured on the full 500-question _s set (48-session haystacks with distractors), scored with the official per-type judge methodology, reader glm-5.2, independent judge deepseek-v4-pro. Reproduce: benchmarks/README.md. Full run history including the failures: benchmarks/RESULTS.md.

Read the RotBench row carefully: it scores the vault this harness builds, so it is a statement about this run, not a promise that your vault will score 99. Pointed at a real working vault it usually will not, and that is the tool doing its job. See benchmarks/ROTBENCH.md for what the score does and does not measure.

What we will and won't claim: recall is elite and reader-independent; QA is honest and mid — published systems self-report higher on their own harnesses (Mem0 94.4%, Zep 63.8% independent); we publish the harness, the judge, and every failed experiment instead. No number here is from a harness you can't run yourself.

Design

• Cross-platform. Pure Python except for cryptography, which does the signing. CI: ubuntu / macos / windows. The pack's bundled verifier stays stdlib-only, so a recipient checks a pack with nothing installed, which is the part that matters.
• Degrades explicitly. qmd 2.1+ is optional. MCP failure falls back to the dedicated CLI; qmd failure falls back to a read-only scan. Machine-readable output names the engine and reason instead of silently pretending the fast path worked.
• Local by default. The HTTP API binds loopback with bearer auth + DNS-rebind protection; the MCP server is stdio (client-spawned). Nothing phones home.
• Temporal. Changelog lines make history queryable: hsm history note --as-of DATE.

Status

v0.2, building in public. Roadmap: ROADMAP.md. Break our benchmark: benchmarks/ROTBENCH.md — adversarial fixtures get merged.

MIT © Kinetic Labs Inc. · a FuckBigTech / HOMESTEAD project.