References and lineage

aizk is a mix of published memory mechanisms, open source infrastructure, product comparisons, and work designed here. This page records which is which, so a reader can trace any behavior back to either a paper or a deliberate choice. A citation on this page does not mean aizk copied an implementation, and it does not mean the cited project endorses aizk. Every code path named here was checked against the tree.

How to read the map

  published work ──adopted, follows the design closely──▶ shipped mechanism
  published work ──adapted, idea kept, shape changed────▶ shipped mechanism
  published work ┄┄compared, no code┄┄▶ product boundary
  published work ┄┄workflow, no runtime┄┄▶ how we change the code
  designed for aizk ──original──▶ shipped mechanism

Label	Meaning
adopted	the shipped mechanism follows the cited design closely
adapted	the source supplied the idea and aizk changed its shape
compared	the source helped define a product boundary but supplied no implementation
workflow	the source influenced how the code is changed or checked
original	the mechanism was designed for aizk and is not claimed from the cited systems

Memory and retrieval

Feature	Lineage	What aizk does	Code
temporal entity and fact graph	adopted from Zep and Graphiti	immutable content plus valid-time and recorded-time claims	store/models/tables/, store/models/views/live_fact.py
add, update, no-op consolidation	adapted from Mem0	rules settle the confident cases and only ambiguity reaches the LLM	graph/consolidation.py, graph/writer.py
associative multi-hop recall	adapted from HippoRAG 2	personalized PageRank inside the SQL statement, over visible current facts only	retrieval/lanes/facts.py
community summaries	adapted from GraphRAG and LightRAG	communities as a rebuildable global-evidence projection	graph/communities.py, retrieval/lanes/vector.py
recursive summary tree	adopted from RAPTOR	grounded summaries rolled into bounded higher levels	graph/raptor.py, retrieval/lanes/overview.py
reflective observations	adapted from A-MEM	optional observations that never replace their grounding facts	graph/insight.py
entity profiles	adapted from GAM	one evidence-grounded profile per entity and scope set	graph/profiles.py
raw evidence as authority	supported by Does Memory Need Graphs	source chunks stay primary and each graph lane earns its cost in ablation	retrieval/lanes/sources.py, eval/plans.py
append-only corrective history	supported by APEX-MEM	contradicted knowledge has its range closed rather than being deleted	store/models/tables/fact.py
speaker-aware group memory	adapted from GroupMemBench and Hindsight	objective state kept apart from observations, opinions, experiences, preferences	provenance.py, graph/grounding.py, eval/groupmem.py
forgetting-aware evaluation	adopted from Memora	scores current evidence without rewarding expired memory	eval/metrics.py
workflow and premise categories	planned from LongMemEval-V2	kept in evaluation until a production schema earns them	eval/
action-memory boundary	compared with Mem2ActBench	no action-selection claim is made from a retrieval-only score	External benchmarks
dense and lexical fusion	adapted from Reciprocal Rank Fusion	typed lane ranks fused inside one SQL recall program	retrieval/recall/program.py, retrieval/lanes/sources.py
merit ordering and maximal recall	original	every lane stays available and one cross-encoder ranks the candidates together	retrieval/recall/orchestrator.py, retrieval/rerank/rescore.py
public evidence provenance	original	internal lanes collapse into source, derived, and session evidence with exact scope descriptions	retrieval/models/result.py, retrieval/templates/recall.md.j2

Paths in that table are relative to src/. RAPTOR supports hierarchical summaries, GraphRAG supports community summaries, HippoRAG supports associative graph retrieval, and GAM and A-MEM support consolidated representations. None of them argues that the agent on the other side of the API should ever see a lane name. The three public provenance classes are therefore an interface choice made here, based on what a consumer needs in order to judge evidence rather than on how the engine happened to find it.

Sharing and identity

The split between private and shared memory is informed by Collaborative Memory. aizk turns that paper’s policy graph into one PostgreSQL-native scope lattice, where every row carries a sorted nonempty set of scope UUIDs and a reader has to stand in every member. Scope sets in depth has the mechanics.

The intersection model, full-authority reads with one explicit write destination, and the source-preserving share operation are original. Logto stays authoritative for users, organizations, roles, and public organization metadata, and aizk derives stable IDs from verified token claims without storing an identity or membership mirror at all.

Concern	Source	The aizk boundary
identity and organization authority	Logto	OIDC discovery, signed tokens, current org roles, no local identity tables
OAuth protected MCP	FastMCP	dynamic client registration and an OIDC proxy over persistent encrypted state
database authorization	PostgreSQL row security and the house rlsalchemy package	forced policies on both content and scoped claims
multi-user memory model	Collaborative Memory	private, organization, and intersection scopes with immutable capture provenance

Infrastructure and model lanes

Responsibility	Project	Use in aizk
relational, temporal, lexical, vector, and policy execution	PostgreSQL	one durable state engine and the preferred place for filtering, ranking, hashing, and temporal logic
vector index	VectorChord and pgvector	low-memory production vector search with a portable fallback
ORM and validation	SQLModel, SQLAlchemy, Pydantic	typed models, PostgreSQL statements, and wire contracts
durable jobs	PgQueuer	graph projection and scheduled passes without a bespoke workflow ledger
document conversion	Docling and Docling Serve	private conversion of accepted bytes into structured JSON and normalized Markdown
immutable object bytes	SeaweedFS and obstore	private S3-compatible storage behind opaque keys
malware scanning	ClamAV	fail-closed streaming scan before any object is persisted
log collection, storage, inspection	Grafana Alloy, Loki, Grafana	labeled Docker logs, one bounded store, a loopback-only viewer
log event vocabulary	OpenTelemetry Logs Data Model	structured events while PostgreSQL stays the durable usage authority
MCP transport and OAuth	FastMCP	the public tools and the Logto OIDC proxy
browser application	SvelteKit and @logto/sveltekit	the optional web interface over the browser JSON API
model serving	vLLM with structured outputs	replaceable OpenAI-compatible endpoints with grammar-constrained extraction
typed LLM calls and judging	Pydantic AI and Pydantic Evals	schema-constrained extraction and isolated evaluation
chunking	Chonkie	bounded prose and source windows
fast entity gate	GLiNER2	a cheap GPU gate and an experimental extractor, never the production graph authority
production embedding	Qwen3-VL-Embedding-2B	text and image vectors through a generic client
production reranking	Qwen3-Reranker-4B	cross-encoder merit ordering across every lane
production extraction	Gemma 4 12B	grounded graph extraction through the generic LLM client
profiling, environments, remote runs	the house mainboard, chefe, and lote packages	stage timing, reproducible tasks, and deployment
typed patterns and SQL primitives	the house patos package	shared model, registry, and patos.sql column abstractions

Model names are deployment choices and not domain names in the code. Embedding, reranking, gating, and extraction each sit behind a client, so another compatible provider can take over without any part of the memory engine being renamed.

Original files and derived data

Several mature systems separate an authoritative original from replaceable interpretation, which is exactly the shape the artifact path takes.

Reference	Useful mechanism	The aizk adaptation
Paperless-ngx	preserve the original, track checksums, index the derivative	one original blob stays authoritative while Markdown and structured data live in PostgreSQL
Docling formats	emit normalized Markdown plus a lossless structured document	both derivatives are stored against the exact original revision
Unstructured elements	normalize many formats into typed elements with source metadata	source metadata is retained without adopting a second element store
ColPali	retrieve pages visually rather than through lossy text	one supplemental image vector beside authoritative Docling structure
VisRAG	answer from page images	authorized files stay available on demand, and recall transfers no bytes
M3DocRAG	combine visual and textual evidence	page-level and video retrieval stay deferred until they measure better

Knowledge organization

Source	Inherited idea	The aizk adaptation
The PARA Method	Projects are finite outcomes and Areas are ongoing responsibilities	Areas and Projects are ontology entities rather than folders
Second Brain and Zettelkasten	PARA gives action context while a Zettelkasten gives atomic durable knowledge	one maintained brief per Area or Project, with atomic notes tagged into it
the author’s own Zettelkasten structure notes	#project and #area identify structure notes	key-value source tags name an exact entity of any live ontology kind and imply no status or access

What is original to aizk

Cite the following as design done here rather than attributing it to one upstream paper.

• Arbitrary nonempty scope sets with intersection visibility under forced PostgreSQL RLS.
• Content-addressed graph content held separate from scoped bi-temporal claims.
• Full-authority recall paired with one explicit write destination.
• A source-preserving share that creates provenance-linked copies rather than moving a row.
• One maximal recall plan whose cross-encoder orders every lane by merit.
• A single prompt-ready MCP recall string produced by a token-budget prefix.
• Exact artifact revision resources that stay authorized by PostgreSQL and transfer no bytes during recall.
• An original-only blob model with database derivatives, metadata fallback, adaptive compression, shared physical bytes, a fail-closed scan gate, and no Redis anywhere.
• Durable actor and scope usage accounting kept apart from expiring operational logs.
• A health snapshot that checks schema, policy, jobs, models, scopes, graph freshness, and a real recall in under five seconds.

Workflow influences

The Bun Rust rewrite report and its original PORTING.md commit shaped how large refactors are run here and not the runtime. The reusable parts are a written mapping before a broad change, small trial cells, bounded ownership, an independent adversarial audit, errors treated as a work queue, and a test suite as the final authority. No Bun code is copied, and compiling is never taken as proof of behavior.

Next

• Comparison puts these mechanisms beside other systems.
• Rejected and deferred covers the sources whose ideas were not taken.
• Design principles explains the rules the original work follows.