YantrikDB — A Cognitive Memory Engine for Persistent AI Systems

The memory engine for AI that actually knows you.

Get Started in 60 Seconds

For AI agents (MCP — works with Claude, Cursor, Windsurf, Copilot)

pip install yantrikdb-mcp

Add to your MCP client config:

{
  "mcpServers": {
    "yantrikdb": {
      "command": "yantrikdb-mcp"
    }
  }
}

That's it. The agent auto-recalls context, auto-remembers decisions, and auto-detects contradictions — no prompting needed. See yantrikdb-mcp for full docs.

As a Python library

pip install yantrikdb

The engine ships a default embedder (potion-base-2M, ~7 MB, distilled from BGE-base-en-v1.5) — record_text() / recall_text() work out of the box. No sentence-transformers install. No first-run model download. No ONNX runtime. Just one pip install.

import yantrikdb

# Default: bundled embedder, dim=64. Just works.
db = yantrikdb.YantrikDB.with_default("memory.db")

db.record("Alice is the engineering lead", importance=0.8, domain="people")
db.record("Project deadline is March 30", importance=0.9, domain="work")
db.record("User prefers dark mode", importance=0.6, domain="preference")

results = db.recall("who leads the team?", top_k=3)
# → [{"text": "Alice is the engineering lead", "score": 1.0}, ...]

db.relate("Alice", "Engineering", "leads")
db.get_edges("Alice")

db.think()  # consolidate, detect conflicts, mine patterns

db.close()

Want higher-quality embeddings?

Three opt-in upgrade paths, in increasing weight:

# 1. Larger bundled variant — downloads on first call, caches under
#    your user data dir. Self-hosted from yantrikos/yantrikdb-models;
#    no HuggingFace dependency, no rate limits.
db = yantrikdb.YantrikDB("memory.db", embedding_dim=256)
db.set_embedder_named("potion-base-8M")   # ~28 MB, ~92% MiniLM
# or:  db.set_embedder_named("potion-base-32M")  # ~121 MB, ~95% MiniLM

# 2. Bring your own embedder (sentence-transformers, fastembed, custom).
from sentence_transformers import SentenceTransformer
db = yantrikdb.YantrikDB("memory.db", embedding_dim=384)
db.set_embedder(SentenceTransformer("all-MiniLM-L6-v2"))

# 3. Slim build (no bundled embedder, must set_embedder yourself).
#    For deployments where the ~7 MB bundle is intolerable.
#    Rust:  yantrikdb = { version = "0.7", default-features = false }

Path	Quality vs MiniLM	Size on disk	Install network
Bundled default (`with_default`)	~89%	~7 MB (bundled)	none
`set_embedder_named("potion-base-8M")`	~92%	~28 MB (cached)	first call only
`set_embedder_named("potion-base-32M")`	~95%	~121 MB (cached)	first call only
`set_embedder(MiniLM)`	100% (baseline)	~80 MB	sentence-transformers' own download

As a Rust crate

[dependencies]
yantrikdb = "0.7"

# Want set_embedder_named() for runtime model upgrades?
# yantrikdb = { version = "0.7", features = ["embedder-download"] }

# Slim build (no bundled embedder, no network code path):
# yantrikdb = { version = "0.7", default-features = false }

The Problem

Current AI memory is:

Store everything → Embed → Retrieve top-k → Inject into context → Hope it helps.

That's not memory. That's a search engine with extra steps.

Real memory is hierarchical, compressed, contextual, self-updating, emotionally weighted, time-aware, and predictive. YantrikDB is built for that.

Why Not Existing Solutions?

Solution	What it does	What it lacks
Vector DBs (Pinecone, Weaviate)	Nearest-neighbor lookup	No decay, no causality, no self-organization
Knowledge Graphs (Neo4j)	Structured relations	Poor for fuzzy memory, not adaptive
Memory Frameworks (LangChain, Mem0)	Retrieval wrappers	Not a memory architecture — just middleware
File-based (CLAUDE.md, memory files)	Dump everything into context	O(n) token cost, no relevance filtering

Benchmark: Selective Recall vs. File-Based Memory

Memories	File-Based	YantrikDB	Token Savings	Precision
100	1,770 tokens	69 tokens	96%	66%
500	9,807 tokens	72 tokens	99.3%	77%
1,000	19,988 tokens	72 tokens	99.6%	84%
5,000	101,739 tokens	53 tokens	99.9%	88%

At 500 memories, file-based exceeds 32K context windows. At 5,000, it doesn't fit in any context window — not even 200K. YantrikDB stays at ~70 tokens per query. Precision improves with more data — the opposite of context stuffing.

Evidence (reproducible)

Every claim here points at a runnable harness — not a static number. Each is gated in CI (.github/workflows/benchmark.yml) so a regression fails the build.

Recall doesn't degrade as the corpus grows, and stays fast. python -m yantrikdb.eval.benchmark holds a fixed signal corpus while adding distractors and measures recall + latency at each scale. Sample run: recall@k 0.938 → 0.929 as memories grow 7×, with p95 recall latency under 3 ms. regression_check() is the CI gate.
The knowledge graph earns its keep on connected data. python -m yantrikdb.eval.graph_lift measures recall with entity-expansion ON vs OFF. Verdict on the connected corpus: +2.5% recall, +1.7% MRR — graph expansion helps where memories are actually linked.
Apples-to-apples vs other memory systems. python -m yantrikdb.eval.competitors scores YantrikDB, mem0, Zep, and Letta on the same corpus, same queries, same metrics, no per-system tuning. (Competitors run once their libraries are installed; results are not pre-tuned.)

These run dependency-free on the bundled embedder, so anyone can reproduce them with one command.

Architecture

Design Principles

Embedded, not client-server — single file, no server process (like SQLite)
Local-first, sync-native — works offline, syncs when connected
Cognitive operations, not SQL — record(), recall(), relate(), not SELECT
Living system, not passive store — does work between conversations
Thread-safe — Send + Sync with internal Mutex/RwLock, safe for concurrent access

Five Indexes, One Engine

┌──────────────────────────────────────────────────────┐
│                   YantrikDB Engine                    │
│                                                      │
│  ┌──────────┬──────────┬──────────┬──────────┐       │
│  │  Vector  │  Graph   │ Temporal │  Decay   │       │
│  │  (HNSW)  │(Entities)│ (Events) │  (Heap)  │       │
│  └──────────┴──────────┴──────────┴──────────┘       │
│  ┌──────────┐                                        │
│  │ Key-Value│  WAL + Replication Log (CRDT)          │
│  └──────────┘                                        │
└──────────────────────────────────────────────────────┘

Vector Index (HNSW) — semantic similarity search across memories
Graph Index — entity relationships, profile aggregation, bridge detection
Temporal Index — time-aware queries ("what happened Tuesday", "upcoming deadlines")
Decay Heap — importance scores that degrade over time, like human memory
Key-Value Store — fast facts, session state, scoring weights

Decoupled Write Path (v0.6.6+)

The vector index is structured as a two-tier LSM: a small mutable delta and an immutable HNSW cold tier swapped atomically via ArcSwap. Foreground writes only touch the delta (brief lock, O(1) push); HNSW work amortizes on a dedicated compactor thread. This is what eliminated the production wedge where sustained writes starved readers — see CONCURRENCY.md and docs/decoupled_write_path_rfc.md.

flowchart LR
    subgraph CLIENT["Caller"]
        C1["record / record_with_rid"]
        C2["recall / recall_with_seq"]
    end

    subgraph FG["Foreground — P1, brief locks only"]
        F1["assign_seq<br/>vec_seq.fetch_add<br/>(or fetch_max for cluster seq)"]
        F2["DeltaIndex.append<br/>brief RwLock&lt;Vec&gt; push"]
        F3["bump_visible_seq<br/>DashMap + AtomicU64<br/>(lock-free)"]
        F4["log_op → SQLite WAL"]
    end

    subgraph IDX["DeltaIndex (per engine)"]
        D1[("delta<br/>RwLock&lt;Vec&lt;DeltaEntry&gt;&gt;<br/>cap = delta_max (256)")]
        D2[("cold<br/>ArcSwap&lt;HnswIndex&gt;<br/>lock-free read")]
    end

    subgraph BG["Background — P3, dedicated threads"]
        B1["Compactor (1s tick)<br/>fires when delta past half-cap<br/>OR oldest entry > max_dirty_age"]
        B2["Materializer pool<br/>N = cores / 2<br/>drains pending oplog ops"]
    end

    subgraph STORE["SQLite (WAL mode, single file)"]
        S1["memories"]
        S2["oplog"]
        S3["entity_edges, sessions, ..."]
    end

    C1 --> F1
    F1 --> F2
    F2 --> D1
    F1 --> F3
    F1 --> F4
    F4 --> S2

    C2 -.->|"optional<br/>wait_for_visible_seq"| F3
    C2 --> D1
    C2 --> D2

    B1 -->|"seal + clone + ArcSwap.store"| D1
    B1 --> D2
    B2 --> S2
    B2 --> S1
    B2 --> S3

The structural invariant. Foreground (P1) and background (P3) do not share a lock primitive that holds for non-O(1) work. The cold tier is read lock-free via ArcSwap; the delta's RwLock is held for the O(1) push only. This is what makes "no single background task can wedge reads, writes, or recovery" enforceable — see CONCURRENCY.md Rules 2 and 3 for the names and failure modes if violated.

Cluster Mode (RFC 010 + Phase 6 RYW)

For multi-node deployments, yantrikdb-server wraps the engine with openraft for leader-elected replication. The four cluster-mutation primitives take the openraft commit-log index as their seq, so all nodes agree on a single global monotonic sequence — read-your-writes works across the cluster, not just within a node.

flowchart LR
    L["Leader<br/>HTTP request"]
    LR["Leader engine<br/>record_with_rid(seq=Some(log_idx))"]
    OR["openraft<br/>commit log"]
    F1["Follower 1 applier<br/>record_with_rid(seq=Some(log_idx))"]
    F2["Follower 2 applier<br/>record_with_rid(seq=Some(log_idx))"]
    R["Reader on any node<br/>recall_with_seq(min_seq=log_idx)"]

    L --> LR
    LR --> OR
    OR -->|replicate + apply| F1
    OR -->|replicate + apply| F2
    F1 -.->|"visible_seq[ns] reaches log_idx"| R
    F2 -.->|"visible_seq[ns] reaches log_idx"| R
    LR -.->|"visible_seq[ns] reaches log_idx"| R

Each record_with_rid / tombstone_with_rid / upsert_entity_edge_with_id / delete_entity_edge_with_id accepts an optional seq: Option<u64>. Single-node callers pass None and the engine allocates; cluster appliers pass Some(commit_log_index) and the engine ratchets vec_seq up to at least that value via fetch_max. After apply, visible_seq[namespace] reaches the log index, so any subsequent recall_with_seq(min_seq=N) blocks just long enough for the local node to have applied through index N — and no longer.

Memory Types (Tulving's Taxonomy)

Type	What it stores	Example
Semantic	Facts, knowledge	"User is a software engineer at Meta"
Episodic	Events with context	"Had a rough day at work on Feb 20"
Procedural	Strategies, what worked	"Deploy with blue-green, not rolling update"

All memories carry importance, valence (emotional tone), domain, source, certainty, and timestamps — used in a multi-signal scoring function that goes far beyond cosine similarity.

Key Capabilities

Relevance-Conditioned Scoring

Not just vector similarity. Every recall combines:

Semantic similarity (HNSW) — what's topically related
Temporal decay — recent memories score higher
Importance weighting — critical decisions beat trivia
Graph proximity — entity relationships boost connected memories
Retrieval feedback — learns from past recall quality

Weights are tuned automatically from usage patterns.

Conflict Detection & Resolution

When memories contradict, YantrikDB doesn't guess — it creates a conflict segment:

"works at Google" (recorded Jan 15) vs. "works at Meta" (recorded Mar 1)
→ Conflict: identity_fact, priority: high, strategy: ask_user

Resolution is conversational: the AI asks naturally, not programmatically.

Semantic Consolidation

After many conversations, memories pile up. think() runs:

Consolidation — merge similar memories, extract patterns
Conflict scan — find contradictions across the knowledge base
Pattern mining — cross-domain discovery ("work stress correlates with health entries")
Trigger evaluation — proactive insights worth surfacing

Proactive Triggers

The engine generates triggers when it detects something worth reaching out about:

Memory conflicts needing resolution
Approaching deadlines (temporal awareness)
Patterns detected across domains
High-importance memories about to decay
Goal tracking ("how's the marathon training?")

Every trigger is grounded in real memory data — not engagement farming.

Multi-Device Sync (CRDT)

Local-first with append-only replication log:

CRDT merging — graph edges, memories, and metadata merge without conflicts
Vector indexes rebuild locally — raw memories sync, each device rebuilds HNSW
Forget propagation — tombstones ensure forgotten memories stay forgotten
Conflict detection — contradictions across devices are flagged for resolution

Sessions & Temporal Awareness

sid = db.session_start("default", "claude-code")
db.record("decided to use PostgreSQL")  # auto-linked to session
db.record("Alice suggested Redis for caching")
db.session_end(sid)
# → computes: memory_count, avg_valence, topics, duration

db.stale(days=14)    # high-importance memories not accessed recently
db.upcoming(days=7)  # memories with approaching deadlines

Full API

Operation	Methods
Core	`record`, `record_batch`, `recall`, `recall_with_response`, `recall_refine`, `forget`, `correct`
Knowledge Graph	`relate`, `get_edges`, `search_entities`, `entity_profile`, `relationship_depth`, `link_memory_entity`
Cognition	`think`, `get_patterns`, `scan_conflicts`, `resolve_conflict`, `derive_personality`
Triggers	`get_pending_triggers`, `acknowledge_trigger`, `deliver_trigger`, `act_on_trigger`, `dismiss_trigger`
Sessions	`session_start`, `session_end`, `session_history`, `active_session`, `session_abandon_stale`
Temporal	`stale`, `upcoming`
Procedural	`record_procedural`, `surface_procedural`, `reinforce_procedural`
Lifecycle	`archive`, `hydrate`, `decay`, `evict`, `list_memories`, `stats`
Sync	`extract_ops_since`, `apply_ops`, `get_peer_watermark`, `set_peer_watermark`
Maintenance	`rebuild_vec_index`, `rebuild_graph_index`, `learned_weights`

Technical Decisions

Decision	Choice	Rationale
Core language	Rust	Memory safety, no GC, ideal for embedded engines
Architecture	Embedded (like SQLite)	No server overhead, sub-ms reads, single-tenant
Bindings	Python (PyO3), TypeScript	Agent/AI layer integration
Storage	Single file per user	Portable, backupable, no infrastructure
Sync	CRDTs + append-only log	Conflict-free for most operations, deterministic
Thread safety	Mutex/RwLock, Send+Sync	Safe concurrent access from multiple threads
Query interface	Cognitive operations API	Not SQL — designed for how agents think

Ecosystem

Package	What	Install
yantrikdb	Rust engine	`cargo add yantrikdb`
yantrikdb	Python bindings (PyO3)	`pip install yantrikdb`
yantrikdb-mcp	MCP server for AI agents	`pip install yantrikdb-mcp`

Roadmap

V0 — Embedded engine, core memory model (record, recall, relate, consolidate, decay)
V1 — Replication log, CRDT-based sync between devices
V2 — Conflict resolution with human-in-the-loop
V3 — Proactive cognition loop, pattern detection, trigger system
V4 — Sessions, temporal awareness, cross-domain pattern mining, entity profiles
V5 — Multi-agent shared memory, federated learning across users

Worked example: Wirecard (RFC 008 substrate — with honest limits)

For nearly a decade, Wirecard's filings and EY's audit attested to €1.9B in Philippine escrow accounts. In June 2020 both banks and the central bank formally denied the accounts existed.

When the source_lineage fields are hand-populated — EY as [wirecard, ey] to capture audit dependence on Wirecard-provided documents, BSP as [bsp, bpi, bdo] to capture restatement of the commercial banks — RFC 008's ⊕ discounts the dependent claims, and the contest operator's temporal split distinguishes present-tense contradictions from historical state changes. On this hand-populated data, the substrate produces useful annotations.

Honest limits (surfaced by Phase 2 empirical testing, Apr 2026):

On naturalistic evidence where a real agent populates the fields, the substrate's gates don't reliably fire. Cases B and C of the Phase 2 eval need an extractor/canonicalizer (not yet built) to work; Case A exposed that ⊕ is mathematically incapable of flipping decisions at realistic N, regardless of coefficient tuning.
Current claim: structured schema for evidence provenance/temporal/conflict annotation, useful for audit and inspection. The dependence-discount operator works on curated inputs but needs replacement before it can drive decisions.
Not a current claim: "decision-improvement substrate for AGI-capable agents." That framing is withdrawn pending RFC 009.

See docs/showcase/wirecard.md for the full walkthrough including the Phase 2 negative result and the gold-state ablation that partitioned operator failure from extraction failure. Run the hand-populated demonstration directly:

cargo run --example showcase_wirecard

Research & Publications

📄 Skill as Memory, Not Document (May 2026)

Sarkar, P. (2026). Skill as Memory, Not Document: A Database-Native Substrate for Agent Skill Catalogs. Zenodo.

A measurement paper at 5K-skill scale: token cost vs filesystem catalogs (with the honest 1.49× ablation), retrieval latency (87.3 ms p50), and invalid-skill admission (0% YantrikDB vs 97% document-only baseline). Reproducible scripts + raw CSVs at yantrikdb-server/benchmarks/skill_recall/. Companion blog: yantrikdb.com/papers/skill-substrate.

Earlier work

U.S. Patent Application 19/573,392 (March 2026): "Cognitive Memory Database System with Relevance-Conditioned Scoring and Autonomous Knowledge Management"
Zenodo (software): YantrikDB: A Cognitive Memory Engine for Persistent AI Systems

Author

Pranab Sarkar — ORCID · LinkedIn · developer@pranab.co.in

License

AGPL-3.0. See LICENSE for the full text.

The MCP server is MIT-licensed — using the engine via the MCP server does not trigger AGPL obligations on your code.

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SHA256	`bdfd1c4e2379c8d01cc2e732b5ddba7afb13c884fe0d83cfd4ae8a5f7b33efdf`
MD5	`0b54ae40594d773c870700c33f02657f`
BLAKE2b-256	`12768d75862718f19e49bb95fff204dd63985e382ae0fda58c5598fa6654a83c`

Algorithm	Hash digest
SHA256	`063f68209ce5b25c546c1578b06b05130ddbc0bc6beb76418612eecadcd7c0ac`
MD5	`fba155c153feffaaf55d9ab8afb7cb12`
BLAKE2b-256	`ab8d12063c1b2cd70eee76d0fefc110879998159023ab06eaa16c8ce81d37e7f`

Algorithm	Hash digest
SHA256	`8f4fd672e631ac051c4d734f98dc6a164f4a70ad4d44aad6f40ad09be8db69ee`
MD5	`9de96289768393deec2dcede8d20588d`
BLAKE2b-256	`934be41ea79edea50d91fd40af4b2cd3ff0eb36e2674fdc068de46347bb047e2`

Algorithm	Hash digest
SHA256	`1b7d82fbcf241f56a1c598f900a827e1aec369aba26a3ec726f16f4cd3475c1b`
MD5	`d42552c72b8429bde5cac59f36442452`
BLAKE2b-256	`dda34cca486fc9e40550cddd4a6df26ab0ff9bc1f347eae2a0f0c616a78770cf`

Algorithm	Hash digest
SHA256	`4bcd7ef7a20f810df1ec65e096e6f3f2a84f180b2fe9f6c6075ea694d0c8764f`
MD5	`396d0eb7aca9d6165d76568058e4ee52`
BLAKE2b-256	`d3e057bb12fc806da179d413f19442bbc90711161308ed5d2dc4ae5a19af50aa`

Algorithm	Hash digest
SHA256	`c775d0f79960a81fff8ac83e0e8831afcb0954d3a0acdc3b368bb358f47ba77f`
MD5	`b5f9c69050f16fceb4687b58ea3de376`
BLAKE2b-256	`4366b7683637d86bcbaedc0c53ff1b475f89f1610407272042e331f298be87dd`

Algorithm	Hash digest
SHA256	`dfbe5e2831f6e9ca75e6773edadf701d1b50947ecf20cd481db60cb381c9ddb6`
MD5	`0b776c3131f5210e654dbf172a9608fe`
BLAKE2b-256	`830b7d8b16e228e5549bc6a3cd5ed24e324e182f458a1e4bff6421cbe235dec3`

Algorithm	Hash digest
SHA256	`4b4d2dbe3d2c3d0e4409b237be3e9ef1b49b84552c20155622cd76a83f34a1dd`
MD5	`05a941a01f00e7e7df3f72bcc04ad879`
BLAKE2b-256	`4fa848596992d19058dacc465c11dec513a36c28d9584739f6736e1d1f6d4fe6`

Algorithm	Hash digest
SHA256	`d178c02cf897cf4d90134550e55df07097aa5390dd6968dbfec6cda71c839dd6`
MD5	`ac0d40fb9c067881bb894d0b43c00c75`
BLAKE2b-256	`8f270254549753ea30c627b7194ce796a1a0ff8b5f123876b9787052e6f1aa3c`

Algorithm	Hash digest
SHA256	`f5eb96a2bc5e78a3a8d7fba12030d3e9ddf5e4f0a538b4f000733c782aec3be4`
MD5	`35cced1fe61527952d0bc6987e3c6ffd`
BLAKE2b-256	`94465febe00f803aa5ea27711964e438befaff94bdd4797b70208b758277c2fa`

Algorithm	Hash digest
SHA256	`4a5e7bb5640f34ee1fd7a5838107759e8357677cd385eb81aa2bc86477976c9c`
MD5	`bdb7be9b2df142e612bf5d11d29368e4`
BLAKE2b-256	`ea929c3b8ccb76bf0a49a894983aa0bd52cbb8598e165ce43428a65f33a2e97a`

Algorithm	Hash digest
SHA256	`8fde13290b31613f6f366eabd99778731c6dda206f4fe3b678d035aa63919afa`
MD5	`a0fa480bcd57d715180f3ecf05c602f8`
BLAKE2b-256	`c9636b12e36c118c837c25943a70d9a601a348e072479eaefdb61d5862ab920e`

Algorithm	Hash digest
SHA256	`6a6479594d417d1ef9eb611487cbacb24b15f035cc30b7f11adc207ee0664092`
MD5	`a07b80ce890aa5e8be7963ddadc37db5`
BLAKE2b-256	`b3657519094645cad8bf0305441b6d5644c1f2b94b63f81a47c3d1f98123e62f`

Algorithm	Hash digest
SHA256	`06cb487b6b141a7cd6261e44bca2aa030ce1017e2e96495072da8ddf381ef6d8`
MD5	`4f2430e4691bbc22a4b8dc12588e5d87`
BLAKE2b-256	`3bfe399e41599f070c73baa5622fea3d754ccfc25756c473b1d697ec7d944534`

Algorithm	Hash digest
SHA256	`3043f37f6fea61a5406adc12c4b69cb3d5a74903c319000df261d7ec74e811a8`
MD5	`20028def11b4961ffa1f214521fef5a4`
BLAKE2b-256	`f4b98ccd1159f4583e89e964b6e71221dbb5ab623baf251fbecf5481e4a609db`

yantrikdb 0.10.0

YantrikDB — A Cognitive Memory Engine for Persistent AI Systems

Get Started in 60 Seconds

For AI agents (MCP — works with Claude, Cursor, Windsurf, Copilot)

As a Python library

Want higher-quality embeddings?

As a Rust crate

The Problem

Why Not Existing Solutions?

Benchmark: Selective Recall vs. File-Based Memory

Evidence (reproducible)

Architecture

Design Principles

Five Indexes, One Engine

Decoupled Write Path (v0.6.6+)

Cluster Mode (RFC 010 + Phase 6 RYW)

Memory Types (Tulving's Taxonomy)

Key Capabilities

Relevance-Conditioned Scoring

Conflict Detection & Resolution

Semantic Consolidation

Proactive Triggers

Multi-Device Sync (CRDT)

Sessions & Temporal Awareness

Full API

Technical Decisions

Ecosystem

Roadmap

Worked example: Wirecard (RFC 008 substrate — with honest limits)

Research & Publications

📄 Skill as Memory, Not Document (May 2026)

Earlier work

Author

License

Project links

Key dates

Owner

Credits

GitHub Statistics

License expression

Requires

Provides Extra

Tags

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Download files

Source Distribution

Built Distributions

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Algorithm	Hash digest
SHA256	`d28322a1aec9b72398733e6adb26a0a46432befc0c2db6a8e43e368a86d56949`
MD5	`92e63b024e42e3dbf0f14dca921e7f6e`
BLAKE2b-256	`be4d2fa9490c17f101149d6a5c658ff52ae66fd55fa020ffcb80153f956194c0`

Algorithm	Hash digest
SHA256	`7bbd879e3a8b19fa9944e88ef5c851e083d977434015a7c1238fac74246abdef`
MD5	`ee49e0f644ac458f2a4ec9c3cd3cd7e4`
BLAKE2b-256	`49034a3f4a03f2d062eed41f30b48a75d1cc41dea88cbf1b6c11e9ff4ca3f6d4`

Algorithm	Hash digest
SHA256	`0ed5e2f354e10997e214faa9cf7b59eaf439ff8ca11927a4dfd80b083a9c5106`
MD5	`1f1774f6c9fd208814b92170013ff417`
BLAKE2b-256	`897d37d209dc040c9d94bc54898396961149b14f3ed6d9b6d44bf0017e874760`

Algorithm	Hash digest
SHA256	`23a33323aaeddc9cb85b9882c0bf2abd0e578a4d3f8fefbe0a4754f050b4afb9`
MD5	`69a15a07cc3e60b75acd1ef63a7af4a9`
BLAKE2b-256	`01cd4c291c61a6f8958399546c5225c7fd13991aefa88caa88e8609a12cd7c72`

Algorithm	Hash digest
SHA256	`5d15dbbc64ca24e4a8dd64d3fb308616a02feebcf4c35e8b9a173acd2be656f9`
MD5	`f50a28b5d7e69ff71798e1df2269329f`
BLAKE2b-256	`8c3d0770e4c0bbfbdacdd606b24aa56fe5027aee5ccef11e5ba3d50710228f3d`