engram-subnet 0.1.2

Decentralized IPFS-style vector database on Bittensor — the permanent semantic memory layer for AI.

Engram

Decentralized Vector Database on Bittensor

Permanent, content-addressed semantic memory for AI — no central authority, no single point of failure.

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What is Engram?

Engram applies the IPFS insight to AI memory: every piece of knowledge gets a content identifier (CID) derived deterministically from its embedding. The same text always maps to the same CID — regardless of which miner stores it.

• Content-addressed — v1::a3f2b1... uniquely identifies an embedding, not a location
• Decentralized — embeddings are replicated across competing miners on Bittensor
• Incentivized — miners earn TAO for provably storing and serving vectors
• Verifiable — HMAC challenge-response proofs ensure miners actually hold the data

         store("The transformer architecture changed everything.")
                              │
                              ▼
              ┌───────────────────────────────┐
              │   CID: v1::a3f2b1c4d5e6f7...  │
              │   Embedding: [0.02, -0.14, ...]│
              │   Stored on: miners 3, 7, 11  │
              └───────────────────────────────┘
                              │
                  query("how does attention work?")
                              │
                              ▼
              ┌───────────────────────────────┐
              │   score: 0.9821  cid: v1::a3f │
              │   score: 0.8744  cid: v1::b2e │
              │   score: 0.8291  cid: v1::c1d │
              └───────────────────────────────┘

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Quick Start

Install

pip install engram-subnet

Or from source:

git clone https://github.com/Dipraise1/-Engram-.git
cd -Engram-
pip install -e .

Configure

cp .env.example .env
# Edit: WALLET_NAME, NETUID, SUBTENSOR_NETWORK
# Optional: USE_LOCAL_EMBEDDER=true  (no OpenAI key needed)

Python SDK

from engram.sdk import EngramClient

client = EngramClient("http://127.0.0.1:8091")

# Store text — returns a permanent CID
cid = client.ingest("The transformer architecture changed everything.")
print(cid)  # v1::a3f2b1...

# Semantic search
results = client.query("how does attention work?", top_k=5)
for r in results:
    print(f"{r['score']:.4f}  {r['cid']}")

# Batch ingest from JSONL
cids = client.batch_ingest_file("data/corpus.jsonl")

CLI

engram ingest "Some important knowledge"
engram ingest --file corpus.jsonl
engram ingest --dir ./docs          # recursive directory ingest

engram query "what is self-attention?"

engram status                        # local store info
engram status --live --netuid 42     # live metagraph + miner health

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Framework Integrations

# LangChain
from engram.sdk.langchain import EngramVectorStore
store = EngramVectorStore(miner_url="http://127.0.0.1:8091", embeddings=your_embeddings)
retriever = store.as_retriever(search_kwargs={"k": 5})

# LlamaIndex
from engram.sdk.llama_index import EngramVectorStore
store = EngramVectorStore(miner_url="http://127.0.0.1:8091")
index = VectorStoreIndex.from_documents(
    documents,
    storage_context=StorageContext.from_defaults(vector_store=store)
)

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Running a Miner

# Create wallet
btcli wallet new_coldkey --wallet.name engram
btcli wallet new_hotkey --wallet.name engram --wallet.hotkey miner

# Register on subnet
btcli subnet register --netuid 42 --wallet.name engram --wallet.hotkey miner

# Start
python neurons/miner.py --wallet.name engram --wallet.hotkey miner --netuid 42

Full setup: docs/miner.md

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Running a Validator

btcli subnet register --netuid 42 --wallet.name engram --wallet.hotkey validator
python neurons/validator.py --wallet.name engram --wallet.hotkey validator --netuid 42

Full setup: docs/validator.md

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Scoring

Validators score miners every 120 seconds:

composite_score = 0.50 × recall@10
               + 0.30 × latency_score     (1.0 at ≤100ms, 0.0 at ≥500ms)
               + 0.20 × proof_success_rate

Miners with proof success rate below 50% receive weight 0.

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Architecture

┌──────────────────────────────────────────────────────────────┐
│                       Bittensor Chain                        │
│               (metagraph · weight setting · TAO)             │
└─────────────────────┬──────────────────────┬─────────────────┘
                      │                      │
              ┌───────▼──────┐    ┌──────────▼──────┐
              │  Validator   │    │      Miner       │
              │              │    │                  │
              │ • challenge  │───▶│ • FAISS index    │
              │ • score      │    │ • embedder       │
              │ • set weights│◀───│ • proof service  │
              └──────────────┘    └──────────┬───────┘
                                             │
                                   ┌─────────▼────────┐
                                   │   engram-core     │
                                   │   (Rust / PyO3)   │
                                   │ • CID generation  │
                                   │ • HMAC proofs     │
                                   └──────────────────┘

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Repository Structure

engram/
├── engram/              # Python package
│   ├── miner/           # Ingest, query, embedder, store, rate limiter
│   ├── validator/       # Scoring, challenge, weight setting
│   ├── sdk/             # Client, LangChain, LlamaIndex adapters
│   └── protocol.py      # Synapse types (IngestSynapse, QuerySynapse)
├── engram-core/         # Rust core — CID generation + storage proofs
├── engram-web/          # Next.js frontend (theengram.space)
├── neurons/             # miner.py, validator.py entry points
├── scripts/             # Demo, ground truth generation, utilities
├── tests/               # pytest suite
└── docs/                # Architecture, SDK, CLI, protocol reference

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Documentation

Guide	Description
docs/architecture.md	System design, data flows, component overview
docs/miner.md	Miner setup, configuration, optimization
docs/validator.md	Validator setup and scoring loop
docs/sdk.md	Python SDK full reference
docs/cli.md	CLI command reference
docs/protocol.md	Wire protocol, CID spec, scoring formulas

Full web docs: theengram.space/docs

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Tests

pytest tests/ -q
cargo test --manifest-path engram-core/Cargo.toml --no-default-features

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Network

Property	Value
Network	Bittensor (TAO)
Type	Infrastructure / Storage
Status	Testnet
Subnet UID	42 (testnet)
Canonical embedding model	text-embedding-3-small (1536d)
Vector index	FAISS (IVF-flat)
Proof type	HMAC-SHA256 challenge-response

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Links

• Website — theengram.space
• Docs — theengram.space/docs
• Dashboard — theengram.space/dashboard
• API — api.theengram.space/health

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2026 — Permanent semantic memory for AI.