solvec 0.1.0a10

Python SDK for Recall by VecLabs - cryptographic memory layer for AI agents

recall-sdk-python

Python SDK for VecLabs Recall — decentralized vector memory for AI agents.

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Install

pip install solvec --pre

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Quick Start

from solvec import SolVec

sv = SolVec(api_key="your-api-key")
collection = sv.collection("agent-memory", dimensions=1536)

# Upsert vectors
collection.upsert([{
    "id": "mem_001",
    "values": [...],
    "metadata": {"text": "User prefers dark mode"}
}])

# Query
results = collection.query(vector=[...], top_k=5)

# Verify collection integrity against on-chain Merkle root
proof = collection.verify()
print(proof.solana_explorer_url)

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Authentication

Get an API key at app.veclabs.xyz.

import os
from solvec import SolVec

sv = SolVec(api_key=os.environ["RECALL_API_KEY"])

Self-hosted with Shadow Drive (bring your own Solana wallet):

sv = SolVec(
    network="devnet",
    wallet="~/.config/solana/id.json",
    shadow_drive=True
)

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API Reference

SolVec(api_key?, network?, wallet?, shadow_drive?)

Creates a client. Use api_key for hosted mode or wallet + shadow_drive=True for self-hosted.

sv.collection(name, dimensions?)

Returns a collection handle. Creates the collection on first write. dimensions required on first write, inferred after.

collection = sv.collection("my-collection", dimensions=1536)

collection.upsert(vectors)

Insert or update vectors. Each vector requires id and values. metadata is optional.

collection.upsert([
    {"id": "v1", "values": [...], "metadata": {"source": "gpt-4"}},
    {"id": "v2", "values": [...]}
])

After every upsert, a SHA-256 Merkle root of all vector IDs is posted to the Solana Anchor program on-chain.

collection.query(vector, top_k?, metric?)

Nearest-neighbor search. Returns top-k results with scores and metadata.

results = collection.query(
    vector=[...],
    top_k=10,              # default: 10
    metric="cosine"        # cosine (default), euclidean, dot
)

for match in results.matches:
    print(match.id, match.score, match.metadata)

collection.delete(ids)

Delete vectors by ID.

collection.delete(["v1", "v2"])

collection.verify()

Fetches the on-chain Merkle root from Solana and verifies it against the current collection state.

proof = collection.verify()
print(proof.valid)               # bool
print(proof.on_chain_root)       # str
print(proof.computed_root)       # str
print(proof.solana_explorer_url) # str

collection.stats()

Returns collection statistics.

stats = collection.stats()
print(stats.vector_count)   # int
print(stats.dimensions)     # int
print(stats.merkle_root)    # str

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Migrating from Pinecone

The API is intentionally shaped to match Pinecone's client. Migration is three line changes:

# Before
from pinecone import Pinecone
pc = Pinecone(api_key="YOUR_KEY")
index = pc.Index("my-index")

# After
from solvec import SolVec
sv = SolVec(api_key="YOUR_KEY")
index = sv.collection("my-index")

# Everything below stays identical
index.upsert(vectors=[...])
index.query(vector=[...], top_k=10)
index.verify()  # new — Pinecone has no equivalent

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Usage with LangChain

from langchain.vectorstores import VecLabsRecall
from langchain.embeddings import OpenAIEmbeddings

vectorstore = VecLabsRecall(
    api_key="your-api-key",
    collection="langchain-memory",
    embedding=OpenAIEmbeddings()
)

vectorstore.add_texts(["User prefers dark mode", "Meeting at 3pm"])
docs = vectorstore.similarity_search("user preferences", k=5)

LangChain integration is in progress. Star the repo to follow along.

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Development

git clone https://github.com/veclabs/recall-sdk-python
cd recall-sdk-python

pip install hatch
hatch build
pytest tests/ -v   # 48 tests

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Status

Feature	Status
Hosted API (api key mode)	✅ Live
Shadow Drive (self-host)	✅ Available — shadow_drive=True
Merkle verification	✅ Complete
TypeScript parity	✅ 48/48 tests — full parity
LangChain integration	📋 In progress
LlamaIndex integration	📋 Planned
AutoGen integration	📋 Planned

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Related

• Rust core engine → veclabs/recall
• TypeScript SDK → veclabs/recall-sdk-js
• Hosted API → api.veclabs.xyz
• Dashboard → app.veclabs.xyz

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Contributing

See CONTRIBUTING.md.

Priority: LangChain integration, LlamaIndex integration, AutoGen integration.

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License

MIT. See LICENSE.

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veclabs.xyz · @veclabs · Discord