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Most vector libraries optimize for a static corpus. Real systems do not stay still: agents write memories continuously, documents get corrected, users invoke the right to erasure, and teams need to reproduce what a retriever saw last Tuesday.

ChronoVec is an approximate-nearest-neighbour index built for that case. Every record carries a version interval, so queries can read the index as of any past moment, deletions physically reclaim space on a bounded budget, and continuous writes never force a rebuild — or block a reader.

pip install chronovec

The same native core is available through the other mainstream package managers once published:

cargo add chronovec
npm install @chronovec/native

Building from source requires a C++20 compiler and CMake — pip install . handles that automatically. See Getting started for full instructions.

Portable builds use baseline CPU instructions. If every deployment CPU supports AVX2, opt in with -DCHRONOVEC_ENABLE_AVX2=ON for faster x86 distance kernels.

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Why ChronoVec exists

1. Query the past

t1 = index.insert(1, embedding)
index.delete(1)

index.search(query, k=10)               # now: id 1 is gone
index.search(query, k=10, snapshot=t1)  # as of t1: id 1 is there

Audit, reproducible evaluation, and retrieval regression debugging all need this. ChronoVec makes the snapshot part of the retrieval API.

2. Delete for real, on a budget

index.delete(user_vector_id)
index.vacuum(oldest_snapshot=index.clock + 1, budget_versions=64)

vacuum physically reclaims at most budget_versions expired records — no full rebuild. For compliance workflows, logical deletion is immediate and physical reclamation is explicit and bounded.

3. Write continuously without blocking readers

Writes never block reads. A reader snapshots an immutable view and queries it lock-free, even while a batch is in progress. Reads and writes compose: insert, delete, and query concurrently from multiple threads with no coordination at the reader.

4. Branch and speculate

from chronovec import AgentMemory

memory = AgentMemory(384)
memory.add(1, embedding, text="base knowledge")

plan = memory.branch("hypothesis")
plan.add(2, other, text="speculative — not yet committed")
plan.search(query)    # sees base + speculation
memory.search(query)  # main never saw the speculation
plan.discard()        # abandon speculation; purge later

Agent memory with snapshot isolation: spawn a branch, explore speculatively, discard or merge. Each branch gets its own private delta index by default, so there's no fixed branch-count ceiling.

5. Survive a crash

index = Index(768, wal_path="index.wal")
index.insert_many(ids, vectors)
# process dies here —
index = Index(768, wal_path="index.wal")  # replays the log; nothing lost

Writes are logged before they are applied. A torn tail from a crash mid-append is detected by CRC, dropped, and the file truncated cleanly.

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Quick start

Quickstart

from chronovec import Collection

# In-memory collection, for easy prototyping. Add persistence easily below!
collection = Collection(dimensions=3)

# Add records in batches; update() and delete() use the same ids-oriented API.
collection.add(
    ids=["doc1", "doc2"],   # unique per record
    embeddings=[[1, 0, 0], [0, 1, 0]],  # or pass embedding_function= and add documents only
    documents=["This is document1", "This is document2"],
    metadatas=[{"source": "notion"}, {"source": "google-docs"}],  # filter on these!
)

# Query the 2 most similar results. get() fetches by id/filter without a search.
results = collection.query(
    [1, 0, 0],
    k=2,
    # where={"source": "notion"},   # optional metadata filter
    # snapshot=collection.snapshot(),  # optional: read as of an earlier point in time
)

For text embedding, pass either the original batch callable or ChronoVec's provider-neutral adapter. The adapter keeps document and query embedding separate when your model needs different instructions:

from chronovec import Collection, CustomEmbedding

embedder = CustomEmbedding(
    embed_documents=my_embed_documents,  # list[str] -> list[list[float]]
    embed_query=my_embed_query,           # str -> list[float]
)
collection = Collection(dimensions=384, embedding_function=embedder)
collection.add(ids=["doc1"], documents=["This is document1"])
results = collection.query(query_text="find document one", k=1)

Objects exposing embed_documents and embed_query are accepted directly, as are LlamaIndex-style get_text_embedding_batch and get_query_embedding objects. ChronoVec does not install or select a model provider; use the provider library that fits your application.

For local Sentence Transformers models, install the optional adapter:

pip install "chronovec[sentence-transformers]"

from chronovec import Collection, SentenceTransformerEmbedding

embedding = SentenceTransformerEmbedding("sentence-transformers/all-MiniLM-L6-v2")
collection = Collection(384, embedding_function=embedding)

For hosted providers, ChronoVec wraps LiteLLM so one adapter covers OpenAI, Cohere, Bedrock, Azure, and the rest of LiteLLM's provider list without ChronoVec depending on any of them directly:

pip install "chronovec[litellm]"

Pass the provider secret explicitly from your environment:

import os

from chronovec import Collection, ProviderEmbedding

embedding = ProviderEmbedding(
    provider="openai",
    model="text-embedding-3-small",
    api_key=os.environ["OPENAI_API_KEY"],
)
collection = Collection(1536, embedding_function=embedding)

Add persistence with a Client, which opens (or creates) named collections that checkpoint themselves to disk after every mutation — no separate save call:

from chronovec import Client

client = Client("./data")  # omit the path for "./.chronovec"

# get_collection, list_collections, delete_collection also available!
collection = client.get_or_create_collection("docs", dimensions=3)
collection.add(ids=["doc1"], embeddings=[[1, 0, 0]], documents=["This is document1"])

Inspect a persistent store with chronovec list ./data or chronovec inspect ./data docs.

# Install from source — cmake runs automatically
pip install ".[dev]"

from chronovec import Index, Collection

# Low-level API: int64 ids, NumPy vectors
index = Index(384, metric="cosine", page_capacity=256, nprobe=16)
t1 = index.insert(1, embedding)
index.insert(2, other_embedding)

for hit in index.search(query, k=10):
    print(hit.id, hit.distance)

index.delete(1)
index.vacuum(oldest_snapshot=index.clock + 1, budget_versions=64)
index.save("memories.cvec")
restored = Index.load("memories.cvec")

# High-level API: string ids, metadata, rich filtering
memory = Collection(dimensions=768, metric="cosine")
memory.add(ids=["doc-a"], embeddings=[v], metadatas=[{"lang": "en", "score": 0.9}])

before = memory.snapshot()
memory.add(ids=["doc-a"], embeddings=[corrected])

memory.query(q, k=5, where={"lang": "en"})                  # latest
memory.query(q, k=5, where={"lang": "en"}, snapshot=before) # as of `before`

where supports $eq $ne $gt $gte $lt $lte $in $nin $contains $regex $and $or.

# Async API for FastAPI / asyncio — same shape as Collection, awaited
from chronovec import AsyncCollection

memory = AsyncCollection(768, embedding_function=my_embed)
await memory.add(ids=["doc-a"], documents=["the user prefers dark mode"])
results = await memory.query(query_text="appearance settings", k=5)

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Framework integrations

# LangChain — branching LCEL chains
from chronovec.integrations.langchain import ChronoVecVectorStore

store = ChronoVecVectorStore(embedding=embeddings, dimensions=384)
store.add_texts(["the user prefers dark mode"])

with store.branch("hypothesis") as scratch:
    scratch.add_texts(["speculative memory"])
    scratch.similarity_search("theme")  # sees both
store.similarity_search("theme")         # speculation discarded

# LlamaIndex
from chronovec.integrations.llamaindex import ChronoVecLlamaStore

store = ChronoVecLlamaStore(dimensions=768)
index = VectorStoreIndex.from_vector_store(store)

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Language support

See the support and maturity matrix for the exact compatibility and guarantee boundary.

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Performance

Measured fresh against chromadb, faiss-ivfflat, and hnswlib on SIFT-128, GloVe-25, and GIST-960, up to 500,000 live vectors — ChronoVec leads on the workload it's built for, and the margin grows with scale rather than shrinking:

• Write-heavy workloads: chronovec holds ~140–150k replacement-pairs/s at high recall across every churn epoch tested at 200k live vectors — 16.5x faiss's steady-state throughput at that scale (vs. 2x at 10k; faiss's remove_ids cost compounds with corpus size). hnswlib stays roughly 70x below chronovec throughout, and chroma didn't complete this workload above 10,000 live vectors — three independent attempts at 50k/100k/200k all exceeded their 5–24 minute time budgets.
• Concurrent reads during writes: chronovec is the only one of the four that's both provably safe (snapshot isolation) and fast — hnswlib and faiss don't document concurrent read+write as safe at all, and chroma is safe but ~980x slower at p99 at 200,000 live vectors.
• Metadata-filtered search: chronovec's page-skipping gets faster as the filter tightens, an advantage that holds from 50k through 500k live vectors; hnswlib gets up to 100x slower at tight filters, and faiss's post-filter approach is fast but falls short of its recall target by up to 80 points and, at scale, stops returning k results for the majority of queries.

For a corpus that's built once and never mutated afterward, hnswlib and faiss both out-query chronovec by ~1.5–2x at matched recall on SIFT-128/GloVe-25 — that's the price of the versioning machinery above, and if your index never changes, either is a fine choice. ChronoVec overtakes hnswlib again at 960 dimensions. ChronoVec is for the other workload: agents writing memories, RAG corpora receiving corrections, CDC streams, and systems that need deletion, historical replay, or filtered queries against a corpus that keeps changing.

See docs/performance.md for full numbers, methodology, and the regression gate protocol.

The public comparison suite is reproducible from a clean checkout: install the benchmark extra, fetch the named ANN-Benchmarks files, then run benchmarks/run_all.sh. The command writes machine-readable JSON results and skips only engines that are not installed; see the benchmark instructions.

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Examples

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Flagship demo: agent memory

Run the offline demo—no model key, database, or service required:

pip install chronovec
python examples/agent_memory.py

It captures an interaction, retries a mistaken memory update on an isolated branch, and merges the corrected result without contaminating the original timeline. For the broader mutable-RAG workflow, see examples/mutable_rag.py.

Documentation

Using ChronoVec from a coding agent

If you are building or modifying code that uses ChronoVec inside Claude Code, a skill file is included that teaches the agent the API idioms, snapshot timing, branching patterns, and common footguns. It activates automatically when relevant. The skill lives at .claude/skills/chronovec/chronovec/SKILL.md and can be invoked explicitly with /chronovec.

Citing ChronoVec

If ChronoVec contributes to your research or publication, please cite it:

@software{chronovec,
  title  = {ChronoVec: A Versioned Vector Index with Snapshot Isolation},
  author = {Rottoli, Michael},
  year   = {2026},
  url    = {https://github.com/mchl-labs/chronovec},
  license = {Apache-2.0}
}

The repository also includes a machine-readable CITATION.cff file.

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Status

• Solid: MVCC correctness, bounded reclamation, lock-free reads, checkpoint save/load, sanitizer targets (ASan/UBSan/TSan), C-ABI fuzzing, comprehensive test suite (3,700+ lines).
• Stable: The Python API and C ABI are production-supported. Writers serialize on a single MVCC lock (lock-free reads, not multiwriter mutation).
• Supported alpha: Rust, Go, and Node.js bindings have release CI but do not yet carry the Python API's compatibility promise.
• Experimental: DuckDB adapter (not thread-safe, minimal test coverage), SQLite dqlite failover.
• Async: AsyncCollection is available for asyncio/FastAPI applications; the low-level Index can be called through asyncio.to_thread() when needed.