logosdb 1.0.1

Fast semantic vector database (HNSW + mmap) with Python bindings

LogosDB is a fast semantic vector database written in C/C++ that provides approximate nearest-neighbor search over embedding vectors with associated text metadata.

Authors: Jose (@jose-compu)

Contributing: See CONTRIBUTING.md for build instructions, style guide, and PR workflow.

Features

• Vectors and metadata are stored as flat binary files, memory-mapped for zero-copy reads.
• Approximate nearest-neighbor search via HNSW (hnswlib), O(log n) query time.
• Each vector row carries optional text and ISO 8601 timestamp metadata (JSONL sidecar).
• The basic operations are Put(embedding, text, timestamp) and Search(query, top_k).
• Timestamp range filtering: search within a time window (e.g., "last 24 hours").
• Multiple distance metrics: inner product, cosine similarity (auto-normalized), or L2 Euclidean.
• Bulk vector access for direct tensor construction (e.g. loading into GPU memory).
• Thread-safe API on a single open handle: an internal mutex serializes operations that touch the index and metadata (including search).
• Crash recovery: HNSW index is automatically backfilled from the append-only vector store on open.
• Scales to millions of vectors.
• Framework integrations: LangChain and LlamaIndex VectorStore adapters.
• MCP server: first-class Claude Code integration via logosdb-mcp-server.

Documentation

The public interface is in include/logosdb/logosdb.h. Callers should not include or rely on the details of any other header files in this package. Those internal APIs may be changed without warning.

Planning a deployment? See docs/sizing.md for disk/RAM estimates based on N×dim, or run python -m logosdb.sizing --rows 1_000_000 --dim 768.

Guide to header files:

• include/logosdb/logosdb.h: Main interface to the DB. Start here. Contains:
  • C API with opaque handles and errptr convention (RocksDB/LevelDB style)
  • C++ convenience wrapper (logosdb::DB) with RAII and exceptions
  • logosdb::Options for HNSW tuning and distance metric selection
  • logosdb::SearchHit result struct
  • logosdb_search_ts_range() for timestamp-filtered search

Limitations

• This is not a general-purpose vector database. It is purpose-built for embedding-based memory retrieval in LLM inference (funes.cpp).
• Only a single process (possibly multi-threaded) can access a particular database at a time.
• There is no client-server support built into the library. An application that needs such support will have to wrap their own server around the library.
• For inner-product distance (LOGOSDB_DIST_IP, the default), vectors must be L2-normalized before insertion. Use LOGOSDB_DIST_COSINE for automatic normalization.
• Embedding generation is external — the caller provides pre-computed float vectors.

Roadmap

Work is tracked in open issues. 1.0.0 shipped on 2026-05-17; the public C API and supported language bindings follow Semantic Versioning on the 1.x line:

Bump	When
Patch (1.0.Z)	Bug fixes, security fixes, documentation, CI/build-only changes, and internal tooling that does not alter the supported contract of the public C API in include/logosdb/logosdb.h or the stable surface of language bindings.
Minor (1.Y.z)	Backward-compatible additions: new functions or options, new storage or index behaviors behind explicit settings, new integrations, new platforms, and additive file-format upgrades with automatic or guided migration.
Major (2.0.0)	Breaking changes to the public C API, on-disk format without a migration path, or binding contracts that cannot remain compatible.

Target versions and issue assignment match GitHub milestones (each open roadmap issue has exactly one milestone).

Milestones → issues

Past releases are summarized in the CHANGELOG and the closed milestones.

SHIPPED 0.9.0 — minor — throughput and scale

• #87 — Streaming NDJSON import/export (logosdb_export_ndjson / logosdb_import_ndjson in C / C++ / Python) with chunk_size, byte-offset --checkpoint, and --resume. Parquet output is a follow-up.
• #80 — Batch ingest v2: logosdb_put_batch is now chunked and WAL-aware (LOGOSDB_BATCH_CHUNK_SIZE, default 1024). Durability matches per-row logosdb_put; speed-up vs single put is workload-dependent (≈1.4× at dim=64 on the bench; HNSW per-row cost dominates at larger dims).

SHIPPED 0.10.0 — minor — search and metadata

• #85 — Hybrid retrieval mode: ANN score + lexical score fusion
• #84 — Filter API v2: structured metadata predicates beyond timestamp range

SHIPPED 0.11.0 — minor — multi-tenancy and tooling

• #86 — Multi-tenant namespaces: quota and isolation within one DB root
• #89 — Recall benchmarking utility for HNSW tuning

SHIPPED 0.12.0 — minor — integrations

• #78 — Feature: Codex plugin marketplace integration for LogosDB

SHIPPED 1.0.0 — first stable release

• Smart chunking, UTF-8/NFC policy, path-hardening, and expanded security tests (#98, #108–#112). See CHANGELOG 1.0.0.

SHIPPED 1.0.1 — patch — release hygiene

• #116 — Sync CHANGELOG, version macros, package metadata, and post-1.0 docs.

Open — platform / hardening (post-1.0)

• #10 — Windows support: abstract POSIX file I/O behind a portable layer
• #111 — Finish Python integration-adapter security audit (partially addressed in 1.0.0 for vibe.py)

Stability (since 1.0.0)

As of 1.0.0, the project commits to stable semver for the public C API and supported language bindings:

• Breaking changes to include/logosdb/logosdb.h or on-disk layout require a future major version, with migration notes.
• Tier-1 platforms for 1.x are Linux and macOS; Windows remains tracked in #10.
• Backup/restore and doctor tooling shipped in earlier 0.x releases; further hardening continues on the 1.x line.

Getting the Source

git clone --recurse-submodules <repository-url>
cd logosdb

Building

This project supports CMake out of the box.

Quick start:

mkdir -p build && cd build
cmake -DCMAKE_BUILD_TYPE=Release .. && cmake --build .

This builds:

Target	Description
logosdb	Static library (liblogosdb.a)
logosdb-cli	Command-line tool for put, search, info
logosdb-bench	Benchmark: HNSW vs brute-force, with ChromaDB comparison
logosdb-test	Unit tests

Usage (C API)

#include <logosdb/logosdb.h>

char *err = NULL;
logosdb_options_t *opts = logosdb_options_create();
logosdb_options_set_dim(opts, 2048);

logosdb_t *db = logosdb_open("/tmp/mydb", opts, &err);
logosdb_options_destroy(opts);

float vec[2048] = { /* ... unnormalized vector ... */ };

// L2-normalize for inner-product distance (returns 0 on success, -1 if zero norm)
if (logosdb_l2_normalize(vec, 2048) == 0) {
    logosdb_put(db, vec, 2048, "My commute is 42 minutes",
                "2025-06-25T10:00:00Z", &err);
}

logosdb_search_result_t *res = logosdb_search(db, query_vec, 2048, 5, &err);
for (int i = 0; i < logosdb_result_count(res); i++) {
    printf("#%d score=%.4f text=%s\n", i,
           logosdb_result_score(res, i),
           logosdb_result_text(res, i));
}
logosdb_result_free(res);
logosdb_close(db);

Search with timestamp range filter

#include <logosdb/logosdb.h>

char *err = NULL;
logosdb_options_t *opts = logosdb_options_create();
logosdb_options_set_dim(opts, 2048);

logosdb_t *db = logosdb_open("/tmp/mydb", opts, &err);

// Search for top-5 matches within the last 24 hours
logosdb_search_result_t *res = logosdb_search_ts_range(
    db, query_vec, 2048, 5,
    "2025-04-21T10:00:00Z",  // from (inclusive), NULL for no lower bound
    "2025-04-22T10:00:00Z",  // to (inclusive), NULL for no upper bound
    50,                      // candidate_k: internal fetch multiplier (10x top_k recommended)
    &err);

for (int i = 0; i < logosdb_result_count(res); i++) {
    printf("#%d score=%.4f ts=%s text=%s\n", i,
           logosdb_result_score(res, i),
           logosdb_result_timestamp(res, i),
           logosdb_result_text(res, i));
}
logosdb_result_free(res);
logosdb_close(db);

Using different distance metrics

#include <logosdb/logosdb.h>

char *err = NULL;
logosdb_options_t *opts = logosdb_options_create();
logosdb_options_set_dim(opts, 2048);

// Use cosine similarity (automatically normalizes vectors)
logosdb_options_set_distance(opts, LOGOSDB_DIST_COSINE);

// Or use L2 Euclidean distance
// logosdb_options_set_distance(opts, LOGOSDB_DIST_L2);

// Default is LOGOSDB_DIST_IP (inner product on L2-normalized vectors)

logosdb_t *db = logosdb_open("/tmp/mydb", opts, &err);
logosdb_options_destroy(opts);

// For cosine: vectors are automatically normalized on put/search
float vec[2048] = { /* ... unnormalized vector ... */ };
logosdb_put(db, vec, 2048, "entry", "2025-04-22T10:00:00Z", &err);

logosdb_close(db);

Usage (C++ wrapper)

#include <logosdb/logosdb.h>
#include <vector>

// Basic usage with default inner-product distance
logosdb::DB db("/tmp/mydb", {.dim = 2048});

// L2-normalize your vectors before insertion (required for inner-product distance)
std::vector<float> embedding = load_some_vector();  // unnormalized
if (logosdb::l2_normalize(embedding)) {
    db.put(embedding, "My commute is 42 minutes", "2025-06-25T10:00:00Z");
}

// Or use l2_normalized() to get a normalized copy
auto normalized = logosdb::l2_normalized(query);
auto results = db.search(normalized, 5);
for (auto &r : results) {
    printf("id=%llu score=%.4f text=%s\n", r.id, r.score, r.text.c_str());
}

C++: Search with timestamp filter

#include <logosdb/logosdb.h>

logosdb::DB db("/tmp/mydb", {.dim = 2048});

// Search within a time window
auto results = db.search_ts_range(
    query, 5,
    "2025-04-21T00:00:00Z",  // from timestamp
    "2025-04-22T00:00:00Z",  // to timestamp
    50);                     // candidate_k (optional, defaults to 10x top_k)

for (auto &r : results) {
    printf("id=%llu score=%.4f ts=%s\n", r.id, r.score, r.timestamp.c_str());
}

C++: Using cosine or L2 distance

#include <logosdb/logosdb.h>

// Cosine similarity - vectors are automatically normalized
logosdb::DB db("/tmp/mydb", {.dim = 2048, .distance = LOGOSDB_DIST_COSINE});

// Put unnormalized vectors - they will be normalized automatically
db.put(unnormalized_embedding, "entry", "2025-04-22T10:00:00Z");

auto results = db.search(query, 5);
// scores are cosine similarities in [0, 1]

// L2 Euclidean distance
// logosdb::DB db("/tmp/mydb", {.dim = 2048, .distance = LOGOSDB_DIST_L2});

Python

LogosDB ships Python bindings built with pybind11 and scikit-build-core.

Install from PyPI (binary wheels provided for Linux x86_64/aarch64 and macOS x86_64/arm64 on CPython 3.9–3.13):

pip install logosdb

Or build from source in a clone:

pip install .

Usage:

import logosdb
from sentence_transformers import SentenceTransformer

# Local Hugging Face embeddings model (runs on your machine)
model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
dim = model.get_sentence_embedding_dimension()

# Use cosine distance so LogosDB auto-normalizes vectors
db = logosdb.DB("/tmp/agent_memory", dim=dim, distance=logosdb.DIST_COSINE)

# Three learnings captured by an AI agent
learnings = [
    ("Retrying API calls with exponential backoff reduced transient failures by 42%.", "2026-05-06T09:00:00Z"),
    ("Splitting long tasks into smaller batches improved throughput and lowered memory spikes.", "2026-05-06T09:05:00Z"),
    ("Adding idempotency keys prevented duplicate writes during network retries.", "2026-05-06T09:10:00Z"),
]

for text, ts in learnings:
    emb = model.encode(text).astype("float32")
    db.put(emb, text=text, timestamp=ts)

# Ask a natural-language question
question = "How can we avoid duplicate writes when retries happen?"
q_emb = model.encode(question).astype("float32")

hits = db.search(q_emb, top_k=3)
for h in hits:
    print(f"{h.score:.4f}  {h.text}")

Python: Using cosine distance (no manual normalization needed)

import numpy as np
import logosdb

# With cosine distance, vectors are automatically normalized
db = logosdb.DB("/tmp/mydb", dim=128, distance=logosdb.DIST_COSINE)

# No need to normalize - just put raw vectors
v = np.random.randn(128).astype(np.float32)
rid = db.put(v, text="unnormalized vector", timestamp="2025-04-22T10:00:00Z")

# Search also works with unnormalized queries
query = np.random.randn(128).astype(np.float32)
hits = db.search(query, top_k=5)

Python: Batch ingest and streaming NDJSON (0.9.0)

import numpy as np
import logosdb

db = logosdb.DB("/tmp/mydb", dim=128, distance=logosdb.DIST_COSINE)

# Chunked, WAL-aware batch insert (LOGOSDB_BATCH_CHUNK_SIZE controls fsync cadence).
embeddings = np.random.randn(10_000, 128).astype(np.float32)
ids = db.put_batch(embeddings, texts=[f"row {i}" for i in range(10_000)])

# Bounded-memory NDJSON export/import; --resume from a checkpoint on interruption.
db.export_ndjson("/tmp/rows.ndjson")
restored = logosdb.DB("/tmp/restored", dim=128, distance=logosdb.DIST_COSINE)
restored.import_ndjson("/tmp/rows.ndjson", chunk_size=1024,
                      checkpoint_path="/tmp/rows.cp", resume=False)

Run the Python tests and examples:

pip install ".[test]"
pytest tests/python/

python examples/python/basic_usage.py

# sentence-transformers demo (optional heavy dep)
pip install ".[examples]"
python examples/python/sentence_transformers_demo.py

Memory-Efficient On-Prem RAG

LogosDB is designed for memory-efficient retrieval-augmented generation (RAG) that runs entirely on your hardware.

RAM Model

LogosDB uses mmap() for zero-copy access. Your RAM usage scales with query patterns, not dataset size:

Dataset	Dim	Disk	Typical Query RAM
100K	384	153 MB	<20 MB
1M	384	1.5 GB	<100 MB
10M	384	15 GB	<200 MB

The OS caches hot index pages; cold data stays on disk. No explicit loading/unloading needed.

Quick RAG Example

import numpy as np
import logosdb
from sentence_transformers import SentenceTransformer

# 1. Load model (runs locally)
model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
dim = model.get_sentence_embedding_dimension()

# 2. Create DB with cosine distance (auto-normalizes)
db = logosdb.DB("/data/knowledge", dim=dim, distance=logosdb.DIST_COSINE)

# 3. Index documents
for text in documents:
    emb = model.encode(text)
    db.put(emb, text=text)  # Auto-normalized with cosine distance

# 4. Query (only touched pages load into RAM)
query_emb = model.encode("What is HNSW?")
for hit in db.search(query_emb, top_k=3):
    print(f"{hit.score:.4f}  {hit.text}")

See docs/rag-on-prem.md for complete guide including:

• Time-sharding for infinite retention
• External quantization patterns
• Architecture patterns for production

See docs/sizing.md for detailed disk/RAM formulas and the python -m logosdb.sizing calculator.

Run the memory-efficient RAG example:

pip install ".[examples]"
python examples/python/memory_efficient_rag.py

Python: LlamaIndex VectorStore

pip install 'logosdb[llama-index]'

from logosdb import LogosDBIndex
from llama_index.core import Document
from llama_index.core.schema import TextNode
from llama_index.core.vector_stores import VectorStoreQuery
import numpy as np

# Create the vector store
db = LogosDBIndex(uri="/tmp/mydb", dim=128)

# Add nodes with pre-computed embeddings
node = TextNode(
    text="My commute is 42 minutes",
    embedding=np.random.randn(128).astype(np.float32).tolist(),
    metadata={"timestamp": "2025-04-28T10:00:00Z"}
)
db.add([node])

# Query
query_emb = np.random.randn(128).astype(np.float32).tolist()
query = VectorStoreQuery(query_embedding=query_emb, similarity_top_k=5)
results = db.query(query)

for node, score in zip(results.nodes, results.similarities):
    print(f"Score: {score:.4f}, Text: {node.text}")

# Timestamp range filtering
results = db.query(query, ts_from="2025-04-01T00:00:00Z", ts_to="2025-04-30T23:59:59Z")

The LogosDBIndex class implements LlamaIndex's VectorStore interface, supporting:

• add(nodes) - Add nodes with embeddings
• delete(node_id) - Delete by node ID
• query(VectorStoreQuery) - Similarity search by vector
• count() / len(store) - Number of live documents
• Timestamp filtering via ts_from and ts_to kwargs

CLI

# Database info
logosdb-cli info /tmp/mydb

# Search with a binary query vector file
logosdb-cli search /tmp/mydb --query-file q.bin --top-k 5

# Streaming NDJSON export / import (#87) — bounded memory, optional resume
logosdb-cli export /tmp/mydb --output rows.ndjson [--start-id N --end-id M]
logosdb-cli import /tmp/mydb --dim 768 --input rows.ndjson \
    --chunk-size 1024 --checkpoint rows.cp [--resume]

Node.js

MCP Server — Claude Code integration

logosdb-mcp-server is a Model Context Protocol server that exposes LogosDB to Claude Code (and any other MCP client) over stdio. It lets Claude index files, persist knowledge across sessions, and do semantic search without leaving the conversation.

Claude Code: complete recipe

Follow these steps in order. Claude Code spawns the MCP server with working directory = your project root (the folder you opened); paths in mcp.json and LOGOSDB_PATH are resolved relative to that cwd unless you use absolute paths.

1. Prerequisites

• Node.js 18 or newer on your PATH (node -v, npm -v). For the published package you also need npx.
• Claude Code installed and signed in (overview).
• Decide where the DB lives: LOGOSDB_PATH (default ./.logosdb) is created under the project root; add the directory to .gitignore if you do not want it committed.

2. Install the server (pick one)

Option A — This repository (local node, no npx). From the repository root:

npm install

That installs the mcp workspace and runs prepare, which compiles TypeScript to mcp/dist/index.js. After you change MCP sources, run npm run mcp:build at the root or npm run build inside mcp/.

Option B — Any other project (published logosdb-mcp-server). In your app’s root:

npm install logosdb-mcp-server

You can still invoke it without a project dependency via npx -y logosdb-mcp-server (see Option C).

3. Register the MCP server in Claude Code

Project-local config lives in .claude/mcp.json at the repository root. User-wide config is ~/.claude.json (same mcpServers shape). Only one definition named logosdb is needed.

If you use Option A (this clone): the repo already contains .claude/mcp.json:

{
  "mcpServers": {
    "logosdb": {
      "command": "node",
      "args": ["./mcp/dist/index.js"],
      "env": {
        "LOGOSDB_PATH": "./.logosdb"
      }
    }
  }
}

Open this folder as the Claude Code project so ./mcp/dist/index.js resolves. If the client’s cwd is not the repo root, set "args" to an absolute path to mcp/dist/index.js (see Installing locally — Option C).

If you use Option B or prefer always-latest from npm (Option C): create or merge .claude/mcp.json:

{
  "mcpServers": {
    "logosdb": {
      "command": "npx",
      "args": ["-y", "logosdb-mcp-server"],
      "env": {
        "LOGOSDB_PATH": "./.logosdb"
      }
    }
  }
}

Default embeddings are local Transformers.js (no API keys); first run may download model weights. Optional Ollama / OpenAI / Voyage env vars are documented in mcp/README.md.

4. Path rules for logosdb_index_file

The server only indexes files that resolve under process.cwd() or LOGOSDB_INDEX_ROOT (if set). Symlinks that escape those roots are rejected. Keep indexing paths inside the project you opened, or set LOGOSDB_INDEX_ROOT to an absolute allowed root (details in mcp/README.md — Path confinement).

5. Reload Claude Code and verify

• Restart Claude Code or reload MCP configuration after editing mcp.json.
• The logosdb server starts on first tool use (stdio).
• Ask the agent to run logosdb_list (or check the MCP tools panel). You should see namespaces (possibly empty) rather than a spawn error.

6. Index, search, delete

• Natural language: e.g. “Index src/ into the code namespace”, “Search the codebase for JWT validation”.
• Slash commands (optional): if .claude/commands/ is present in the project (this repo ships them), use /index, /search, /forget — see the table below.
• Snippet memory: the agent can call logosdb_index for short text; logosdb_index_file chunks files and directories.

Use one embedding backend consistently per namespace on disk (do not change model dimension on existing data). See Environment variables and mcp/README.md.

7. Optional: agent instructions

Copy the CLAUDE.md template block from Agent instructions (CLAUDE.md and similar) into your project so the agent indexes and searches without being reminded every session.

8. Troubleshooting

Symptom	What to try
MCP fails to start / “Cannot find module”	From the same cwd Claude uses, run node ./mcp/dist/index.js (Option A) or npx -y logosdb-mcp-server (Option C) in a terminal; fix missing npm install or broken path.
Relative ./mcp/dist/index.js not found	Use an absolute args path to index.js, or open the correct folder as the project root.
logosdb_index_file rejects a path	Path is outside cwd / LOGOSDB_INDEX_ROOT; use a path inside the project or set LOGOSDB_INDEX_ROOT.
Search looks wrong after changing model	New embeddings must use a fresh LOGOSDB_PATH or new namespace; dimensions must match.

Google Antigravity: same stdio MCP pattern (node + local script, or npx + package). Step-by-step: mcp/README.md — Google Antigravity.

Claude Code slash commands

This repo ships project slash commands under .claude/commands/ (in addition to .claude/mcp.json for the MCP server):

Command	Role
/index	Index a file or directory via logosdb_index_file with incremental: true (new/changed files only; commands/index.md)
/search	Semantic search; optional ISO ts_from / ts_to on the MCP tool (commands/search.md)
/forget	Delete by row id or by natural-language query (commands/forget.md)

Agent instructions (CLAUDE.md and similar)

The MCP server does not index the repository by itself: the agent must call the tools (or you rely on slash commands / hooks). To make LogosDB feel automatic without typing /index every time, add a block like the following to your project’s CLAUDE.md (or any instructions file your agent reads on every session). Adjust namespaces and paths to your repo.

## LogosDB (semantic memory via MCP)

The **logosdb** MCP server is configured. Data lives on disk under `LOGOSDB_PATH` (see `.claude/mcp.json`); it **persists across sessions**.

**Namespaces:** Use separate namespaces for different concerns (e.g. `code` for `src/`, `docs` for `docs/`, `decisions` for short architectural notes). Search only the namespace that matches the user’s task.

**When starting substantive work on this codebase:**
1. If the user has not indexed recently and you need broad code context, call **`logosdb_index_file`** with **`incremental: true`** on the smallest useful path (e.g. `src/` or a package directory), not the whole monorepo unless asked.
2. Before answering “where is X implemented?” or similar, call **`logosdb_search`** with a tight natural-language `query`, `namespace` set appropriately, and `top_k` between **3** and **8**. Do not paste entire trees into the chat—retrieve, then read only the cited files.
3. For “what did we decide recently?” style questions, use **`logosdb_search`** with optional **`ts_from` / `ts_to`** (ISO 8601 inclusive bounds) on the `decisions` or `docs` namespace when timestamps matter.
4. When the user states a durable fact worth remembering (API contract, policy, workaround), call **`logosdb_index`** into the right namespace with concise text (timestamps are stored automatically; optional **`metadata`** can label the source).

**After large refactors or dependency upgrades:** Re-run **`logosdb_index_file`** with **`incremental: true`** on affected paths so search stays aligned without duplicating chunks for unchanged files.

**Deletion:** Use **`logosdb_delete`** with **`id`** from a prior search hit, or with **`query`** + optional **`match_rank`** / **`search_top_k`** to remove a semantically matched row when the user asks to forget something.

Environment variables

Variable	Default	Description
LOGOSDB_PATH	./.logosdb	Root directory for all namespace databases
EMBEDDING_PROVIDER	(local)	Omit for Transformers.js on-device; or ollama, openai, voyage
TRANSFORMERS_MODEL	Xenova/all-MiniLM-L6-v2	Local embedding model (bundled MCP path)
OLLAMA_*	—	See mcp/README.md when using Ollama
OPENAI_API_KEY	—	Required when EMBEDDING_PROVIDER=openai
VOYAGE_API_KEY	—	Required when EMBEDDING_PROVIDER=voyage
LOGOSDB_CHUNK_SIZE	800	Target characters per chunk for "legacy" and "section" chunking modes
LOGOSDB_CHUNK_MODE	auto	Chunking strategy: auto (per-extension), line (code), section (markdown/rst), legacy (original paragraph/char). See mcp/README.md — Chunking strategies.

Voyage AI (voyage-3, dim=1024) is Anthropic's recommended cloud embedding model:

"env": {
  "LOGOSDB_PATH": "./.logosdb",
  "EMBEDDING_PROVIDER": "voyage",
  "VOYAGE_API_KEY": "<your-voyage-api-key>"
}

Available tools

Tool	Description
logosdb_index	Embed and store a text snippet in a namespace
logosdb_index_file	Chunk, embed, and store a file or tree; optional incremental: true (skip unchanged, replace changed, prune deleted under a directory). When the path lives inside a Git working tree the walker honours .gitignore (root + nested + .git/info/exclude + global excludes) by default; disable with respect_gitignore: false or LOGOSDB_RESPECT_GITIGNORE=0.
logosdb_search	Semantic search; optional ts_from / ts_to (ISO 8601) for timestamp-window filter
logosdb_list	List all namespaces
logosdb_info	Stats for a namespace (count, dimension, path)
logosdb_delete	Delete by row id, or by natural-language query (search_top_k, match_rank)

Installing locally (without npx)

Option A — global CLI on your machine

npm install -g logosdb-mcp-server

In .claude/mcp.json, point the server at the global binary:

"logosdb": {
  "command": "logosdb-mcp-server",
  "args": [],
  "env": { "LOGOSDB_PATH": "./.logosdb" }
}

Ensure the directory containing the global npm binaries is on your PATH when Claude Code spawns the process (same shell you use for npm install -g).

Option B — project-local node_modules (no global install)

From your app repo:

npm install logosdb-mcp-server

Then use npx so the binary resolves from ./node_modules:

"logosdb": {
  "command": "npx",
  "args": ["-y", "logosdb-mcp-server"],
  "env": { "LOGOSDB_PATH": "./.logosdb" }
}

Omit -y if you prefer a fixed local install only. You can also call the entry script explicitly:

"command": "node",
"args": ["./node_modules/logosdb-mcp-server/dist/index.js"],
"env": { "LOGOSDB_PATH": "./.logosdb" }

Option C — development build from a LogosDB clone

cd mcp && npm install && npm run build

This repository’s checked-in .claude/mcp.json uses a relative path ./mcp/dist/index.js after npm install from the repo root (workspace prepare). For other clients or if the process cwd is not the repo root, use an absolute path to mcp/dist/index.js:

"logosdb": {
  "command": "node",
  "args": ["/absolute/path/to/logosdb/mcp/dist/index.js"],
  "env": { "LOGOSDB_PATH": "./.logosdb" }
}

The same env block (LOGOSDB_PATH, embedding variables) applies to every option. Restart Claude Code or reload MCP config after editing .claude/mcp.json.

Performance

Here is a performance report from the included logosdb-bench program. The results are somewhat noisy, but should be enough to get a ballpark performance estimate.

Setup

We use databases with 1K, 10K, and 100K vectors. Each vector has 2048 dimensions (matching typical LLM embedding sizes). Vectors are L2-normalized random unit vectors.

(Numbers below are illustrative ballparks; absolute values depend on hardware and build flags. Relative HNSW vs brute-force behavior is what to focus on.)

LogosDB:    version 1.0.1
CPU:        Apple M-series (ARM64)
Dim:        2048
HNSW M:     16, ef_construction: 200, ef_search: 50

Write performance

put (1K vectors):    ~50 µs/op   (~20,000 inserts/sec)
put (10K vectors):   ~80 µs/op   (~12,500 inserts/sec)
put (100K vectors):  ~120 µs/op  (~8,300 inserts/sec)

Each "op" above corresponds to a write of a single vector + metadata + HNSW index update.

Search performance

HNSW top-5 (1K):     ~0.1 ms/query
HNSW top-5 (10K):    ~0.3 ms/query
HNSW top-5 (100K):   ~1.2 ms/query

Brute-force top-5 (1K):    ~0.3 ms/query
Brute-force top-5 (10K):   ~2.5 ms/query
Brute-force top-5 (100K):  ~25 ms/query

HNSW maintains sub-linear scaling while brute-force grows linearly with database size. At 100K vectors, HNSW is roughly 20x faster.

VectorDB Bench

VectorDB Bench is the standard open-source harness for comparing vector databases at realistic scale (load, optimize, serial/concurrent search, recall). LogosDB is integrated as a first-class backend:

Location	Role
vectordb_bench/backend/clients/logosdb/	Client: config.py, logosdb.py, cli.py
vectordb_bench/backend/clients/__init__.py	DB.LogosDB enum + init_cls / config_cls / case_config_cls mappings
vectordb_bench/cli/vectordbbench.py	CLI subcommand logosdb
pyproject.toml	Optional extra logosdb (installs the Python bindings used by the client)

Results — Performance1536D50K (2026-05-17)

OpenAI embeddings, 50K vectors, 1536 dimensions, cosine distance. Serial search only (--skip-search-concurrent). Absolute timings depend on hardware; recall/NDCG are the primary cross-database comparators.

Metric	Value
Load duration	340.4 s
Insert duration	339.6 s
Optimize duration	0.8 s
Serial latency p99	4.6 ms
Serial latency p95	4.0 ms
Recall@100	0.9347
NDCG	0.9464

Raw JSON: vectordb_bench/results/LogosDB/result_20260517_*.json (under your VectorDB Bench checkout).

Re-run

From a VectorDB Bench tree with the LogosDB client installed (pip install -e '.[logosdb]' or equivalent):

.venv/bin/python3.12 -m vectordb_bench.cli.vectordbbench logosdb \
  --uri /tmp/vectordbbench_logosdb \
  --case-type Performance1536D50K \
  --skip-search-concurrent

Use a fresh --uri directory per run. Set OPENAI_API_KEY (or the embedding provider your case expects) before starting.

Results — glove-25-angular (qdrant/vector-db-benchmark)

Standard qdrant/vector-db-benchmark harness, 1.18M × 25 vectors, cosine distance (angular). Serial search.

Metric	Value
Upload time	345 s
Mean precision@10	0.939
RPS	9,401
Mean latency	0.092 ms
p99 latency	0.146 ms

Source: qdrant/vector-db-benchmark.

Benchmark vs ChromaDB

logosdb-bench --dim 2048 --counts 1000,10000,100000

Metric	ChromaDB	LogosDB
Language	Python + C (hnswlib)	Pure C/C++
Search algorithm	HNSW	HNSW (same hnswlib)
Storage	SQLite + Parquet	Binary mmap + JSONL
Startup overhead	Python runtime + deps	Zero (linked library)
Embedding generation	Built-in (Sentence Transformers)	External (caller provides vectors)
Target use case	General-purpose vector store	Embedded LLM inference memory
Search latency (100K, dim=2048)	~5-10 ms	~1-3 ms
Memory footprint (100K, dim=2048)	~1.5 GB (Python + SQLite)	~800 MB (mmap)
Cold start	~2-5 s (Python imports)	<10 ms
Dependencies	Python, NumPy, SQLite, hnswlib	hnswlib (header-only, vendored)

LogosDB uses the same HNSW implementation as ChromaDB (hnswlib) but eliminates Python overhead, SQLite serialization, and Sentence Transformer coupling. The result is a leaner library optimized for the single use case of embedded semantic memory for LLM inference.

Repository contents

include/logosdb/logosdb.h     Public C/C++ API (start here)
src/logosdb.cpp               Core engine: wires storage + index + metadata
src/storage.h / storage.cpp   Fixed-stride binary vector file with mmap
src/metadata.h / metadata.cpp Append-only JSONL text + timestamp store
src/hnsw_index.h / .cpp       Thin wrapper around hnswlib
tools/logosdb-cli.cpp         Command-line interface
tools/logosdb-bench.cpp       Benchmark tool
tests/test_doctest.cpp        C++ unit tests (doctest, incl. CLI integration)
tests/test_stress.cpp         Stress/property tests (opt-in via --test-suite=stress)
tests/python/test_smoke.py    Python smoke tests (pytest)
python/src/bindings.cpp       pybind11 Python bindings
python/logosdb/               Python package (logosdb._core + stubs)
examples/python/              Python usage examples
pyproject.toml                Python build/config (scikit-build-core)
third_party/hnswlib/          Vendored hnswlib (header-only)
package.json / package-lock.json   Private npm workspace (MCP + `nodejs` logosdb); `npm install` at repo root only
mcp/                          MCP server (logosdb-mcp-server npm package)
.claude/mcp.json              Claude Code MCP config (local `node ./mcp/dist/index.js`)
.claude/commands/             Slash command prompts (/index, /search, /forget)
CHANGELOG                     Release history
LICENSE                       MIT license text

Contributing

We welcome contributions. See CONTRIBUTING.md for:

• Building from source
• Running tests and benchmarks
• Code style and PR workflow

Please review our Code of Conduct and Security Policy.

License

MIT — see LICENSE for the full text.