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

Name: logosdb
Author: jose-compu

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. Version bumps follow Semantic Versioning with these project conventions while the library is pre-1.0 (0.x.y):

Bump	When
Patch (`0.x.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 (`0.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 `1.0.0`	First stable release: a documented compatibility promise for the public C API and for on-disk formats (including upgrade paths). Intended when the Path to 1.0.0 criteria (below) are satisfied so downstream authors can depend on semver semantics without surprise breakage across patch/minor lines.

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

Milestones → issues

SHIPPED 0.7.7 — patch (`0.x.Z`) SHIPPED

#74 — security: harden encoding and injection surfaces across MCP, CLI, and integrations
#9 — libFuzzer JSON harness in CI; local ASan/UBSan builds possible; full-tree sanitizer CI not enabled (vendored hnswlib noise)

SHIPPED 0.7.8 — patch (`0.x.Z`) SHIPPED

#94 — feat(mcp): expose timestamp-range search (search_ts_range / from_ts, to_ts)
#95 — docs: document Claude Code slash commands (/index, /search, /forget) in main README
#96 — feat(mcp) or docs: semantic delete / forget-by-query vs ID-only logosdb_delete

SHIPPED 0.8.0 — minor — operations and durability SHIPPED

#88 — DB doctor/upgrade: compatibility checks and guided migrations
#83 — Snapshots and backup: consistent point-in-time export/restore
#82 — Metrics and observability: expose query/ingest/index health counters
#81 — Compaction/vacuum: reclaim space from tombstones and fragmented files

0.9.0 — minor — throughput and scale

#87 — Streaming import/export for very large corpora
#80 — Batch ingest v2: high-throughput put_batch with WAL-aware commit

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

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

0.12.0 — minor — integrations

#78 — Feature: Codex plugin marketplace integration for LogosDB

0.13.0 — minor — platform

#10 — Windows support: abstract POSIX file I/O behind a portable layer

1.0.0 — stable release tag

No roadmap issues are assigned only to this milestone: it marks publishing 1.0.0 once the criteria below are satisfied; implementation work stays on the milestones above until shipped.

Path to 1.0.0

1.0.0 is not a single mega-release of every open issue; it is the milestone where the project commits to stable semver for the public API and supported persistence story. Practically, 1.0.0 is targeted when:

The public C API in include/logosdb/logosdb.h is treated as stable: breaking changes only in future major versions, with migration notes.
On-disk layout and version negotiation are documented, with doctor/upgrade guidance (#88) and a credible backup/restore path (#83).
The security baseline in #74 is addressed for MCP, CLI, and bundled integrations.
Tier-1 platforms for the release are explicit (first-class Windows (#10) or a documented Unix-only 1.0, decided at release time).

Features such as hybrid retrieval (#85), filter predicates (#84), or multi-tenant namespaces (#86) can ship in 0.x minors as they land; they do not all block 1.0.0 unless adopted into the stable API surface.

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)

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

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 file indexing

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)
`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.

(Report below was produced with an older logosdb-bench binary labeled 0.5.0; scaling and relative HNSW vs brute-force behavior are still representative of current builds.)

LogosDB:    version 0.8.0
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.

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_basic.cpp          C++ unit tests
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.