openalex-local 0.3.1

Local OpenAlex database with 284M+ works, abstracts, and semantic search

OpenAlex Local

Local OpenAlex database with 284M+ scholarly works, abstracts, and semantic search.

Why OpenAlex Local?

Built for the LLM era - features that matter for AI research assistants:

Feature	Benefit
284M Works	More coverage than CrossRef
Abstracts	~45-60% availability for semantic search
Concepts & Topics	Built-in classification
Author Disambiguation	Linked to institutions
Open Access Info	OA status and URLs

Perfect for: RAG systems, research assistants, literature review automation.

Installation

pip install openalex-local

From source:

git clone https://github.com/ywatanabe1989/openalex-local
cd openalex-local && make install

Database setup (~300 GB, ~1-2 days to build):

# Check system status
make status

# 1. Download OpenAlex Works snapshot (~300GB)
make download-screen  # runs in background

# 2. Build SQLite database
make build-db

# 3. Build FTS5 index
make build-fts

Python API

from openalex_local import search, get, count

# Full-text search (title + abstract)
results = search("machine learning neural networks")
for work in results:
    print(f"{work.title} ({work.year})")
    print(f"  Abstract: {work.abstract[:200]}...")
    print(f"  Concepts: {[c['name'] for c in work.concepts]}")

# Get by OpenAlex ID or DOI
work = get("W2741809807")
work = get("10.1038/nature12373")

# Count matches
n = count("CRISPR")

CLI

openalex-local search "CRISPR genome editing" -n 5
openalex-local search-by-doi W2741809807
openalex-local search-by-doi 10.1038/nature12373
openalex-local status  # Configuration and database stats

With abstracts (-a flag):

$ openalex-local search "neural network" -n 1 -a

Found 1,523,847 matches in 45.2ms

1. Deep learning for neural networks (2015)
   OpenAlex ID: W2741809807
   Abstract: This paper presents a comprehensive overview of deep learning
   techniques for neural network architectures...

HTTP API

Start the FastAPI server:

openalex-local relay --host 0.0.0.0 --port 31292

Endpoints:

# Search works (FTS5)
curl "http://localhost:31292/works?q=CRISPR&limit=10"

# Get by ID or DOI
curl "http://localhost:31292/works/W2741809807"
curl "http://localhost:31292/works/10.1038/nature12373"

# Batch lookup
curl -X POST "http://localhost:31292/works/batch" \
  -H "Content-Type: application/json" \
  -d '{"ids": ["W2741809807", "10.1038/nature12373"]}'

# Database info
curl "http://localhost:31292/info"

HTTP mode (connect to running server):

# On local machine (if server is remote)
ssh -L 31292:127.0.0.1:31292 your-server

# Python client
from openalex_local import configure_http
configure_http("http://localhost:31292")

# Or via CLI
openalex-local --http search "CRISPR"

MCP Server

Run as MCP (Model Context Protocol) server:

openalex-local mcp start

Local MCP client configuration:

{
  "mcpServers": {
    "openalex-local": {
      "command": "openalex-local",
      "args": ["mcp", "start"],
      "env": {
        "OPENALEX_LOCAL_DB": "/path/to/openalex.db"
      }
    }
  }
}

Remote MCP via HTTP:

# On server: start persistent MCP server
openalex-local mcp start -t http --host 0.0.0.0 --port 8083

{
  "mcpServers": {
    "openalex-remote": {
      "url": "http://your-server:8083/mcp"
    }
  }
}

Diagnose setup:

openalex-local mcp doctor        # Check dependencies and database
openalex-local mcp list-tools    # Show available MCP tools
openalex-local mcp installation  # Show client config examples

Available tools:

• search - Full-text search across 284M+ papers
• search_by_id - Get paper by OpenAlex ID or DOI
• enrich_ids - Batch lookup with metadata
• status - Database statistics

Related Projects

crossref-local - Sister project with CrossRef data:

Feature	crossref-local	openalex-local
Works	167M	284M
Abstracts	~21%	~45-60%
Update frequency	Real-time	Monthly
DOI authority	Yes (source)	Uses CrossRef
Citations	Raw references	Linked works
Concepts/Topics	No	Yes
Author IDs	No	Yes
Best for	DOI lookup, raw refs	Semantic search

When to use CrossRef: Real-time DOI updates, raw reference parsing, authoritative metadata. When to use OpenAlex: Semantic search, citation analysis, topic discovery.

Documentation

Full documentation available at openalex-local.readthedocs.io

• Installation Guide
• Quickstart
• CLI Reference
• HTTP API Reference
• Python API

Data Source

Data from OpenAlex, an open catalog of scholarly works. Updated monthly from their snapshot.

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