OpenAlex Local (openalex-local)

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

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

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

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")

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...

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"

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

We provide precomputed SciTeX Impact Factors calculated from OpenAlex citation data. These follow the JCR formula but use OpenAlex as the data source.

Validation against JCR 2024 (17,042 matched journals):

Export SciTeX IF:

# Export all SciTeX IF values
openalex-local export-if -o scitex_if.csv
openalex-local export-if -o scitex_if.json

# Top 1000
openalex-local export-if -o top1000.csv --limit 1000

Use in search results:

openalex-local search "machine learning" --with-if

Formula:

SciTeX IF(Year) = Citations in Year to articles from (Year-1, Year-2)
                  ─────────────────────────────────────────────────────
                  Citable articles published in (Year-1, Year-2)

Note: "SciTeX IF" is our calculation using OpenAlex data. It is not the trademarked "Journal Impact Factor" from Clarivate/JCR.

crossref-local - Sister project with CrossRef data:

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

Full documentation available at openalex-local.readthedocs.io

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

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

from openalex_local import search, get, enrich_ids

results = search("neural oscillations", limit=10)
work = get("W2741809807")
enriched = enrich_ids(["W2741809807", "W3000000000"])

openalex-local search "query"
openalex-local search-by-doi 10.1038/nature12373

openalex-local mcp start

Agent skill pages live under src/openalex_local/_skills/openalex-local/.