HAL Open Archive API
SkillDev toolsAccess French and European research via the HAL open archive API
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Then ask your AI: use the HAL Open Archive API skill
What this skill tells your AI
The instructions your AI receives, as published by brycewang-stanford/auto-empirical-research-skills in skills/43-wentorai-research-plugins/skills/literature/fulltext/hal-archive-api/SKILL.md and read by ahel’s review.
Overview
HAL (Hyper Articles en Ligne) is France's national open archive for scholarly deposits. Managed by CNRS, it hosts 4M+ full-text documents from French research institutions and international collaborators. The API provides Solr-based search with full metadata, PDF links, and OAI-PMH harvesting. Free, no authentication required.
API Endpoints
Search API
# Keyword search
curl "https://api.archives-ouvertes.fr/search/?q=machine+learning&rows=20&wt=json"
# Search specific fields
curl "https://api.archives-ouvertes.fr/search/?q=title_s:\"deep learning\"&wt=json"
# Filter by document type
curl "https://api.archives-ouvertes.fr/search/?q=neural+networks&\
fq=docType_s:ART&rows=20&wt=json"
# Filter by year and language
curl "https://api.archives-ouvertes.fr/search/?q=climate+change&\
fq=producedDateY_i:[2023 TO 2026]&fq=language_s:en&wt=json"
# Filter by institution
curl "https://api.archives-ouvertes.fr/search/?q=robotics&\
fq=structId_i:441569&wt=json"
# Return specific fields
curl "https://api.archives-ouvertes.fr/search/?q=CRISPR&\
fl=halId_s,title_s,authFullName_s,producedDateY_i,uri_s,files_s&wt=json"
Search Fields
| Field | Description | Example |
|---|---|---|
title_s | Title | title_s:"attention mechanism" |
authFullName_s | Author name | authFullName_s:"Yann LeCun" |
abstract_s | Abstract | abstract_s:transformer |
keyword_s | Keywords | keyword_s:"natural language" |
producedDateY_i | Year | producedDateY_i:2024 |
docType_s | Document type | docType_s:ART |
language_s | Language | language_s:en |
domain_s | Domain/subject | domain_s:info.info-ai |
journalTitle_s | Journal name | journalTitle_s:"Nature" |
structId_i | Institution ID | Lab/university ID |
Document Types
| Code | Type |
|---|---|
ART | Journal article |
COMM | Conference paper |
THESE | PhD thesis |
HDR | Habilitation thesis |
REPORT | Report |
COUV | Book chapter |
OUV | Book |
POSTER | Poster |
UNDEFINED | Preprint/other |
Query Parameters
| Parameter | Description |
|---|---|
q | Solr query |
fq | Filter query |
fl | Fields to return |
rows | Results per page (max 10000) |
start | Pagination offset |
sort | Sort order (e.g., producedDateY_i desc) |
wt | Format: json, xml, csv |
Response Structure
{
"response": {
"numFound": 12500,
"start": 0,
"docs": [
{
"halId_s": "hal-01234567",
"title_s": ["Deep Learning for Climate Modeling"],
"authFullName_s": ["Marie Dupont", "Jean Martin"],
"producedDateY_i": 2024,
"docType_s": "ART",
"journalTitle_s": "Environmental Modelling",
"uri_s": "https://hal.science/hal-01234567",
"files_s": ["https://hal.science/hal-01234567/document"],
"domain_s": ["sde.es", "info.info-ai"],
"abstract_s": ["We propose a novel deep learning approach..."],
"language_s": ["en"]
}
]
}
}
Python Usage
import requests
BASE_URL = "https://api.archives-ouvertes.fr/search/"
def search_hal(query: str, rows: int = 20,
doc_type: str = None, from_year: int = None,
language: str = None) -> list:
"""Search HAL open archive."""
params = {
"q": query,
"wt": "json",
"rows": rows,
"fl": "halId_s,title_s,authFullName_s,producedDateY_i,"
"uri_s,files_s,docType_s,journalTitle_s,abstract_s",
"sort": "producedDateY_i desc",
}
fq = []
if doc_type:
fq.append(f"docType_s:{doc_type}")
if from_year:
fq.append(f"producedDateY_i:[{from_year} TO 2030]")
if language:
fq.append(f"language_s:{language}")
if fq:
params["fq"] = fq
resp = requests.get(BASE_URL, params=params)
resp.raise_for_status()
data = resp.json()
results = []
for doc in data.get("response", {}).get("docs", []):
title = doc.get("title_s", [""])[0] if isinstance(
doc.get("title_s"), list) else doc.get("title_s", "")
results.append({
"hal_id": doc.get("halId_s"),
"title": title,
"authors": doc.get("authFullName_s", []),
"year": doc.get("producedDateY_i"),
"type": doc.get("docType_s"),
"journal": doc.get("journalTitle_s"),
"url": doc.get("uri_s"),
"pdf": doc.get("files_s", [None])[0],
})
return results
def search_theses(topic: str, from_year: int = 2020) -> list:
"""Find French PhD theses on a topic."""
return search_hal(topic, rows=50, doc_type="THESE",
from_year=from_year)
def get_institution_publications(struct_id: int,
from_year: int = 2023) -> list:
"""Get publications from a specific institution."""
params = {
"q": "*:*",
"fq": [f"structId_i:{struct_id}",
f"producedDateY_i:[{from_year} TO 2030]"],
"wt": "json",
"rows": 100,
"fl": "halId_s,title_s,authFullName_s,producedDateY_i,docType_s",
"sort": "producedDateY_i desc",
}
resp = requests.get(BASE_URL, params=params)
resp.raise_for_status()
return resp.json().get("response", {}).get("docs", [])
# Example: find recent French AI research
papers = search_hal("intelligence artificielle", from_year=2024)
for p in papers:
pdf = " [PDF]" if p["pdf"] else ""
print(f"[{p['year']}] {p['title']}{pdf}")
# Example: find PhD theses on NLP
theses = search_theses("natural language processing")
for t in theses:
print(f"{t['title']} — {', '.join(t['authors'][:2])}")
HAL Domains
| Code | Domain |
|---|---|
info | Computer Science |
math | Mathematics |
phys | Physics |
sde | Environmental Sciences |
sdv | Life Sciences |
shs | Social Sciences & Humanities |
chim | Chemistry |
spi | Engineering Sciences |
References
Signals
- GitHub stars
- 4k
- Forks
- 531
- Last commit
- Sep 2026
Advanced
- Item type
- skill
- Key
hal-archive-api- Source
- github.com/brycewang-stanford/auto-empirical-research-skills
github.com/brycewang-stanford/auto-empirical-research-skills
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