Mekler AI Academic Paper Search

x402

Mekler AI Academic Paper Search is a paid API for AI agents from api.mekler.ai, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-18).

Search for academic research papers across multiple sources with deduplicated DOI-based identifiers, retraction filtering, and provenance metadata

Price / call$0.01

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StatusUnknown

How this service works

Find research papers by topic or seed DOI/OpenAlex ID. Return ranked publication metadata with identifiers, source provenance, conservative duplicate/version grouping and reported retraction flags from OpenAlex and Crossref. Filter by publication year; retrieve up to20 results. No abstracts, full text, author identities, exhaustive literature review or quality/retraction clearance.

Output

A JSON object containing ranked, deduplicated paper results each with DOI, URL, title, year, venue, OpenAlex ID, source records from Crossref and OpenAlex, retraction status, and version grouping. Also includes coverage statistics, ranking methodology notes, cost breakdown per data source, and warnings about metadata completeness.

Example use cases

Literature review for research proposal

“Find me 20 academic papers on large language model alignment published between 2021 and 2024 — I need deduplicated results with DOIs so I can build a literature review, and please exclude any retracted papers.”

Seed paper expansion for related work

“I have a paper with DOI 10.1038/s41586-021-03819-2 — can you find up to 10 related research papers using it as a seed, from 2019 onward, with their venue and provenance info?”

Quick topic survey for competitive analysis

“Search for academic papers on protein structure prediction published in the last 5 years — give me up to 15 results with their sources and retraction status so I can see what's been published in this space.”

Technical details

Endpoint

POSThttps://api.mekler.ai/v1/research/papers/find-research-papers?utm_source=zero.xyz

When to prefer

Choose this endpoint when you need deduplicated, provenance-tracked academic paper search that merges results from both Crossref and OpenAlex under a single normalized identifier scheme. It is especially useful when you need retraction filtering, version grouping, and per-source attribution — features not available in simple single-source academic search APIs. It is less suitable for full-text retrieval or citation graph traversal.

Side effects

Query too short (under 3 characters) returns validation error
No results found for highly specific or niche queries
Retraction flags may be incomplete or stale — absence of a flag is not clearance
OpenAlex or Crossref upstream timeouts may reduce coverage
Year range filters that are inverted or out of bounds return errors
Rate limits or payment failures via x402 prevent call execution

Input

Schema · Request body

Field	Type	Description
seed	string
limit	integer
query	string
to_year	integer
from_year	integer
request_id	string
include_retracted	boolean

Output

Schema · Response

{
  "type": "json",
  "example": {
    "cost": {
      "source_attempts": [
        {
          "state": "ok",
          "source": "crossref",
          "operation": "search",
          "elapsed_ms": 0,
          "list_price_usd": 0
        },
        {
          "state": "ok",
          "source": "openalex",
          "operation": "search",
          "elapsed_ms": 0,
          "list_price_usd": 0.001
        }
      ],
      "supplier_access": "anonymous free allowance; no paid balance",
      "hosting_cost_usd": null,
      "charged_supplier_usd": 0,
      "openalex_list_price_usd": 0.001
    },
    "mode": "topic",
    "status": "ok",
    "ranking": "reciprocal_rank_fusion_k60; explicit identity/version groups; not a quality assessment",
    "results": [
      {
        "id": "doi:10.5555/mekler.graph.1",
        "doi": "10.5555/mekler.graph.1",
        "url": "https://doi.org/10.5555/mekler.graph.1",
        "type": "article",
        "year": 2024,
        "title": "Graph neural networks for synthetic benchmark data",
        "venue": "Synthetic Research Journal",
        "sources": [
          "crossref",
          "openalex"
        ],
        "versions": [],
        "openalex_id": "W9999999901",
        "source_records": [
          {
            "id": "W9999999901",
            "rank": 1,
            "source": "openalex"
          },
          {
            "id": "10.5555/mekler.graph.1",
            "rank": 1,
            "source": "crossref"
          }
        ],
        "retraction_status": "not_reported",
        "is_retraction_notice": false
      }
    ],
    "coverage": {
      "crossref_records": 1,
      "openalex_records": 1,
      "normalized_records": 2,
      "complete_source_requests": true,
      "retracted_groups_excluded": 0,
      "duplicate_or_version_records_merged": 1
    },
    "returned": 1,
    "warnings": [
      "Metadata coverage and retraction flags can be incomplete or stale. Absence of a flag is not clearance."
    ],
    "retrieved_at": "2026-10-17T00:00:00+00:00"
  }
}

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