aie-europe-2026

name	aie-europe-2026
description	Query AI Engineer Europe 2026 conference data — speakers, talks, schedule, and more. Use when building apps, AI integrations, or tools on top of conference data. Provides REST endpoints (JSON + plain text), an MCP server for agent tool calls, and a CLI. Covers 150+ talks, 150+ speakers, workshops, and the full 3-day schedule.
license	MIT
compatibility	Requires network access to https://ai.engineer
metadata	{"author":"aidotengineer","version":"1.0","conference-dates":"April 8-10, 2026","conference-location":"London, UK","conference-venue":"Park Plaza Westminster Bridge"}

AI Engineer Europe 2026

Conference data APIs, MCP server, and CLI for AI Engineer Europe 2026 (April 8-10, London).

When to use this skill

• Building apps or dashboards with conference data
• Querying speakers, talks, or schedule programmatically
• Connecting an AI agent to the conference MCP server
• Generating conference summaries, recommendations, or search features
• Looking up who's speaking, what talks are on which day/track, or session details

Endpoints

Base URL: https://ai.engineer

Endpoint	Format	Description
/europe/llms.txt	Plain text	Conference overview optimized for LLM consumption
/europe/llms-full.txt	Plain text	Full details — every talk, speaker bio, schedule
/europe/sessions.json	JSON	All sessions (talks + workshops) with titles, descriptions, speakers, times, rooms, tracks
/europe/speakers.json	JSON	All speakers with roles, companies, social links, photos, talks
/europe/mcp	JSON-RPC	MCP server — tool calls for querying conference data
/europe/speakers-embeddings.json	JSON	All speakers with 128-dim Gemini Embedding 2 vectors
/europe/sessions-embeddings.json	JSON	All sessions with 128-dim Gemini Embedding 2 vectors

All endpoints are public, free, and CORS-enabled. Data is cached (s-maxage=3600, stale-while-revalidate=86400).

Embeddings

Pre-computed Gemini Embedding 2 vectors for semantic search, clustering, and recommendations.

• Model: gemini-embedding-2-preview
• Dimensions: 128 (truncated from 3072 via Matryoshka Representation Learning)
• Task type: RETRIEVAL_DOCUMENT

The embedding JSON files include full metadata (name, company, role, talks, etc.) alongside the 128-dim vector for each speaker/session.

Fetch embeddings (curl)

# Speaker embeddings (128-dim Gemini Embedding 2, MRL)
curl https://ai.engineer/europe/speakers-embeddings.json | jq '.speakers[:2]'

# Session embeddings
curl https://ai.engineer/europe/sessions-embeddings.json | jq '.sessions[:2]'

JavaScript — cosine similarity search

const res = await fetch('https://ai.engineer/europe/speakers-embeddings.json');
const { speakers } = await res.json();

// Cosine similarity
const dot = (a, b) => a.reduce((s, v, i) => s + v * b[i], 0);
const norm = (a) => Math.sqrt(dot(a, a));
const cosine = (a, b) => dot(a, b) / (norm(a) * norm(b));

// Compare against a query embedding (128-dim from Gemini)
const ranked = speakers
  .map(s => ({ name: s.name, score: cosine(queryEmbedding, s.embedding) }))
  .sort((a, b) => b.score - a.score)
  .slice(0, 5);
console.log(ranked);

Python — cosine similarity search

import json, urllib.request, numpy as np

data = json.loads(urllib.request.urlopen('https://ai.engineer/europe/speakers-embeddings.json').read())
speakers = data['speakers']

def cosine(a, b):
    a, b = np.array(a), np.array(b)
    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))

ranked = sorted(speakers, key=lambda s: cosine(query_emb, s['embedding']), reverse=True)[:5]
for s in ranked:
    print(s['name'], s.get('company', ''))

Generate your own query embedding (to search against pre-computed vectors)

# Use the Gemini API with the same model + MRL dimensionality
curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-embedding-2-preview:embedContent?key=$GEMINI_API_KEY" \
  -H 'Content-Type: application/json' \
  -d '{
    "model": "models/gemini-embedding-2-preview",
    "content": { "parts": [{ "text": "autonomous coding agents" }] },
    "taskType": "RETRIEVAL_QUERY",
    "outputDimensionality": 128
  }'

Quick start

curl

# Plain text overview (good for piping to an LLM)
curl https://ai.engineer/europe/llms.txt

# Full conference dump
curl https://ai.engineer/europe/llms-full.txt

# Structured JSON
curl https://ai.engineer/europe/sessions.json | jq '.sessions[:3]'
curl https://ai.engineer/europe/speakers.json | jq '.speakers[:3]'

CLI (@aidotengineer/aie)

No install needed — runs via npx:

npx @aidotengineer/aie --list              # List all conferences
npx @aidotengineer/aie europe              # Europe conference info
npx @aidotengineer/aie eu speakers         # All speakers
npx @aidotengineer/aie eu speakers --search "Anthropic"
npx @aidotengineer/aie eu sessions --day "April 9"
npx @aidotengineer/aie eu sessions --type workshop
npx @aidotengineer/aie eu search "agents"  # Full-text search
npx @aidotengineer/aie eu speakers --json  # Raw JSON output
npx @aidotengineer/aie eu mcp             # MCP connection info

JavaScript / TypeScript

const res = await fetch('https://ai.engineer/europe/speakers.json');
const { speakers, totalSpeakers } = await res.json();

// Find speakers from Anthropic
const anthropic = speakers.filter(s =>
  s.company?.toLowerCase().includes('anthropic')
);
console.log(`${anthropic.length} speakers from Anthropic`);

// Get all keynotes
const data = await fetch('https://ai.engineer/europe/sessions.json').then(r => r.json());
const keynotes = data.sessions.filter(t => t.type === 'keynote');
console.log(keynotes.map(k => `${k.time}: ${k.title} — ${k.speakers.join(', ')}`));

Python

import requests

# --- Fetch and explore sessions ---
data = requests.get('https://ai.engineer/europe/sessions.json').json()
print(f"{data['totalSessions']} sessions across {data['dates']}")

# Day 2 sessions
day2 = [s for s in data['sessions'] if s.get('day') == 'April 9']
for s in day2:
    speakers = ', '.join(s.get('speakers', []))
    print(f"{s.get('time', '?')}: {s.get('title', 'TBA')} — {speakers}")

# Sessions about agents
agent_sessions = [s for s in data['sessions']
                  if 'agent' in (s.get('title') or '').lower()
                  or s.get('track', '').lower() == 'ai agents']
print(f"\n{len(agent_sessions)} sessions about agents")

# --- Fetch speakers ---
sp = requests.get('https://ai.engineer/europe/speakers.json').json()

# Speakers with GitHub profiles
with_github = [s for s in sp['speakers'] if s.get('github')]
print(f"\n{len(with_github)} speakers with GitHub profiles")
for s in with_github[:5]:
    print(f"  {s['name']} ({s.get('company', '?')}): {s['github']}")

# Group speakers by company
from collections import Counter
companies = Counter(s.get('company') for s in sp['speakers'] if s.get('company'))
for company, count in companies.most_common(10):
    print(f"  {company}: {count} speakers")

Python — MCP tool call

import requests
import json

MCP_URL = 'https://ai.engineer/europe/mcp'

def mcp_call(tool_name: str, arguments: dict = {}) -> dict:
    """Call an MCP tool and return the parsed result."""
    resp = requests.post(MCP_URL, json={
        'jsonrpc': '2.0',
        'id': 1,
        'method': 'tools/call',
        'params': {'name': tool_name, 'arguments': arguments}
    }, headers={'Content-Type': 'application/json', 'Accept': 'application/json'})
    result = resp.json()['result']['content'][0]['text']
    return json.loads(result)

# Get conference info
info = mcp_call('get_conference_info')
print(f"{info['name']} — {info['dates']} — {info['location']}")

# Search speakers
speakers = mcp_call('list_speakers', {'search': 'Google'})
for s in speakers['speakers']:
    print(f"  {s['name']}: {s.get('role', '')} @ {s.get('company', '')}")

# Get Day 2 keynotes
keynotes = mcp_call('list_sessions', {'day': 'April 9', 'type': 'keynote'})
for t in keynotes['sessions']:
    print(f"  {t['time']}: {t['title']} — {', '.join(t['speakers'])}")

# Get schedule for one day
schedule = mcp_call('get_schedule', {'day': 'April 10'})
for session in schedule['days'][0]['sessions']:
    print(f"  {session.get('time', '?')}: {session.get('title', 'TBA')}")

MCP server

The MCP server at https://ai.engineer/europe/mcp implements JSON-RPC 2.0 over Streamable HTTP.

Client config

Add to your Claude Desktop, Cursor, Windsurf, or any MCP client config:

{
  "mcpServers": {
    "aie-europe": {
      "url": "https://ai.engineer/europe/mcp"
    }
  }
}

Available tools

Tool	Description	Optional params
get_conference_info	Dates, venue, links, metadata	—
list_speakers	Speakers with roles, companies, socials, sessions	search
list_sessions	Sessions with descriptions, times, rooms, tracks	day, type, track, search
get_schedule	Full schedule organized by day	day

Example tool call (curl)

curl -X POST https://ai.engineer/europe/mcp \
  -H "Content-Type: application/json" \
  -H "Accept: application/json" \
  -d '{
    "jsonrpc": "2.0",
    "id": 1,
    "method": "tools/call",
    "params": {
      "name": "list_speakers",
      "arguments": { "search": "Anthropic" }
    }
  }'

Example tool call (Python)

import requests, json

resp = requests.post('https://ai.engineer/europe/mcp', json={
    'jsonrpc': '2.0', 'id': 1,
    'method': 'tools/call',
    'params': {'name': 'list_sessions', 'arguments': {'track': 'MCP'}}
}, headers={'Content-Type': 'application/json', 'Accept': 'application/json'})

result = json.loads(resp.json()['result']['content'][0]['text'])
for s in result['sessions']:
    print(f"{s['title']} — {', '.join(s['speakers'])}")

Initialize + discover tools

# Initialize session
curl -X POST https://ai.engineer/europe/mcp \
  -H "Content-Type: application/json" \
  -d '{"jsonrpc":"2.0","id":1,"method":"initialize","params":{"protocolVersion":"2025-03-26","capabilities":{},"clientInfo":{"name":"my-client","version":"1.0.0"}}}'

# List tools
curl -X POST https://ai.engineer/europe/mcp \
  -H "Content-Type: application/json" \
  -d '{"jsonrpc":"2.0","id":2,"method":"tools/list"}'

# GET also returns server info + tool definitions
curl https://ai.engineer/europe/mcp | jq .

See references/MCP.md for full tool schemas and response formats.

Data model

Talk

type PublicTalk = {
  title?: string;
  description?: string;
  day?: string;        // "April 8" | "April 9" | "April 10"
  time?: string;       // "9:00-9:30am"
  room?: string;       // "Keynote" | "Abbey" | "Fleming" | "Moore" | "St. James" | "Westminster"
  type?: string;       // "keynote" | "talk" | "workshop" | "panel" | "break"
  track?: string;      // "AI Agents" | "Coding Agents" | "MCP" | "Open Source" | "GPUs & LLM Infrastructure" | ...
  speakers: string[];
};

Speaker

type PublicSpeaker = {
  name: string;
  role?: string;
  company?: string;
  companyDescription?: string;
  twitter?: string;    // Full URL: "https://x.com/handle"
  linkedin?: string;   // Full URL
  github?: string;     // Full URL
  website?: string;
  photoUrl?: string;   // "https://ai.engineer/europe-speakers/name.jpg"
  sessions: PublicTalk[];
};

See references/SCHEMAS.md for full response envelope structures and real API response examples.

See references/EXAMPLES.md for 4 complete TS + Python recipes: topic index, semantic embed/cluster/retrieve, multi-track day planner, and speaker social graph.

Edge cases

• Some sessions have empty description — fall back to title + speakers
• speakers array can be empty for break/logistics sessions
• type field may be missing for some sessions — treat as "talk"
• Speaker photos are served from ai.engineer/europe-speakers/ — some speakers may not have a photo
• Social links (twitter, linkedin, github) are full URLs when present, undefined when absent
• The track field only exists on Day 2-3 multi-track sessions; Day 1 workshops don't have tracks

Sensitive fields (never exposed)

These fields exist in the source data but are stripped from all public endpoints:

• contact.email — speaker email addresses
• notes — internal organizer notes
• acceleventsSpeakerId — internal Accelevents IDs
• sessionId — internal session identifiers
• invited — whether speaker was invited vs CFP
• cfpData — call for papers submissions and review status

Links

• Website: ai.engineer/europe
• Source code: github.com/aiDotEngineer/aiecode2025
• CLI on npm: @aidotengineer/aie
• Twitter: @aiDotEngineer
• Gemini Embedding 2 docs: ai.google.dev/gemini-api/docs/models/gemini-embedding-2-preview