Seedlist for AI Agents

Quick Start

All endpoints return static JSON. No API key needed. Fetch with curl, Python, or any HTTP client. Data is rebuilt daily; check last_updated field on the root object for freshness.

Tip: For quick lookups by slug, use the lightweight /investor-lookup.json (~150KB). For full search/filter across all investors, use /enrichment-index.json (~400KB).

curl

curl -s https://seedlist.com/enrichment-index.json | python3 -m json.tool | head -50

Python

import json, urllib.request

# Load the full investor index
data = json.loads(urllib.request.urlopen("https://seedlist.com/enrichment-index.json").read())

# Find seed-stage fintech investors
# ⚠️ Always lowercase sector values before comparing
matches = [
    inv for inv in data["investors"]
    if "seed" in inv["stage_focus"]
    and "fintech" in [s.lower() for s in inv["sector_focus"]]
]

for inv in matches[:5]:
    # Use tldr for summaries; fall back to thesis_summary
    summary = inv.get("tldr") or inv["thesis_summary"][:80]
    # firm_name may be empty — fall back to firm slug
    firm = inv["firm_name"] or inv["firm"]
    print(f"{inv['name']} ({firm}) — {summary}")

See Data Notes at the bottom for field-level details and edge cases. Or read llms.txt for a machine-readable summary.

Endpoints

Common Tasks

import json, urllib.request

data = json.loads(urllib.request.urlopen("https://seedlist.com/enrichment-index.json").read())

my_stage = "seed"
my_sectors = ["fintech", "developer-tools"]

results = []
for inv in data["investors"]:
    score, reasons = 0, []

    # Stage match (30 pts)
    if my_stage in inv["stage_focus"]:
        score += 30
        reasons.append(f"Invests at {my_stage}")

    # Sector overlap (up to 40 pts) — weighted by specificity
    # An investor with 3 sectors who matches > one with 15 sectors
    inv_sectors = [s.lower() for s in inv["sector_focus"]]
    overlap = [s for s in my_sectors if s in inv_sectors]
    if overlap:
        specificity = min(1.0, 5 / max(len(inv_sectors), 1))
        sector_pts = int(min(40, len(overlap) * 15) * (0.5 + 0.5 * specificity))
        score += sector_pts
        reasons.append(f"Sectors: {', '.join(overlap)}")

    # Recency (15 pts)
    last = inv.get("last_active", "")
    if last >= "2025": score += 15
    elif last >= "2024": score += 8

    if score >= 30:
        tier = 1 if score >= 70 else 2 if score >= 45 else 3
        results.append({
            "name": inv["name"],
            "firm": inv["firm_name"] or inv["firm"],
            "tier": tier,
            "why": "; ".join(reasons),
            "tldr": inv.get("tldr", ""),
            "url": f"https://seedlist.com/investors/{inv['slug']}.html",
        })

results.sort(key=lambda x: x["tier"])
for r in results:
    label = ["Strong fit", "Worth pursuing", "Stretch"][r["tier"] - 1]
    print(f"[{label}] {r['name']} ({r['firm']}) — {r['why']}")

import json, urllib.request
from collections import defaultdict

data = json.loads(urllib.request.urlopen("https://seedlist.com/startup-investor-map.json").read())

comparables = ["stripe", "plaid", "brex"]

# Count unique startups per investor (not rounds!)
investor_companies = defaultdict(lambda: {"name": "", "companies": set(), "has_profile": False})
for slug in comparables:
    # Deduplicate: one entry per investor per startup
    seen = set()
    for inv in data["startup_investors"].get(slug, []):
        if inv["slug"] in seen or inv["slug"] == "independent":
            continue
        seen.add(inv["slug"])
        entry = investor_companies[inv["slug"]]
        entry["name"] = inv["name"]
        entry["companies"].add(slug)
        entry["has_profile"] = inv.get("has_profile", False)

results = sorted(investor_companies.values(), key=lambda x: -len(x["companies"]))
for r in results[:15]:
    cos = ", ".join(r["companies"])
    print(f"{r['name']}: backed {len(r['companies'])}/{len(comparables)} ({cos})")

graph = requests.get("https://seedlist.com/investor-graph.json").json()

target = "ron-conway"

# Co-investors (people who frequently invest alongside the target)
co = graph["co_investments"].get(target, {})
for slug, info in sorted(co.items(), key=lambda x: -x[1]["count"])[:10]:
    name = graph["investor_names"].get(slug, slug)
    print(f"Co-investor: {name} ({info['count']} shared deals)")

# Firm colleagues
for firm_slug, firm in graph["firms"].items():
    if target in firm["members"]:
        print(f"Firm: {firm['name']} — colleagues: {firm['members']}")

# Portfolio founders who can intro
for startup_slug, backers in graph["startup_backers"].items():
    if target in backers:
        founders = graph["startup_founders"].get(startup_slug, [])
        name = graph["startup_names"].get(startup_slug, startup_slug)
        for f in founders:
            print(f"Founder intro: {f['name']} ({f['role']} at {name})")

import csv, io

data = requests.get("https://seedlist.com/enrichment-index.json").json()

# Build a name lookup (lowercase for fuzzy matching)
by_name = {}
for inv in data["investors"]:
    by_name[inv["name"].lower()] = inv

# Your investor list
my_investors = ["Ron Conway", "Mike Maples", "Unknown Person"]

for name in my_investors:
    match = by_name.get(name.lower())
    if match:
        print(f"MATCH: {match['name']} | {match['firm_name']} | "
              f"Stage: {match['stage_focus']} | Sectors: {match['sector_focus']}")
    else:
        print(f"NO MATCH: {name}")

rounds = requests.get("https://seedlist.com/rounds-feed.json").json()

# Latest 10 rounds
for r in rounds[:10]:
    amt = r["amount"] or "undisclosed"
    print(f"{r['date']} | {r['company']} | {r['round']} | {amt} | Lead: {r['lead']}")

Claude Skill

If you use Claude Code, you can install a Seedlist skill that teaches Claude how to query these endpoints for fundraising assistance.

Install

# Download the skill file
curl -o seedlist-skill.md https://raw.githubusercontent.com/ericries/seedlist/main/docs/seedlist-skill.md

# Add to your Claude Code project skills
cp seedlist-skill.md .claude/skills/seedlist-skill.md

Or add the URL directly as a custom skill in Claude Code settings. The skill teaches Claude to fetch Seedlist JSON endpoints and build investor target lists, find warm intro paths, and enrich investor CSVs.

Schema Reference

Root Object (enrichment-index.json)

Investor (enrichment-index.json → investors[])

One entry per published investor profile.

Quick Lookup (investor-lookup.json)

Lightweight slug-keyed dictionary for O(1) investor lookups. ~150KB vs ~400KB for the full index. Use when you know the investor slug and just need basic info.

Firm (enrichment-index.json)

The firms array contains one entry per published firm profile.

Funding Round (rounds-feed.json)

Array of up to 500 funding rounds, sorted by date descending.

Startup-Investor Map (startup-investor-map.json)

Investor Graph (investor-graph.json)

Data Notes

Edge cases and field-level details for robust integrations.

• Sectors are normalized lowercase strings — no near-duplicates. Direct string matching is safe. Use tldr for investor summaries (2-4 sentences, 95% populated) — prefer over thesis_summary.
• firm is a slug, firm_name is display. firm_name may be empty — fall back: inv["firm_name"] or inv["firm"]
• last_active format varies — "2025-11-15", "2025-03", "~2022", or empty. String comparison works for recency: inv["last_active"] >= "2024"
• check_size is human-readable — e.g., "$250K-$2M". Parse with regex for range comparison.
• Startup-investor map has duplicates — an investor appears once per round. Deduplicate by slug when counting comparables. Filter out "independent".
• queued array — investors in the research pipeline, not yet profiled. Tell founders: "we know about this investor but don't have a profile yet."