Python SDK for the Pounce v2 Entity API — search, look up, and enrich 65M+ verified B2B companies.
Project description
pounce-agent-data
Python SDK for the Pounce v2 Entity API — search, look up, and enrich 65M+ verified B2B companies.
Installation
pip install pounce-agent-data
Quick Start
from pounce_agent_data import PounceClient
client = PounceClient(api_key="your_key")
# Search with advanced filters
results, rate = client.search(
q="AI companies", country="CH", city="Zurich",
founded_min=2020, sort="newest", limit=10
)
for company in results["items"]:
print(company["canonical_name"], company["primary_domain"])
# AI semantic search (2 credits)
results, rate = client.semantic_search("fintech startups in Switzerland")
# Lookup by domain
company, rate = client.lookup("stripe.com")
# Full company profile
detail, rate = client.detail(entity_id=12345)
# Analytics
countries, rate = client.analytics_countries()
sectors, rate = client.analytics_sectors(country="CH")
Generic Request
For any endpoint not covered by a convenience method — including new endpoints
added after this SDK version — use request():
data, rate = client.request("GET", "/entities/export", params={"country": "CH", "format": "csv"})
data, rate = client.request("POST", "/webhooks", json={"url": "https://...", "events": ["entity.updated"]})
Authentication
Pass your API key directly or set the POUNCE_API_KEY environment variable:
export POUNCE_API_KEY=your_key
client = PounceClient() # reads from env
API Methods
Search & Discovery
| Method | Credits | Description |
|---|---|---|
search() |
1 | Filter by keyword, country, city, founding year, employee range, domain, sort |
semantic_search() |
2 | AI-powered natural language search |
autocomplete() |
0 | Fast name autocomplete for type-ahead |
lookup(domain) |
1 | Find a company by domain |
Entity Detail
| Method | Credits | Description |
|---|---|---|
detail(entity_id) |
1 | Full company profile |
officers(entity_id) |
1 | Company officers & key people |
changes(entity_id) |
1 | Entity change timeline (Developer+) |
similar(entity_id) |
2 | Find similar entities via vector search |
Bulk & Export
| Method | Credits | Description |
|---|---|---|
bulk_lookup(domains) |
1/found | Bulk lookup up to 100 domains |
export() |
5 | Export as CSV/JSON (Developer+) |
Analytics
| Method | Credits | Description |
|---|---|---|
analytics_overview() |
1 | Platform statistics & coverage (Developer+) |
analytics_countries() |
1 | Entity counts per country (Developer+) |
analytics_sectors() |
1 | Top sectors for a country (Developer+) |
analytics_formations() |
1 | Entity formation trends (Developer+) |
analytics_geography() |
1 | Entity density by city (Developer+) |
Webhooks
| Method | Credits | Description |
|---|---|---|
create_webhook() |
0 | Register a webhook URL (Developer+) |
list_webhooks() |
0 | List registered webhooks (Developer+) |
delete_webhook() |
0 | Delete a webhook |
test_webhook() |
0 | Send a test event |
Other
| Method | Credits | Description |
|---|---|---|
match_score(a, b) |
3 | Similarity score between two companies (Business+) |
stats() |
0 | Public platform statistics |
request(method, path) |
varies | Generic request for any v2 endpoint |
Search Filters
results, rate = client.search(
q="fintech", # keyword search
country="CH", # ISO country code
city="Zurich", # city name
founded_min=2020, # minimum founding year
founded_max=2025, # maximum founding year
first_seen_after="2025-01-01", # only entities indexed after this date
employee_range="11-50", # 1-10, 11-50, 51-200, 201-500, 500+
has_domain=True, # only companies with a known domain
sort="newest", # relevance, newest, oldest, quality
limit=25,
offset=0,
)
Rate Limits
Every response includes rate limit info:
results, rate = client.search(q="test")
print(rate.remaining) # calls remaining this month
print(rate.credits_used) # credits consumed by this call
Plans
| Plan | Monthly Calls | Rate/Min |
|---|---|---|
| Free | 100 | 30 |
| Developer (€19/mo) | 5,000 | 60 |
| Business (€49/mo) | 25,000 | 300 |
| Enterprise (custom) | 200,000 | 1,000 |
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