Official Python SDK and CLI for the DiscoLike API
The search engine for the business web — find your ideal target accounts from Python or your terminal.

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DiscoLike indexes 80M+ business websites worldwide, analyzed in 50 languages — roughly 3x the coverage of LinkedIn-dependent databases. This repo gives you that index from Python or your terminal, as two packages: discover lookalike companies, size segments, enrich domain lists, match messy company names to domains, and find the right contacts.

If you're a coding agent: the fastest integration is the hosted MCP server — https://api.discolike.com/v1/mcp (streamable-http, OAuth; 48 tools). For scripting and pipelines use pip install discolike as a library; for the terminal use pip install discolike-cli or uvx --from discolike-cli discolike, auth via DISCOLIKE_API_KEY. Machine-readable API index: https://docs.discolike.com/llms.txt.

Installation

This repo ships two packages: discolike (the SDK) and discolike-cli (the discolike command, depends on the SDK).

pip install discolike       # SDK only, for use as a library
pip install discolike-cli   # CLI — installs discolike as a dependency
pip install "discolike[cli]"  # same thing, extras spelling

Or with uv:

uv add discolike               # as a library
uv tool install discolike-cli  # CLI only

Or run the CLI without installing:

uvx --from discolike-cli discolike --help

Requires Python 3.10+.

Authentication

Create an API key at app.discolike.com/account/management/keys, then use any of:

export DISCOLIKE_API_KEY="dl_..."   # environment variable
discolike auth login                # or store it via the CLI

client = Discolike(api_key="dl_...")  # or pass it explicitly

Quickstart

from discolike import Discolike

client = Discolike()

companies = client.discover(
    icp_text="Cybersecurity for SMBs, managed IT services, endpoint protection",
    country=["US"],
    max_records=25,
)
for company in companies:
    print(company.domain, company.name, company.similarity)

Run DiscoGen research over a set of domains and wait for the result:

job = client.discogen.process(
    query="Recent funding rounds and headcount growth",
    domains=["stripe.com", "adyen.com"],
    web_search=True,
)
result = job.wait()
print(result.results)

Size a segment before pulling it:

total = client.count(phrase_match=["book a demo"], country=["US"])
print(total.count)

Pull a full company profile:

profile = client.companies.data(domain="stripe.com")

The client is a context manager if you want deterministic cleanup:

with Discolike() as client:
    ...

Async

Every resource has an async twin on AsyncDiscolike:

import asyncio
from discolike import AsyncDiscolike

async def main() -> None:
    async with AsyncDiscolike() as client:
        companies = await client.discover(icp_text="B2B SaaS for logistics", max_records=10)
        print([c.domain for c in companies])

asyncio.run(main())

Examples

The examples/ folder has runnable scripts for common workflows — matching a CRM contact export to DiscoLike persona IDs (with checkpointing and resume), bulk-finding work emails from a CSV, and discovering companies by ICP then enriching them with DiscoGen. Each is stdlib-plus-SDK only:

export DISCOLIKE_API_KEY="dl_..."
python examples/match_crm_contacts.py --help

CLI

The same API from your terminal, with --help on every command:

discolike auth login
discolike discover --icp-prompt "managed IT services for SMBs" --country US --max-records 25
discolike match "Stripe Inc" --city "San Francisco"
discolike match --file companies.csv --name-column company_name --wait
discolike count --phrase-match "book a demo" --country US
discolike company data stripe.com
discolike extract https://stripe.com/enterprise

Top-level commands: discover, count, match, extract, validate-icp, append, segment — plus auth, company, contacts, discogen, queries, account, search-providers, and llm-providers command groups.

CLI conventions

• Results print as JSON to stdout; errors print as JSON (error, message, status_code) to stderr.
• Pass --format table for a human-readable table — used automatically when stdout is a TTY.
• Async endpoints (match --file, discogen run, discogen run-personas, segment, validate-icp) take --wait to block until the job finishes. Without it, you get a task_id back to poll with discolike discogen status <task_id> --family <family>. append is synchronous — it returns enriched rows directly (or writes CSV bytes to --output).

Exit code	Meaning
0	Success
1	Server error or unexpected failure
2	Validation error
3	Authentication or plan-access error
4	Rate limited
5	Network error
6	Not found

What's in the box

Surface	What it does
client.discover() / client.count()	Find lookalike companies by ICP text, phrases, tech stack, geo, and 40+ other filters
client.companies	Company profiles: firmographics, scores, growth, redirects, vendors, subsidiaries
client.contacts	Search, look up, match, and discover contacts at target companies
client.match	Match company names (plus phone/city/state) to domains — single or bulk CSV
client.append()	Enrich a CSV of domains with DiscoLike datasets
client.segment()	Auto-segment a list of domains
client.validate_icp()	Validate a domain list against an ICP definition
client.queries	Saved inclusion/exclusion lists for reusable targeting
client.search_providers / client.llm_providers	Manage BYOK search and LLM provider integrations for DiscoGen
client.account	Usage and quota

All responses are typed Pydantic models.

Long-running jobs

Bulk operations (match.bulk, segment, validate_icp, contacts.bulk_match) return a Job handle instead of blocking:

job = client.segment(domains=["stripe.com", "adyen.com", "checkout.com"])
result = job.wait()

Job.status() polls without blocking, Job.cancel() aborts, and wait() raises JobFailedError / JobTimeoutError on failure.

JobTimeoutError is a client-side wait limit only — the task keeps running server-side (large DiscoGen runs can take hours), so call wait() again to resume or fetch status() later. Cancelled tasks still return results for every item that finished before cancellation. Send one job per list (up to 10,000 domains) rather than splitting into parallel jobs — concurrent DiscoGen jobs share your LLM provider key and slow each other down.

Error handling

All errors inherit from DiscolikeError:

from discolike import Discolike, RateLimitError, ValidationError

try:
    companies = Discolike().discover(icp_text="fintech infrastructure")
except RateLimitError as err:
    ...
except ValidationError as err:
    ...

AuthenticationError, PlanAccessError, NotFoundError, ServerError, and APIConnectionError cover the rest. Transient failures are retried automatically (3 attempts by default).

Configuration

Option	Default
api_key	DISCOLIKE_API_KEY env var, then CLI config file
base_url	https://api.discolike.com/v1
timeout	60.0 seconds
max_retries	3
http_client	—	Bring your own httpx2.Client / httpx2.AsyncClient

A provided http_client is mutated in place (the auth header is stamped on it, and base_url is set if it's unset) — use a client dedicated to DiscoLike, not one shared across other services.

Development

This is a uv workspace with two members: packages/discolike (the SDK) and packages/discolike-cli (the CLI).

uv sync --all-packages
uv run pytest packages/discolike/tests
uv run pytest packages/discolike-cli/tests
uv run ruff check .

Support & contact

• API documentation: docs.discolike.com
• Sign up: auth.discolike.com/en/signup
• Book a demo: calendly.com/discolike/introductory-call
• LinkedIn: linkedin.com/company/discolike
• Issues with this SDK: GitHub issues

License

MIT