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#!/usr/bin/env bash
# AgentEnrich. Pure curl quickstart.

: "${AGENTENRICH_API_KEY:?Set AGENTENRICH_API_KEY first}"
BASE="${AGENTENRICH_BASE_URL:-https://api.agentenrich.com}"
H_AUTH="Authorization: Bearer ${AGENTENRICH_API_KEY}"
H_JSON="Content-Type: application/json"

# 1. Enrich a LinkedIn URL
curl -sS -X POST "${BASE}/v1/enrich" -H "${H_AUTH}" -H "${H_JSON}" \
  -d '{"kind":"linkedin","linkedinUrl":"https://www.linkedin.com/in/satyanadella"}' \
  | jq '{name: .person.fullName, title: .person.title}'

# 2. Find a verified email
curl -sS -X POST "${BASE}/v1/find-email" -H "${H_AUTH}" -H "${H_JSON}" \
  -d '{"kind":"linkedin","linkedinUrl":"https://www.linkedin.com/in/satyanadella"}' \
  | jq '{email, email_verified, mx_found}'

# 3. Find the head of growth at a company
curl -sS -X POST "${BASE}/v1/find-decision-maker" -H "${H_AUTH}" -H "${H_JSON}" \
  -d '{"company_domain":"stripe.com","role":"Head of Growth"}' \
  | jq '.results[] | {name: .fullName, title: .title}'

# 4. Live job-change feed
curl -sS "${BASE}/v1/signals?kind=job-change&limit=5" -H "${H_AUTH}" \
  | jq '.data[] | {who: .personName, new_role: .newRole, at: .companyName}'

# 5. Full account brief in one call
curl -sS -X POST "${BASE}/v1/bundles/sales-deck" -H "${H_AUTH}" -H "${H_JSON}" \
  -d '{"company_domain":"stripe.com","target_role":"VP Engineering"}' \
  | jq '{company: .company.name, dm: .decision_maker.fullName}'

import json, os, urllib.request

API_KEY = os.environ["AGENTENRICH_API_KEY"]
BASE = "https://api.agentenrich.com"
HEADERS = {"Authorization": f"Bearer {API_KEY}", "Content-Type": "application/json"}

def call(path, body=None):
    data = json.dumps(body).encode() if body else None
    req = urllib.request.Request(f"{BASE}{path}", data=data, headers=HEADERS, method="POST")
    with urllib.request.urlopen(req) as r:
        return json.loads(r.read())

# Target accounts
domains = ["stripe.com", "plaid.com", "ramp.com"]

print("domain,name,title,email,verified")
for domain in domains:
    dm = call("/v1/find-decision-maker", {"company_domain": domain, "role": "VP of Sales"})
    if not dm.get("results"): continue
    person = dm["results"][0]
    email_info = call("/v1/find-email", {"kind": "linkedin", "linkedinUrl": person["linkedinUrl"]})
    print(f"{domain},{person.get('fullName')},{person.get('title')},{email_info.get('email')},{'yes' if email_info.get('email_verified') else 'no'}")

import { generateText, tool } from "ai";
import { anthropic } from "@ai-sdk/anthropic";
import { z } from "zod";

const AE = "https://api.agentenrich.com";
const H = { Authorization: `Bearer ${process.env.AGENTENRICH_API_KEY}`, "Content-Type": "application/json" };

const tools = {
  list_funding: tool({
    description: "Recent funding signals. Included on Pro+.",
    parameters: z.object({ limit: z.number().default(10) }),
    execute: async ({ limit }) => (await fetch(`${AE}/v1/signals?kind=funding&limit=${limit}`, { headers: H }).then(r => r.json())).data,
  }),
  find_dm: tool({
    description: "Top buyer at a company by role. 4cr per resolved.",
    parameters: z.object({ company_domain: z.string(), role: z.string() }),
    execute: async (a) => (await fetch(`${AE}/v1/find-decision-maker`, { method: "POST", headers: H, body: JSON.stringify(a) }).then(r => r.json())).results?.[0],
  }),
  prospect_package: tool({
    description: "Email, phone, peers, hooks. 12 credits.",
    parameters: z.object({ linkedinUrl: z.string() }),
    execute: async (a) => fetch(`${AE}/v1/prospect-package`, { method: "POST", headers: H, body: JSON.stringify(a) }).then(r => r.json()),
  }),
};

const { text } = await generateText({
  model: anthropic("claude-sonnet-4-6"), tools, maxSteps: 8,
  system: "You are an AI SDR. Find 3 recently funded B2B companies, get the VP Sales at each, run prospect_package, draft personalized outreach. Output JSON.",
  prompt: "Run the loop.",
});
console.log(text);

import { ChatAnthropic } from "@langchain/anthropic";
import { tool } from "@langchain/core/tools";
import { createReactAgent } from "langchain/agents";
import { z } from "zod";

const AE = "https://api.agentenrich.com";
const H = { Authorization: `Bearer ${process.env.AGENTENRICH_API_KEY}`, "Content-Type": "application/json" };

const competitiveIntel = tool(
  async ({ competitor_domain }) => JSON.stringify(await (await fetch(`${AE}/v1/bundles/competitive-intel`, { method: "POST", headers: H, body: JSON.stringify({ competitor_domain }) })).json()),
  { name: "competitive_intel", description: "Leadership + recent moves + tech + lookalike TAM. 25 cr.", schema: z.object({ competitor_domain: z.string() }) }
);

const salesDeck = tool(
  async (a) => JSON.stringify(await (await fetch(`${AE}/v1/bundles/sales-deck`, { method: "POST", headers: H, body: JSON.stringify(a) })).json()),
  { name: "sales_deck", description: "Full account brief. 25 cr.", schema: z.object({ company_domain: z.string(), target_role: z.string() }) }
);

const agent = await createReactAgent({
  llm: new ChatAnthropic({ model: "claude-sonnet-4-6" }),
  tools: [competitiveIntel, salesDeck],
});

const out = await agent.invoke({ input: "Competitor is segment.com. Find the best lookalike and brief me on it." });
console.log(out);

import json, os, requests
from crewai import Agent, Task, Crew
from crewai.tools import tool

AE = "https://api.agentenrich.com"
H = {"Authorization": f"Bearer {os.environ['AGENTENRICH_API_KEY']}", "Content-Type": "application/json"}

@tool("Find similar candidates")
def find_similar(linkedin_url: str) -> str:
    r = requests.post(f"{AE}/v1/find-similar-people", headers=H, json={"linkedinUrl": linkedin_url, "limit": 10}).json()
    return json.dumps(r.get("similar_people", []))

@tool("Get prospect package")
def prospect_pack(linkedin_url: str) -> str:
    return json.dumps(requests.post(f"{AE}/v1/prospect-package", headers=H, json={"linkedinUrl": linkedin_url}).json())

sourcer = Agent(role="Talent sourcer", goal="Find 10 candidates similar to seed", backstory="Veteran sourcer", tools=[find_similar])
outreach = Agent(role="Outreach copywriter", goal="Draft personalized intros", backstory="Writes like a human", tools=[prospect_pack])

seed = "https://www.linkedin.com/in/some-engineer"
source_task = Task(description=f"Find 10 candidates similar to {seed}", expected_output="JSON list", agent=sourcer)
write_task = Task(description="For each: prospect_pack + 4-line intro email referencing their work", expected_output="JSON list", agent=outreach, context=[source_task])

result = Crew(agents=[sourcer, outreach], tasks=[source_task, write_task]).kickoff()
print(result)

{
  "mcpServers": {
    "agentenrich": {
      "command": "npx",
      "args": ["-y", "@agentenrich/mcp"],
      "env": { "AGENTENRICH_API_KEY": "ae_live_PASTE_YOUR_KEY_HERE" }
    }
  }
}