Examples

These examples show production workflow patterns for Exa Agent, including list building, KYB intelligence, job postings, and structured outputs.

	A	B	C	D	E	F
1	full_name	company	title	gtm_function	company_verified_as_exa_ai	evidence_urls
2	Marcus Holm	Exa	Chief Revenue Officer (CRO)	Revenue / GTM leadership	true	2 links
3	Karan Singh	Exa	VP, Revenue Operations	Revenue Operations	true	2 links
4	Jatin Gupta	Exa	GTM Engineer	GTM Engineering / Growth	true	3 links
5	Gavin Smith	Exa	Founding Strategic Account Executive	Sales	true	1 link
6	Chris Perpignani	Exa	Enterprise Growth at Exa	Enterprise Growth / Sales	true	2 links
7	… plus 7 more

	A	B	C	D	E	F
1	company	category	signal	detail	source	disposition
2	Ramp Business Corporation	Identity	Ramp Business Corporation	Finance operations platform for corporate cards, expense management, AP, procurement, and reporting.	ramp.com/about-us	verified
3	Ramp Business Corporation	Partnership	Microsoft	Microsoft 365, Copilot, and Teams integration highlighted in Ramp press materials.	ramp.com/press	ecosystem
4	Ramp Business Corporation	Partnership	Visa	Deepened payments-network partnership for autonomous AI corporate payments.	PRNewswire	payments
5	Ramp Business Corporation	Investment	Series F	$750M financing round at a $44B valuation.	PRNewswire	2026-06-04
6	Ramp Business Corporation	Risk	Bank / compliance dependency	Financial institution partners and business-only KYB workflows are material diligence areas.	legal terms	review

	A	B	C	D	E	F
1	company	title	function	location	company_verified_as_exa_ai	source_platform
2	Exa	Account Executive @ Exa	Sales	San Francisco, CA	true	Ashby
3	Exa	Sales Development Representative	Sales Development	Not specified	true	Ashby
4	Exa	Founding Account Executive - NYC	Sales	New York City	true	Ashby
5	Exa	Executive Assistant to Founders	Operations	San Francisco, CA	true	Ashby
6	Exa	Software Engineer, Backend	Engineering	San Francisco, CA	true	Ashby

	A	B	C	D	E	F
1	company	domain	overview	buyingSignals	sourceUrls	verified_domain
2	Ramp	ramp.com	Finance automation for cards, expenses, bill pay, and procurement.	enterpriseprocurement	3 links	true
3	Mercury	mercury.com	Business banking, treasury, and financial workflows for startups.	startup focusbanking	2 links	true

	A	B	C	D	E	F
1	company	contact_label	role	relevanceReason	evidence_scope	contact_cost_bound
2	LangChain	Contact A	Partnerships lead	Owns ecosystem integrations and partner motion.	role evidence	max 2
3	Modal	Contact B	GTM lead	Relevant for compute, developer, and platform partnerships.	role evidence	max 2

	A	B	C	D	E	F
1	company	domain	reason	sourceUrl	excluded_match	status
2	Sample output; excluded input rows: Cursor and CodeRabbit
3	PatchPilot	patchpilot.ai	Builds agentic code repair infrastructure.	funding	none	new
4	StackRelay	stackrelay.dev	Routes coding-agent tasks across tools.	homepage	none	new

	A	B	C	D	E	F
1	company	website	hiringPageUrl	hiringEvidence	location	followup_source
2	Sample output continued from a previous run: narrowed prior company results to San Francisco hiring matches
3	Northstar IDE	northstar.dev	/careers/platform	Open platform engineer role.	San Francisco	previous run
4	TraceForge	traceforge.dev	/jobs	Hiring infra engineer for agent runtime team.	San Francisco	previous run

Find allKYC / KYBJob postingsEnrich rowsFind contactsSkip knownContinue workflow

Find all GTM members at Exa.ai

Use a discovery prompt when Agent should find the rows. Be explicit about company disambiguation and require current-employment evidence so similarly named companies do not leak into the result.

from exa_py import Exa

exa = Exa()
run = exa.agent.runs.create(
    query="Find all GTM members currently working at Exa.ai. Verify each person is at the correct company: Exa, the AI search company at exa.ai, not any other company named Exa. Include the company name in every row. Include only people with public evidence that they currently work at Exa and whose role is go-to-market, such as sales, business development, partnerships, customer success, marketing, growth, or revenue. Return one row per person.",
    effort="high",
    output_schema={
        "type": "object",
        "required": ["gtm_members"],
        "properties": {
            "gtm_members": {
                "type": "array",
                "maxItems": 100,
                "items": {
                    "type": "object",
                    "required": ["full_name", "company", "title", "gtm_function", "company_verified_as_exa_ai", "evidence_urls"],
                    "properties": {
                        "full_name": {"type": "string"},
                        "company": {"type": "string"},
                        "title": {"type": "string"},
                        "gtm_function": {"type": "string"},
                        "company_verified_as_exa_ai": {"type": "boolean"},
                        "evidence_summary": {"type": "string"},
                        "profile_url": {"type": "string", "format": "uri"},
                        "evidence_urls": {
                            "type": "array",
                            "items": {"type": "string", "format": "uri"},
                            "minItems": 1,
                        },
                    },
                },
            }
        },
    },
)
print(run)

import Exa from "exa-js";

const exa = new Exa();
const run = await exa.agent.runs.create({
  query: "Find all GTM members currently working at Exa.ai. Verify each person is at the correct company: Exa, the AI search company at exa.ai, not any other company named Exa. Include the company name in every row. Include only people with public evidence that they currently work at Exa and whose role is go-to-market, such as sales, business development, partnerships, customer success, marketing, growth, or revenue. Return one row per person.",
  effort: "high",
  outputSchema: {
    type: "object",
    required: ["gtm_members"],
    properties: {
      gtm_members: {
        type: "array",
        maxItems: 100,
        items: {
          type: "object",
          required: ["full_name", "company", "title", "gtm_function", "company_verified_as_exa_ai", "evidence_urls"],
          properties: {
            full_name: { type: "string" },
            company: { type: "string" },
            title: { type: "string" },
            gtm_function: { type: "string" },
            company_verified_as_exa_ai: { type: "boolean" },
            evidence_summary: { type: "string" },
            profile_url: { type: "string", format: "uri" },
            evidence_urls: {
              type: "array",
              items: { type: "string", format: "uri" },
              minItems: 1
            }
          }
        }
      }
    }
  }
});
console.log(run);

curl -s -X POST "https://api.exa.ai/agent/runs" \
  -H "Content-Type: application/json" \
  -H "x-api-key: $EXA_API_KEY" \
  -d '
{
  "query": "Find all GTM members currently working at Exa.ai. Verify each person is at the correct company: Exa, the AI search company at exa.ai, not any other company named Exa. Include the company name in every row. Include only people with public evidence that they currently work at Exa and whose role is go-to-market, such as sales, business development, partnerships, customer success, marketing, growth, or revenue. Return one row per person.",
  "effort": "high",
  "outputSchema": {
    "type": "object",
    "required": ["gtm_members"],
    "properties": {
      "gtm_members": {
        "type": "array",
        "maxItems": 100,
        "items": {
          "type": "object",
          "required": ["full_name", "company", "title", "gtm_function", "company_verified_as_exa_ai", "evidence_urls"],
          "properties": {
            "full_name": { "type": "string" },
            "company": { "type": "string" },
            "title": { "type": "string" },
            "gtm_function": { "type": "string" },
            "company_verified_as_exa_ai": { "type": "boolean" },
            "evidence_summary": { "type": "string" },
            "profile_url": { "type": "string", "format": "uri" },
            "evidence_urls": {
              "type": "array",
              "items": { "type": "string", "format": "uri" },
              "minItems": 1
            }
          }
        }
      }
    }
  }
}
'

KYC / KYB intelligence

Use Agent when a partner or vendor review needs public web intelligence, not just a single profile page. Ask for identity verification, business context, public signals, investment activity, and explicit risk notes in one structured object.

from exa_py import Exa

exa = Exa()
run = exa.agent.runs.create(
    query="Research Ramp (ramp.com) as a potential partner for a fintech infrastructure company. Verify the company identity. Return concise KYB intelligence covering company identity, brand or ecosystem partnerships, public mentions or notable news, recent investment activity, and risk notes. Use public sources and include source URLs.",
    effort="medium",
    output_schema={
        "type": "object",
        "required": ["company_intel"],
        "properties": {
            "company_intel": {
                "type": "object",
                "required": [
                    "company",
                    "domain",
                    "identity_verified",
                    "business_summary",
                    "brand_partnerships",
                    "public_mentions",
                    "investment_activity",
                    "risk_notes",
                    "source_urls",
                ],
                "properties": {
                    "company": {"type": "string"},
                    "domain": {"type": "string"},
                    "identity_verified": {"type": "boolean"},
                    "business_summary": {"type": "string"},
                    "brand_partnerships": {
                        "type": "array",
                        "maxItems": 3,
                        "items": {
                            "type": "object",
                            "required": ["partner", "relationship", "evidence"],
                            "properties": {
                                "partner": {"type": "string"},
                                "relationship": {"type": "string"},
                                "evidence": {"type": "string"},
                            },
                        },
                    },
                    "public_mentions": {
                        "type": "array",
                        "maxItems": 3,
                        "items": {
                            "type": "object",
                            "required": ["mention", "source"],
                            "properties": {
                                "mention": {"type": "string"},
                                "date": {"type": "string"},
                                "source": {"type": "string", "format": "uri"},
                            },
                        },
                    },
                    "investment_activity": {
                        "type": "array",
                        "maxItems": 3,
                        "items": {
                            "type": "object",
                            "required": ["event", "source"],
                            "properties": {
                                "event": {"type": "string"},
                                "amount_or_valuation": {"type": "string"},
                                "date": {"type": "string"},
                                "source": {"type": "string", "format": "uri"},
                            },
                        },
                    },
                    "risk_notes": {
                        "type": "array",
                        "maxItems": 3,
                        "items": {"type": "string"},
                    },
                    "source_urls": {
                        "type": "array",
                        "maxItems": 8,
                        "items": {"type": "string", "format": "uri"},
                    },
                },
            }
        },
    },
)
print(run)

import Exa from "exa-js";

const exa = new Exa();
const run = await exa.agent.runs.create({
  query: "Research Ramp (ramp.com) as a potential partner for a fintech infrastructure company. Verify the company identity. Return concise KYB intelligence covering company identity, brand or ecosystem partnerships, public mentions or notable news, recent investment activity, and risk notes. Use public sources and include source URLs.",
  effort: "medium",
  outputSchema: {
    type: "object",
    required: ["company_intel"],
    properties: {
      company_intel: {
        type: "object",
        required: [
          "company",
          "domain",
          "identity_verified",
          "business_summary",
          "brand_partnerships",
          "public_mentions",
          "investment_activity",
          "risk_notes",
          "source_urls"
        ],
        properties: {
          company: { type: "string" },
          domain: { type: "string" },
          identity_verified: { type: "boolean" },
          business_summary: { type: "string" },
          brand_partnerships: {
            type: "array",
            maxItems: 3,
            items: {
              type: "object",
              required: ["partner", "relationship", "evidence"],
              properties: {
                partner: { type: "string" },
                relationship: { type: "string" },
                evidence: { type: "string" }
              }
            }
          },
          public_mentions: {
            type: "array",
            maxItems: 3,
            items: {
              type: "object",
              required: ["mention", "source"],
              properties: {
                mention: { type: "string" },
                date: { type: "string" },
                source: { type: "string", format: "uri" }
              }
            }
          },
          investment_activity: {
            type: "array",
            maxItems: 3,
            items: {
              type: "object",
              required: ["event", "source"],
              properties: {
                event: { type: "string" },
                amount_or_valuation: { type: "string" },
                date: { type: "string" },
                source: { type: "string", format: "uri" }
              }
            }
          },
          risk_notes: {
            type: "array",
            maxItems: 3,
            items: { type: "string" }
          },
          source_urls: {
            type: "array",
            maxItems: 8,
            items: { type: "string", format: "uri" }
          }
        }
      }
    }
  }
});
console.log(run);

curl -s -X POST "https://api.exa.ai/agent/runs" \
  -H "Content-Type: application/json" \
  -H "x-api-key: $EXA_API_KEY" \
  -d '
{
  "query": "Research Ramp (ramp.com) as a potential partner for a fintech infrastructure company. Verify the company identity. Return concise KYB intelligence covering company identity, brand or ecosystem partnerships, public mentions or notable news, recent investment activity, and risk notes. Use public sources and include source URLs.",
  "effort": "medium",
  "outputSchema": {
    "type": "object",
    "required": ["company_intel"],
    "properties": {
      "company_intel": {
        "type": "object",
        "required": [
          "company",
          "domain",
          "identity_verified",
          "business_summary",
          "brand_partnerships",
          "public_mentions",
          "investment_activity",
          "risk_notes",
          "source_urls"
        ],
        "properties": {
          "company": { "type": "string" },
          "domain": { "type": "string" },
          "identity_verified": { "type": "boolean" },
          "business_summary": { "type": "string" },
          "brand_partnerships": {
            "type": "array",
            "maxItems": 3,
            "items": {
              "type": "object",
              "required": ["partner", "relationship", "evidence"],
              "properties": {
                "partner": { "type": "string" },
                "relationship": { "type": "string" },
                "evidence": { "type": "string" }
              }
            }
          },
          "public_mentions": {
            "type": "array",
            "maxItems": 3,
            "items": {
              "type": "object",
              "required": ["mention", "source"],
              "properties": {
                "mention": { "type": "string" },
                "date": { "type": "string" },
                "source": { "type": "string", "format": "uri" }
              }
            }
          },
          "investment_activity": {
            "type": "array",
            "maxItems": 3,
            "items": {
              "type": "object",
              "required": ["event", "source"],
              "properties": {
                "event": { "type": "string" },
                "amount_or_valuation": { "type": "string" },
                "date": { "type": "string" },
                "source": { "type": "string", "format": "uri" }
              }
            }
          },
          "risk_notes": {
            "type": "array",
            "maxItems": 3,
            "items": { "type": "string" }
          },
          "source_urls": {
            "type": "array",
            "maxItems": 8,
            "items": { "type": "string", "format": "uri" }
          }
        }
      }
    }
  }
}
'

Job postings

Use Agent when open roles are spread across company pages, aggregators, and startup directories. This example constrains the source to Exa’s Ashby-hosted job board and still requires company disambiguation.

from exa_py import Exa

exa = Exa()
run = exa.agent.runs.create(
    query="Find current open job postings for Exa on Ashby. Use Ashby-hosted postings, preferably jobs.ashbyhq.com/exa, and verify each job is for Exa, the AI search company at exa.ai. Return up to 5 postings with company name, title, function, location, Ashby posting URL, and why it is relevant.",
    effort="medium",
    output_schema={
        "type": "object",
        "required": ["job_postings"],
        "properties": {
            "job_postings": {
                "type": "array",
                "maxItems": 5,
                "items": {
                    "type": "object",
                    "required": [
                        "title",
                        "company",
                        "function",
                        "location",
                        "company_verified_as_exa_ai",
                        "posting_url",
                        "source_platform",
                        "why_relevant",
                    ],
                    "properties": {
                        "title": {"type": "string"},
                        "company": {"type": "string"},
                        "function": {"type": "string"},
                        "location": {"type": "string"},
                        "company_verified_as_exa_ai": {"type": "boolean"},
                        "posting_url": {"type": "string", "format": "uri"},
                        "source_platform": {"type": "string"},
                        "why_relevant": {"type": "string"},
                    },
                },
            }
        },
    },
)
print(run)

import Exa from "exa-js";

const exa = new Exa();
const run = await exa.agent.runs.create({
  query: "Find current open job postings for Exa on Ashby. Use Ashby-hosted postings, preferably jobs.ashbyhq.com/exa, and verify each job is for Exa, the AI search company at exa.ai. Return up to 5 postings with company name, title, function, location, Ashby posting URL, and why it is relevant.",
  effort: "medium",
  outputSchema: {
    type: "object",
    required: ["job_postings"],
    properties: {
      job_postings: {
        type: "array",
        maxItems: 5,
        items: {
          type: "object",
          required: [
            "title",
            "company",
            "function",
            "location",
            "company_verified_as_exa_ai",
            "posting_url",
            "source_platform",
            "why_relevant"
          ],
          properties: {
            title: { type: "string" },
            company: { type: "string" },
            function: { type: "string" },
            location: { type: "string" },
            company_verified_as_exa_ai: { type: "boolean" },
            posting_url: { type: "string", format: "uri" },
            source_platform: { type: "string" },
            why_relevant: { type: "string" }
          }
        }
      }
    }
  }
});
console.log(run);

curl -s -X POST "https://api.exa.ai/agent/runs" \
  -H "Content-Type: application/json" \
  -H "x-api-key: $EXA_API_KEY" \
  -d '
{
  "query": "Find current open job postings for Exa on Ashby. Use Ashby-hosted postings, preferably jobs.ashbyhq.com/exa, and verify each job is for Exa, the AI search company at exa.ai. Return up to 5 postings with company name, title, function, location, Ashby posting URL, and why it is relevant.",
  "effort": "medium",
  "outputSchema": {
    "type": "object",
    "required": ["job_postings"],
    "properties": {
      "job_postings": {
        "type": "array",
        "maxItems": 5,
        "items": {
          "type": "object",
          "required": [
            "title",
            "company",
            "function",
            "location",
            "company_verified_as_exa_ai",
            "posting_url",
            "source_platform",
            "why_relevant"
          ],
          "properties": {
            "title": { "type": "string" },
            "company": { "type": "string" },
            "function": { "type": "string" },
            "location": { "type": "string" },
            "company_verified_as_exa_ai": { "type": "boolean" },
            "posting_url": { "type": "string", "format": "uri" },
            "source_platform": { "type": "string" },
            "why_relevant": { "type": "string" }
          }
        }
      }
    }
  }
}
'

Enrich input rows

Use input.data when your system already has the records and Agent should research or enrich each one. This pattern keeps row identity in structured input instead of burying it in the prompt.

from exa_py import Exa

exa = Exa()
run = exa.agent.runs.create(
    query="For each input company, produce a concise research brief. Use recent, reputable sources. Return one report per input row.",
    effort="medium",
    input={"data": [{"company": "Ramp", "domain": "ramp.com"}, {"company": "Mercury", "domain": "mercury.com"}]},
    output_schema={
        "type": "object",
        "required": ["reports"],
        "properties": {
            "reports": {
                "type": "array",
                "maxItems": 2,
                "items": {
                    "type": "object",
                    "required": ["company", "domain", "overview", "buyingSignals", "sourceUrls", "verified_domain"],
                    "properties": {
                        "company": {"type": "string"},
                        "domain": {"type": "string"},
                        "overview": {"type": "string"},
                        "buyingSignals": {
                            "type": "array",
                            "maxItems": 3,
                            "items": {"type": "string"},
                        },
                        "sourceUrls": {
                            "type": "array",
                            "minItems": 1,
                            "items": {"type": "string", "format": "uri"},
                        },
                        "verified_domain": {"type": "boolean"},
                    },
                },
            }
        },
    },
)
print(run)

import Exa from "exa-js";

const exa = new Exa();
const run = await exa.agent.runs.create({
  query: "For each input company, produce a concise research brief. Use recent, reputable sources. Return one report per input row.",
  effort: "medium",
  input: { data: [{ company: "Ramp", domain: "ramp.com" }, { company: "Mercury", domain: "mercury.com" }] },
  outputSchema: {
    type: "object",
    required: ["reports"],
    properties: {
      reports: {
        type: "array",
        maxItems: 2,
        items: {
          type: "object",
          required: ["company", "domain", "overview", "buyingSignals", "sourceUrls", "verified_domain"],
          properties: {
            company: { type: "string" },
            domain: { type: "string" },
            overview: { type: "string" },
            buyingSignals: {
              type: "array",
              maxItems: 3,
              items: { type: "string" }
            },
            sourceUrls: {
              type: "array",
              minItems: 1,
              items: { type: "string", format: "uri" }
            },
            verified_domain: { type: "boolean" }
          }
        }
      }
    }
  }
});
console.log(run);

curl -s -X POST "https://api.exa.ai/agent/runs" \
  -H "Content-Type: application/json" \
  -H "x-api-key: $EXA_API_KEY" \
  -d '
{
  "query": "For each input company, produce a concise research brief. Use recent, reputable sources. Return one report per input row.",
  "effort": "medium",
  "input": {
    "data": [
      { "company": "Ramp", "domain": "ramp.com" },
      { "company": "Mercury", "domain": "mercury.com" }
    ]
  },
  "outputSchema": {
    "type": "object",
    "required": ["reports"],
    "properties": {
      "reports": {
        "type": "array",
        "maxItems": 2,
        "items": {
          "type": "object",
          "required": ["company", "domain", "overview", "buyingSignals", "sourceUrls", "verified_domain"],
          "properties": {
            "company": { "type": "string" },
            "domain": { "type": "string" },
            "overview": { "type": "string" },
            "buyingSignals": {
              "type": "array",
              "maxItems": 3,
              "items": { "type": "string" }
            },
            "sourceUrls": {
              "type": "array",
              "minItems": 1,
              "items": { "type": "string", "format": "uri" }
            },
            "verified_domain": { "type": "boolean" }
          }
        }
      }
    }
  }
}
'

Enrich rows with bounded contact fields

Contact-oriented workflows can trigger separate contact enrichment charges. This anonymized docs version demonstrates the shape without publishing personal names, emails, phone numbers, or profile URLs.

from exa_py import Exa

exa = Exa()
run = exa.agent.runs.create(
    query="For each company, find the best sales or partnerships contact. Prefer leadership or go-to-market roles. Return anonymized contact labels in examples and include evidence for why each role is relevant.",
    effort="high",
    input={"data": [{"company": "LangChain", "domain": "langchain.com"}, {"company": "Modal", "domain": "modal.com"}]},
    output_schema={
        "type": "object",
        "required": ["contacts"],
        "properties": {
            "contacts": {
                "type": "array",
                "maxItems": 2,
                "items": {
                    "type": "object",
                    "required": ["company", "contact_label", "role", "relevanceReason"],
                    "properties": {
                        "company": {"type": "string"},
                        "contact_label": {"type": "string"},
                        "role": {"type": "string"},
                        "relevanceReason": {"type": "string"},
                        "contact_cost_bound": {"type": "string"},
                    },
                },
            }
        },
    },
)
print(run)

import Exa from "exa-js";

const exa = new Exa();
const run = await exa.agent.runs.create({
  query: "For each company, find the best sales or partnerships contact. Prefer leadership or go-to-market roles. Return anonymized contact labels in examples and include evidence for why each role is relevant.",
  effort: "high",
  input: { data: [{ company: "LangChain", domain: "langchain.com" }, { company: "Modal", domain: "modal.com" }] },
  outputSchema: {
    type: "object",
    required: ["contacts"],
    properties: {
      contacts: {
        type: "array",
        maxItems: 2,
        items: {
          type: "object",
          required: ["company", "contact_label", "role", "relevanceReason"],
          properties: {
            company: { type: "string" },
            contact_label: { type: "string" },
            role: { type: "string" },
            relevanceReason: { type: "string" },
            contact_cost_bound: { type: "string" }
          }
        }
      }
    }
  }
});
console.log(run);

curl -s -X POST "https://api.exa.ai/agent/runs" \
  -H "Content-Type: application/json" \
  -H "x-api-key: $EXA_API_KEY" \
  -d '
{
  "query": "For each company, find the best sales or partnerships contact. Prefer leadership or go-to-market roles. Return anonymized contact labels in examples and include evidence for why each role is relevant.",
  "effort": "high",
  "input": {
    "data": [
      { "company": "LangChain", "domain": "langchain.com" },
      { "company": "Modal", "domain": "modal.com" }
    ]
  },
  "outputSchema": {
    "type": "object",
    "required": ["contacts"],
    "properties": {
      "contacts": {
        "type": "array",
        "maxItems": 2,
        "items": {
          "type": "object",
          "required": ["company", "contact_label", "role", "relevanceReason"],
          "properties": {
            "company": { "type": "string" },
            "contact_label": { "type": "string" },
            "role": { "type": "string" },
            "relevanceReason": { "type": "string" },
            "contact_cost_bound": { "type": "string" }
          }
        }
      }
    }
  }
}
'

Exclude known records

Use input.exclusion when Agent should avoid records your product has already reviewed, shown, or rejected. The sheet preview uses synthetic returned company names so the exclusion pattern is visible without implying those rows came from a live production run.

from exa_py import Exa

exa = Exa()
run = exa.agent.runs.create(
    query="Find 10 seed-stage companies building infrastructure for AI coding agents. Do not return companies in the exclusion list.",
    effort="auto",
    input={"exclusion": [{"company": "Cursor", "domain": "cursor.com"}, {"company": "CodeRabbit", "domain": "coderabbit.ai"}]},
    output_schema={
        "type": "object",
        "required": ["companies"],
        "properties": {
            "companies": {
                "type": "array",
                "maxItems": 10,
                "items": {
                    "type": "object",
                    "required": ["company", "domain", "reason", "sourceUrl"],
                    "properties": {
                        "company": {"type": "string"},
                        "domain": {"type": "string"},
                        "reason": {"type": "string"},
                        "sourceUrl": {"type": "string", "format": "uri"},
                        "excluded_match": {"type": "string"},
                        "status": {"type": "string"},
                    },
                },
            }
        },
    },
)
print(run)

import Exa from "exa-js";

const exa = new Exa();
const run = await exa.agent.runs.create({
  query: "Find 10 seed-stage companies building infrastructure for AI coding agents. Do not return companies in the exclusion list.",
  effort: "auto",
  input: { exclusion: [{ company: "Cursor", domain: "cursor.com" }, { company: "CodeRabbit", domain: "coderabbit.ai" }] },
  outputSchema: {
    type: "object",
    required: ["companies"],
    properties: {
      companies: {
        type: "array",
        maxItems: 10,
        items: {
          type: "object",
          required: ["company", "domain", "reason", "sourceUrl"],
          properties: {
            company: { type: "string" },
            domain: { type: "string" },
            reason: { type: "string" },
            sourceUrl: { type: "string", format: "uri" },
            excluded_match: { type: "string" },
            status: { type: "string" }
          }
        }
      }
    }
  }
});
console.log(run);

curl -s -X POST "https://api.exa.ai/agent/runs" \
  -H "Content-Type: application/json" \
  -H "x-api-key: $EXA_API_KEY" \
  -d '
{
  "query": "Find 10 seed-stage companies building infrastructure for AI coding agents. Do not return companies in the exclusion list.",
  "effort": "auto",
  "input": {
    "exclusion": [
      { "company": "Cursor", "domain": "cursor.com" },
      { "company": "CodeRabbit", "domain": "coderabbit.ai" }
    ]
  },
  "outputSchema": {
    "type": "object",
    "required": ["companies"],
    "properties": {
      "companies": {
        "type": "array",
        "maxItems": 10,
        "items": {
          "type": "object",
          "required": ["company", "domain", "reason", "sourceUrl"],
          "properties": {
            "company": { "type": "string" },
            "domain": { "type": "string" },
            "reason": { "type": "string" },
            "sourceUrl": { "type": "string", "format": "uri" },
            "excluded_match": { "type": "string" },
            "status": { "type": "string" }
          }
        }
      }
    }
  }
}
'

Continue a completed workflow

Use previousRunId when the next request should build on a completed run’s context. The sheet preview uses synthetic returned company names to illustrate the continuation pattern without exposing a real prior run.

from exa_py import Exa

exa = Exa()
run = exa.agent.runs.create(
    query="From the companies you found, narrow the list to those hiring platform engineers in San Francisco. Add the hiring page URL for each match.",
    previous_run_id="agent_run_01j...",
    output_schema={
        "type": "object",
        "required": ["companies"],
        "properties": {
            "companies": {
                "type": "array",
                "maxItems": 10,
                "items": {
                    "type": "object",
                    "required": ["company", "website", "hiringPageUrl"],
                    "properties": {
                        "company": {"type": "string"},
                        "website": {"type": "string", "format": "uri"},
                        "hiringPageUrl": {"type": "string", "format": "uri"},
                        "hiringEvidence": {"type": "string"},
                        "location": {"type": "string"},
                        "followup_source": {"type": "string"},
                    },
                },
            }
        },
    },
)
print(run)

import Exa from "exa-js";

const exa = new Exa();
const run = await exa.agent.runs.create({
  previousRunId: "agent_run_01j...",
  query: "From the companies you found, narrow the list to those hiring platform engineers in San Francisco. Add the hiring page URL for each match.",
  outputSchema: {
    type: "object",
    required: ["companies"],
    properties: {
      companies: {
        type: "array",
        maxItems: 10,
        items: {
          type: "object",
          required: ["company", "website", "hiringPageUrl"],
          properties: {
            company: { type: "string" },
            website: { type: "string", format: "uri" },
            hiringPageUrl: { type: "string", format: "uri" },
            hiringEvidence: { type: "string" },
            location: { type: "string" },
            followup_source: { type: "string" }
          }
        }
      }
    }
  }
});
console.log(run);

curl -s -X POST "https://api.exa.ai/agent/runs" \
  -H "Content-Type: application/json" \
  -H "x-api-key: $EXA_API_KEY" \
  -d '{
  "previousRunId": "agent_run_01j...",
  "query": "From the companies you found, narrow the list to those hiring platform engineers in San Francisco. Add the hiring page URL for each match.",
  "outputSchema": {
    "type": "object",
    "required": ["companies"],
    "properties": {
      "companies": {
        "type": "array",
        "maxItems": 10,
        "items": {
          "type": "object",
          "required": ["company", "website", "hiringPageUrl"],
          "properties": {
            "company": { "type": "string" },
            "website": { "type": "string", "format": "uri" },
            "hiringPageUrl": { "type": "string", "format": "uri" },
            "hiringEvidence": { "type": "string" },
            "location": { "type": "string" },
            "followup_source": { "type": "string" }
          }
        }
      }
    }
  }
}'

Broad list building with Agent Max

Use effort: "max" for work where completeness and thoroughness matter more than latency or cost, including large list building, deep multi-source research, and criteria that are hard to verify. Agent Max is in public beta: send Exa-Beta: agent-max-effort-2026-07-27 with the request. The header accepts a comma-separated list of beta tokens.

from exa_py import Exa

exa = Exa()
run = exa.beta.agent.runs.create(
    query="Find all companies building browser automation tools in the United States.",
    effort="max",
    budget={"maxCostDollars": 10},
    betas=["agent-max-effort-2026-07-27"],
)
print(run)

import Exa from "exa-js";

const exa = new Exa();
const run = await exa.beta.agent.runs.create({
  query: "Find all companies building browser automation tools in the United States.",
  effort: "max",
  budget: { maxCostDollars: 10 },
  betas: ["agent-max-effort-2026-07-27"]
});
console.log(run);

curl -s -X POST "https://api.exa.ai/agent/runs" \
  -H "Content-Type: application/json" \
  -H "x-api-key: $EXA_API_KEY" \
  -H "Exa-Beta: agent-max-effort-2026-07-27" \
  -d '{
    "query": "Find all companies building browser automation tools in the United States.",
    "effort": "max",
    "budget": { "maxCostDollars": 10 }
  }'

Attach a data partner with Exa Connect

Use dataSources to let Exa Agent pull from a premium data partner during a run. Here it enriches each company with traffic from Similarweb and funding from Harmonic, blended with web research. See Exa Connect for the full list of partners.

from exa_py import Exa

exa = Exa()
run = exa.agent.runs.create(
    query="For each input company, report estimated monthly visits and total funding raised. Use the attached data partners.",
    effort="medium",
    data_sources=[
        {"provider": "similarweb"},
        {"provider": "harmonic"},
    ],
    input={
        "data": [
            {"company": "Ramp", "domain": "ramp.com"},
            {"company": "Mercury", "domain": "mercury.com"},
        ]
    },
    output_schema={
        "type": "object",
        "required": ["companies"],
        "properties": {
            "companies": {
                "type": "array",
                "maxItems": 2,
                "items": {
                    "type": "object",
                    "required": ["company", "domain", "monthlyVisits", "totalFunding"],
                    "properties": {
                        "company": {"type": "string"},
                        "domain": {"type": "string"},
                        "monthlyVisits": {"type": "number", "description": "from Similarweb"},
                        "totalFunding": {"type": "string", "description": "from Harmonic"},
                    },
                },
            }
        },
    },
)
print(run)

import Exa from "exa-js";

const exa = new Exa();
const run = await exa.agent.runs.create({
  query: "For each input company, report estimated monthly visits and total funding raised. Use the attached data partners.",
  effort: "medium",
  dataSources: [{ provider: "similarweb" }, { provider: "harmonic" }],
  input: {
    data: [
      { company: "Ramp", domain: "ramp.com" },
      { company: "Mercury", domain: "mercury.com" }
    ]
  },
  outputSchema: {
    type: "object",
    required: ["companies"],
    properties: {
      companies: {
        type: "array",
        maxItems: 2,
        items: {
          type: "object",
          required: ["company", "domain", "monthlyVisits", "totalFunding"],
          properties: {
            company: { type: "string" },
            domain: { type: "string" },
            monthlyVisits: { type: "number", description: "from Similarweb" },
            totalFunding: { type: "string", description: "from Harmonic" }
          }
        }
      }
    }
  }
});
console.log(run);

curl -s -X POST "https://api.exa.ai/agent/runs" \
  -H "Content-Type: application/json" \
  -H "x-api-key: $EXA_API_KEY" \
  -d '{
  "query": "For each input company, report estimated monthly visits and total funding raised. Use the attached data partners.",
  "effort": "medium",
  "dataSources": [
    { "provider": "similarweb" },
    { "provider": "harmonic" }
  ],
  "input": {
    "data": [
      { "company": "Ramp", "domain": "ramp.com" },
      { "company": "Mercury", "domain": "mercury.com" }
    ]
  },
  "outputSchema": {
    "type": "object",
    "required": ["companies"],
    "properties": {
      "companies": {
        "type": "array",
        "maxItems": 2,
        "items": {
          "type": "object",
          "required": ["company", "domain", "monthlyVisits", "totalFunding"],
          "properties": {
            "company": { "type": "string" },
            "domain": { "type": "string" },
            "monthlyVisits": { "type": "number", "description": "from Similarweb" },
            "totalFunding": { "type": "string", "description": "from Harmonic" }
          }
        }
      }
    }
  }
}'

Production checklist

Give Agent a specific query that names the unit of work and the desired source quality.
Use input.data for known records instead of embedding rows in the prompt.
Use input.exclusion for records that should not be returned again.
Add outputSchema whenever downstream code consumes the result.
Use maxItems on arrays when you need predictable scope and cost.
Store the returned run id so you can poll, replay events, inspect costs, or continue from the run later.

API

Data

MCP

SDKs

Integrations

Agentic payments

Admin

Additional Resources

Find all GTM members at Exa.ai

KYC / KYB intelligence

Job postings

Enrich input rows

Enrich rows with bounded contact fields

Exclude known records

Continue a completed workflow

Broad list building with Agent Max

Attach a data partner with Exa Connect

Production checklist

​Find all GTM members at Exa.ai

​KYC / KYB intelligence

​Job postings

​Enrich input rows

​Enrich rows with bounded contact fields

​Exclude known records

​Continue a completed workflow

​Broad list building with Agent Max

​Attach a data partner with Exa Connect

​Production checklist

Find all GTM members at Exa.ai

KYC / KYB intelligence

Job postings

Enrich input rows

Enrich rows with bounded contact fields

Exclude known records

Continue a completed workflow

Broad list building with Agent Max

Attach a data partner with Exa Connect

Production checklist