> ## Documentation Index
> Fetch the complete documentation index at: https://exa.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Combining providers

> Use several data partners together in a single Exa Agent run.

Attaching a partner to `dataSources` makes it available to the Exa Agent as a tool — it does **not** force the agent to call it. Whether a partner fires depends on your `query` and `outputSchema`: name the kind of result you want from each partner, and the Exa Agent reaches for the matching tool instead of guessing from a web page. You can attach up to five partners per run; the Exa Agent picks which to call for each step, with Exa web search available alongside them. Need more than five for a single run? [Contact us](mailto:sales@exa.ai) to raise the limit.

## Two partners in one run

List several partners together and the Exa Agent draws on each where it's strongest. Two is just an example here — attach up to five partners to `dataSources`, and the same principle applies: ask for each one's data explicitly. This investor-briefing run combines [Financial Datasets](/reference/agent-api/connect/financialdatasets) for ticker news with [Particle](/reference/agent-api/connect/particle) for podcast commentary. The query asks for each partner's distinctive data and the schema splits the output into `financialNews` and `podcastChatter`, so the Exa Agent calls **both** partners in the same run.

<CodeGroup>
  ```python Python theme={null}
  from exa_py import Exa

  exa = Exa()
  run = exa.agent.runs.create(
      query=(
          "Give me an investor briefing on NVIDIA (NVDA): (1) the latest financial and "
          "earnings news, and (2) what podcast hosts and guests have recently been saying "
          "about NVIDIA, with speaker-attributed quotes and their stance."
      ),
      data_sources=[
          {"provider": "financial_datasets"},
          {"provider": "particle_news"},
      ],
      output_schema={
          "type": "object",
          "required": ["ticker", "financialNews", "podcastChatter"],
          "properties": {
              "ticker": {"type": "string"},
              "financialNews": {
                  "type": "array",
                  "maxItems": 6,
                  "items": {
                      "type": "object",
                      "required": ["title", "source", "date", "theme"],
                      "properties": {
                          "title": {"type": "string"},
                          "source": {"type": "string"},
                          "date": {"type": "string"},
                          "theme": {"type": "string", "description": "earnings, guidance, analyst rating, product, or market"},
                      },
                  },
              },
              "podcastChatter": {
                  "type": "array",
                  "maxItems": 6,
                  "items": {
                      "type": "object",
                      "required": ["podcast", "speaker", "quote", "stance"],
                      "properties": {
                          "podcast": {"type": "string"},
                          "speaker": {"type": "string"},
                          "quote": {"type": "string"},
                          "stance": {"type": "string", "description": "bullish, bearish, or neutral"},
                      },
                  },
              },
          },
      },
  )
  run = exa.agent.runs.poll_until_finished(run.id)
  ```

  ```typescript TypeScript theme={null}
  import Exa from "exa-js";

  const exa = new Exa();
  const run = await exa.agent.runs.create({
    query:
      "Give me an investor briefing on NVIDIA (NVDA): (1) the latest financial and " +
      "earnings news, and (2) what podcast hosts and guests have recently been saying " +
      "about NVIDIA, with speaker-attributed quotes and their stance.",
    dataSources: [
      { provider: "financial_datasets" },
      { provider: "particle_news" },
    ],
    outputSchema: {
      type: "object",
      required: ["ticker", "financialNews", "podcastChatter"],
      properties: {
        ticker: { type: "string" },
        financialNews: {
          type: "array",
          maxItems: 6,
          items: {
            type: "object",
            required: ["title", "source", "date", "theme"],
            properties: {
              title: { type: "string" },
              source: { type: "string" },
              date: { type: "string" },
              theme: { type: "string", description: "earnings, guidance, analyst rating, product, or market" },
            },
          },
        },
        podcastChatter: {
          type: "array",
          maxItems: 6,
          items: {
            type: "object",
            required: ["podcast", "speaker", "quote", "stance"],
            properties: {
              podcast: { type: "string" },
              speaker: { type: "string" },
              quote: { type: "string" },
              stance: { type: "string", description: "bullish, bearish, or neutral" },
            },
          },
        },
      },
    },
  });
  ```

  ```bash cURL theme={null}
  curl -s -X POST "https://api.exa.ai/agent/runs" \
    -H "Content-Type: application/json" \
    -H "x-api-key: $EXA_API_KEY" \
    -d '{
      "query": "Give me an investor briefing on NVIDIA (NVDA): (1) the latest financial and earnings news, and (2) what podcast hosts and guests have recently been saying about NVIDIA, with speaker-attributed quotes and their stance.",
      "dataSources": [
        { "provider": "financial_datasets" },
        { "provider": "particle_news" }
      ],
      "outputSchema": {
        "type": "object",
        "required": ["ticker", "financialNews", "podcastChatter"],
        "properties": {
          "ticker": { "type": "string" },
          "financialNews": {
            "type": "array",
            "maxItems": 6,
            "items": {
              "type": "object",
              "required": ["title", "source", "date", "theme"],
              "properties": {
                "title": { "type": "string" },
                "source": { "type": "string" },
                "date": { "type": "string" },
                "theme": { "type": "string", "description": "earnings, guidance, analyst rating, product, or market" }
              }
            }
          },
          "podcastChatter": {
            "type": "array",
            "maxItems": 6,
            "items": {
              "type": "object",
              "required": ["podcast", "speaker", "quote", "stance"],
              "properties": {
                "podcast": { "type": "string" },
                "speaker": { "type": "string" },
                "quote": { "type": "string" },
                "stance": { "type": "string", "description": "bullish, bearish, or neutral" }
              }
            }
          }
        }
      }
    }'
  ```
</CodeGroup>

<Tip>
  Make each partner's data explicit in your *query* — name the kind of result you want from each one (here: ticker financial news vs. speaker-attributed podcast quotes). If the request is generic ("latest news"), the Exa Agent tends to fall back to web search instead of a partner. Mirroring those distinct asks in your `outputSchema` fields reinforces it.
</Tip>