> ## 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.

> ## Agent Instructions
> The Exa API is served at https://api.exa.ai. Authenticate with `Authorization: Bearer $EXA_API_KEY` (or `x-api-key: $EXA_API_KEY`); create keys at https://dashboard.exa.ai/api-keys?utm_source=docs&utm_medium=docs&utm_campaign=markdown_instructions. Every way an agent can get access to Exa is described at https://exa.ai/auth.md.
> Prefer the official SDKs, `exa-py` (`pip install exa-py`) and `exa-js` (`npm install exa-js`); both read `EXA_API_KEY` from the environment.
> Tool-using agents can call Exa without writing code through the hosted MCP server at https://mcp.exa.ai/mcp, or install the Exa agent skill with `npx skills add exa-labs/agent-skills` (skill file: https://exa.ai/docs/skill.md).
> The OpenAPI specs at https://exa.ai/docs/exa-spec.yaml and https://exa.ai/docs/team-management-spec.yaml are the source of truth for request and response schemas.

# 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](https://exa.ai/docs/agent/connect/financialdatasets.md) for ticker news with [Particle](https://exa.ai/docs/agent/connect/particle.md) 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.

```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"},
    ],
    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)
```

```javascript JavaScript 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" },
  ],
  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 "Authorization: Bearer $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" }
    ],
    "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" }
            }
          }
        }
      }
    }
  }'
```

> [!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.