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Documentation Index

Fetch the complete documentation index at: https://docs.ai-stats.phaseo.app/llms.txt

Use this file to discover all available pages before exploring further.

Use this recipe when one agent workflow should:
  • run through the TypeScript Agent SDK
  • rely on managed web search
  • return strict structured JSON
  • recover near-valid malformed JSON deterministically

​
1. Install the SDKs

npm install @ai-stats/sdk @ai-stats/agent-sdk

​
2. Define one narrow output contract

Keep the first research output small enough that operators can inspect it easily in logs. 
type ResearchBrief = {
  topic: string;
  summary: string;
  sources: Array<{
    title: string;
    url: string;
  }>;
};

​
3. Create the agent

Use parseOutput so the runtime returns typed data instead of raw text. 
import { createAgent } from "@ai-stats/agent-sdk";

export const researchBriefAgent = createAgent<string, ResearchBrief>({
  id: "research-brief-agent",
  model: "ai-stats/free",
  instructions:
    "Research the user's topic with web search when needed and return a concise JSON brief with cited sources.",
  parseOutput(text) {
    return JSON.parse(text) as ResearchBrief;
  },
});

​
4. Configure the gateway-backed adapter once

This is the important part:
  • responseFormat keeps the output schema explicit
  • plugins enables response healing for near-valid JSON
  • gatewayTools exposes managed web search to the model
  • toolChoice forces the search tool when this workflow should always ground itself
import {
  createGatewayAgentClient,
} from "@ai-stats/agent-sdk";

const client = createGatewayAgentClient({
  clientOptions: {
    apiKey: process.env.AI_STATS_API_KEY!,
  },
  responseFormat: {
    type: "json_schema",
    name: "research_brief",
    schema: {
      type: "object",
      properties: {
        topic: { type: "string" },
        summary: { type: "string" },
        sources: {
          type: "array",
          items: {
            type: "object",
            properties: {
              title: { type: "string" },
              url: { type: "string" },
            },
            required: ["title", "url"],
            additionalProperties: false,
          },
          minItems: 1,
        },
      },
      required: ["topic", "summary", "sources"],
      additionalProperties: false,
    },
  },
  plugins: [{ id: "response-healing" }],
  gatewayTools: [
    { type: "gateway:web_search", parameters: { max_results: 5 } },
  ],
  toolChoice: "gateway:web_search",
  webSearchOptions: { search_context_size: "high" },
});

​
5. Run the workflow

const result = await researchBriefAgent.run({
  input: "Summarize the latest browser automation support patterns for coding agents.",
  client,
  onEvent(event) {
    console.log(event.type, event.runId);
  },
});

console.log(result.output);

​
6. What to verify in logs

After one successful run, inspect the request detail view and confirm:
  • requested native web search tools or managed search activity is present
  • search results and citations were persisted
  • plugin execution shows response-healing only when recovery was needed
  • the final output matches the JSON schema you asked for

​
7. When to use this pattern

Use this recipe when:
  • the workflow is mostly one-shot research, not a deep local-tool loop
  • the model should ground itself before answering
  • downstream callers want typed JSON instead of prose
Do not use this pattern when:
  • the workflow already knows the exact URLs and gateway:web_fetch is enough
  • the agent needs rich local tools more than upstream-native tools
  • the output contract is loose enough that strict JSON adds more overhead than value
Last modified on May 19, 2026