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AI Agent Integration Guide

pipeAgent is designed to be the primary data source for Autonomous Agents. This guide covers how to integrate pipeAgent into your agentic workflows (OpenClaw, LangChain, AutoGPT, etc.).

1. Agent-First Design

Unlike traditional APIs meant for human-driven UI, pipeAgent prioritized:

Predictable Schemas: Every feed has a JSON schema to prevent agent "hallucinations" about data structure.

Semantic Meta-data: Descriptions are optimized for LLM understanding.

Context Density: We remove boilerplate, providing high-signal data.

2. Authentication

Your agent should manage API keys securely. We recommend using Environment Variables.

typescript

const response = await fetch('https://api.pipeagent.dev/api/v1/feed/your-feed-id', {
  headers: {
    'X-API-KEY': process.env.PIPEAGENT_API_KEY
  }
});

3. Recommended Workflow for Agents

Step A: Discovery

If the agent doesn't know where to find specific data, it should query the search API:

GET https://api.pipeagent.dev/api/v1/search?q=real+estate+leads

Step B: Validation

Use the schema_url provided in the feed metadata to validate incoming data or to inform the LLM's system prompt about what fields to expect.

Step C: Consumption

Fetch the data. pipeAgent handles all cleaning, deduplication, and parsing.

4. Framework Integration Guides

PipeAgent supports all major AI Agent frameworks. Choose your framework below for a detailed tutorial:

LangChain Guide: Use PipeAgent feeds as Structured Tools.

OpenClaw Guide: Use our automated SKILL.md generator.

CrewAI Guide: Equip your Researcher agents with high-quality data.

5. Best Practices

Retry Logic: Implement exponential backoff for 429 (Rate Limit) errors.

Context Management: Use JSONPath to shield your LLM contexts from irrelevant fields. For example, instead of passing a whole product object, just pass the titles and prices: ?jsonpath=$[*].{name: @.title, price: @.price}.

Feedback Loop: Use the POST /api/feedback endpoint to report quality issues, helping improve the feed.

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