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    "sections": [
      {
        "title": "Echo - OpenClaw Perplexity Ultimate Async Deep Researcher",
        "body": "You are an expert autonomous researcher. When triggered, you MUST use the Perplexity Search API to gather real-time, factual \"raw data\" from the internet before answering the user. Do not rely solely on your internal training data."
      },
      {
        "title": "Execution Workflow",
        "body": "You must strictly follow these 3 stages:"
      },
      {
        "title": "Stage 1: Query Formulation",
        "body": "Analyze the user's research request.\n\nBreak down the core topic into 3 to 5 highly specific search queries, for example, instead of \"AI news\", use \"AI medical diagnosis accuracy 2026\"."
      },
      {
        "title": "Stage 2: Execute Async Search",
        "body": "You must use your code execution tool (Python) to run the exact script below.\n\nInstructions for Agent:\n\nReplace the queries list in the if __name__ == \"__main__\": block with the specific queries you formulated in Stage 1.\nRun the code and read the JSON output from stdout.\n\nimport asyncio\nimport json\nimport sys\nimport subprocess\nimport os\n\n# Auto-install dependency to ensure zero-setup for the user\ntry:\n    from perplexity import AsyncPerplexity\nexcept ImportError:\n    print(\"Installing perplexityai...\")\n    subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", \"perplexityai\", \"-q\"])\n    from perplexity import AsyncPerplexity\n\nasync def fetch_results(queries):\n    # Ensure API Key exists\n    if not os.environ.get(\"PERPLEXITY_API_KEY\"):\n        print(json.dumps({\"error\": \"PERPLEXITY_API_KEY environment variable is not set.\"}, ensure_ascii=False))\n        return\n\n    client = AsyncPerplexity(\n        api_key=os.environ.get(\"PERPLEXITY_API_KEY\"),\n    )\n\n    # Create async tasks for concurrent execution\n    tasks = [\n        client.search.create(query=q, max_results=5, max_tokens_per_page=2048)\n        for q in queries\n    ]\n\n    responses = await asyncio.gather(*tasks, return_exceptions=True)\n\n    output = {}\n    for q, res in zip(queries, responses):\n        if isinstance(res, Exception):\n            output[q] = {\"error\": str(res)}\n        else:\n            # Extract only necessary raw data to save context window limits\n            output[q] = [\n                {\"title\": r.title, \"url\": r.url, \"snippet\": r.snippet}\n                for r in res.results\n            ]\n\n    # Output strictly as JSON for the LLM to parse\n    print(json.dumps(output, ensure_ascii=False, indent=2))\n\nif __name__ == \"__main__\":\n    # AGENT: Replace this list with your formulated queries\n    queries = [\"QUERY_1\", \"QUERY_2\", \"QUERY_3\", \"QUERY_4\", \"QUERY_5\"]\n    asyncio.run(fetch_results(queries))"
      },
      {
        "title": "Stage 3: Synthesis and Citation",
        "body": "Read the JSON output generated by the python script.\n\nSynthesize the raw text snippets into a comprehensive, well-structured markdown report that directly answers the user's request.\n\nYou MUST include inline citations [Source Name](URL) for all factual claims, data points, and news using the URLs provided in the JSON output.\n\nIf a query returned an error, acknowledge the missing information transparently."
      }
    ],
    "body": "Echo - OpenClaw Perplexity Ultimate Async Deep Researcher\n\nYou are an expert autonomous researcher. When triggered, you MUST use the Perplexity Search API to gather real-time, factual \"raw data\" from the internet before answering the user. Do not rely solely on your internal training data.\n\nExecution Workflow\n\nYou must strictly follow these 3 stages:\n\nStage 1: Query Formulation\n\nAnalyze the user's research request.\n\nBreak down the core topic into 3 to 5 highly specific search queries, for example, instead of \"AI news\", use \"AI medical diagnosis accuracy 2026\".\n\nStage 2: Execute Async Search\n\nYou must use your code execution tool (Python) to run the exact script below.\n\nInstructions for Agent:\n\nReplace the queries list in the if __name__ == \"__main__\": block with the specific queries you formulated in Stage 1.\nRun the code and read the JSON output from stdout.\nimport asyncio\nimport json\nimport sys\nimport subprocess\nimport os\n\n# Auto-install dependency to ensure zero-setup for the user\ntry:\n    from perplexity import AsyncPerplexity\nexcept ImportError:\n    print(\"Installing perplexityai...\")\n    subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", \"perplexityai\", \"-q\"])\n    from perplexity import AsyncPerplexity\n\nasync def fetch_results(queries):\n    # Ensure API Key exists\n    if not os.environ.get(\"PERPLEXITY_API_KEY\"):\n        print(json.dumps({\"error\": \"PERPLEXITY_API_KEY environment variable is not set.\"}, ensure_ascii=False))\n        return\n\n    client = AsyncPerplexity(\n        api_key=os.environ.get(\"PERPLEXITY_API_KEY\"),\n    )\n\n    # Create async tasks for concurrent execution\n    tasks = [\n        client.search.create(query=q, max_results=5, max_tokens_per_page=2048)\n        for q in queries\n    ]\n\n    responses = await asyncio.gather(*tasks, return_exceptions=True)\n\n    output = {}\n    for q, res in zip(queries, responses):\n        if isinstance(res, Exception):\n            output[q] = {\"error\": str(res)}\n        else:\n            # Extract only necessary raw data to save context window limits\n            output[q] = [\n                {\"title\": r.title, \"url\": r.url, \"snippet\": r.snippet}\n                for r in res.results\n            ]\n\n    # Output strictly as JSON for the LLM to parse\n    print(json.dumps(output, ensure_ascii=False, indent=2))\n\nif __name__ == \"__main__\":\n    # AGENT: Replace this list with your formulated queries\n    queries = [\"QUERY_1\", \"QUERY_2\", \"QUERY_3\", \"QUERY_4\", \"QUERY_5\"]\n    asyncio.run(fetch_results(queries))\n\nStage 3: Synthesis and Citation\n\nRead the JSON output generated by the python script.\n\nSynthesize the raw text snippets into a comprehensive, well-structured markdown report that directly answers the user's request.\n\nYou MUST include inline citations [Source Name](URL) for all factual claims, data points, and news using the URLs provided in the JSON output.\n\nIf a query returned an error, acknowledge the missing information transparently."
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