Conversational flow agents

Introduced 2.13

Similarly to a flow agent, a conversational flow agent is configured with a set of tools that it runs in order. The difference between them is that a conversational flow agent stores the conversation in an index, in the following example, the conversation_index. The following agent runs the VectorDBTool and then the MLModelTool:

POST /_plugins/_ml/agents/_register
{
  "name": "population data analysis agent",
  "type": "conversational_flow",
  "description": "This is a demo agent for population data analysis",
  "app_type": "rag",
  "memory": {
    "type": "conversation_index"
  },
  "tools": [
    {
      "type": "VectorDBTool",
      "name": "population_knowledge_base",
      "parameters": {
        "model_id": "YOUR_TEXT_EMBEDDING_MODEL_ID",
        "index": "test_population_data",
        "embedding_field": "population_description_embedding",
        "source_field": [
          "population_description"
        ],
        "input": "${parameters.question}"
      }
    },
    {
      "type": "MLModelTool",
      "name": "bedrock_claude_model",
      "description": "A general tool to answer any question",
      "parameters": {
        "model_id": "YOUR_LLM_MODEL_ID",
        "prompt": """

Human:You are a professional data analyst. You will always answer question based on the given context first. If the answer is not directly shown in the context, you will analyze the data and find the answer. If you don't know the answer, just say don't know. 

Context:
${parameters.population_knowledge_base.output:-}

${parameters.chat_history:-}

Human:${parameters.question}

Assistant:"""
      }
    }
  ]
}

For more information about the Register Agent API request fields, see Request body fields.

For a step-by-step tutorial, see Agents and tools tutorial.

Using external MCP tools

Introduced 3.8

Starting in OpenSearch 3.8, conversational flow agents can call external MCP server tools in a fixed pipeline alongside OpenSearch tools such as MLModelTool. Configure parameters.mcp_connectors and declare each MCP tool explicitly in the tools array using McpStreamableHttpTool or McpSseTool. For setup steps and examples, see Using MCP tools with flow agents and conversational flow agents.

Next steps

To learn more about registering agents, see Register Agent API.
For a list of supported tools, see Tools.
For a step-by-step tutorial, see Agents and tools tutorial.
For supported APIs, see Agent APIs.
To use agents and tools in configuration automation, see Automating configurations.