How to Use the pgvector (Vector Database) MCP in AutoGen

Q: Is my pgvector (Vector Database) schema accessible to AutoGen?

Yes. You can use listtables so your agents can inspect and understand your database structure before they act.

Q: Does AutoGen support indexing for pgvector (Vector Database)?

Yes. Your agents can call createindex to maintain search performance. They can even debate the best index type for your workload.

Enable multi-agent debate and decision-making on your PostgreSQL data with this MCP Server for AutoGen.

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Connect pgvector (Vector Database) MCP to AutoGen

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Setup pgvector (Vector Database) with AutoGen

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Coordinate agents with pgvector tools

Let your agents debate which data to pull using `search_vectors`. One agent can propose a query while another verifies the quality of the results. This ensures your database is queried correctly. You get a consensus-based approach to data retrieval that reduces errors in your pipeline.

Manage database state with AutoGen

Allow your agents to call `create_table` when they identify a need for new storage. They coordinate to ensure your database schema evolves safely. You avoid manual bottlenecks. Agents handle the structural changes required to support their ongoing discussions and tasks.

Audit vector operations in AutoGen

Use `list_tables` to keep agents informed about the current database state. They use this information to decide which tables to target for specific tasks. It keeps the multi-agent system synchronized. Every participant knows exactly where the data is stored and how to access it.

Setup guide

Set up pgvector (Vector Database) MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes pgvector (Vector Database) tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="pgvector (Vector Database)_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent pgvector (Vector Database) data")
print(result.messages[-1].content)

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Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="pgvector (Vector Database)_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent pgvector (Vector Database) data")
    print(result.messages[-1].content)

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