How to Use the Biconomy (Account Abstraction) MCP in AutoGen
Enable AutoGen agents to negotiate transaction costs and execute Biconomy Supertransactions through multi-agent debate.
Works with every AI agent you already use
…and any MCP-compatible client
Connect Biconomy (Account Abstraction) MCP to AutoGen
Create your Vinkius account to connect Biconomy (Account Abstraction) to AutoGen and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.
Negotiate transaction quotes in AutoGen
The `get_quote` tool provides the exact cost parameters for your requested Supertransaction. In an AutoGen setup, a budget agent can analyze this quote and debate with an execution agent whether the gas fees are acceptable. This consensus-driven approach ensures you never execute transactions during peak gas spikes. The agents coordinate to fetch new quotes until they meet your pre-defined budget rules.
Multi-agent execution via AutoGen MCP Server
The `execute_supertx` tool handles the final on-chain submission of your signed quote. Your security agent verifies the signatures in the `payloadToSign` field before the execution agent calls this tool. This separation of duties prevents unauthorized or malformed payloads from reaching the blockchain. The MCP Server acts as the secure gateway, executing only when all agents reach consensus.
Track and verify execution status collectively
The `get_explorer_status` tool retrieves the real-time status of your submitted Supertransaction. Once executed, a monitoring agent continuously checks this endpoint to verify completion. The monitoring agent then reports the final status back to the group. If a failure occurs, the agents can collectively decide to request a new quote and retry.
Set up Biconomy (Account Abstraction) 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 includesmcp_server_toolsfor 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
AssistantAgentand callagent.run(). The agent invokes Biconomy (Account Abstraction) tools and returns structured results.
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="Biconomy (Account Abstraction)_assistant",
model_client=OpenAIChatCompletionClient(model="gpt-4o"),
tools=tools,
)
result = await agent.run("List recent Biconomy (Account Abstraction) data")
print(result.messages[-1].content) 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.
McpWorkbenchis 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=workbenchto your agent. State is preserved across multiple tool calls within the same session.
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="Biconomy (Account Abstraction)_assistant",
model_client=OpenAIChatCompletionClient(model="gpt-4o"),
workbench=workbench,
)
result = await agent.run("List recent Biconomy (Account Abstraction) data")
print(result.messages[-1].content) Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Biconomy. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
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Common questions about Biconomy (Account Abstraction) MCP in AutoGen
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