Using Pipelock with AutoGen#

Pipelock wraps MCP servers used by AutoGen agents as a stdio proxy, scanning
every request and response for credential leaks, prompt injection, and tool
description poisoning. This guide covers StdioServerParams integration with
AutoGen v0.4+ and Docker Compose deployment.

Quick Start#

# 1. Install pipelock
go install github.com/luckyPipewrench/pipelock/cmd/pipelock@latest

# 2. Generate a config (or copy a preset)
pipelock generate config --preset balanced > pipelock.yaml

# 3. Verify
pipelock version

import asyncio
from autogen_ext.tools.mcp import StdioServerParams, mcp_server_tools

async def main():
    server_params = StdioServerParams(
        command="pipelock",
        args=[
            "mcp", "proxy",
            "--config", "pipelock.yaml",
            "--",
            "npx", "-y", "@modelcontextprotocol/server-filesystem", "/workspace"
        ],
    )

    tools = await mcp_server_tools(server_params)
    # tools is a list of standard AutoGen tool objects

asyncio.run(main())

That's it. The tools list contains standard AutoGen tool objects backed by
Pipelock-scanned MCP calls.

How It Works#

AutoGen Agent <--> pipelock mcp proxy <--> MCP Server
   (client) (scan both ways) (subprocess)

Pipelock scans three things:

Outbound requests. Catches credentials leaking through tool arguments
(API keys, tokens, private key material).
Inbound responses. Catches prompt injection in tool results.
Tool descriptions. Catches poisoned tool definitions and mid-session
rug-pull changes.

Integration Patterns#

Pattern A: AssistantAgent with MCP Tools#

The standard pattern: create MCP tools and pass them to an AssistantAgent.

import asyncio
from autogen_agentchat.agents import AssistantAgent
from autogen_agentchat.ui import Console
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import StdioServerParams, mcp_server_tools

async def main():
    server_params = StdioServerParams(
        command="pipelock",
        args=[
            "mcp", "proxy",
            "--config", "pipelock.yaml",
            "--",
            "npx", "-y", "@modelcontextprotocol/server-filesystem", "/workspace"
        ],
    )

    tools = await mcp_server_tools(server_params)

    agent = AssistantAgent(
        name="file_analyst",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        tools=tools,
    )

    await Console(agent.run_stream(task="List all files in the workspace"))

asyncio.run(main())

Pattern B: Multiple MCP Servers#

Create tools from multiple Pipelock-wrapped servers and combine them:

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

async def main():
    model_client = OpenAIChatCompletionClient(model="gpt-4o")

    filesystem_params = StdioServerParams(
        command="pipelock",
        args=["mcp", "proxy", "--config", "pipelock.yaml", "--",
              "npx", "-y", "@modelcontextprotocol/server-filesystem", "/workspace"],
    )

    database_params = StdioServerParams(
        command="pipelock",
        args=["mcp", "proxy", "--config", "pipelock.yaml", "--",
              "python", "-m", "mcp_server_sqlite", "--db", "/data/app.db"],
    )

    fs_tools = await mcp_server_tools(filesystem_params)
    db_tools = await mcp_server_tools(database_params)

    agent = AssistantAgent(
        name="multi_tool_agent",
        model_client=model_client,
        tools=fs_tools + db_tools,
    )

asyncio.run(main())

Pattern C: Mixed Transports#

Wrap stdio servers with Pipelock. Remote servers connect directly and are not
covered by the stdio proxy:

import asyncio
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import StdioServerParams, StreamableHttpServerParams, mcp_server_tools

async def main():
    model_client = OpenAIChatCompletionClient(model="gpt-4o")

    # Local server: wrap with pipelock
    local_params = StdioServerParams(
        command="pipelock",
        args=["mcp", "proxy", "--config", "pipelock.yaml", "--",
              "npx", "-y", "@modelcontextprotocol/server-filesystem", "/tmp"],
    )

    # Remote server: NOT scanned by pipelock
    # Pipelock's MCP proxy only wraps stdio servers.
    # For remote servers, vet the server before connecting.
    remote_params = StreamableHttpServerParams(
        url="https://api.example.com/mcp",
        headers={"Authorization": "Bearer token"},
    )

    local_tools = await mcp_server_tools(local_params)
    remote_tools = await mcp_server_tools(remote_params)

    agent = AssistantAgent(
        name="hybrid_agent",
        model_client=model_client,
        tools=local_tools + remote_tools,
    )

asyncio.run(main())

Note: SseServerParams is deprecated. Use StreamableHttpServerParams
for new remote MCP connections.

Note: Pipelock's MCP proxy only wraps stdio-based servers. Remote HTTP/SSE
MCP connections go directly to the remote endpoint and bypass Pipelock. For
outbound HTTP traffic from your agent code (API calls, web fetches), route those
through pipelock run as a fetch proxy. See the
HTTP fetch proxy section below.

Pattern D: Multi-Agent Teams#

AutoGen's team abstractions (RoundRobinGroupChat, SelectorGroupChat, etc.)
allow agents with different Pipelock configs to collaborate:

import asyncio
from autogen_agentchat.agents import AssistantAgent
from autogen_agentchat.teams import RoundRobinGroupChat
from autogen_agentchat.conditions import TextMentionTermination
from autogen_agentchat.ui import Console
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import StdioServerParams, mcp_server_tools

async def main():
    model_client = OpenAIChatCompletionClient(model="gpt-4o")

    # Researcher: broad access, warn mode
    research_tools = await mcp_server_tools(StdioServerParams(
        command="pipelock",
        args=["mcp", "proxy", "--config", "pipelock-warn.yaml", "--",
              "npx", "-y", "@modelcontextprotocol/server-fetch"],
    ))

    researcher = AssistantAgent(
        name="researcher",
        model_client=model_client,
        tools=research_tools,
        system_message="Research topics. Say TERMINATE when done.",
    )

    # Writer: restricted access, block mode
    writer_tools = await mcp_server_tools(StdioServerParams(
        command="pipelock",
        args=["mcp", "proxy", "--config", "pipelock-strict.yaml", "--",
              "npx", "-y", "@modelcontextprotocol/server-filesystem", "/output"],
    ))

    writer = AssistantAgent(
        name="writer",
        model_client=model_client,
        tools=writer_tools,
        system_message="Write reports based on research. Say TERMINATE when done.",
    )

    team = RoundRobinGroupChat(
        participants=[researcher, writer],
        termination_condition=TextMentionTermination("TERMINATE"),
    )

    await Console(team.run_stream(task="Research and write a report on workspace contents"))

asyncio.run(main())

Docker Compose#

Network-isolated deployment where the agent container has no direct internet
access:

networks:
  pipelock-internal:
    internal: true
    driver: bridge
  pipelock-external:
    driver: bridge

services:
  pipelock:
    # Pin to a specific version for production (e.g., ghcr.io/luckypipewrench/pipelock:2.1.2)
    image: ghcr.io/luckypipewrench/pipelock:latest
    networks:
      - pipelock-internal
      - pipelock-external
    command: ["run", "--listen", "0.0.0.0:8888", "--config", "/config/pipelock.yaml"]
    volumes:
      - ./pipelock.yaml:/config/pipelock.yaml:ro
    healthcheck:
      test: ["/pipelock", "healthcheck"]
      interval: 10s
      timeout: 3s
      start_period: 5s
      retries: 3

  autogen-agent:
    build: .
    networks:
      - pipelock-internal
    environment:
      - OPENAI_API_KEY=${OPENAI_API_KEY}
      - PIPELOCK_FETCH_URL=http://pipelock:8888/fetch
    depends_on:
      pipelock:
        condition: service_healthy

The agent container can only reach the pipelock service. All HTTP traffic goes
through the fetch proxy. MCP servers running as subprocesses inside the agent
container are wrapped with pipelock mcp proxy as shown above.

You can also generate this template with:

pipelock generate docker-compose --agent generic

HTTP Fetch Proxy#

For scanning HTTP traffic from AutoGen agents (web fetches, API calls), run
Pipelock as a fetch proxy:

pipelock run --config pipelock.yaml

Configure your agent to route HTTP requests through http://localhost:8888/fetch:

import requests

def fetch_through_pipelock(url: str) -> str:
    resp = requests.get(
        "http://localhost:8888/fetch",
        params={"url": url},
        headers={"X-Pipelock-Agent": "autogen-research"},
        timeout=30,
    )
    resp.raise_for_status()
    data = resp.json()
    if data.get("blocked"):
        raise RuntimeError(f"Pipelock blocked request: {data.get('block_reason')}")
    return data.get("content", "")

TLS Interception#

When using pipelock as an HTTP forward proxy (HTTPS_PROXY), CONNECT tunnels
are opaque by default: pipelock only sees the hostname, not the request body or
response content. Enabling TLS interception closes this gap by performing a MITM
on HTTPS connections, giving you full DLP on request bodies and response
injection detection through CONNECT tunnels.

To enable it:

Generate a CA and enable TLS interception (see the TLS Interception Guide)
Trust the CA in your Python environment:

export SSL_CERT_FILE=~/.pipelock/ca.pem
# Or for requests/httpx specifically:
export REQUESTS_CA_BUNDLE=~/.pipelock/ca.pem

MCP proxy mode (stdio wrapping) does not require TLS interception. It scans
traffic in both directions without certificates.

Choosing a Config#

Config	Action	Best For
`balanced`	warn (default)	Recommended starting point (`--preset balanced`)
`strict`	block (default)	High-security, production (`--preset strict`)
`generic-agent.yaml`	warn (default)	Agent-specific tuning (copy from `configs/`)
`claude-code.yaml`	block (default)	Unattended coding agents (copy from `configs/`)

Start with balanced to log detections without blocking. Review the logs,
tune thresholds, then switch to strict for production.

Troubleshooting#

MCP server not starting#

Verify the command works without Pipelock first:

npx -y @modelcontextprotocol/server-filesystem /tmp

Then wrap it:

pipelock mcp proxy -- npx -y @modelcontextprotocol/server-filesystem /tmp

Seeing Pipelock output#

Pipelock logs to stderr. To see real-time output during development:

echo '{"jsonrpc":"2.0","id":1,"method":"tools/list"}' | \
  pipelock mcp proxy --config pipelock.yaml -- npx -y @modelcontextprotocol/server-filesystem /tmp

False positives#

Switch to warn mode to see what's being flagged without blocking:

response_scanning:
  action: warn
mcp_input_scanning:
  action: warn
mcp_tool_scanning:
  action: warn

Review stderr output, then tighten thresholds.

Config file not found#

Use absolute paths if relative paths don't resolve:

StdioServerParams(
    command="pipelock",
    args=["mcp", "proxy", "--config", "/etc/pipelock/config.yaml", "--",
          "npx", "-y", "@modelcontextprotocol/server-filesystem", "/tmp"],
)