Overview

The named-pipes library is designed to facilitate low-latency inter-process communication (IPC) on the same machine by leveraging named pipes as the transport layer for building persistent agentic tool servers. These tool servers can provide a wide array of capabilities, including Large Language Model (LLM) inference, text-to-speech (TTS), vector search, and browser automation.

Low Latency and Statefulness

Named pipes route data through kernel memory, which enhances speed and reduces the complexity associated with traditional IPC methods like HTTP or shared memory. This results in a significant reduction in latency, making the library especially suitable for real-time applications such as voice agents. By retaining state in memory, the tool servers avoid the overhead of reloading model weights or data from disk on each invocation, leading to enhanced performance.

Core Features

• Persistent Server Processes: Each tool server operates as a persistent process, allowing it to maintain in-memory states, such as model weights or browser states, vastly improving response times for repeated requests.
• Ad-hoc Command Execution: A command-line utility, cpipe, is included for sending commands to any running server directly from the terminal, enabling quick interactions with tool servers.
• Integration with Coding Tools: The library supports direct interaction with agentic coding tools such as Claude Code, allowing for seamless command execution and server discovery.

Why Choose Named Pipes?

Named pipes are a robust choice for IPC because:

• Statefulness: They maintain the state between calls, making them ideal for applications requiring quick responses to commands.
• Minimal Latency: Named pipes are significantly faster than traditional IPC methods, accelerating communication and enhancing user experience.

Architectural Advantages

Unlike Command Line Interface (CLI) tools that spawn new processes for each command and thus incur startup costs, or frameworks like MCP which add unnecessary layers of complexity, named pipes allow for direct messaging between the orchestrator and the tool server. This efficient communication eliminates protocol overhead, streamlining the entire process for local applications.

Example Use Cases

The library supports various applications, including but not limited to:

• LLM inference servers for real-time data processing.
• Speech-to-text and text-to-speech streaming servers for multimedia applications.
• In-memory key-value stores for quick data retrieval.
• Vector databases for efficient data searching and analysis.
• Browser automation servers for enhanced application interaction.

API Overview

The library offers a set of abstractions over named pipes, facilitating easy development:

from named_pipes import ToolNamedPipe

with ToolNamedPipe("example_tool") as tool:
    # Tool logic here
    pass

Developers can implement custom protocols on top of the base classes TextNamedPipe and DataNamedPipe, enabling rapid development of new capabilities tailored to specific needs.

Command-Line Utility: cpipe

The cpipe command-line tool further enhances usability, allowing users to:

• Send commands to named-pipe servers.
• Discover running servers and their capabilities.

Example usage includes:

cpipe /tmp/tool-chat chat --data '{"messages": [{"role":"user","content":"Hello"}]}'

In summary, the named-pipes library provides an effective and efficient way to build agentic tool servers with low latency, while keeping the architecture simple and accessible for developers.