running

A set of tools for running fast-agent programs interactively or from the command line. fast-agent is a Python framework for configuring and running AI agents.

In plain words
What is it for?
Use it to start an interactive agent session, run an agent with a command-line message, select a particular agent, or override the default model.
Why use it?
It lets developers test agents by chatting with them, send them specific messages, and choose a model without changing the program.

Agent

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add agents/evalstate/fast-agent/running
Clone the repo
git clone --depth 1 https://github.com/evalstate/fast-agent
Per session 0 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 867 The whole file, excluding the scripts and references it only reads on demand.
Security scan C 1 finding. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00000 $0.00867
Opus 5 $0.00000 $0.00434
Sonnet 5 $0.00000 $0.00173
Haiku 4.5 $0.00000 $0.00087

Measured 2d ago against content hash fcf1a2540ce6, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade C, and why

running scanned grade C with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Hidden instructionshighPrompt injection

Directives inside HTML comments, invisible characters or bidirectional overrides are read by the model and not by the person reviewing the file.

<!-- ### Connecting to MCP Servers Connect to MCP servers from other FastAgent applications by configuring them in your `fast-agent.yaml`: ```yaml mcp: servers: my_remote_agent: transport: "sse" url: "http://localhost:80
docs/docs/agents/running.md · 157 lines

How it starts

The opening of the file, as written. The whole thing — 157 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Deploy and Run

fast-agent provides flexible deployment options to meet a variety of use cases, from interactive development to production server deployments.

Interactive Mode

Run fast-agent programs interactively for development, debugging, or direct user interaction.

import asyncio
from fast_agent.core.fastagent import FastAgent

fast = FastAgent("My Interactive Agent")


@fast.agent(instruction="You are a helpful assistant")
async def main():
    async with fast.run() as agent:
        # Start interactive prompt
        await agent()


if __name__ == "__main__":
    asyncio.run(main())

When started with uv run agent.py, this begins an interactive prompt where you can chat directly with the configured agents, apply prompts, save history and so on.

Command Line Execution

fast-agent supports command-line arguments to run agents and workflows with specific messages.

# Send a message to a specific agent
uv run agent.py --agent default --message "Analyze this dataset"

# Override the default model
uv run agent.py --model gpt-4o --agent default --message "Complex question"

# Run with minimal output
uv run agent.py --quiet --agent default --message "Background task"

This is perfect for scripting, automation, or one-off queries.

The --quiet flag switches off the Progress, Chat and Tool displays.

MCP Server Deployment

Any fast-agent application can be deployed as an MCP server with a simple command-line switch.

Starting an MCP Server

# Start as a Streamable HTTP server (http://localhost:8080/mcp)
uv run agent.py --transport http --port 8080

# Start as a stdio server
uv run agent.py --transport stdio

Each configured agent is exposed as an MCP tool for sending messages to that agent.

The MCP Server can also be started programmatically.

Programmatic Server Startup

import asyncio
from fast_agent.core.fastagent import FastAgent

fast = FastAgent("Server Agent")


@fast.agent(instruction="You are an API agent")
async def main():
    # Start as a server programmatically
    await fast.start_server(
        transport="http",
        port=8080,
        server_name="API-Agent-Server",
        server_description="Provides API access to my agent",
        tool_description="Send a message to the {agent} agent",
    )


if __name__ == "__main__":
    asyncio.run(main())

Read the full file on GitHub · 157 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 157 lines · 0 tokens per session scan C fcf1a2540ce6

Subscribe to this mod's changes

running is an agent published in the GitHub repository evalstate/fast-agent (3,904 stars, last pushed 2d ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 867 tokens. A static security scan graded it C with 1 finding (hidden instructions). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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