running-agents

An explanation of how to run agents with one or more messages, either waiting for the complete answer or receiving it piece by piece as it is produced. The latter approach is called streaming.

In plain words
What is it for?
Use it to call agents synchronously or asynchronously, run non-streaming requests, and process streamed response updates in C# or Python.
Why use it?
It helps developers choose between a complete response for simple workflows and incremental updates for interfaces that should show progress immediately.

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/managedcode/dotnet-skills/running-agents
Clone the repo
git clone --depth 1 https://github.com/managedcode/dotnet-skills
Per session 8 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 3,925 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. 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 $0.00008 $0.03925
Opus 5 $0.00004 $0.01962
Sonnet 5 $0.00002 $0.00785
Haiku 4.5 $0.00001 $0.00392

Measured 2d ago against content hash e18241156cca, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

running-agents scanned grade A with 0 findings 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.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

catalog/Frameworks/Microsoft-Agent-Framework/skills/microsoft-agent-framework/references/official-docs/concepts/agents/running-agents.md · 421 lines

How it starts

The opening of the file, as written. The whole thing — 421 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Running Agents

The base Agent abstraction exposes various options for running the agent. Callers can choose to supply zero, one, or many input messages. Callers can also choose between streaming and non-streaming. Let's dig into the different usage scenarios.

Streaming and non-streaming

Microsoft Agent Framework supports both streaming and non-streaming methods for running an agent.

::: zone pivot="programming-language-csharp"

For non-streaming, use the RunAsync method.

Console.WriteLine(await agent.RunAsync("What is the weather like in Amsterdam?"));

For streaming, use the RunStreamingAsync method.

await foreach (var update in agent.RunStreamingAsync("What is the weather like in Amsterdam?"))
{
    Console.Write(update);
}

::: zone-end ::: zone pivot="programming-language-python"

For non-streaming, use the run method.

result = await agent.run("What is the weather like in Amsterdam?")
print(result.text)

For streaming, use the run method with stream=True. This returns a ResponseStream object that can be iterated asynchronously:

async for update in agent.run("What is the weather like in Amsterdam?", stream=True):
    if update.text:
        print(update.text, end="", flush=True)

ResponseStream

The ResponseStream object returned by run(..., stream=True) supports two consumption patterns:

Pattern 1: Async iteration — process updates as they arrive for real-time display:

response_stream = agent.run("Tell me a story", stream=True)
async for update in response_stream:
    if update.text:
        print(update.text, end="", flush=True)

Pattern 2: Direct finalization — skip iteration and get the complete response:

response_stream = agent.run("Tell me a story", stream=True)
final = await response_stream.get_final_response()
print(final.text)

Pattern 3: Combined — iterate for real-time display, then get the aggregated result:

response_stream = agent.run("Tell me a story", stream=True)

# First, iterate to display streaming output
async for update in response_stream:
    if update.text:
        print(update.text, end="", flush=True)

# Then get the complete response (uses already-collected updates, does not re-iterate)
final = await response_stream.get_final_response()
print(f"\n\nFull response: {final.text}")
print(f"Messages: {len(final.messages)}")

Read the full file on GitHub · 421 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 · 421 lines · 8 tokens per session scan A e18241156cca

Subscribe to this mod's changes

running-agents is an agent published in the GitHub repository managedcode/dotnet-skills (477 stars, last pushed 2d ago), licensed MIT. It adds 8 tokens to every session and 3,925 once invoked, about $0.0000 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.