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.
npx skills add NicolaiLassen/orxhestra --skill agent-streaminggit clone --depth 1 https://github.com/NicolaiLassen/orxhestraWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/nicolailassen/orxhestra/agent-streaming)<a href="https://agentmods.dev/skills/nicolailassen/orxhestra/agent-streaming"><img src="https://agentmods.dev/badge/skills/nicolailassen/orxhestra/agent-streaming/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/nicolailassen/orxhestra/agent-streaming"><img src="https://agentmods.dev/badge/skills/nicolailassen/orxhestra/agent-streaming.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00029 | $0.00507 |
| Opus 5 | $0.00015 | $0.00253 |
| Sonnet 5 | $0.00006 | $0.00101 |
| Haiku 4.5 | $0.00003 | $0.00051 |
Grade A, and why
agent-streaming 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 10d 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.
What it actually says
Agent Streaming
All agents stream via astream(), yielding Event objects.
Basic streaming
from orxhestra.events.event import Event, EventType
async for event in agent.astream("Write about distributed systems"):
if event.type == EventType.AGENT_MESSAGE and event.partial:
print(event.text, end="", flush=True)
elif event.is_final_response():
print(f"\n[DONE] {event.text}")
Sub-agent streaming via AgentTool
Sub-agent events stream through the parent in real-time. Events carry branch and agent_name fields.
from orxhestra import LlmAgent
from orxhestra.tools.agent_tool import AgentTool
weather_agent = LlmAgent(name="WeatherAgent", model=model, tools=[get_weather])
travel_agent = LlmAgent(name="TravelAgent", model=model, tools=[get_attractions])
planner = LlmAgent(
name="TripPlanner",
model=model,
tools=[AgentTool(weather_agent), AgentTool(travel_agent)],
instructions="Use the sub-agents to plan a trip.",
)
async for event in planner.astream("Plan a trip to Copenhagen"):
if event.branch:
print(f" [{event.agent_name}] {event.text}", end="")
elif event.is_final_response():
print(f"\nFinal: {event.text}")
How it works
LlmAgentcreates anasyncio.Queueand setsctx.event_callback = queue.put_nowait.AgentToolcallsctx.event_callback(event)for each child event.- Events yield from the queue concurrently while tools run.
event_callbackpropagates throughctx.derive()for nested sub-agents.
Any custom tool can use ctx.event_callback to push events.
With Runner
async for event in runner.astream(
user_id="user-1",
session_id="session-1",
new_message="Write me a long essay.",
):
if event.is_final_response():
print(f"\n[DONE] {event.text}")
elif event.type == EventType.AGENT_MESSAGE and event.partial:
print(event.text, end="", flush=True)
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.
- 10d ago First seen · 69 lines · 29 tokens per session scan A 3c82d5d4b4f4
agent-streaming is a skill published in the GitHub repository NicolaiLassen/orxhestra (21 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 29 tokens to every session and 507 once invoked, about $0.0001 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.
Other skills, from other repositories
ampersend
Give an agent a way to pay for things on the internet. Use when the user wants the agent to be able to pay for things online, when an HTTP call returns 402 Payment Required, when calling an endpoint that charges per request, when the user names a capability they want without a specific URL in mind, or when the user is…
unit-converter
Converts values between metric and imperial units, using the project's agreed factors.
agent-framework-py-release
Use when cutting a Python release for the microsoft/agent-framework monorepo. Triggers on "bump py versions", "cut a python release", "prepare release PR for python", "release py packages", "bump python to X.Y.Z", or similar requests to bump Python package versions and prepare a release PR. Handles all four lifecycle…
foundry-hosted-agent-validation
Step-by-step process for validating a Python Foundry hosted agent sample (under python/samples/04-hosting/foundry-hosted-agents/) end to end — running it locally (native runtime and azd ai agent run) and after deploying it to an Azure AI Foundry project with azd. Use this when asked to validate a hosted agent sample.
build-and-test
How to build and test .NET projects in the Agent Framework repository. Use this when verifying or testing changes.
python-feature-lifecycle
Guidance for package and feature lifecycle in the Agent Framework Python codebase, including stage meanings, feature-stage decorators, feature enums, and how to move APIs from one stage to the next.