Microsoft Agent Framework is a multi-language framework for building, coordinating, and running AI agents and workflows in Python and .NET. Teams use it for agent systems that need orchestration, collaboration, durability, observability, governance, or human involvement beyond a single prompt or chat loop. Catalogue add-ons provide skills and instructions for working with the framework.
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 microsoft/agent-framework --skill foundry-config-setupgit clone --depth 1 https://github.com/microsoft/agent-frameworkWrote 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/microsoft/agent-framework/foundry-config-setup)<a href="https://agentmods.dev/skills/microsoft/agent-framework/foundry-config-setup"><img src="https://agentmods.dev/badge/skills/microsoft/agent-framework/foundry-config-setup/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/microsoft/agent-framework/foundry-config-setup"><img src="https://agentmods.dev/badge/skills/microsoft/agent-framework/foundry-config-setup.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk pass
- NVIDIA SkillSpector pass
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 | $0.00065 | $0.00430 |
| Opus 5.5 | $0.00026 | $0.00172 |
| Sonnet 5.5 | $0.00013 | $0.00086 |
| Haiku 4.5 | $0.00006 | $0.00043 |
Grade A, and why
foundry-config-setup 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 1mo 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
Usage
Some samples (notably those under 01-get-started) hardcode the Foundry
project endpoint and model directly in the FoundryChatClient constructor
using placeholder values, for example:
client = FoundryChatClient(
project_endpoint="https://your-project.services.ai.azure.com",
model="gpt-4o",
credential=AzureCliCredential(),
)
These placeholder values are not real and the sample cannot run as written. The validation environment provides the real values through environment variables, so the sample must read them from the environment instead.
When you detect a hardcoded/placeholder project_endpoint (or model) that
is causing the sample to fail with missing or invalid setup:
- Replace the hardcoded
project_endpointvalue with a read from theFOUNDRY_PROJECT_ENDPOINTenvironment variable. - Replace the hardcoded
modelvalue with a read from theFOUNDRY_MODELenvironment variable. - Ensure
import osis present at the top of the file.
The corrected constructor should look like:
import os
client = FoundryChatClient(
project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
model=os.environ["FOUNDRY_MODEL"],
credential=AzureCliCredential(),
)
These samples are intentionally written with hardcoded placeholders, so this
is expected setup—not a defect in the sample. After applying the change,
re-run the sample and report the result as a success if it now runs.
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.
- 1mo ago First seen · 53 lines · 65 tokens per session scan A 72279cefeddb
foundry-config-setup is a skill published in the GitHub repository microsoft/agent-framework (14,148 stars, last pushed today), licensed MIT. It adds 65 tokens to every session and 430 once invoked, about $0.0003 per session on Opus 5.5. 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.
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