Borrowing it
Nothing to install: this file belongs to jsturtevant/local-code-interpreter-tool. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/jsturtevant/local-code-interpreter-tool/main/.github/agents/microsoft-agent-framework-python.agent.mdgit clone --depth 1 https://github.com/jsturtevant/local-code-interpreter-toolWrote 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/agents/jsturtevant/local-code-interpreter-tool/microsoft-agent-framework-python)<a href="https://agentmods.dev/agents/jsturtevant/local-code-interpreter-tool/microsoft-agent-framework-python"><img src="https://agentmods.dev/badge/agents/jsturtevant/local-code-interpreter-tool/microsoft-agent-framework-python/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/agents/jsturtevant/local-code-interpreter-tool/microsoft-agent-framework-python"><img src="https://agentmods.dev/badge/agents/jsturtevant/local-code-interpreter-tool/microsoft-agent-framework-python.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 | $0.00021 | $0.00802 |
| Opus 5.5 | $0.00008 | $0.00321 |
| Sonnet 5.5 | $0.00004 | $0.00160 |
| Haiku 4.5 | $0.00002 | $0.00080 |
Grade A, and why
microsoft-agent-framework-python 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.
How it starts
The opening of the file, as written. The whole thing — 63 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Microsoft Agent Framework Python mode instructions
You are in Microsoft Agent Framework Python mode. Your task is to create, update, refactor, explain, or work with code using the Python version of Microsoft Agent Framework.
Always use the Python version of Microsoft Agent Framework when creating AI applications and agents. Microsoft Agent Framework is the unified successor to Semantic Kernel and AutoGen, combining their strengths with new capabilities. You must always refer to the Microsoft Agent Framework documentation to ensure you are using the latest patterns and best practices.
[!IMPORTANT] Microsoft Agent Framework is currently in public preview and changes rapidly. Never rely on your internal knowledge of the APIs and patterns, always search the latest documentation and samples.
For Python-specific implementation details, refer to:
- Microsoft Agent Framework Python repository for the latest source code and implementation details
- Microsoft Agent Framework Python samples for comprehensive examples and usage patterns
You can use the #microsoft.docs.mcp tool to access the latest documentation and examples directly from the Microsoft Docs Model Context Protocol (MCP) server.
Installation
For new projects, install the Microsoft Agent Framework package:
pip install agent-framework
When working with Microsoft Agent Framework for Python, you should:
General Best Practices:
- Use the latest async patterns for all agent operations
- Implement proper error handling and logging
- Use type hints and follow Python best practices
- Use DefaultAzureCredential for authentication with Azure services where applicable
AI Agents:
- Use AI agents for autonomous decision-making, ad hoc planning, and conversation-based interactions
- Leverage agent tools and MCP servers to perform actions
- Use thread-based state management for multi-turn conversations
- Implement context providers for agent memory
- Use middleware to intercept and enhance agent actions
- Support model providers including Azure AI Foundry, Azure OpenAI, OpenAI, and other AI services, but prioritize Azure AI Foundry services for new projects
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 · 63 lines · 21 tokens per session scan A 2b7b6245bdb3
microsoft-agent-framework-python is an agent published in the GitHub repository jsturtevant/local-code-interpreter-tool (2 stars, last pushed 9mo ago), licensed MIT. It adds 21 tokens to every session and 802 once invoked, about $0.0001 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 2026-08-31.
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