Agent Framework — AI Agent Framework: Live Stats & TrendScore

Live GitHub stats, community sentiment, and trend data for Agent Framework. TrendingBots tracks star velocity, fork activity, and what developers are saying — updated from real data sources.

GitHub data synced: May 6, 2026 • Sentiment updated: Apr 12, 2026

GitHub Statistics

• GitHub Stars: 10,135
• Forks: 1,656
• Open Issues: 815
• Primary Language: Python
• License: MIT
• Weekly Star Velocity: +214 stars/week

Community Sentiment

Community Buzz: Meet the new era of agent-building: Microsoft Agent Framework has officially hit Release Candidate, as mentioned on Twitter by a user, and also 'I built an python AI agent framework that doesn't make me want to mass-delete my venv' from a Reddit user

Pros & Cons

What People Love

Easy to chain agents into a workflow, Richer workflows and simpler agent creation, Support for human-in-the-loop approval

Common Complaints

Limited support for shared state/context across tools and agents, Steep learning curve for beginners

Biggest Positive: Easy LLM Framework

Biggest Negative: Limited State Support

Why Agent Framework Stands Out

Microsoft Agent Framework is valuable because it provides a comprehensive and flexible framework for building, orchestrating, and deploying AI agents. Its graph-based orchestration capabilities and support for various LLM providers make it an ideal choice for complex AI workflows. Additionally, its built-in OpenTelemetry integration and flexible middleware system provide a high degree of observability and customizability. Unlike other frameworks, Microsoft Agent Framework provides a consistent API across both Python and .NET implementations, making it a great choice for multi-language projects.

Built With

Build a multi-agent workflow that automates data processing and analysis — Microsoft Agent Framework enables this through its graph-based orchestration capabilities, Build a chatbot that integrates with Azure services — Microsoft Agent Framework provides a simple and efficient way to do this using its FoundryChatClient, Build a research agent that reads and summarizes large amounts of text — Microsoft Agent Framework's support for various LLM providers makes this possible, Build a custom middleware system for request/response processing and exception handling — Microsoft Agent Framework's flexible middleware system enables this, Build a distributed tracing and monitoring system for AI agents — Microsoft Agent Framework's built-in OpenTelemetry integration makes this easy

Getting Started

1. Run `pip install agent-framework` to install the Microsoft Agent Framework
2. Import the necessary modules and initialize an Agent object, such as `agent = Agent(client=FoundryChatClient(credential=AzureCliCredential()))`
3. Configure the Agent object with the necessary settings, such as `agent.name = 'HaikuBot'` and `agent.instructions = 'You are an upbeat assistant that writes beautifully.'`
4. Use the `agent.run()` method to execute the agent, such as `print(await agent.run('Write a haiku about Microsoft Agent Framework.'))`
5. Try running a simple workflow to verify that it works, such as creating a haiku using the `FoundryChatClient`

About

A framework for building, orchestrating and deploying AI agents and multi-agent workflows with support for Python and .NET.

Official site: https://aka.ms/agent-framework

Category & Tags

Category: multi-agent

Tags: agent-framework, agentic-ai, agents, ai, dotnet, multi-agent, orchestration, python, sdk, workflows

Market Context

Competing with other AI frameworks like LangChain and CrewAI