Microsoft Agent Framework
Microsoft Agent Framework is an open source SDK and runtime designed to help developers build, orchestrate, and deploy AI agents and multi-agent workflows using languages such as .NET and Python. It combines the simple agent abstractions of AutoGen with the enterprise-grade capabilities of Semantic Kernel, including session-based state management, type safety, middleware, telemetry, and broad model and embedding support, creating a unified platform for both experimentation and production use. It introduces graph-based workflows that give developers explicit control over how multiple agents interact, execute tasks, and coordinate complex processes, enabling structured orchestration across sequential, concurrent, or branching scenarios. It supports long-running and human-in-the-loop workflows through robust state management, allowing agents to maintain context, reason through multi-step problems, and operate continuously over time.
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Claude Managed Agents
Claude Managed Agents is a pre-built, configurable agent system from Anthropic designed to run long-running, asynchronous tasks on managed infrastructure without requiring developers to build their own agent loops. It acts as a complete “agent harness,” allowing developers to define goals while the system handles execution, orchestration, and state management behind the scenes. Unlike direct model prompting, which requires step-by-step interaction, Managed Agents are designed for tasks that unfold over time, such as research, automation, or multi-step workflows, where the agent can continue working independently after being started. It supports advanced capabilities such as multi-agent orchestration, where a primary agent can coordinate specialized sub-agents that operate in parallel with isolated contexts, improving both speed and output quality.
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BabyAGI
This Python script is an example of an AI-powered task management system. The system uses OpenAI and Chroma to create, prioritize, and execute tasks. The main idea behind this system is that it creates tasks based on the result of previous tasks and a predefined objective. The script then uses OpenAI's natural language processing (NLP) capabilities to create new tasks based on the objective, and Chroma to store and retrieve task results for context. This is a pared-down version of the original Task-Driven Autonomous Agent.
The script works by running an infinite loop that does the following steps:
1. Pulls the first task from the task list.
2. Sends the task to the execution agent, which uses OpenAI's API to complete the task based on the context.
3. Enriches the result and stores it in Chroma.
4. Creates new tasks and reprioritizes the task list based on the objective and the result of the previous task.
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Bahama
These are step-by-step AI automations that flow sequentially towards a final outcome, with each step building upon the previous to deliver consistent, repeatable outcomes every time. Flows can be saved and re-run as needed, automating tasks as diverse as responding to customer service emails or producing a newsletter by gathering, summarizing, and publishing the latest news. Create an internal knowledge base to give your AI agents the data they need to take informed action on your specific projects. Provide support docs to empower an AI customer service agent, a readme to support an AI code reviewer, or product specs to inform an AI sales agent.
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