agentic ai playground

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Basic Information

Agentic AI Playground is a small research-focused repository for experimenting with agentic, multi-agent AI systems built on the smolagents library. It serves as a local research assistant framework where multiple autonomous agents can be instantiated to perform tasks and interact. The README indicates the project is intended for local runs and experimentation rather than a hosted service. It documents which open-source models are used to run agents locally and gives operational guidance about model selection. The project targets researchers and developers who want to prototype agentic behaviors, orchestration patterns, and code-generating agents using locally available LLMs. The repository emphasizes practical, model-centered experimentation with agents that can execute Python code when driven by a coding-optimized language model.

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Features
The README highlights use of the smolagents library to build multi-agent systems and lists concrete model configurations used to run agents locally, including llama3.1:8b-instruct-q8_0, llama3.1:8b-q8_0, qwen2.5:7b-instruct-q8_0, and qwen2.5-coder:7b-instruct-q8_0. It calls out that agents are capable of writing actual Python code as part of their actions, which requires stronger coding-optimized models for reliable behavior. The repo is oriented to local execution and model selection guidance, and it provides practical notes advising the use of coder-oriented LLMs for best performance. The content is concise and focused on enabling reproducible local experiments with multi-agent setups and code-writing agents.
Use Cases
This repository is helpful for researchers and developers exploring agentic AI and multi-agent research because it consolidates a lightweight playground and clear operational notes about running agents locally with specific open-source models. It clarifies that agents may generate and run Python code and therefore recommends coding-optimized LLMs such as qwen2.5-coder:7b-instruct-q8_0 for improved reliability. The listed models give immediate starting points for local experimentation and evaluation. The repo lowers the barrier to testing agent behaviors, agent-to-agent coordination, and code-generating workflows by documenting model choices and practical considerations rather than providing an abstract design. It is most useful for prototyping and benchmarking multi-agent interactions on local hardware.

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