agent-framework vs llm_agents

Common questions

What is the difference between agent-framework and llm_agents?

agent-framework: Framework for building and deploying AI agents and multi-agent workflows. llm_agents: Library to build agents controlled by LLMs. See the comparison table for live GitHub stats and shared categories.

When should I choose agent-framework over llm_agents?

Choose agent-framework over llm_agents when Requirements: Python version 3.6 or newer is required for Python installations.; The .NET Core SDK must be installed for utilizing the .NET packages.; Tags unique to agent-framework: agent-framework, agentic-ai, agents, multi-agent; Also covers Developer Tools; Choose agent-framework if your project requires support for both Python and .NET, allowing you to develop across different ecosystems.

When should I choose llm_agents over agent-framework?

Choose llm_agents over agent-framework when Requirements: Min 1 GB RAM; Requires the installation of requirements specified by running pip install -r requirements.txt followed by pip install -e ..; Dependencies include setting up environment variables for OPENAI_API_KEY to use OpenAI API and optionally SERPAPI_API_KEY if Google search tool is utilized.; Tags unique to llm_agents: deep-learning, langchain, llms, machine-learning; Use llm_agents when you require a lightweight solution for building agents controlled by LLMs with specific tools such as Python REPL execution or Hacker News search.

When should I avoid agent-framework?

Avoid using the agent-framework if your team does not have proficiency in either Python or.NET, as this may cause difficulties in leveraging its features effectively. Do not opt for agent-framework if you only need lightweight support for AI agents without a comprehensive orchestration and deployment framework.

When should I avoid llm_agents?

Avoid llm_agents if you need a more robust and feature-rich product for complex tasks that require extensive integration capabilities beyond Python REPL, Google search, and Hacker News. Do not choose it when you are looking for advanced abstraction layers like those found in LangChain, as llm_agents aims to remain simple with fewer files and a straightforward core.

Is agent-framework or llm_agents more popular on GitHub?

agent-framework has more GitHub stars (12,718 vs 1,053). Stars measure visibility, not whether either tool fits your constraints.

Are agent-framework and llm_agents open source?

Yes - both are open-source projects on GitHub (agent-framework: MIT, llm_agents: MIT).

Where can I find alternatives to agent-framework or llm_agents?

GraphCanon lists graph-backed alternatives at agent-framework alternatives and llm_agents alternatives (agent-framework markdown twin, llm_agents markdown twin), ranked by typed relationship edges rather than popularity votes.

Is there a machine-readable version of this comparison?

Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

Which is better maintained, agent-framework or llm_agents?

agent-framework: Very active. llm_agents: Dormant. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.

Where are the full trust reports for agent-framework and llm_agents?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: agent-framework trust report; llm_agents trust report.