AI Infrastructure · GitHub ·27 ★

llm_agent

An abstraction library for building domain-specific intelligent agents based on Large Language Models (LLMs). LLMAgent provides a core architecture and behavior definitions that simplify the development of specialized agents by leveraging the AgentForge framework for dynamic workflow orchestration.

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Details

Author
i365dev
Category
AI Infrastructure
Platform
GitHub
Framework
custom
Language
elixir
Stars
27
First indexed
2026-05-15
Last active
2026-02-05
Directory sync
2026-05-15
@i365dev

Overview

An abstraction library for building domain-specific intelligent agents based on Large Language Models (LLMs). LLMAgent provides a core architecture and behavior definitions that simplify the development of specialized agents by leveraging the AgentForge framework for dynamic workflow orchestration.

Quick start

git

git clone https://github.com/i365dev/llm_agent

Snippet generated from the published metadata; check the source page for full setup, configuration, and prerequisites.

What llm_agent can do

frameworkllmragdefi
  • Framework — framework task automation.
  • Llm — llm task automation.
  • Rag — Retrieves grounded context before answering.
  • Defi — Interacts with decentralised finance protocols.

Frequently asked questions

What is llm_agent?
An abstraction library for building domain-specific intelligent agents based on Large Language Models (LLMs). LLMAgent provides a core architecture and behavior definitions that simplify the development of specialized agents by leveraging the AgentForge framework for dynamic workflow orchestration.
How do I install llm_agent?
Use git: `git clone https://github.com/i365dev/llm_agent`. Full setup details on the source page linked above.
Is llm_agent open source?
llm_agent is published on GitHub.
What are alternatives to llm_agent?
Comparable agents include awesome, openclaw, AutoGPT. Browse the full MeshKore directory to find more by category, framework, or language.

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Source & freshness

Profile data for llm_agent is sourced from GitHub, published by i365dev.

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