AgentForge

Name: AgentForge Introduction Video
Uploaded: 2025-05-02T16:28:03Z
Duration: 1 min 33 s
Description: A Python framework for creating, testing, and deploying LLM-powered agents.

About AgentForge

Launched Aug 29, 2024

Introduction Video

Description

A Python framework for creating, testing, and deploying LLM-powered agents.

AgentForge is a low-code framework tailored for the rapid development, testing, and iteration of AI-powered autonomous agents and Cognitive Architectures. Compatible with a range of LLM models — currently supporting OpenAI, Google's Gemini, Anthropic's Claude, and Ollama or LMStudio for local LLMs — it offers the flexibility to run different models for different agents based on your specific needs.

AgentForge Key Features

Customizable Agents, Custom Tools & Actions, Dynamic Prompt Templates, Knowledge Graph Functionality, LLM Agnostic Agents (Each Agent can call different LLMs if needed), On-The-Fly Prompt Editing, OpenAI, Google & Anthropic API Support, Open-Source Model Support

AgentForge Use Cases

API Automation, Custom AI Agent Development, Task Automation, Multi-Agent Simulation, Production Deployment.

Pros

Low-code framework allows for rapid development, making it accessible for users with varying levels of programming expertise.
Supports a wide range of LLM models including OpenAI, Google's Gemini, Anthropic's Claude, and local LLMs like Ollama and LMStudio, offering flexibility in model selection.
Facilitates the creation of AI-powered autonomous agents and cognitive architectures, expanding the scope of AI applications.
Allows for testing and iterative development, aiding in refining and improving agent performance.

Cons

Might have a learning curve for users unfamiliar with cognitive architectures or LLMs.
Dependence on external LLMs may lead to potential costs depending on the commercial model used.
Could require significant computational resources, especially when handling complex tasks or large models locally.
The framework's performance might vary depending on the model and computational resources available.