AgentForge
Overview
Low-code framework for rapid development of AI-powered autonomous agents and cognitive architectures.
AgentForge is a low-code framework designed for the swift creation, testing, and iteration of AI-powered autonomous agents and cognitive architectures. It supports multiple Large Language Models (LLMs), including OpenAI, Google's Gemini, Anthropic's Claude, and local models via Ollama or LMStudio. The platform offers customizable agents with features like dynamic prompt templates, on-the-fly prompt editing, and custom tools and actions. Its database-agnostic design ensures seamless integration with various databases, making it adaptable for diverse AI projects.
Product overview
Capabilities, pricing and fit
DataBassGit's AgentForge is a GPL-3.0, low-code Python framework for building and testing agents and cognitive architectures, not a hosted end-user agent. It represents individual agents through configuration and composes them into declarative Cogs for branching, loops, memory, and multi-agent workflows.
Information as of September 4, 2026
What it can do
- Declarative Cogs
YAML-defined Cogs coordinate agents, branching logic, loops, fallbacks, guards, and shared memory into reusable workflows.[1], [2]
- Configurable agent components
Agents can use prompt templates, personas, optional memory and storage, custom APIs, and editable provider settings.[1], [2]
- Multiple model backends
The framework documents OpenAI, Google, and Anthropic APIs plus local models through Ollama and LM Studio, allowing different agents to use different backends.[1]
Where it fits best
Pricing and availability
AgentForge is free open-source software under GPL-3.0. Model-provider, local compute, storage, and deployment costs are separate.[1]
Platforms: Python package and CLI, Local development environment[1], [2]
Access: Self-hosted with cloud-model APIs, Self-hosted with Ollama or LM Studio, Credential-free local debug mode[1], [2]
Important considerations
- AgentForge is a development framework rather than a finished task-specific agent, so users must define prompts, flows, storage, tools, and a model path appropriate to their application.[1], [2]
- Real model runs require either a supported cloud-provider setup or a locally operated model service, and redistribution must comply with the repository's GPL-3.0 license.[1], [2]
Sources (2)
- DataBassGit/AgentForgeGitHub · checked 2026-09-04
- AgentForge DocumentationDocumentation · checked 2026-09-04
Autonomy level
80/100
Our subjective rating of how independently the product carries out its documented tasks, considering the human setup, input, approval and review it needs. This is not a measured success rate.
Comparisons
Custom Comparisons
Some of the use cases of AgentForge:
- Rapid prototyping of AI-powered autonomous agents.
- Developing complex cognitive architectures with minimal coding.
- Integrating various Large Language Models into AI projects.
- Customizing agent behaviors with dynamic prompts and tools.
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Popularity level: 66%
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