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Average Ratings 0 Ratings

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ease
features
design
support

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Write a Review

Description

Swamp is a versatile automation framework and command-line utility designed to assist both AI agents and humans in creating, executing, and reusing workflows that can be repeated. Users can specify various behaviors and methods, integrate them into comprehensive workflows, and preserve structured, versioned outputs for future reference and analysis. The command-line interface caters to AI coding systems, including Claude Code, Cursor, and Codex, as well as individuals operating through a terminal. Additional extensions enrich the platform by incorporating functionalities for services like GitHub, Kubernetes, Slack, various cloud services, and monitoring applications. Swamp also provides features such as encrypted credential vaults, workflows that can be triggered on a schedule or via webhooks, access control mechanisms, audit logs, and the ability for remote execution, making it suitable for collaborative team environments. Teams have the option to maintain data in a local repository, or they can set up their own server and shared datastore infrastructure. While the software is available at no cost for individual users, there are subscription plans available for team usage. Additionally, its user-friendly approach allows for seamless integration into various existing workflows, enhancing productivity and efficiency.

Description

TensorFlow Agents (TF-Agents) is an extensive library tailored for reinforcement learning within the TensorFlow framework. It streamlines the creation, execution, and evaluation of new RL algorithms by offering modular components that are both reliable and amenable to customization. Through TF-Agents, developers can quickly iterate on code while ensuring effective test integration and performance benchmarking. The library features a diverse range of agents, including DQN, PPO, REINFORCE, SAC, and TD3, each equipped with their own networks and policies. Additionally, it provides resources for crafting custom environments, policies, and networks, which aids in the development of intricate RL workflows. TF-Agents is designed to work seamlessly with Python and TensorFlow environments, presenting flexibility for various development and deployment scenarios. Furthermore, it is fully compatible with TensorFlow 2.x and offers extensive tutorials and guides to assist users in initiating agent training on established environments such as CartPole. Overall, TF-Agents serves as a robust framework for researchers and developers looking to explore the field of reinforcement learning.

API Access

Has API Yes 

API Access

Has API Yes 

Screenshots View All

No images available

Screenshots View All

Integrations

Claude Code Yes 
Cursor Yes 
GitHub Yes 
Kubernetes Yes 
OpenAI Codex Yes 
Python No 
Slack Yes 
TensorFlow No 

Integrations

Claude Code No 
Cursor No 
GitHub No 
Kubernetes No 
OpenAI Codex No 
Python Yes 
Slack No 
TensorFlow Yes 

Pricing Details

$0
The Personal plan is free. The Team plan starts at $9 per user per month with annual billing.
Free Trial Yes 
Free Version Yes 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Deployment

Web-Based No 
On-Premises Yes 
iPhone App No 
iPad App No 
Android App No 
Windows Yes 
Mac Yes 
Linux Yes 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support No 

Customer Support

Business Hours Yes 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person Yes 

Vendor Details

Company Name

Swamp Club

Founded

2026

Country

United States

Website

swamp-club.com

Vendor Details

Company Name

Tensorflow

Founded

2015

Country

United States

Website

www.tensorflow.org/agents

Product Features

Product Features

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