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What is Deep Agents?

Deep Agents is an open-source library and agent framework that facilitates the development of agents powered by large language models (LLMs) to perform complex, multi-step tasks. The library includes an agent loop that offers features such as planning, persistent memory, access to the file system, and the capability to assign tasks to subagents, allowing developers to easily integrate various model providers and tools. Targeted at software developers and teams aiming to create customized agent applications, it accommodates a wide range of workflows, including research, software programming, and any other situations that require structured planning and action over several steps. Moreover, developers can further expand the framework's functionalities by adding their own tools, middleware, and backend systems, as well as seamlessly integrating with LangGraph and LangSmith for efficient orchestration, tracing, and deployment. In essence, Deep Agents serves as a robust software development framework that enables users to design and manage agents tailored to their specific applications and environments, rather than offering a ready-made solution for end-users. This adaptability and extensive range of features position Deep Agents as a crucial resource for innovators in the field of agent-based application development, fostering creativity and efficiency in their projects. As the demand for sophisticated agent solutions grows, the importance of frameworks like Deep Agents becomes even more pronounced.

What is Agno?

Agno is an innovative framework tailored for the development of agents that possess memory, knowledge, tools, and reasoning abilities. It enables developers to create a wide array of agents, including those that reason, operate multimodally, collaborate in teams, and execute complex workflows. With an appealing user interface, Agno not only facilitates seamless interaction with agents but also includes features for monitoring and assessing their performance. Its model-agnostic nature guarantees a uniform interface across over 23 model providers, effectively averting the challenges associated with vendor lock-in. Agents can be instantiated in approximately 2 microseconds on average, which is around 10,000 times faster than LangGraph, while utilizing merely 3.75KiB of memory—50 times less than LangGraph. The framework emphasizes reasoning, allowing agents to engage in "thinking" and "analysis" through various reasoning models, ReasoningTools, or a customized CoT+Tool-use strategy. In addition, Agno's native multimodality enables agents to process a range of inputs and outputs, including text, images, audio, and video. The architecture of Agno supports three distinct operational modes: route, collaborate, and coordinate, which significantly enhances agent interaction flexibility and effectiveness. Overall, by integrating these advanced features, Agno establishes a powerful platform for crafting intelligent agents capable of adapting to a multitude of tasks and environments, promoting innovation in agent-based applications.

Media

Media

Integrations Supported

LangSmith
dcode

Integrations Supported

AG-UI
Dock
Ejentum
Gemini Managed Agents
Hindsight
Oxylabs
Python
Weaviate
xpander.ai

API Availability

Has API

API Availability

Has API

Pricing Information

Free
Open source
Free Version

Pricing Information

Free
Free Version

Supported Platforms

SaaS
On-Prem

Supported Platforms

SaaS

Customer Service / Support

Not specified

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub

Training Options

Documentation Hub

Company Facts

Organization Name

LangChain

Date Founded

2023

Company Location

United States

Company Website

www.langchain.com/deep-agents

Company Facts

Organization Name

Agno

Company Location

United States

Company Website

github.com/agno-agi/agno

Categories and Features

AI Agent Frameworks

Not specified

Categories and Features

Agentic AI

Not specified

AI Agent Builders

Not specified

AI Agent Frameworks

Not specified

AI Agents

Not specified

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