Agenthive – An open ecosystem for AI agents | By Abhijit Menon | February 2025

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9 hours ago

Agenthive is an open source initiative designed to provide a flexible and extensible framework for AI -focused agents. It allows users to interact with several AI agents, each designed for specialized tasks, promoting a collaborative AI ecosystem.

In the rapidly evolving digital landscape today, professionals often juggle several tools to perform specialized tasks. Agenthive Consolidates these capacities in a single friendly platform, offering access to the request to AI agents adapted to tasks ranging from the code examination to the preparation of the interviews. This centralized approach rationalizes not only workflows but also democratizes access to advanced AI features, which allows users of all technical backgrounds to improve their productivity.

Imagine a digital market where AI agents with specialized skills can be hired on demand, much like the self -employed on Fiverr or Upwork. Agenthive Aims to revolutionize the way in which the tools fueled by AI are used, offering a transparent platform where users can engage intelligent agents for specific tasks, from the generation of curriculum vitae to preparing interviews.

This article plunges into Agenthivecovering Goal, current features, underlying technology, roadmap for future improvementsAnd how the community can contribute to this growing ecosystem.

The platform is built using a modern technological battery:

• Front: react with types Script and Material-i
• Backend: Fastapi (Python)
• Database: SQLALCHEMY with SQLITE
• Authentication: Authentication of tokens based on JWT
• API: Restful termination points with OPENAPI documentation

1. Architecture Fronend

• React with TypeScript: provides a development of type components
• Material-IU: implements a coherent and modern user interface design
• Management of the context based on the context: manages authentication and user condition
• React router: manages the routing of applications and navigation

2. Backend architecture

• Framework Fastapi: offers high performance API end points
• Sqlalchemy Orm: provides abstraction and database management
• JWT authentication: provides secure user sessions
• Middleware: manages the cors and the processing of requests

3. AI Agents

Each agent is implemented as a separate module with specialized capacities:

• Code review agent: analyzes the quality of the code and suggests improvements
• CV chiprament: evaluates the curriculum vitae and provides comments
• Preparation of interviews: help users to prepare for technical interviews
• Writing assistant: improves the quality of writing and style
• Technical convenience store: help technical problem solving

• Secure recording and authentication
• JWT -based session management
• User profile and lament balance monitoring

The market displays AI agents available

Each agent provides:

• Specialized entry interfaces
• Real -time treatment
• Detailed response formatting
• Monitoring and user story

• Payment model by use
• Purchase and management of tokens
• Use monitoring and invoicing

• Encrypted communication
• Secure token storage
• Limitation of rates and abuse prevention
• Validation and disinfection of entries

• Allows users to use agents in other workflows via the API.
• Users can generate API keys to use each agent.

# Agent Management
GET /agents
Response:
[
{
"id": integer,
"name": string,
"description": string,
"price_per_token": float
}
]
POST /agents/{id}/invoke
Request:
{
"input_data": string,
"context": string (optional)
}
Response:
{
"output_text": string,
"tokens_used": integer,
"cost": float
}

The project has several improvements in the pipeline, classified as short -term improvements and long -term objectives.

Improved agent capacities: Widen existing agents to perform more focus on action such as:

• PDF generation for CVs according to work descriptions. To do this, we will integrate the capabilities of another LLM -based agent on which I worked.

• Generate interview questions based on the roles of use.
• Perform demonstration interview sessions with users.

Improvement of the quality of the response: Improvement of AI -centered responses for more precise and useful outings.

More specialized use case: Development of agents for workflows specific to industry.

Better error management: Improvement of tolerance to defects to ensure smooth interactions.

User experience improvements::

• Improve the visualization of the response to present results in an intuitive manner.

Characteristics of the platform::

• Presentation of prize processing capacities to effectively manage several requests.
• Allowing users to configure personalized agent settings.

Extension of the platform::

• Adding more AI agents specializing in different fields. An example is another project on which I worked who is a chess coach based at LLM.

• Creation of a development framework for users to create personalized agents.
• Establish an integration market for the third -party models and services of the AI.

Corporate features::

• Allow team management tools for collaborative use.
• Implementation of use analyzes to follow the efficiency of agents.
• Provide personalized deployment options for companies.

AI capabilities::

• Incorporate advanced language models for better reasoning and contextual understanding.
• Management of multimodal treatment to work with text, images and audio.
• Allowing real -time collaboration between AI agents and users.

Agenthive is an open source project and contributions are strongly encouraged. Ways to get involved:

• Develop new agents: Add specialized agents for unique use cases.
• Improve infrastructure: Optimize performance, scalability and safety.
• Expand the documentation: Create tutorials and guides to facilitate integration.
• Correction and improvements in bugs: Identify and solve problems to improve stability.
• We also saddle if you are interested in going to production through deployment and integration with Stripe for Payments.

For detailed technical details and to contribute, please consult the GitHub repository-https://github.com/akmenon1996/ai-agent-marketplace/blob/main/

To follow the work I do, follow me on medium and do not hesitate to contact me to chat on LinkedIn!

Linkedin: https://www.linkedin.com/in/abhijit-krishna-menon/

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