AgentServe is a Python-based open-source platform that lets developers define, serve, and orchestrate AI agents as HTTP services. It supports multiple LLMs, custom tool integrations, concurrency control, and built-in observability, so teams can rapidly prototype and scale agent-driven workflows in production environments.
AI Platforms & Frameworks
#AI Agents
#RESTful API
#Chatbot Framework
#multi-agent systems
#Automated Workflows
#scalable AI
#custom integration
#agent deployment
#parallel processing
#model routing
#Open-source tools
#health checks
#logging and metrics
#agent orchestration
#Rapid Prototyping
#flexible architecture
#community support
#plugin integration
#LLM support
#agent scheduling
...
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AgentServe
AgentServe is a Python-based open-source platform that lets developers define, serve, and orchestrate AI agents as HTTP services. It supports multiple LLMs, custom tool integrations, concurrency control, and built-in observability, so teams can rapidly prototype and scale agent-driven workflows in production environments.
AI Platforms & Frameworks
#AI Agents
#RESTful API
#Chatbot Framework
#multi-agent systems
#Automated Workflows
#scalable AI
#custom integration
#agent deployment
#parallel processing
#model routing
#Open-source tools
#health checks
#logging and metrics
#agent orchestration
#Rapid Prototyping
#flexible architecture
#community support
#plugin integration
#LLM support
#agent scheduling
...
Featured
What is AgentServe?
AgentServe provides a unified interface for creating and deploying AI agents. Users define agent behaviors in configuration files or code, integrate external tools or knowledge sources, and expose agents over REST endpoints. The framework handles model routing, parallel requests, health checks, logging, and metrics out of the box. AgentServe’s modular design allows plugging in new models, custom tools, or scheduling policies, making it ideal for building chatbots, automated workflows, and multi-agent systems in a scalable, maintainable way.
Who will use AgentServe?
- Machine learning engineers
- Backend developers
- AI researchers
- Startup teams
- Enterprise automation teams
How to use the AgentServe?
- Step1: Install via pip: pip install agentserve
- Step2: Define your agent in a YAML or JSON config with model, prompt, and tools
- Step3: Register any custom tool functions or external API hooks
- Step4: Launch the server: agentserve run --config path/to/config.yaml
- Step5: Send HTTP requests to the /agent/
/invoke endpoint
Platform
- mac
- windows
- linux
AgentServe's Core Features & Benefits
The Core Features
- RESTful API deployment of AI agents
- Support for multiple LLM providers
- Custom tool and plugin integration
- Concurrent request handling and routing
- Built-in logging, metrics, and health checks
- Multi-agent orchestration and scheduling
The Benefits
- Rapid agent prototyping and deployment
- Scalable, production-ready agent hosting
- Flexible, model-agnostic architecture
- Extensible with custom tools and workflows
- Open-source transparency and community support
AgentServe's Main Use Cases & Applications
- Customer support and helpdesk chatbots
- Automated email triage and response systems
- Data-driven research assistants
- Workflow automation for sales and marketing
- Multi-agent simulation and orchestration
FAQs of AgentServe
AgentServe Company Information
- PropsAI
AgentServe Reviews
5/5
AgentServe's Main Competitors and alternatives?
- LangChain
- Rasa
- Microsoft Bot Framework
- Haystack
- OpenAI Functions
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