
PolyAI
The only conversational AI platform delivering both unlimited scale and complete control, proven at enterprise level.

The leading open-source conversational AI platform for building AI agents you can trust to handle real-world complexity.

Rasa is a comprehensive conversational AI platform designed for building robust and reliable AI agents. It combines machine learning models, including LLMs, with custom business logic, providing developers with full control over the behavior and performance of their agents. The platform emphasizes transparency and control, avoiding 'black box' approaches by offering full visibility into the agent's decision-making process. Rasa supports both pro-code and no-code development via Rasa Pro and Rasa Studio respectively. Key features include intent recognition, dialogue management, entity extraction, and integration with various channels, including web, mobile, messaging apps, and voice gateways. It enables businesses to automate customer experience, sales processes, and employee support, adapting AI to specific business needs rather than the other way around. With the Model Context Protocol (MCP), Rasa agents can seamlessly access external tools, services, and data through APIs, making tool access discoverable, reusable, and orchestration-ready.
Rasa is a comprehensive conversational AI platform designed for building robust and reliable AI agents.
Explore all tools that specialize in dialogue management. This domain focus ensures Rasa delivers optimized results for this specific requirement.
Combines traditional NLU with large language models for improved dialogue understanding and generation. Allows agents to handle more complex and nuanced conversations.
A universal format for agents to access tools, services, and data through APIs. Turns tool access into something discoverable, reusable, and orchestration-ready.
A visual interface for business users to build, analyze, and optimize AI assistants without coding. Enables content management, flow building, and conversation analysis.
Includes features like PII data management, dependency vulnerability protection, secrets management (powered by Vault), and SSO/RBAC for secure deployment in enterprise environments.
Offers comprehensive monitoring and logging capabilities using OpenTelemetry standards. Allows developers to track agent performance, identify bottlenecks, and troubleshoot issues.
Allows developers to use and manage multiple LLMs within the same Rasa project, selecting the best LLM for specific tasks or conversation flows.
Install Rasa Open Source or sign up for Rasa Pro/Studio.
Define your bot's intents and entities using NLU training data.
Create dialogue flows and stories to manage conversation turns.
Implement custom actions using the Rasa SDK to integrate with external systems.
Configure channel connectors to deploy your AI agent across different platforms (e.g., web, mobile, messaging).
Train the model using the `rasa train` command or the Rasa Studio UI.
Test your agent using the command line or the interactive testing panel.
Monitor performance using Rasa X (if applicable) or custom observability tools like OpenTelemetry.
Iterate on your model based on user feedback and performance metrics.
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Verified feedback from other users.
"Generally positive reviews highlighting the platform's flexibility, control, and enterprise-grade features. Some users mention a steeper learning curve compared to simpler chatbot platforms."
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