Agent Operating Procedures (AOPs) for natural-language workflow definitionOmnichannel support: voice, chat, and email in unified platformVoice AI agents with natural dialog and brand customizationTesting, observability, and experimentation toolsAnalytics suite for conversation insights and customer understandingCross-channel memory for connected interactionsRapid iteration without engineering sprints
Features
Multi-language support (Chinese, English, etc.)Long context window (up to 32K tokens in base model)Code understanding and generationMathematical reasoningInstruction followingFunction calling capabilitiesChat and completion modesTool use and agent capabilities
Pricing Tiers
Pricing Tiers—
Pricing Tiers
Open SourceFree/one-time
Self-hosted deployment
Integrations
Integrations—
Integrations
Hugging FaceollamavLLMLM Studio
Platforms
Platforms
webapi
Platforms
apiweb
Pros
Pros
Multi-channel deployment across voice, chat, and email with unified agent memory
Human-like conversation quality with brand consistency across customer interactions
Agent Operating Procedures enable customizable and optimizable AI agent behavior
Enterprise-focused platform designed for scalable customer service automation
Seamless cross-channel experience reduces customer friction and improves satisfaction
Pros
Free access to powerful AI models without subscription fees or usage limits
Multi-platform availability through web, mobile, and desktop applications
OpenAI-compatible API format enables seamless integration with existing tools
Advanced Deep Research capability performs intelligent multi-step web searches
Open-access design promotes transparency and community-driven development
Cons
Cons
Limited information available about pricing and cost transparency for enterprises
Requires technical expertise and integration effort for deployment and optimization
Potential challenges maintaining accuracy and context in complex multi-turn conversations
Competition from established AI platforms with larger ecosystems and customer bases
Dependency on continuous training and tuning for optimal agent performance
Cons
Limited brand recognition compared to established competitors like ChatGPT
Potential language and cultural biases in training data from Chinese origins
Unclear data privacy policies regarding user conversations and information storage
Less extensive third-party integration ecosystem compared to market leaders
Community support and documentation may be less comprehensive than competitors
