Building an Advanced Chatbot
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Building an Advanced Chatbot
Powered by GenAI
Lyzr vs Kore.ai vs Langchain.com
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| What is? | Lyzr is a low-code agent framework with an ‘agentic’ approach to building generative AI applications. Its fully integrated agents come with pre-built RAG pipelines, allowing you to build and launch in minutes. | Kore helps in chatbot development by providing a SaaS platform that enables businesses to create, manage, and deploy digital assistants and chatbots. | LangChain is a framework designed for the creation of applications using LLMs. The framework's core components include chains and links that enable developers to build LLM applications. |
| Development Approach | Agentic approach with low-code agent framework | No-code approach | Functions and Chains approach to building applications |
| Deployment | Locally deployable agent SDKs | SaaS platform | Locally Deployable code |
| Where does data reside? | Data remains in customer’s cloud environment | Data remains on Kore’s cloud | Data remains in customer’s cloud environment |
| Future Expansion | Lyzr Agents integrate seamlessly into Lyzr’s Multi-Agent Automation Platform for seamless expansion to complex workflows in the future. | Kore is a chatbot platform without any scope to expand into a workflow automation pipeline. | Langchain framework helps in development of LLM applications and hence allows scope expansion. |
| 24*7 Enterprise support |
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Community Support |
| Simple to build and launch |
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| Documentation |
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| SLA for upgrades | 24 hours |
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Typically 1 week |
| LLM Choices | 200+ LLMs |
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LLM integration through libraries like LiteLLM |
| Technology choices | Integrates with all leading vector databases, embedding models and runs on any cloud | SaaS | Integrates with all leading vector databases, embedding models and runs on any cloud |
| Open Source Ui |
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| SOTA RAG Architecture | Yes. Blog post here. |
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| Pricing | Flat - $399 per month. No throttling. No usage-based pricing. | Usage-based pricing. | Open Source. But the APIs are charged per user. |
| AI Management | Lyzr AIMS for comprehensive AI Agents management with Agent SDK analytics | In-built analytics of chatbots | Langchain LangSmith for LLM observability |
| Agent Integrations | Lyzr Chat Agent can cross-integrate with other Lyzr agents like RAG, Data Analysis, Search and Lyzr Automata (workflow automation). |
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No native integration. New functions and programs are required to integrate new functionalities. |
| Customizations | 600+ RAG pipeline configurations to customize the chat agent as per the customer’s need. |
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All customizations are manual. No out-of-the-box pre-built pipelines are available. |
| Partners Network |
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| AWS Partnership |
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| AWS Funding for Deployment | Yes. Between $10,000 to $75,000. |
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| Free POC Development | Yes. In 48 hours |
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How do you build a Klarna-style user-aware,
self-improving chatbot?
Klarna has been all over the news the past few days as they released the performance metrics of their customer support chat assistant, which managed to automate 700 full-time agent jobs, handle 2.3 million conversations per month without dropping the CSAT score, and eventually help Klarna save $40M per annum.
So how did they do it? What goes behind the scenes?
At Lyzr AI, we took a crack at building the architecture with Lyzr’s Chat Agent SDK. And here is how it works. 👇
- Klarna has been all over the news the past few days as they released the performance metrics of their customer support chat assistant, which managed to automate 700 full-time agent jobs, handle 2.3 million conversations per month without dropping the CSAT score, and eventually help Klarna save $40M per annum.
- So how did they do it? What goes behind the scenes?
- At Lyzr AI, we took a crack at building the architecture with Lyzr’s Chat Agent SDK. And here is how it works. 👇
- The user-aware function helps maintain the user’s profile, updating it in real-time
- The QA example set helps the LLM with few-shot learning to generate user preferred responses
- The long-term memory ensures that the chat agent does not lose context of all previous interactions
- The in-session short-term memory enables seamless chat exchange
- The RLHF function enriches the QA example set
Try our vanilla chat agent demo (still quite impressive) here: https://chatagent.lyzr.ai/
Or our perplexity style knowledge agent here: https://lnkd.in/eD5G_a42
Planning to build one for your organization? Book a demo today – https://lnkd.in/eh6ih-9q
