AgentLah
Channel-agnostic conversational agent framework
A channel-agnostic conversational agent framework, deployed and live — one codebase powers a WhatsApp property-sales assistant and the real-estate sales agent delivered for a freelance client. Property-listing RAG, knowledge-base RAG, human-takeover dashboard, and pluggable LLM providers.
Problem
Real-estate sales conversations happen on WhatsApp, on web chat, over email — and a bot built for one channel is a rewrite for the next. Meanwhile the conversations themselves need real substance: listing knowledge, lead qualification, and a way for a human to take over the moment the AI is out of its depth.
Approach
AgentLah separates the agent from the channel: one core handles conversation memory, RAG, and tool use; thin adapters connect WhatsApp Cloud API, web chat, or direct API. Two retrieval layers — property-listing RAG and knowledge-base RAG — ground answers in the client's actual inventory.
LLM providers are pluggable (Anthropic / OpenAI / Google), so the same agent can be tuned for cost or quality per deployment. A human-takeover dashboard lets an operator watch conversations and step in live, and MCP tool-use connects the agent to CRM and scheduling.
Outcomes
- Deployed and live at agent-lah.vercel.app.
- v1 of an autonomous sales agent delivered to a real-estate client (under NDA); the engagement is ongoing.
- The framework is open source and generalises beyond property — the channel-adapter pattern is the reusable part.