TwinLlamas AI
Try Replicant →
TwinLlamas AI
Try Replicant →
TwinLlamas AI builds the persistent knowledge layer AI agents actually need — a living, structured memory system that accumulates, links, and improves over time.
Not another chunk, embed, retrieve, hallucinate, repeat pipeline.
Most AI knowledge systems are disposable. Chunk text, embed it, retrieve fragments, and hope the model fills the gaps. Every session starts from zero. Nothing is learned. Nothing is retained.
| Traditional RAG | TwinLlamas AI |
|---|---|
| Chunk, embed, retrieve, forget | Accumulate, structure, link, reuse |
| Stateless sessions | Persistent agent memory |
| Flat text retrieval | Knowledge graph with relationships |
| No provenance | Full source tracing and attribution |
| Contradictions ignored | Contradictions surfaced and resolved |
| Developer-only interface | Rich UI for knowledge exploration |
An agentic AI platform for building intelligent digital replicas of professional knowledge — combining local LLM inference, multi-agent collaboration, and a living knowledge layer.
Replicant transforms raw documents into structured, interconnected knowledge that agents can reason over, explore, and build upon — session after session. Knowledge is a first-class citizen, not a byproduct of retrieval.
Seven capabilities that separate a real knowledge infrastructure from a search wrapper.
Knowledge persists across sessions as tracked Knowledge Units with metadata, relationships, and full provenance history.
Raw documents are transformed into interconnected, structured knowledge — not just chunked into vectors. PDFs, markdown, web pages, all formats.
A living, explorable system. Browse topics, inspect relationships, review updates, trace answers to sources, and spot contradictions.
Purpose-built memory infrastructure for AI agents. Conversational memory grows with every interaction. Shared access across multi-agent systems.
Local inference via Ollama. No data leaves your environment. Zero external API dependencies. Enterprise-ready deployment.
Visual knowledge exploration. Source attribution on every response. Contradiction surfacing. Update review workflows. Not just for developers.
Full REST API for completions, chat, RAG ingestion, and document management. Slot into any agent workflow.
A five-stage cycle that transforms raw information into a living, compounding knowledge layer.
Give enterprise AI agents persistent, structured memory across departments, sessions, and systems.
Capture expert knowledge that compounds over time instead of disappearing into email threads and docs.
Build a living internal wiki where agents and humans collaborate on a shared knowledge base.
Full source attribution and update history for every piece of knowledge — audit-ready by design.
Stop building on top of amnesia. Start with a knowledge layer that actually compounds.