ritual-dapp-llm

On-chain LLM inference for Ritual dApps works by orchestrating off-chain model execution with a TEEs-verified precompile, enabling streaming responses and deterministic settlement within Ritual Chain 1979. It uses the 0x0802 precompile for commitment and replay settlement.

You can store conversation history for on-chain AI inference off-chain using GCS, HuggingFace, or Pinata. This data is made accessible to the precompile through proper StorageRef-based convoHistory and TTL parameters.

Yes, you can stream LLM responses directly on-chain by interacting with the 0x0000000000000000000000000000000000000802 precompile. This precompile manages commitment and replay settlement for interactive dApps.

Yes, Ritual Chain 1979 supports deterministic agent behavior through model policy pinning. It requires pinning the zai-org/GLM-4.7-FP8 model and configuring proper model-ABI parameters for reliable execution.

The best way to deploy an interactive dApp with on-chain AI inference is to use a standardized pattern that queries the Ritual LLM precompile. This involves pinning the model policy and configuring StorageRef-based history for secure settlement.

You need a TEEs-verified precompile for on-chain LLM execution to ensure secure off-chain model processing and deterministic settlement. This guarantees reliable agent behavior and verifiable computation within dApps.