ECHO: A Locally-Deployable Agentic Health Assistant with Temporal Memory, Safety Guardrails, and Speech Assessment
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Observed public metrics from 1 member.
Merged summary
TL;DR - ECHO is a locally-deployable agentic health assistant for chronic care that combines a LangGraph/ReAct chatbot with persistent temporal memory, a two-stage safety layer, and multimodal speech assessment. It matters because it shows a full privacy-preserving clinical agent stack running on consumer hardware without sending patient data off-device.
- Core agent uses a ReAct loop over LangGraph with 17 clinical tools and a temporal knowledge graph for cross-session memory; 94.9% tool-execution pass rate on a 59-scenario benchmark with GPT-5 Mini.
- Hybrid safety layer: sub-1ms rule-based filtering for explicit crisis/jailbreak signals, plus a signed GNN with APPNP-style propagation for boundary cases — 88.8% accuracy and 90.6% unsafe recall on 2,537 annotated Turkish health queries, beating zero-shot LLM baselines including Llama 3.3 70B.
- Speech module fuses Whisper acoustic and BERT text encodings via cross-attention to estimate emotion, depression, and pain, at 0.652 mean macro F1.
- Delivered as a web app running entirely on consumer hardware with no external data transmission, targeting GDPR and KVKK compliance.
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ECHO: A Locally-Deployable Agentic Health Assistant with Temporal Memory, Safety Guardrails, and Speech Assessment
TL;DR - ECHO is a locally-deployable agentic health assistant for chronic care that combines a LangGraph/ReAct chatbot with persistent temporal memory, a two-stage safety layer, and multimodal speech assessment. It matters because it shows a full privacy-preserving clinical agent stack running on consumer hardware without sending patient data off-device.
- Core agent uses a ReAct loop over LangGraph with 17 clinical tools and a temporal knowledge graph for cross-session memory; 94.9% tool-execution pass rate on a 59-scenario benchmark with GPT-5 Mini.
- Hybrid safety layer: sub-1ms rule-based filtering for explicit crisis/jailbreak signals, plus a signed GNN with APPNP-style propagation for boundary cases — 88.8% accuracy and 90.6% unsafe recall on 2,537 annotated Turkish health queries, beating zero-shot LLM baselines including Llama 3.3 70B.
- Speech module fuses Whisper acoustic and BERT text encodings via cross-attention to estimate emotion, depression, and pain, at 0.652 mean macro F1.
- Delivered as a web app running entirely on consumer hardware with no external data transmission, targeting GDPR and KVKK compliance.