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HydraDB for

Healthcare AI

Clinical copilots fail when they treat every interaction as a blank slate or rely on naive vector search that conflates "has COPD" with "COPD ruled out." HydraDB gives healthcare AI engineering teams a persistent, temporal memory layer that structures EMR data and tracks evolving patient conditions with audit-grade lineage.

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90%

Recall accuracy on LongMemEval

<200ms

Context retrieval latency

1B+

Documents ingested

40%

Reduction in repeat contacts

// The Problem //

Why healthcare AI keeps failing

Healthcare AI agents struggle because patient data is complex, temporal, and scattered across systems. Standard vector databases and context windows cannot handle the nuanced requirements of clinical memory.

Medical negations confuse semantic search

Vector databases rely on semantic similarity, which fails in clinical contexts. When searching for respiratory conditions, standard RAG retrieves "pneumonia ruled out" as confidently as "diagnosed with pneumonia," leading to dangerous hallucinations.

Medical negations confuse semantic search

Medical negations confuse semantic search

Patient histories get destructively overwritten

DIY memory stacks often replace old data with new updates, destroying the temporal context needed to understand when a medication changed and why. Without historical baselines, agents cannot track disease progression.

Patient histories get destructively overwritten

EMR data remains fragmented

Clinical notes, lab results, and discharge summaries exist in disconnected silos across HL7 feeds, FHIR APIs, and legacy systems. Engineers waste months building brittle ETL pipelines that still leave agents with incomplete patient context.

EMR data remains fragmented

Black-box retrieval blocks compliance

Healthcare requires audit trails. When an agent recommends a treatment or flags a risk, engineers need infrastructure that traces exactly which clinical note or medical guideline drove that output. Standard vector search provides no lineage.

Black-box retrieval blocks compliance

// See It In Action //

An agent that actually remembers

HydraDB structures your EMR data and clinical notes into a temporal graph, assembling full patient context before your LLM responds.

Live support session — HydraDB context active

Retrieval : 94ms

What is the status of the patient's hypertension management, and did cardiology approve the medication change we discussed last month?

Clinician

Patient care workflow

Clinical context request

HydraDB recalled:

Patient reported dizziness on Lisinopril on Oct 10

Hypertension medication switched from Lisinopril to Losartan on Oct 12

Dr. Smith (Cardiology) authorized Losartan prescription on Oct 12

Blood pressure normalized per lab results Nov 1, no adverse reactions

The patient reported dizziness while on Lisinopril on October 10. Dr. Smith from Cardiology authorized switching to Losartan on October 12. Lab results from November 1 show normalized blood pressure with no adverse reactions. Current active medication is Losartan.

// How Hydra Helps //

Context Infrastructure for Clinical AI

HydraDB replaces brittle vector search and custom memory logic with a unified memory layer designed for longitudinal patient tracking.

Persistent memory

HydraDB stores complete patient histories as structured memory. Clinical notes, lab results, and physician decisions become a continuous, retrievable graph that gives your agents persistent context across years of care.

Persistent memory

Persistent memory

Relationship-aware retrieval

While vector search returns isolated text chunks, HydraDB connects lab results to diagnoses, medications to physicians, and symptoms to outcomes through its graph-native architecture. Your agent receives fully connected clinical context.

Relationship-aware retrieval

Temporal context

HydraDB uses a versioned temporal graph to track evolving conditions without destructive updates. Your agent understands what the patient's baseline was last year versus today, tracking disease progression with 90.97% temporal accuracy.

Temporal context

Structured ingestion from healthcare systems

Map your FHIR APIs, HL7 feeds, Epic, Cerner, or data lake records to HydraDB's ingestion format. HydraDB resolves medical entities, links related records, and extracts clinical facts at ingestion time.

Structured ingestion from healthcare systems

// Get Started //

Three steps to production-ready memory

HydraDB works with any LLM or framework. Replace custom memory logic with structured context retrieval.

Connect your EMR and clinical systems

Pull records from your FHIR APIs, HL7 feeds, or enterprise data lakes.

import os
from hydra_db import HydraDB

client = HydraDB(token=os.environ.get("HYDRA_KEY"))

# Retrieve clinical data using your existing integrations
clinical_note = fhir_client.get_document(patient_id, doc_type="consult")
lab_result = fhir_client.get_observation(patient_id, category="lab")

import os
from hydra_db import HydraDB

client = HydraDB(token=os.environ.get("HYDRA_KEY"))

# Retrieve clinical data using your existing integrations
clinical_note = fhir_client.get_document(patient_id, doc_type="consult")
lab_result = fhir_client.get_observation(patient_id, category="lab")

import os
from hydra_db import HydraDB

client = HydraDB(token=os.environ.get("HYDRA_KEY"))

# Retrieve clinical data using your existing integrations
clinical_note = fhir_client.get_document(patient_id, doc_type="consult")
lab_result = fhir_client.get_observation(patient_id, category="lab")

Connect your EMR and clinical systems

Pull records from your FHIR APIs, HL7 feeds, or enterprise data lakes.

import os
from hydra_db import HydraDB

client = HydraDB(token=os.environ.get("HYDRA_KEY"))

# Retrieve clinical data using your existing integrations
clinical_note = fhir_client.get_document(patient_id, doc_type="consult")
lab_result = fhir_client.get_observation(patient_id, category="lab")

Connect your EMR and clinical systems

Pull records from your FHIR APIs, HL7 feeds, or enterprise data lakes.

import os
from hydra_db import HydraDB

client = HydraDB(token=os.environ.get("HYDRA_KEY"))

# Retrieve clinical data using your existing integrations
clinical_note = fhir_client.get_document(patient_id, doc_type="consult")
lab_result = fhir_client.get_observation(patient_id, category="lab")

Ingest and structure patient data

Map clinical records to HydraDB's format for entity resolution and temporal tracking.

import json

client.context.ingest(
    type="knowledge",
    tenant_id="hospital_network",
    sub_tenant_id="patient_12345",
    app_knowledge=json.dumps([
        {
            "id": "note_2024_10_12",
            "tenant_id": "hospital_network",
            "sub_tenant_id": "patient_12345",
            "title": "Cardiology Consult",
            "type": "clinical_note",
            "content": {"text": clinical_note.text},
            "metadata": {"physician": "Dr. Smith", "action": "medication_change"}
        },
        {
            "id": "lab_2024_11_01",
            "tenant_id": "hospital_network",
            "sub_tenant_id": "patient_12345",
            "title": "Blood Pressure Panel",
            "type": "lab_result",
            "content": {"text": lab_result.value},
            "metadata": {"status": "normalized"}
        }
    ])
)

import json

client.context.ingest(
    type="knowledge",
    tenant_id="hospital_network",
    sub_tenant_id="patient_12345",
    app_knowledge=json.dumps([
        {
            "id": "note_2024_10_12",
            "tenant_id": "hospital_network",
            "sub_tenant_id": "patient_12345",
            "title": "Cardiology Consult",
            "type": "clinical_note",
            "content": {"text": clinical_note.text},
            "metadata": {"physician": "Dr. Smith", "action": "medication_change"}
        },
        {
            "id": "lab_2024_11_01",
            "tenant_id": "hospital_network",
            "sub_tenant_id": "patient_12345",
            "title": "Blood Pressure Panel",
            "type": "lab_result",
            "content": {"text": lab_result.value},
            "metadata": {"status": "normalized"}
        }
    ])
)

import json

client.context.ingest(
    type="knowledge",
    tenant_id="hospital_network",
    sub_tenant_id="patient_12345",
    app_knowledge=json.dumps([
        {
            "id": "note_2024_10_12",
            "tenant_id": "hospital_network",
            "sub_tenant_id": "patient_12345",
            "title": "Cardiology Consult",
            "type": "clinical_note",
            "content": {"text": clinical_note.text},
            "metadata": {"physician": "Dr. Smith", "action": "medication_change"}
        },
        {
            "id": "lab_2024_11_01",
            "tenant_id": "hospital_network",
            "sub_tenant_id": "patient_12345",
            "title": "Blood Pressure Panel",
            "type": "lab_result",
            "content": {"text": lab_result.value},
            "metadata": {"status": "normalized"}
        }
    ])
)

Ingest and structure patient data

Map clinical records to HydraDB's format for entity resolution and temporal tracking.

import json

client.context.ingest(
    type="knowledge",
    tenant_id="hospital_network",
    sub_tenant_id="patient_12345",
    app_knowledge=json.dumps([
        {
            "id": "note_2024_10_12",
            "tenant_id": "hospital_network",
            "sub_tenant_id": "patient_12345",
            "title": "Cardiology Consult",
            "type": "clinical_note",
            "content": {"text": clinical_note.text},
            "metadata": {"physician": "Dr. Smith", "action": "medication_change"}
        },
        {
            "id": "lab_2024_11_01",
            "tenant_id": "hospital_network",
            "sub_tenant_id": "patient_12345",
            "title": "Blood Pressure Panel",
            "type": "lab_result",
            "content": {"text": lab_result.value},
            "metadata": {"status": "normalized"}
        }
    ])
)

Ingest and structure patient data

Map clinical records to HydraDB's format for entity resolution and temporal tracking.

import json

client.context.ingest(
    type="knowledge",
    tenant_id="hospital_network",
    sub_tenant_id="patient_12345",
    app_knowledge=json.dumps([
        {
            "id": "note_2024_10_12",
            "tenant_id": "hospital_network",
            "sub_tenant_id": "patient_12345",
            "title": "Cardiology Consult",
            "type": "clinical_note",
            "content": {"text": clinical_note.text},
            "metadata": {"physician": "Dr. Smith", "action": "medication_change"}
        },
        {
            "id": "lab_2024_11_01",
            "tenant_id": "hospital_network",
            "sub_tenant_id": "patient_12345",
            "title": "Blood Pressure Panel",
            "type": "lab_result",
            "content": {"text": lab_result.value},
            "metadata": {"status": "normalized"}
        }
    ])
)

Retrieve full context at inference time

Query HydraDB for precise patient history before LLM generation.

result = client.query(
    tenant_id="hospital_network",
    sub_tenant_id="patient_12345",
    query="hypertension medication status and cardiology approval",
    type="knowledge",
    mode="thinking",
    query_apps=True
)

# Pass structured context to your LLM
response = llm.generate(
    system_prompt=build_clinical_prompt(result.data),
    user_query="Summarize for attending physician"
)

result = client.query(
    tenant_id="hospital_network",
    sub_tenant_id="patient_12345",
    query="hypertension medication status and cardiology approval",
    type="knowledge",
    mode="thinking",
    query_apps=True
)

# Pass structured context to your LLM
response = llm.generate(
    system_prompt=build_clinical_prompt(result.data),
    user_query="Summarize for attending physician"
)

result = client.query(
    tenant_id="hospital_network",
    sub_tenant_id="patient_12345",
    query="hypertension medication status and cardiology approval",
    type="knowledge",
    mode="thinking",
    query_apps=True
)

# Pass structured context to your LLM
response = llm.generate(
    system_prompt=build_clinical_prompt(result.data),
    user_query="Summarize for attending physician"
)

Retrieve full context at inference time

Query HydraDB for precise patient history before LLM generation.

result = client.query(
    tenant_id="hospital_network",
    sub_tenant_id="patient_12345",
    query="hypertension medication status and cardiology approval",
    type="knowledge",
    mode="thinking",
    query_apps=True
)

# Pass structured context to your LLM
response = llm.generate(
    system_prompt=build_clinical_prompt(result.data),
    user_query="Summarize for attending physician"
)

Retrieve full context at inference time

Query HydraDB for precise patient history before LLM generation.

result = client.query(
    tenant_id="hospital_network",
    sub_tenant_id="patient_12345",
    query="hypertension medication status and cardiology approval",
    type="knowledge",
    mode="thinking",
    query_apps=True
)

# Pass structured context to your LLM
response = llm.generate(
    system_prompt=build_clinical_prompt(result.data),
    user_query="Summarize for attending physician"
)

// Why HydraDB //

HydraDB vs. Standard Vector Search for Healthcare

HydraDB gives clinical copilots persistent longitudinal patient memory, relationship-aware retrieval, temporal context, and medical negation handling where standard vector search treats every query as isolated semantic lookup.

Features

HydraDB

Standard Vector Search

Cross-session memory

Persistent longitudinal patient memory across years

Isolated lookups with no historical continuity

Relationship-aware retrieval

Connects labs, notes, and approvals in a graph

Returns isolated text chunks by similarity

Temporal context

Tracks evolving conditions with versioned state

Flattens time, surfaces outdated data as current

Medical negation handling

Structures facts to differentiate positive from ruled-out

Conflates "absent" with positive matches

Best fit

Clinical copilots requiring audit-grade lineage

Static medical document search

Features

Cross-session memory

Relationship-aware retrieval

Temporal context

Medical negation handling

Best fit

HydraDB

Persistent longitudinal patient memory across years

Connects labs, notes, and approvals in a graph

Tracks evolving conditions with versioned state

Structures facts to differentiate positive from ruled-out

Clinical copilots requiring audit-grade lineage

Standard Vector Search

Isolated lookups with no historical continuity

Returns isolated text chunks by similarity

Flattens time, surfaces outdated data as current

Conflates "absent" with positive matches

Static medical document search

// What Teams Are Saying //

Trusted by teams building healthcare AI

HydraDB eliminated our hallucination problems. Vector databases kept retrieving 'ruled out' conditions as positive matches. HydraDB structures EMR data so our agents understand actual patient state.

Sarah Chen

Head of AI, Clinical Intelligence Platform

Standard RAG confused historical labs with current baselines. HydraDB's temporal graph lets our decision-support agents track multi-year care plans with audit-grade accuracy.

Dr. Marcus Rivera

CTO, Enterprise Healthcare Solutions

Don’t see your use case?

HydraDB is general-purpose graph-native memory infrastructure. If your AI needs persistent, structured context, it can probably help — talk to us.

Read the docs

Talk to us

Don’t see your use case?

HydraDB is general-purpose graph-native memory infrastructure. If your AI needs persistent, structured context, it can probably help — talk to us.

Read the docs

Talk to us

Frequently Asked questions

What is HydraDB? How is it different from other graph databases?

HydraDB is an object-store-native distributed graph database built in Rust, designed to serve as the context layer for AI systems. Unlike traditional graph databases, where storage is tied more closely to database servers or dedicated cluster volumes, HydraDB makes object storage itself the source of truth. This lets query nodes and indexers scale, restart, or be replaced independently without moving or replicating the graph. It also supports snapshot-consistent OpenCypher, GraphBLAS-accelerated traversal, Neo4j-compatible Bolt, and HTTP APIs.

Why use a graph database like HydraDB for AI agents?

A graph database like HydraDB gives AI agents something vector search alone cannot: relationships and state. Instead of retrieving isolated chunks that merely look similar to a query, an agent can follow explicit connections between people, projects, events, policies, documents, and past actions to understand how the current situation fits together.

When should I use HydraDB instead of a vector database?

Use a vector database when your main problem is semantic search over mostly static content: finding documents, chunks, tickets, or products that are similar to a query. Use HydraDB when your AI agent needs to understand how information connects and changes over time. If the agent needs to answer questions like who owns this project, what is blocking it, which policy applies to this customer, or what changed since the last session, similarity search alone is not enough. HydraDB lets the agent traverse entities and relationships and work with structured state instead of only retrieving nearby embeddings.

Does HydraDB work with GraphRAG?

Yes. HydraDB can be used as the graph database behind a GraphRAG system. GraphRAG is a retrieval approach rather than a specific database: it builds or uses a knowledge graph, then retrieves context by traversing relationships instead of relying only on vector similarity. Microsoft’s GraphRAG architecture is explicitly designed around a storage-agnostic knowledge model and allows custom storage and workflow implementations.

How does HydraDB improve retrieval for AI agents?

HydraDB improves retrieval by combining vector search with graph traversal, exact-match search, and temporal context. Instead of returning isolated similar chunks, it reconstructs the relationships, dependencies, and latest valid state around a query, which gives AI agents a smaller, more relevant set of evidence to reason over.

Can I use HydraDB with my existing AI stack?

Yes. HydraDB plugs into your existing AI stack through Neo4j-compatible Bolt and HTTP APIs. Keep your current models, agents, and retrieval pipeline, and add HydraDB as the graph layer for connected, stateful context.

How does HydraDB use context graphs for AI agents?

HydraDB turns fragmented agent memory into a connected context graph. This lets agents retrieve not only the relevant fact, but also how that fact relates to other entities, where it came from, and what the current state is, which gives multiple agents and workflows a consistent view of the same domain.

What AI use cases is HydraDB best suited for?

HydraDB is best for AI agents that need persistent, connected context such as support agents, coding agents, research agents, GraphRAG systems, and enterprise copilots. It is especially useful when the agent needs to remember history, follow relationships, and understand how state changes over time.

Don’t see your use case?

HydraDB is general-purpose graph-native memory infrastructure. If your AI needs persistent, structured context, it can probably help — talk to us.

Read the docs

Talk to us

X / Twitter

Discord

X / Twitter

Discord

Frequently Asked questions

What is AI agent memory for healthcare?

AI agent memory gives clinical AI systems persistent context across a patient’s history. Instead of treating every encounter as a new interaction, the agent can retrieve previous diagnoses, symptoms, medications, lab results, physician decisions, and other relevant events before responding. This creates a longitudinal view of the patient rather than relying only on information from the current encounter.

Why isn't vector search enough for healthcare AI?

Vector search retrieves information primarily based on semantic similarity, but clinical meaning often depends on state, relationships, and time. For example, “pneumonia diagnosed” and “pneumonia ruled out” may be semantically similar while representing opposite clinical facts. HydraDB structures clinical information as connected entities and relationships, allowing agents to retrieve the relevant patient state and history rather than relying only on similar text chunks.

How does HydraDB help AI agents understand a patient's longitudinal history?

HydraDB stores clinical notes, lab results, diagnoses, medications, physician decisions, and other events as persistent structured memory. These records can be connected across years of care, giving an agent access to the patient’s relevant history without repeatedly reconstructing context from disconnected records.

How does HydraDB handle patient information that changes over time?

HydraDB uses a versioned temporal graph to preserve how patient state changes over time instead of simply overwriting old information. For example, a medication may be prescribed, changed because of a side effect, and later replaced by another treatment. HydraDB preserves that sequence so an agent can understand what was true at a particular point in time and what the current state is today.

How does HydraDB connect clinical information across different records?

HydraDB uses relationship-aware retrieval to connect information such as diagnoses, symptoms, medications, physicians, lab results, procedures, and outcomes. Instead of retrieving each clinical note independently, an agent can retrieve connected context such as which physician authorized a medication change, which symptoms led to it, and what subsequent lab results showed.

Can HydraDB preserve the source of clinical information?

Yes. HydraDB can maintain lineage between extracted clinical facts and the records they came from. This helps applications trace retrieved context back to supporting clinical notes, lab results, or other source records instead of returning facts without their underlying evidence.

What healthcare data can I bring into HydraDB?

You can bring clinical notes, lab results, diagnoses, medications, discharge summaries, physician decisions, and other structured or unstructured healthcare records into HydraDB. Data from FHIR APIs, HL7 feeds, EMR systems, and enterprise data lakes can be mapped into HydraDB, where related medical entities and events can be connected into persistent patient context.

Why is HydraDB a strong choice for building healthcare AI?

Healthcare context is inherently temporal and relational. Understanding a patient’s current state often depends on what happened previously, which treatments changed, who made a clinical decision, what evidence supported it, and how different medical events are connected. HydraDB is built for this kind of long-lived context. Its temporal graph preserves changing patient state, relationship-aware retrieval connects information across clinical records, and fast graph traversal retrieves relevant context without placing an entire patient history into the model’s prompt. Its architecture also makes large, growing histories more affordable to retain while keeping frequently accessed context fast.

How can we bring healthcare data into HydraDB?

HydraDB can work with your existing healthcare data integrations. Records from FHIR APIs, HL7 feeds, systems such as Epic or Cerner, and enterprise data lakes can be mapped to HydraDB’s ingestion format. HydraDB can then resolve medical entities, link related records, extract clinical facts, and preserve changes over time as connected context for your AI applications. Learn more about HydraDB.

Don’t see your use case?

HydraDB is general-purpose graph-native memory infrastructure. If your AI needs persistent, structured context, it can probably help — talk to us.

Read the docs

Talk to us

Frequently Asked questions

What is HydraDB? How is it different from other graph databases?

Why use a graph database like HydraDB for AI agents?

When should I use HydraDB instead of a vector database?

Does HydraDB work with GraphRAG?

How does HydraDB improve retrieval for AI agents?

Can I use HydraDB with my existing AI stack?

How does HydraDB use context graphs for AI agents?

What AI use cases is HydraDB best suited for?

Don’t see your use case?

HydraDB is general-purpose graph-native memory infrastructure. If your AI needs persistent, structured context, it can probably help — talk to us.

Read the docs

Talk to us

X / Twitter

Discord