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Architecture

Benchmarks

Pricing

Use Cases

Resources

Documentation

Agents

HydraDB for

Research Intelligence AI

Building investigative agents that track evolving competitors and traverse entity networks across millions of documents? Standard RAG flattens time, relational databases time out on multi-hop queries, and custom GraphRAG pipelines become engineering nightmares. HydraDB gives your financial and market intelligence agents a persistent, graph-native memory layer that tracks state changes, resolves entities, and reasons over temporal shifts without the maintenance overhead.

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90%

Recall accuracy on LongMemEval

<200ms

Context retrieval latency

1B+

Documents ingested

40%

Reduction in repeat contacts

// The Problem //

Why research intelligence AI keeps failing

Applied AI teams building investigative agents typically stitch together a vector database for documents, a relational database for metadata, and complex ETL pipelines for entity resolution. This creates fragile infrastructure that breaks when asked to perform deep reasoning over time.

Destructive updates erase history

Standard vector RAG assumes facts are static. When a competitor changes their pricing model or an executive leaves, new documents overwrite the old. Your agent loses the ability to answer temporal questions like "What was their strategy last quarter versus today?"

Destructive updates erase history

Destructive updates erase history

Multi-hop traversal timeouts

Investigative workflows require connecting disparate dots. Relational databases choke on these multi-hop queries, leaving agents without the entity context needed to uncover hidden market connections.

Multi-hop traversal timeouts

GraphRAG maintenance nightmare

Building custom GraphRAG pipelines over millions of documents introduces massive engineering overhead. Updating these custom graphs incrementally when new daily market data arrives is slow, expensive, and prone to breaking.

GraphRAG maintenance nightmare

Fragmented entity resolution

Market signals scatter across SEC filings, web scrapes, Notion notes, and data lakes. Without a memory layer that resolves entities during ingestion, your agent treats "Acme Corp," "Acme," and "Acme's European subsidiary" as disconnected concepts.

Fragmented entity resolution

// See It In Action //

An agent that actually remembers

Connect your internal research, data lakes, and document pipelines to HydraDB. It structures the data into a bi-temporal context graph. When your agent analyzes a market shift, HydraDB retrieves the exact timeline, entity relationships, and source documents before the LLM generates.

Live support session — HydraDB context active

Retrieval : 94ms

How has Competitor X's strategy regarding AI server infrastructure changed since Q2, and who is their current primary chip supplier?

Analyst

Research intelligence workflow

Market shift analysis

HydraDB recalled:

Fact: In Q2 (May 14), Competitor X announced an internal server build strategy

Temporal State: Internal build strategy → Abandoned on Aug 22 due to yield issues

Relationship: Competitor X → Signed supply agreement with → Supplier Y (Aug 22)

Document: SEC 8-K filing (Aug 22) confirming $500M contract with Supplier Y

In Q2 (May 14), Competitor X planned to build their own AI servers. However, this strategy was abandoned on August 22 due to yield issues. On the same day, they signed a $500M supply agreement with Supplier Y, who is now their primary chip supplier according to their August 22 SEC 8-K filing.

// How Hydra Helps //

Context Infrastructure for Investigative Agents

HydraDB replaces disconnected vector stores and slow relational databases with a single, graph-native context layer designed for high-stakes reasoning across massive document corpuses.

Persistent memory

HydraDB stores market data as a versioned, append-only graph. When a fact changes, the previous state is preserved. This bi-temporal modeling allows financial agents to track evolving market trends and reason over what used to be true alongside what is true now.

Persistent memory

Persistent memory

Relationship-aware retrieval

HydraDB models complex networks natively. It traverses deep, multi-hop connections in milliseconds, bypassing the timeout limitations of traditional relational database joins.

Relationship-aware retrieval

HydraDB models complex networks natively. It traverses deep, multi-hop connections in milliseconds, bypassing the timeout limitations of traditional relational database joins.

Temporal context

HydraDB's Git-style versioned temporal graph preserves the full history of state changes. Your agents can determine when a competitor abandoned a strategy and why they pivoted.

Temporal context

HydraDB's Git-style versioned temporal graph preserves the full history of state changes. Your agents can determine when a competitor abandoned a strategy and why they pivoted.

Ingestion built for research and market data

HydraDB ingests structured records from your data pipelines, including AWS S3 data lakes and Notion workspaces. Map your extracted documents to HydraDB's format, and it resolves entities and extracts commitments at ingestion time.

Ingestion built for research and market data

// Get Started //

Three steps to production-ready memory

HydraDB integrates directly into your existing data pipelines. Connect your data lakes and document stores, map your unstructured research, and retrieve deep relationship context at inference time.

Connect your data lakes and document stores

Pull raw market reports, web scrapes, and internal notes from AWS S3, Notion, or your enterprise data lake using existing ETL jobs.

import os
import boto3
from hydra_db import HydraDB

client = HydraDB(token=os.environ.get("HYDRA_KEY"))
s3 = boto3.client('s3')

# Retrieve a market report from your S3 data lake
response = s3.get_object(
    Bucket='market-research-lake', 
    Key='reports/competitor_x_q3.txt'
)
market_report_text = response['Body'].read().decode('utf-8')

import os
import boto3
from hydra_db import HydraDB

client = HydraDB(token=os.environ.get("HYDRA_KEY"))
s3 = boto3.client('s3')

# Retrieve a market report from your S3 data lake
response = s3.get_object(
    Bucket='market-research-lake', 
    Key='reports/competitor_x_q3.txt'
)
market_report_text = response['Body'].read().decode('utf-8')

import os
import boto3
from hydra_db import HydraDB

client = HydraDB(token=os.environ.get("HYDRA_KEY"))
s3 = boto3.client('s3')

# Retrieve a market report from your S3 data lake
response = s3.get_object(
    Bucket='market-research-lake', 
    Key='reports/competitor_x_q3.txt'
)
market_report_text = response['Body'].read().decode('utf-8')

Connect your data lakes and document stores

Pull raw market reports, web scrapes, and internal notes from AWS S3, Notion, or your enterprise data lake using existing ETL jobs.

import os
import boto3
from hydra_db import HydraDB

client = HydraDB(token=os.environ.get("HYDRA_KEY"))
s3 = boto3.client('s3')

# Retrieve a market report from your S3 data lake
response = s3.get_object(
    Bucket='market-research-lake', 
    Key='reports/competitor_x_q3.txt'
)
market_report_text = response['Body'].read().decode('utf-8')

Connect your data lakes and document stores

Pull raw market reports, web scrapes, and internal notes from AWS S3, Notion, or your enterprise data lake using existing ETL jobs.

import os
import boto3
from hydra_db import HydraDB

client = HydraDB(token=os.environ.get("HYDRA_KEY"))
s3 = boto3.client('s3')

# Retrieve a market report from your S3 data lake
response = s3.get_object(
    Bucket='market-research-lake', 
    Key='reports/competitor_x_q3.txt'
)
market_report_text = response['Body'].read().decode('utf-8')

Ingest and structure market intelligence

Send extracted documents to HydraDB. The ingestion pipeline automatically extracts entities, resolves relationships, and versions facts to support temporal reasoning.

import json

# HydraDB handles entity resolution and appends changes to temporal graph
client.context.ingest(
    type="knowledge",
    tenant_id="global_macro_fund",
    sub_tenant_id="tech_sector_desk",
    app_knowledge=json.dumps([{
        "id": "report_comp_x_q3",
        "tenant_id": "global_macro_fund",
        "sub_tenant_id": "tech_sector_desk",
        "title": "Competitor X Q3 Strategy Shift",
        "type": "market_report",
        "content": {"text": market_report_text},
        "metadata": {"source": "aws_s3", "date": "2023-08-22"}
    }])
)

import json

# HydraDB handles entity resolution and appends changes to temporal graph
client.context.ingest(
    type="knowledge",
    tenant_id="global_macro_fund",
    sub_tenant_id="tech_sector_desk",
    app_knowledge=json.dumps([{
        "id": "report_comp_x_q3",
        "tenant_id": "global_macro_fund",
        "sub_tenant_id": "tech_sector_desk",
        "title": "Competitor X Q3 Strategy Shift",
        "type": "market_report",
        "content": {"text": market_report_text},
        "metadata": {"source": "aws_s3", "date": "2023-08-22"}
    }])
)

import json

# HydraDB handles entity resolution and appends changes to temporal graph
client.context.ingest(
    type="knowledge",
    tenant_id="global_macro_fund",
    sub_tenant_id="tech_sector_desk",
    app_knowledge=json.dumps([{
        "id": "report_comp_x_q3",
        "tenant_id": "global_macro_fund",
        "sub_tenant_id": "tech_sector_desk",
        "title": "Competitor X Q3 Strategy Shift",
        "type": "market_report",
        "content": {"text": market_report_text},
        "metadata": {"source": "aws_s3", "date": "2023-08-22"}
    }])
)

Ingest and structure market intelligence

Send extracted documents to HydraDB. The ingestion pipeline automatically extracts entities, resolves relationships, and versions facts to support temporal reasoning.

import json

# HydraDB handles entity resolution and appends changes to temporal graph
client.context.ingest(
    type="knowledge",
    tenant_id="global_macro_fund",
    sub_tenant_id="tech_sector_desk",
    app_knowledge=json.dumps([{
        "id": "report_comp_x_q3",
        "tenant_id": "global_macro_fund",
        "sub_tenant_id": "tech_sector_desk",
        "title": "Competitor X Q3 Strategy Shift",
        "type": "market_report",
        "content": {"text": market_report_text},
        "metadata": {"source": "aws_s3", "date": "2023-08-22"}
    }])
)

Ingest and structure market intelligence

Send extracted documents to HydraDB. The ingestion pipeline automatically extracts entities, resolves relationships, and versions facts to support temporal reasoning.

import json

# HydraDB handles entity resolution and appends changes to temporal graph
client.context.ingest(
    type="knowledge",
    tenant_id="global_macro_fund",
    sub_tenant_id="tech_sector_desk",
    app_knowledge=json.dumps([{
        "id": "report_comp_x_q3",
        "tenant_id": "global_macro_fund",
        "sub_tenant_id": "tech_sector_desk",
        "title": "Competitor X Q3 Strategy Shift",
        "type": "market_report",
        "content": {"text": market_report_text},
        "metadata": {"source": "aws_s3", "date": "2023-08-22"}
    }])
)

Retrieve full context at inference time

Before your investigative agent generates analysis, query HydraDB for multi-hop entity relationships and temporal state changes.

# Retrieve structured timeline and entity context before LLM generation
result = client.query(
    tenant_id="global_macro_fund",
    sub_tenant_id="tech_sector_desk",
    query="How has Competitor X's server strategy changed since Q2?",
    type="knowledge",
    mode="thinking",
    query_apps=True
)

# Pass exact temporal context to your preferred LLM
response = llm.chat(
    system=build_investigator_prompt(result.data),
    user="Draft a strategy update brief for the portfolio manager."
)

# Retrieve structured timeline and entity context before LLM generation
result = client.query(
    tenant_id="global_macro_fund",
    sub_tenant_id="tech_sector_desk",
    query="How has Competitor X's server strategy changed since Q2?",
    type="knowledge",
    mode="thinking",
    query_apps=True
)

# Pass exact temporal context to your preferred LLM
response = llm.chat(
    system=build_investigator_prompt(result.data),
    user="Draft a strategy update brief for the portfolio manager."
)

# Retrieve structured timeline and entity context before LLM generation
result = client.query(
    tenant_id="global_macro_fund",
    sub_tenant_id="tech_sector_desk",
    query="How has Competitor X's server strategy changed since Q2?",
    type="knowledge",
    mode="thinking",
    query_apps=True
)

# Pass exact temporal context to your preferred LLM
response = llm.chat(
    system=build_investigator_prompt(result.data),
    user="Draft a strategy update brief for the portfolio manager."
)

Retrieve full context at inference time

Before your investigative agent generates analysis, query HydraDB for multi-hop entity relationships and temporal state changes.

# Retrieve structured timeline and entity context before LLM generation
result = client.query(
    tenant_id="global_macro_fund",
    sub_tenant_id="tech_sector_desk",
    query="How has Competitor X's server strategy changed since Q2?",
    type="knowledge",
    mode="thinking",
    query_apps=True
)

# Pass exact temporal context to your preferred LLM
response = llm.chat(
    system=build_investigator_prompt(result.data),
    user="Draft a strategy update brief for the portfolio manager."
)

Retrieve full context at inference time

Before your investigative agent generates analysis, query HydraDB for multi-hop entity relationships and temporal state changes.

# Retrieve structured timeline and entity context before LLM generation
result = client.query(
    tenant_id="global_macro_fund",
    sub_tenant_id="tech_sector_desk",
    query="How has Competitor X's server strategy changed since Q2?",
    type="knowledge",
    mode="thinking",
    query_apps=True
)

# Pass exact temporal context to your preferred LLM
response = llm.chat(
    system=build_investigator_prompt(result.data),
    user="Draft a strategy update brief for the portfolio manager."
)

// Why HydraDB //

HydraDB vs. Standard Vector Search for Research Intelligence

HydraDB gives investigative agents persistent bi-temporal history, graph-native retrieval, temporal context, and structured ingestion where standard vector search treats every query as isolated semantic lookup.

Features

HydraDB

Standard Vector Search

Cross-session memory

Persistent bi-temporal history across millions of documents

Treats every query as isolated, stateless lookup

Relationship-aware retrieval

Graph-native resolution for deep, multi-hop investigative queries

Relies on semantic similarity; fails at explicit entity connections

Temporal context

Append-only state tracking for market shifts and evolving strategies

Destructively overwrites history or flattens timelines

Integration method

Structured records ingested from your existing data pipelines

Manual chunking and embedding of individual documents

Best fit

Investigative agents requiring temporal reasoning and entity traversal

Static semantic search over isolated knowledge bases

Team hand-off summaries

Creates a shared account brief for every team

Creates fragmented hand-offs

Workflow triggers

Triggers updates when important signals change

Depends on humans noticing every signal

Features

Cross-session memory

Relationship-aware retrieval

Temporal context

Integration method

Best fit

Team hand-off summaries

Workflow triggers

HydraDB

Persistent bi-temporal history across millions of documents

Graph-native resolution for deep, multi-hop investigative queries

Append-only state tracking for market shifts and evolving strategies

Structured records ingested from your existing data pipelines

Investigative agents requiring temporal reasoning and entity traversal

Creates a shared account brief for every team

Triggers updates when important signals change

Standard Vector Search

Treats every query as isolated, stateless lookup

Relies on semantic similarity; fails at explicit entity connections

Destructively overwrites history or flattens timelines

Manual chunking and embedding of individual documents

Static semantic search over isolated knowledge bases

Creates fragmented hand-offs

Depends on humans noticing every signal

// What Teams Are Saying //

Trusted by teams building research intelligence AI

We spent months trying to build a custom GraphRAG pipeline to track competitor supply chains, but it was too brittle to maintain. HydraDB gave us out-of-the-box entity resolution and relationship traversal. Our multi-hop queries went from timing out to returning in under 200ms.

Marcus V.

Head of AI Engineering, Global Strategy Firm

Standard vector databases were actively harming our financial agents because they flattened time. You can't analyze a market shift if your agent doesn't understand the difference between last year's SEC filing and yesterday's 8-K. HydraDB's bi-temporal memory solved our hallucination problem completely.

Sarah L.

CTO, Quantitative Market Research

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 research intelligence?

AI agent memory gives research and investigative agents persistent context across documents, sources, and research sessions. Instead of analyzing every report or filing independently, the agent can remember previously discovered entities, relationships, events, decisions, and how those facts changed over time.

Why isn't standard RAG enough for research intelligence agents?

Standard RAG is good at finding semantically similar documents, but research often requires connecting information across many different sources and points in time. An analyst may need to understand how a company’s strategy changed, which executives were involved, which suppliers are connected, and what evidence supports each change. HydraDB connects these facts in a graph so agents can retrieve relationships, historical state, and supporting documents rather than only similar chunks of text.

How does HydraDB help agents track how companies and markets change over time?

HydraDB stores research context in a versioned temporal graph. When a fact changes, the previous state remains available instead of being overwritten. This allows agents to answer questions such as what a company’s strategy was last quarter, when it changed, what replaced it, and which events or documents explain the change.

How does HydraDB connect information across different research sources?

Research about the same company or person is often scattered across filings, market reports, emails, internal notes, data lakes, and other documents. HydraDB resolves entities and connects relationships across these sources so the agent can understand that different references may describe the same underlying company, person, product, or event. This creates a connected knowledge graph rather than a collection of isolated documents.

Can HydraDB help with multi-hop research and investigative queries?

Yes. HydraDB is designed for relationship-aware retrieval across connected entities. An agent can move through relationships between companies, executives, suppliers, products, investments, events, and documents to uncover context that may require several hops to discover. This is useful for research questions where the answer does not exist in a single document and instead has to be assembled from multiple connected facts.

What research data can I bring into HydraDB?

You can bring market reports, SEC filings, internal research, web data, company documents, emails, notes, data-lake records, and other structured or unstructured research into HydraDB. HydraDB can extract entities and relationships from this information and organize it into persistent context that research agents can retrieve later.

How can we bring research data into HydraDB?

HydraDB connectors can continuously sync data from your existing tools into HydraDB, while ingestion APIs let you bring in data from custom research pipelines, data lakes, and document processing systems. You can connect sources such as Notion, Slack, Gmail, GitHub, and other supported apps, or ingest research from systems such as AWS S3 and enterprise data lakes. HydraDB can then structure the information, resolve entities, preserve changes over time, and make it available as connected context for your research agents. Learn more about HydraDB connectors.

Why is HydraDB a strong choice for building research intelligence AI?

Research intelligence is inherently relational and temporal. The right answer often depends not only on what is true today, but what was true before, when it changed, why it changed, and how companies, people, events, products, and documents are connected. HydraDB is built for this kind of long-lived research context. Its temporal graph preserves historical state, while fast graph traversal helps agents retrieve deep multi-hop relationships across large research corpora. Its architecture also keeps older context in lower-cost object storage while keeping frequently accessed context fast, making it practical to retain growing amounts of research history 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

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