@hydra_db

HydraDB is a graphDB built on object storage: fast, extremely cheap, and purpose-made for use cases like AI memory, ontologies, and company brain.

 San Francisco
hydradb.com/
Joined August 2025
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“No results” from a knowledge graph deserves a closer look. Potoniec, Wiśniewski and Ławrynowicz’s 2021 paper tests the assumptions inside ontology questions before interpreting their answers. Does the vocabulary exist? Does the ontology allow the situation the question assumes? For agent context graphs, keep “no matching facts” separate from “the model cannot support this question.” They need different next steps. The formal method uses OWL 2 and SPARQL-OWL. Applying it to an agent workflow still needs its own validation. PDF: ksiresearch.org/seke/seke21p…
Unchanged first page of the paper “Incorporating Presuppositions of Competency Questions into Test-Driven Development of Ontologies” by Jędrzej Potoniec, Dawid Wiśniewski and Agnieszka Ławrynowicz, SEKE 2021. The page includes the title, authors, abstract and introduction.

ALT Unchanged first page of the paper “Incorporating Presuppositions of Competency Questions into Test-Driven Development of Ontologies” by Jędrzej Potoniec, Dawid Wiśniewski and Agnieszka Ławrynowicz, SEKE 2021. The page includes the title, authors, abstract and introduction.

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Coming back after a week off means catching up on 1,284 unread Slack messages. Your AI asks you to paste everything you missed. HydraDB gives it the context to find what actually matters. Less noise. More context.
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The real version: connect Slack, ask what changed : docs.hydradb.com/essentials/…

Connectors - HydraDB SDK

Connect Slack, GitHub, Notion, or any of 70 apps, and HydraDB keeps their data synced into your database.

docs.hydradb.com
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A knowledge graph can extract the right names and still put them in the wrong structure. OSKGC, a 2025 paper by Dali Wang and Mizuho Iwaihara, makes that an explicit evaluation problem: measure extracted triples and their alignment with a predefined ontology separately. One example is edge direction. A building linked to its architect has a different schema from an architect linked to a building, even when both names are correct. The benchmark adds fine-grained entity types and a type hierarchy to 10,183 short text examples derived from WebNLG. Its Structural Similarity metric gives graded credit based on where a predicted type sits in that hierarchy. The scope matters: these examples contain one to three triples. Enterprise documents, changing schemas and downstream agent behavior need their own evaluation. A useful takeaway for graph builders: track factual extraction, type accuracy and relation direction separately before testing the graph on the questions your application needs to answer. Paper: ceur-ws.org/Vol-4041/paper1.…
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New community session today: Index 0. 7:00 PM IST (1:30 PM UTC) on Discord. Chill intro hang, zero-indexed. Come say hi, meet the folks, see what we're building and where it's headed. No prep, no prior context needed. Everyone's welcome. Link in the reply.
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Join here: discord.gg/YPtwenvxY8

Join the HydraDB Discord Server!

Check out the HydraDB community on Discord - hang out with 1242 other members and enjoy free voice and text chat.

discord.com
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Your agents work as a crew. Now they share context like one. @crewAIInc × @hydraDB 🤝
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Takes one install: pip install crewai-hydradb Docs: docs.hydradb.com/plugins/cre…

CrewAI - HydraDB SDK

Give CrewAI agents memory that survives across runs, backed by HydraDB.

docs.hydradb.com
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A wrong entity merge can give an agent the wrong customer's context. Imagine two companies with similar names. If a knowledge graph combines them, contracts and support history can end up attached to the wrong account. Useful reading: Ditto, from 2020. It fine-tunes a pretrained language model to decide whether two records refer to the same entity, with field names and values preserved in the input. Domain-specific cues help guide the comparison. The practical takeaway: test lookalike names, missing fields and conflicting identifiers before letting an agent rely on merged records. Ditto still needs labeled examples and candidate selection. Its matching benchmarks don't establish end-to-end agent reliability. Paper: arxiv.org/pdf/2004.00584
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HydraDB retweeted
aditya.
@bit2swazOct 15
btw we're doing weekly database paper reading sessions at @hydra_db now :) every thursday, 4pm IST (10:30am UTC) on our discord. focusing mostly on consensus rn if youre into distributed systems or databases, pull up to the discord (everyone's welcome) ^^ link in replies 👇
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Every conversation shouldn't feel like the first !
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We're running our Database discussion session today. 4:00 PM IST (10:30 AM UTC), on Discord. This week is a refresher on the fundamentals: replication, partitioning, transactions, consistency, and consensus. Come hang, bring questions. Link in the reply.
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Join here: discord.gg/YPtwenvxY8

Join the HydraDB Discord Server!

Check out the HydraDB community on Discord - hang out with 1242 other members and enjoy free voice and text chat.

discord.com
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Anthropic just replaced Git internally. Not because Git is bad. Because it was built for humans, and their agents now merge 30,000+ changes a day. Same story in retrieval. Vector search was built to find similar text. Agents need what's current and connected. That's HydraDB.
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Similarity isn't relevance. How HydraDB retrieves current, connected context: benchmarks.hydradb.com Anthropic's post on Scion: anthropic.com/engineering/re…

Hydra DB · Benchmarks

Benchmark and research reports from Hydra DB, the graph-native context layer for AI agents: versioned, relational, time-aware, and built for precise recall.

benchmarks.hydradb.com
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Your chunker cut the sentence that mattered. "Refunds within 30 days" landed in one chunk. "Unless shipped outside the EU" in the next. Search returned only first. HydraDB returns the graph relations between chunks with each search, not just top hits.
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Learn how a HydraDB search returns graph context and related chunks alongside the hits: docs.hydradb.com/essentials/…

How to Use API Results - HydraDB SDK

Turn a query response into an LLM prompt.

docs.hydradb.com
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Jobs run on nodes. Sensors observe nodes. A telemetry agent needs those relationships to answer which machines were hot while a workload ran. Symbolic Separation (Davletiyarov, Khan & Bartolini, 2026) builds a query-specific knowledge graph and checks generated SPARQL against the ontology before execution. The useful pattern: define the allowed relationships, connect the relevant data, then validate the query before running it. This is a preprint. Its benchmarks change models and backend capabilities, so the headline gains don't isolate the graph's contribution. PDF: arxiv.org/pdf/2609.17107
First page of “Symbolic Separation: Grounding Deep Agents in Knowledge Graphs for Trustworthy Operational Data Analytics,” by Baibek Davletiyarov, Junaid Ahmed Khan and Andrea Bartolini, University of Bologna. arXiv:2609.17107v1, 15 September 2026. Title and authors appear above the abstract and introduction in two columns. The paper describes ontology-constrained knowledge graphs for telemetry agents. Page rendered from the original PDF with no content edits. Source: https://arxiv.org/abs/2609.17107. License: CC BY 4.0, https://creativecommons.org/licenses/by/4.0/.

ALT First page of “Symbolic Separation: Grounding Deep Agents in Knowledge Graphs for Trustworthy Operational Data Analytics,” by Baibek Davletiyarov, Junaid Ahmed Khan and Andrea Bartolini, University of Bologna. arXiv:2609.17107v1, 15 September 2026. Title and authors appear above the abstract and introduction in two columns. The paper describes ontology-constrained knowledge graphs for telemetry agents. Page rendered from the original PDF with no content edits. Source: https://arxiv.org/abs/2609.17107. License: CC BY 4.0, https://creativecommons.org/licenses/by/4.0/.

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Debug graph-backed agents at the first failed hop. For a supplier → product → customer question, check each intermediate answer and its retrieved evidence, alongside final-answer accuracy. AgenticRAGTracer (You et al., ACL Findings 2026) provides intermediate questions, answers and supporting documents for multi-hop evaluation. Its tests use Wikipedia-derived text-retrieval tasks. The enterprise-graph takeaway: build the same checks around your own queries. Paper: aclanthology.org/2026.findin…
Unchanged first page of AgenticRAGTracer: A Hop-Aware Benchmark for Diagnosing Multi-Step Retrieval Reasoning in Agentic RAG, by Qijie You, Wenkai Yu, Hao Liang, Zhen Hao Wong and Wentao Zhang. Findings of ACL 2026, July 2–7, 2026, page 1301. The title, authors, affiliations, abstract and opening introduction are visible. © 2026 Association for Computational Linguistics. Source: https://aclanthology.org/2026.findings-acl.66.pdf. CC BY 4.0: https://creativecommons.org/licenses/by/4.0/. Rendered from the original PDF with no content changes.

ALT Unchanged first page of AgenticRAGTracer: A Hop-Aware Benchmark for Diagnosing Multi-Step Retrieval Reasoning in Agentic RAG, by Qijie You, Wenkai Yu, Hao Liang, Zhen Hao Wong and Wentao Zhang. Findings of ACL 2026, July 2–7, 2026, page 1301. The title, authors, affiliations, abstract and opening introduction are visible. © 2026 Association for Computational Linguistics. Source: https://aclanthology.org/2026.findings-acl.66.pdf. CC BY 4.0: https://creativecommons.org/licenses/by/4.0/. Rendered from the original PDF with no content changes.

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For agent graph pipelines, make failed constraints actionable: retain the node, property path and violated rule, then revalidate proposed edits. xpSHACL (Publio & Labra Gayo, 2025) grounds LLM explanations in validation evidence. Passing constraints still leaves factual accuracy to verify. PDF: arxiv.org/pdf/2507.08432v1
Unchanged first page of xpSHACL: Explainable SHACL Validation using Retrieval-Augmented Generation and Large Language Models, by Gustavo Correa Publio and José Emilio Labra Gayo, 11 July 2025. The title, authors, abstract and opening sections are visible. Source: https://arxiv.org/abs/2507.08432. CC BY-SA 4.0: https://creativecommons.org/licenses/by-sa/4.0/. No changes.

ALT Unchanged first page of xpSHACL: Explainable SHACL Validation using Retrieval-Augmented Generation and Large Language Models, by Gustavo Correa Publio and José Emilio Labra Gayo, 11 July 2025. The title, authors, abstract and opening sections are visible. Source: https://arxiv.org/abs/2507.08432. CC BY-SA 4.0: https://creativecommons.org/licenses/by-sa/4.0/. No changes.

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