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Rank #4 of 7 in Vector Databases & Memory Stores

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HelixDB

Open SourceYC X25

HelixDB

helix-db.com ↗Try it →
PA Score32/100AGENT-READY63/100BUILT-IN AI3/100API35/100
MCP✓CLI✓API✓
★ 5.9k▲ 3.3k/yrnpm 3.2k/wkpypi 721/wk
ArenasVector Databases & Memory Stores#4/7

Access

Open app ↗API docs ↗CLI docs ↗MCP docs ↗

Install

curlcurl -sSL "https://install.helix-db.com" | bash

Vendor-official, but review any script before piping it to a shell.

npmnpm install @helix-db/helix-db@3.0.4
pippython -m pip install helix-db==0.3.4

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Alternatives to HelixDB →

Try itExperimental

See what an agent can do with HelixDB before you ever sign up. Pick a story: recorded sessions replay real probe-harness transcripts; commands tagged live-capable can re-run against the real endpoint from our edge, right now (▶ run live — the exact same request, live and recorded lines always labeled); the live MCP handshake runs real requests from our edge, right now — including, where the server allows it, one real read-only tool call (bring your own key for auth-gated servers); sandboxed self-drive sessions are designed and gated (docs/TRY-IT.md).

$curl -s https://docs.helix-db.com/llms.txt | head -6recorded session — replayed, not live
recorded 2026-09-10 · exit 0 · captured verbatim by our probe harness, secrets redacted · pure-HTTP probe — ▶ run live re-runs it from our edge

Verified integrations

No integration evidence found in our corpus for this product yet — that means none was found, never that it doesn’t integrate.

By theme — the product's score on each story themeBy theme

Agenticness — how well agents can access and operate the productAgenticnessevidence →

How well agents can access and operate the product

43.9/100

Automation depth — how much of the product can run unattendedAutomation depthevidence →

How much of the product can run unattended

0.0/100

Data lifecycle — stories about data lifecycle in this arenaData lifecycleevidence →

Stories about data lifecycle in this arena

6.0/100

Deployment modes — stories about deployment modes in this arenaDeployment modesevidence →

Stories about deployment modes in this arena

44.0/100

Embeddings pipeline — stories about embeddings pipeline in this arenaEmbeddings pipelineevidence →

Stories about embeddings pipeline in this arena

0.0/100

Filtering metadata — stories about filtering metadata in this arenaFiltering metadataevidence →

Stories about filtering metadata in this arena

21.6/100

Multi tenancy scale — stories about multi tenancy scale in this arenaMulti tenancy scaleevidence →

Stories about multi tenancy scale in this arena

14.0/100

Openness — open source, data portability, and self-hosting storiesOpennessevidence →

Open source, data portability, and self-hosting stories

26.4/100

Performance latency — stories about performance latency in this arenaPerformance latencyevidence →

Stories about performance latency in this arena

0.0/100

Pricing plans — plan structure and value — what each tier costs and what it unlocksPricing plansevidence →

Plan structure and value — what each tier costs and what it unlocks

10.0/100

Privacy posture — data-handling and privacy storiesPrivacy postureevidence →

Data-handling and privacy stories

0.0/100

Sdk integrations — stories about sdk integrations in this arenaSdk integrationsevidence →

Stories about sdk integrations in this arena

15.0/100

Search quality hybrid — stories about search quality hybrid in this arenaSearch quality hybridevidence →

Stories about search quality hybrid in this arena

49.0/100

Story verdicts — every judged story with its evidenceStory verdicts

What’s free: 2 free · 1 paid · 0 enterprise · 19 not stated in evidence

?

Sorted by importance (agentic first) (high → low) · 53/53 stories · click a row’s chevron for the rationale and evidence

Drive the product through a documented public API G

Agent access

ai-native userAgenticness — how well agents can access and operate the productAgenticness3full8/10T

Connect an agent via an official MCP server G

Agent access

ai-native userAgenticness — how well agents can access and operate the productAgenticness3full7/10T

Delegate tasks to a built-in AI assistant inside the product G

Agentic features

ai-native userAgenticness — how well agents can access and operate the productAgenticness3none0/10

Plug MCP servers into this product so it can use their tools G

Agent access

ai-native userAgenticness — how well agents can access and operate the productAgenticness3n/auntestednone yet

Point an agent at llms.txt or agent-oriented docs G

Agent access

ai-native userAgenticness — how well agents can access and operate the productAgenticness2full9/10T

Build against official SDKs G

Agent access

ai-native userAgenticness — how well agents can access and operate the productAgenticness2full8/10T

Download a machine-readable API spec (OpenAPI or equivalent) G

Api quality

ai-native userAgenticness — how well agents can access and operate the productAgenticness2full8/10T

Issue scoped/least-privilege API credentials for an agent G

Agent access

ai-native userAgenticness — how well agents can access and operate the productAgenticness2full7/10C

Use an official CLI G

Agent access

ai-native userAgenticness — how well agents can access and operate the productAgenticness2full7/10T

Run the product headlessly / in CI for automation G

Agent access

ai-native userAgenticness — how well agents can access and operate the productAgenticness2partial6/10T

Explore an interactive API reference with runnable examples G

Api quality

ai-native userAgenticness — how well agents can access and operate the productAgenticness2partial4/10T

Operate the product with natural-language commands G

Agentic features

ai-native userAgenticness — how well agents can access and operate the productAgenticness2disputed3/10D

Get AI-generated insights and suggestions from my data inside the product G

Agentic features

ai-native userAgenticness — how well agents can access and operate the productAgenticness2noneuntestednone yet

Rely on versioned APIs with a documented deprecation policy G

Api quality

ai-native userAgenticness — how well agents can access and operate the productAgenticness2noneuntestednone yet

Set up automations that run autonomously in the background G

Agentic features

ai-native userAgenticness — how well agents can access and operate the productAgenticness2n/auntestednone yet

Subscribe to events via webhooks G

Agent access

ai-native userAgenticness — how well agents can access and operate the productAgenticness2noneuntestednone yet

Test against a sandbox environment without touching production data G

Api quality

ai-native userAgenticness — how well agents can access and operate the productAgenticness1partial6/10C

Run approximate nearest-neighbor similarity search over embeddings with configurable distance metrics C

Core search

developerSearch quality hybrid — stories about search quality hybrid in this arenaSearch quality hybrid3full8/10T

Self-host the core product G

ai-native userOpenness — open source, data portability, and self-hosting storiesOpenness3fullfree8/10X

Filter vector search by structured metadata conditions without wrecking recall or latency C

Filtering

developerFiltering metadata — stories about filtering metadata in this arenaFiltering metadata3partial6/10X

Combine dense vector search with keyword or sparse (BM25-style) signals in one hybrid query with fusion ranking C

Hybrid

developerSearch quality hybrid — stories about search quality hybrid in this arenaSearch quality hybrid3partial5/10T

Isolate many tenants cheaply using namespaces, partitions, or per-tenant collections with documented limits C

Tenancy

platform-engineerMulti tenancy scale — stories about multi tenancy scale in this arenaMulti tenancy scale3partial3/10C

Export all of my data in open formats and leave G

ai-native userOpenness — open source, data portability, and self-hosting storiesOpenness3none0/10

Have the database generate embeddings at ingest and query time using built-in or configured model providers, instead of running a separate embedding pipeline C

Embeddings

ml-engineerEmbeddings pipeline — stories about embeddings pipeline in this arenaEmbeddings pipeline3none0/10

Define rules that trigger actions automatically on events G

ai-native userAutomation depth — how much of the product can run unattendedAutomation depth3noneuntestednone yet

Prevent my data from being used to train AI models G

ai-native userPrivacy posture — data-handling and privacy storiesPrivacy posture3noneuntestednone yet

Run keyword/full-text search over documents inside the database without bolting on a separate search engine C

Hybrid

developerSearch quality hybrid — stories about search quality hybrid in this arenaSearch quality hybrid2full8/10T

Run the database embedded in-process or as a lightweight local instance for development and small workloads C

Local dev

developerDeployment modes — stories about deployment modes in this arenaDeployment modes2full8/10X

Enforce granular access control (API keys, roles, per-collection permissions) on database operations C

Tenancy

platform-engineerMulti tenancy scale — stories about multi tenancy scale in this arenaMulti tenancy scale2partial6/10C

Build against official SDKs in the major languages (Python, TypeScript, Go, Java) G

Sdks

developerSdk integrations — stories about sdk integrations in this arenaSdk integrations2partial5/10C

Use a fully managed cloud version of the database with programmatic provisioning C

Managed cloud

developerDeployment modes — stories about deployment modes in this arenaDeployment modes2partialpaid5/10T

Read the product's source under an open license G

ai-native userOpenness — open source, data portability, and self-hosting storiesOpenness2disputed4/10D

Upsert and delete records continuously and have changes reflected in search results quickly, with documented freshness/consistency behavior C

Freshness

developerData lifecycle — stories about data lifecycle in this arenaData lifecycle2partial3/10C

Bulk-import and bulk-export vectors plus metadata in documented formats C

Portability

developerData lifecycle — stories about data lifecycle in this arenaData lifecycle2none0/10

Choose where my data is stored (region/residency) G

ai-native userPrivacy posture — data-handling and privacy storiesPrivacy posture2none0/10

Enable vector quantization or compression to cut memory and storage cost with a documented accuracy trade-off C

Index tuning

ml-engineerPerformance latency — stories about performance latency in this arenaPerformance latency2none0/10

Express rich filter conditions (ranges, geo, nested boolean logic, array membership) in queries C

Filtering

developerFiltering metadata — stories about filtering metadata in this arenaFiltering metadata2none0/10

Pay serverless usage-based pricing with transparent per-unit costs instead of provisioning fixed clusters G

Pricing

developerPricing plans — plan structure and value — what each tier costs and what it unlocksPricing plans2none0/10

Plug the database into RAG and agent frameworks (LangChain, LlamaIndex, etc.) through maintained first-class integrations C

Integrations

ml-engineerSdk integrations — stories about sdk integrations in this arenaSdk integrations2none0/10

Replicate data across nodes or zones for high availability with a documented consistency model C

Scaling

platform-engineerMulti tenancy scale — stories about multi tenancy scale in this arenaMulti tenancy scale2none0/10

Rerank search results with built-in or first-party-integrated reranking models C

Reranking

ml-engineerSearch quality hybrid — stories about search quality hybrid in this arenaSearch quality hybrid2none0/10

Scale beyond one node with sharding or distributed deployment C

Scaling

platform-engineerMulti tenancy scale — stories about multi tenancy scale in this arenaMulti tenancy scale2none0/10

See published benchmarks or measured latency/recall numbers backing the database's performance claims C

Benchmarks

platform-engineerPerformance latency — stories about performance latency in this arenaPerformance latency2none0/10

Tune index parameters (HNSW graph settings, index types) to trade recall against latency and memory C

Index tuning

ml-engineerPerformance latency — stories about performance latency in this arenaPerformance latency2none0/10

Back up collections with snapshots and restore them C

Backup

platform-engineerData lifecycle — stories about data lifecycle in this arenaData lifecycle2noneuntestednone yet

Control data retention and deletion G

ai-native userPrivacy posture — data-handling and privacy storiesPrivacy posture2noneuntestednone yet

Do everything through the API that I can do in the UI G

ai-native userOpenness — open source, data portability, and self-hosting storiesOpenness2noneuntestednone yet

Opt out of telemetry and usage tracking G

ai-native userPrivacy posture — data-handling and privacy storiesPrivacy posture2noneuntestednone yet

Perform bulk operations across many items at once G

ai-native userAutomation depth — how much of the product can run unattendedAutomation depth2noneuntestednone yet

Schedule recurring jobs or workflows G

ai-native userAutomation depth — how much of the product can run unattendedAutomation depth2n/auntestednone yet

Prototype on a meaningful free tier before paying anything G

Pricing

developerPricing plans — plan structure and value — what each tier costs and what it unlocksPricing plans1partialfree5/10X

Deploy to production on Kubernetes with an official Helm chart or operator C

Self managed

platform-engineerDeployment modes — stories about deployment modes in this arenaDeployment modes1none0/10

Version, review, and roll back my automations G

ai-native userAutomation depth — how much of the product can run unattendedAutomation depth1noneuntestednone yet

Opportunities — the stories that would move this product's scores, from its own judged verdictsOpportunitiestop 8 of 37 stories with headroom

What would move HelixDB’s scores — derived from its own judged verdicts, biggest headroom first. Each line quotes what the judge found missing; shipping it (or evidencing it publicly) is the fix.

  1. Agenticness — how well agents can access and operate the productDelegate tasks to a built-in AI assistant inside the product

    nonemoves Built-in AIimpact 45

    HelixDB documents an MCP server for external AI agents/tools to connect to it, and a 'helix chef' bootstrapper that scaffolds projects, but there is no evidence of a built-in AI assistant inside the product itself that a user can delegate tasks to.

  2. Automation depth — how much of the product can run unattendedDefine rules that trigger actions automatically on events

    nonemoves PA Scoreimpact 30

    HelixDB is a graph/vector/text database with query and transaction capabilities, but no evidence describes event-driven triggers, rules engines, or automatic actions firing on data events; the evidence pack only covers queries, indexes, SDKs, MCP access, and access control.

  3. Embeddings pipeline — stories about embeddings pipeline in this arenaHave the database generate embeddings at ingest and query time using built-in or configured model providers, instead of running a separate embedding pipeline

    nonemoves PA Scoreimpact 30

    Missing: any documentation of built-in embedding generation, model provider configuration, or automatic text-to-vector conversion at ingest/query time.

  4. Openness — open source, data portability, and self-hosting storiesExport all of my data in open formats and leave

    nonemoves PA Scoreimpact 30

    While HelixDB is Apache 2.0 open source (helixdb-docs-12) and can run embedded/self-hosted (helixdb-docs-2), there is no evidence of an explicit data export/migration tool or open-format data dump capability, and community comments explicitly raise vendor lock-in concerns about the bespoke query language (helixdb-comm-3, helixdb-comm-8) with no rebuttal shown for data portability.

  5. Privacy posture — data-handling and privacy storiesPrevent my data from being used to train AI models

    nonemoves PA Scoreimpact 30

    HelixDB is a graph/vector/text database product; the evidence pack contains no statement about AI-training data usage policies, opt-out mechanisms, or data-use commitments regarding customer data.

  6. Agenticness — how well agents can access and operate the productGet AI-generated insights and suggestions from my data inside the product

    nonemoves Built-in AIimpact 30

    The evidence describes HelixDB as a graph/vector/text database with MCP-based query access and an AI-assisted bootstrapper for scaffolding, but there is no mention of the product itself generating insights, summaries, or suggestions from stored data — it only lets external AI agents issue read/write queries against the data.

  7. Agenticness — how well agents can access and operate the productSubscribe to events via webhooks

    nonemoves agent-readyimpact 30

    No evidence of webhook subscription or event-notification capability anywhere in the docs, CLI, MCP, or API references; the evidence covers queries, indexes, security, and multi-tenancy but nothing about event-driven webhooks.

  8. Agenticness — how well agents can access and operate the productRely on versioned APIs with a documented deprecation policy

    nonemoves API qualityimpact 30

    There is mention of a v3 request model and release notes, but no evidence of a formal API versioning scheme or documented deprecation policy for HelixDB's APIs/SDKs/query language.

Showing the top 8 of 37 — every none/partial verdict in the story verdicts table is headroom.

Think a verdict is wrong? Every verdicts-table row has a Flag link — see the methodology.

Coverage map — which docs area, API section, or community source covers which judged storiesCoverage map8 surfaces · 24 covered stories

Where the cited evidence behind each covered verdict came from — the same citations the verdicts table shows, no extra judging.

Database docs22 stories

GitHub README9 stories

Hacker News7 stories

CLI docs6 stories

llms.txt6 stories

OpenAPI spec4 stories

docs.helix-db.com3 stories

helix-db.com3 stories

Probe proofs — replayable recordings from the probe harnessProbe proofs

Replayable recordings from our probe harness — see the Prove-It protocol to submit one.

$curl -s https://docs.helix-db.com/llms.txt | head -6reproduced
$ curl -s https://docs.helix-db.com/llms.txt | head -6
# HelixDB

> HelixDB combines a property graph, approximate vector search, and BM25 full-text search behind one operation-tree query model. Requests run through Cloud, a local server, or the embedded runtime.

Quickstart (local instance requires Docker or Podman):
proves: Point an agent at llms.txt or agent-oriented docsrecorded 2026-09-10
$curl -si -X POST https://mcp.helix-db.com/mcp -H 'Content-Type: application/json' -d '<jsonrpc initialize>'reproduced
$ curl -si -X POST https://mcp.helix-db.com/mcp -H 'Content-Type: application/json' -d '<jsonrpc initialize>'
HTTP/2 401

date: Thu, 10 Sep 2026 19:14:52 GMT

content-type: application/json

content-length: 25

www-authenticate: Bearer resource_metadata="https://mcp.helix-db.com/.well-known/oauth-protected-resource/mcp"

{"error":"unauthorized"}
proves: Connect an agent via an official MCP serverrecorded 2026-09-10

Claims vs evidence — vendor claims reconciled against independent verdictsClaims vs evidence

8 of 13 testable claims verified · 1 contradicted → integrity 46/100

14 distinct capability claims found in HelixDB’s own claimed-docs/GitHub materials, reconciled against our judge’s independent verdicts.

8

Verified

4

Unverified

1

Contradicted

10

Undersold

Verified (10)
Unverified (5)
Contradicted (1)
Undersold (10)
Claims outside our story set (3)

Real capability claims found in HelixDB’s own materials, but no story in this arena’s taxonomy covers them yet — that’s feedback on the taxonomy, not a mark against the product.

  • “ACID transactions span graph, vector, and text data within a single transaction”

    source ↗

  • “Search and filtering capabilities apply to both nodes and edges, not just nodes”

    source ↗

  • “Database-specific overrides can adjust request rate limits, burst capacity, and query retry budget”

    source ↗
Suggest a story for these →

Business model

open-sourceusage-basedenterprise-custom

Apache-2.0 core is free to self-host; Helix Cloud is sold as monthly workspace plans (Idea, Startup, Growth) with shared read/write rate allowances, plus dedicated/enterprise deployments.

pricing ↗

Score trend

How this product’s scores have moved as evidence and verdicts are re-derived — a point per change, not per day.

PA Scoretracked since Sep 10 '26 — no movement recorded yet
Agent-readytracked since Sep 10 '26 — no movement recorded yet

Try Experimental

Run it in the microterminal →

Recorded agent sessions — and a live MCP handshake where the vendor ships one.

Flag

⚑ Flag a verdict

Think a verdict is wrong? Opens a prefilled GitHub issue — or use the ⚑ next to any verdict above.

Badge

Embed this product's score badge →

Hotlinked SVG — always shows the live current score.

For agents

This page as markdown/llms.txt

Data

Evidence (JSON)Verdicts (JSON)

Agent surface uptime MCP up · llms.txt up · openapi.json up (tracking since Sep 11 '26)