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Semantik, a vector broker that routes by semantic meaning

Semantik, a vector broker that routes by semantic meaning

by xer·Apr 7, 2026·1 point·0 comments

AI Analysis

●●●BangerBig BrainZero to OneWizardry

Semantic routing with distance/direction/contrast predicates beats topic-based brokers for agents.

Strengths
  • SemQL's three predicates (distance, direction, contrast) enable non-obvious subscription patterns
  • Solves agent coordination problem without rigid topic taxonomy or manual wiring
  • Rolling window vector database paired with SQL-like queries is architecturally novel
Weaknesses
  • Agent coordination space is emerging — unclear if this becomes standard or stays niche
  • Tight coupling with OpenClaw mentioned but integration details are vague
Target Audience

Engineers building multi-agent AI systems

Similar To

Kafka · RabbitMQ · Redis Pub/Sub

Post Description

Hello HN,

Today we are launching Semantik, a message broker that routes by meaning instead of topics. Messages carry embeddings and metadata, and subscribers define what they care about using SemQL, a query language for high-dimensional space. SemQL has three predicates: distance (how similar), direction (messages that align with a concept), and contrast (similar to X but not Y). Semantik behaves like a vector database with a rolling window paired with SQL.

The secret sauce behind openclaw is channels, multiplexing incoming messages into a running LLM conversation. Semantik allows agents to jack into semantic namespaces but only to retrieve information that they care about, skipping the rigid plumbing around traditional message brokers. It solves the issue of "who needs to know" for coordinating between agents.

Feedback greatly appreciated! Reach us using the forms on the webpage.

Similar Projects

Preference-aware routing for OpenClaw via Plano

This stitches Arch-Router into Plano so OpenClaw traffic can be steered to different models by task preference — e.g., cheap k2.5 for calendar/email and Opus 4.6 for heavy app-building — which is a sensible, pragmatic way to shave inference costs without manual swapping. The demo looks usable (config.yaml + README + diagram) but stops at integration; I'd like to see performance/latency comparisons, failure handling and more real-world routing rules before I'd trust it in production.

Niche GemSolve My Problem
sparacha
103mo ago