Hey HN — Blake here. We built Novyx because every AI agent framework treats memory as an afterthought. Agents forget between sessions, can't search what they know, and when they make bad decisions there's no way to understand why.

  Novyx is a memory API for AI agents. Store observations, recall them with semantic search, and roll back when things go
  wrong.

  What it does:

  - Store + Recall — Semantic search over agent memories using sentence embeddings. Recency-weighted scoring, auto-linking
  related memories via knowledge graph.
  - Rollback — Point-in-time rollback with dry-run preview. Undo bad writes without redeploying.
  - Replay — Time-travel debugging. Reconstruct what your agent knew at any timestamp. Diff memory states between two points.
   Track individual memories from birth to death.
  - Cortex — Autonomous memory maintenance. Consolidates near-duplicate memories, reinforces frequently recalled ones, decays
   forgotten ones. Runs in the background.
  - Audit trail — Compliance-grade logging of every memory operation. Tamper-evident hash chains.

  Technical details:

  - Postgres + pgvector for storage and search. Redis for auth/rate limiting. CPU-only embeddings (all-MiniLM-L6-v2).
  - Multi-tenant with application-level isolation. ~82 REST endpoints.
  - Python SDK and JS/TS SDK. LangChain, CrewAI, and MCP integrations.
  - Free tier: 5K memories, 5K API calls/mo. Pro ($39/mo): unlimited memories + Replay + Cortex. Enterprise ($199/mo):
  counterfactual recall, drift analysis, insights.

  We're not competing with LangSmith or Langfuse — those are trace debuggers (what the LLM said). We're the layer underneath
  (what the agent knew).

  Live at https://novyxlabs.com. Docs at https://novyxlabs.com/docs.

  Happy to answer questions about the architecture.