Vectorize

Docs in Your IDE: Cursor + Vectorize. You’re midway through a PR review. Someone asks: “What are the downstream dependencies of this service?” Normally, you’d open three browser tabs, search the wiki, and dig through architecture diagrams. By the time you find the answer, your review flow is broken. With Cursor connected to a Vectorize agent: - Ask the question - The agent scopes docs by service metadata - You get the exact diagram and context — right inside your IDE Know more: vectorize.io/blog/docs-in-y…

0.8.2 also ships concurrency and data-integrity fixes that matter for long-running deployments. If you self-manage Hindsight, upgrade. Full release notes: hindsight.vectorize.io/blog/2026/06/1…

More providers and models: Native Nous Portal with built-in OAuth, Gemini Batch API for retain, Gemini embedding-2 support, per-bank cost attribution for gateways like OpenRouter and LiteLLM, and a per-bank connectivity probe to verify config before you rely on it.

Hindsight 0.8.2 is here! Keep secrets out of memory, fix what your agent learned wrong, and control how much each context retains. Plus new providers and fixes every self-managed deployment should pick up. 🧵

Hindsight: Building AI Agents That Actually Learn! Modern AI agents are expected to do more than answer isolated questions. We ask them to manage projects, collaborate with teams, track evolving information, and develop stable viewpoints over time. Yet most agents today still behave like stateless tools—each interaction starts fresh, with little ability to accumulate experience or learn. This gap isn’t a limitation of language models themselves. It’s a limitation of how we design agent memory. At Vectorize, this realization led us to build Hindsight: an open-source agent memory system designed not just for recall, but for learning. Know more about Hindsight: vectorize.io/blog/hindsight…

To handle drift, Hindsight uses a layered approach: incoming data is automatically synthesized into "Observations," which then update our canonical "Mental Models." During every query, the system prioritizes these curated models over raw data, ensuring the agent always relies on the most accurate, up-to-date consensus.

The Open-Source MCP Memory Server Your AI Agent Is Missing. AI agents forget everything between sessions. Hindsight gives them persistent, structured memory via MCP. One Docker, Inc command to run the full stack locally. Connect any MCP-compatible client. Three core operations: retain (store), recall (search), reflect (reason) — plus mental models that auto-update as memories grow. Know more: linkedin.com/pulse/open-sou…

@halaprix @NousResearch Glad your Hermes agent brain on Hindsight

Took me a while to migrate from @NousResearch Hermes built-in memory to @Vectorizeio Hindsight, but I went big ;p Don't tell me if there's an easier way now—too late! Cringe a bit. github.com/NousResearch/h…

An AI agent without memory is like a trader with amnesia. Every day, starts from zero. Does not remember what worked. Cannot learn from mistakes. Cannot build on yesterday's insights. @TheARCTERMINAL ANIMA: → Remembers your portfolio decisions → Tracks what strategies you preferred → Notes your risk tolerance changes over time → Compounds insights across weeks and months The agent on day 90 is fundamentally better than the agent on day 1. That is memory. That is learning. That is what makes it an agent, not a chatbot.

Modern AI agents are evolving beyond simple input-output systems, and Hindsight™ is leading that evolution. When a user invokes an AI agent, it doesn't just process the request in isolation — it draws from a persistent memory layer powered by Hindsight™, the world's most advanced agent memory system. Unlike traditional RAG-based approaches, Hindsight™ enables agents to recall facts, remember previous interactions, and genuinely reflect on past experiences to improve over time — much like human memory. Armed with this intelligence, the central AI agent seamlessly orchestrates specialized sub-agents: a Search Agent for web queries, a Coding Agent for writing and reviewing code, a Data Analysis Agent for interpreting datasets, and a Marketing Agent for generating creative content ideas. The agent also leverages integrated tools to execute tasks efficiently. The result? Curated, context-aware responses delivered back to the user — smarter every time, thanks to Hindsight™. Know more about Hindsight: github.com/vectorize-io/h…