Vectorize

Adding Persistent Memory to OpenAI Codex with Hindsight. Codex is OpenAI's open-source coding agent CLI. You give it a task, it reads your files, runs commands, and iterates until it's done. It's capable and fast — but it has no memory. Every session starts from nothing. Codex doesn't know which libraries your project uses, which patterns you've standardized on, which areas of the codebase are fragile, or what you were working on yesterday. You re-establish this context at the start of every session, either by explaining it directly or by pointing at an AGENTS.md file you've manually maintained. AGENTS.md helps — it's a static markdown file that tells Codex baseline facts about your project on startup. But it captures what you remembered to write down, not what you actually encountered. The Redis TTL discrepancy you noticed Tuesday at 3pm, the JWT edge case that surfaced during code review, the reason you stopped using SQLAlchemy — these live in session transcripts that vanish when the window closes. Nobody updated AGENTS.md. Next session, that knowledge is gone. Know more: linkedin.com/pulse/adding-p…

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…

One Memory for Every AI Tool I Use? Like many, I use different AI tools throughout the day depending on what I'm doing and where I am. Claude primarily, across the desktop and mobile apps, the Claude Code CLI, and the VS Code extension. Plus ChatGPT's mobile app and Codex on desktop. A couple of weeks ago I added an OpenClaw agent to the mix, running on a Mac Mini and connected to Discord. People are split on it, but the hype around some of its capabilities clicked immediately: beyond the model itself, it has a much broader set of tools than conventional AI interfaces. But the most groundbreaking part was memory. Having OpenClaw remember things across conversations felt like a genuine shift, from isolated sessions to something persistent. So why not do this with Claude or ChatGPT? Or better yet, why couldn't they all share one unified memory? That's the shared memory pattern I've been running for the past few weeks. The problem: no shared memory across AI tools. Know more: linkedin.com/pulse/one-memo…

Great read from Nicolò explaining how, with Hindsight, we are tackling the memory issue that faces self-driving agents.

In depth blog here: hindsight.vectorize.io/blog/2026/05/0…

0.6.1 is out - check out the new features!

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…

@henrytdowling We don't expire memories. But we do update observations. Observations contain the latest view, resolving the contradictions. The old memories are real, but new ones can cause observations to be updated.

@Vectorizeio How do you think about memory "expiration"? Eg memories that are stored long-term but you might need to evict if they stop being true?

AI agents rely on sophisticated memory systems to retain information, reason over time, and improve decision-making based on past experiences. Without memory, agents would be unable to learn or adapt effectively. These systems include several key components working in concert. Short-term or working memory temporarily stores active task information—current queries, recent conversation context, and intermediate steps—enabling coherent, context-aware responses. Long-term memory persists across interactions, accumulating patterns, experiences, and instructions that enhance accuracy and efficiency over time. Episodic memory captures event snapshots containing states, actions, outcomes, and rewards, particularly valuable in reinforcement learning for understanding which behaviors lead to success. Semantic memory stores structured world knowledge including concepts, rules, language understanding, and domain expertise, enabling proper reasoning and information interpretation. Retrieval mechanisms are crucial for accessing relevant memories through similarity-based vector embeddings, keywords, or contextual cues, ensuring agents utilize appropriate rather than random information. Finally, memory supports planning by tracking subgoals, progress, and obstacles, enabling strategic multi-step actions beyond immediate reactions. Together, these memory systems transform AI agents from reactive tools into adaptive, learning systems capable of increasingly sophisticated performance across diverse tasks and extended interactions. Well, now you can have a robust human like memory layer for your AI Agents with Hindsight. Yes, Hindsight is a new innovation in the space of agentic memory to help you build sophisticated Agentic systems that can remember everything with proper context. Try Hindsight: hindsight.vectorize.io

contagious memory vs prompt engineering with @Vectorizeio's hindsight, right remembered @composio mcp token failure so good that even after issue is gone, it aggressively refused to execute tasks asking to fix the auth first. Fixed by injecting {{system-*}} tags before user messages with MCP health info.

@xr_bb52547 Yes!

AI agents now have "memory systems." Which means they can remember your preferences, your patterns, your mistakes. The pitch: "More personalized experiences." The reality: More data to monetize, more behavior to predict, more attention to capture. AI doesn't need memory to help you. It needs memory to know what you'll buy next.

Your OpenClaw Agents Are Strangers to Each Other. Hindsight Changes That. You're running more than one OpenClaw instance. Maybe one handles customer support, one serves your dev team, one is a personal assistant. Each instance is doing its job — having conversations, picking up context, learning what matters. But by default, none of that learning is shared. One instance figures something out; every other instance starts from zero. A team of agents that can't share memory isn't really a team. Hindsight solves this with shared memory banks — a single store that every instance reads from and writes to. One agent learns something; every agent knows it. One config change. Know more: linkedin.com/pulse/your-ope…

Agents need more than RAG. @TechJournalist explains why. venturebeat.com/data/the-rag-e…

Curious to know more? Read @nicoloboschi 's release blog here: hindsight.vectorize.io/blog/2026/05/0…

MAJOR release alert 🚨 Introducing Hindsight 0.6.0!

Let's understand the differences between an AI Agent and AI Workflows. An AI Workflow follows a rigid, predefined sequence - starting, calling an LLM with tools, and ending - where the developer controls every step and decision path. It's predictable and deterministic. An AI Agent, by contrast, operates with much greater autonomy; it can loop back, make its own decisions about when to use tools, and dynamically determine how to reach the end goal. The central graph captures this trade-off elegantly: as an agent's level of control increases, the human's oversight decreases. Workflows sit in the high human-control zone, making them safer and more predictable but less flexible. Autonomous agents sit at the opposite end - highly capable of handling complex, open-ended tasks, but requiring more trust since they chart their own course through a problem with minimal human intervention. To build any efficient AI Agent or a workflow, you need a robust agentic memory layer and Hindsight is built just for this. A new approach to agent memory. Best in the world on benchmarks. Best in production for your agents. Mimics human memory with more accuracy. Try Hindsight: github.com/vectorize-io/h…