Vectorize AI, Inc.

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: https://lnkd.in/gspb4e5r

Hindsight 0.7.0 is here! Checkout all the new features below: https://lnkd.in/gWpn_ikc

OpenClaude: Build a Claude Code Agent with Long-Term Memory — and Take It Everywhere. Anthropic just launched Channels for Claude Code: Claude Code sessions connected to messaging platforms. This means Claude Code can now operate as a fully autonomous agent, reachable also from your phone, always running against your codebase (and not just it). Claude Code has a built-in memory system based on markdown files (CLAUDE.md, auto-memory), and it works very well for static preferences and project instructions. But it wasn't designed for conversational memory — it doesn't extract facts from your discussions, doesn't recall relevant context by semantic similarity, and doesn't build up structured knowledge over time. Close the session, and some richness and depth of what you discussed is gone. This guide fixes that. We'll set up Claude Code on Telegram Messenger and wire it to Hindsight for true long-term memory — automatic fact extraction, semantic recall, and a knowledge base that grows with every conversation. The result is a persistent AI (coding) assistant you can talk to from anywhere, that actually learns from your interactions. Know more: https://lnkd.in/g9EsPSv4

Adding Long-Term Memory to LangGraph and LangChain Agents. LangGraph agents are stateful by design — checkpointers save graph state between steps, and the Store API persists data across threads. But neither gives agents true long-term memory: the ability to extract meaning from conversations, build up knowledge over time, and recall it semantically when relevant. That's what Hindsight adds. Hindsight is a memory layer for LLM applications that automatically extracts facts from conversations, builds entity graphs, and retrieves relevant context using four parallel recall strategies. The hindsight-langgraph package brings that to LangGraph — and since the memory tools are standard LangChain @tool functions, they work with plain LangChain too. Know more: https://lnkd.in/gvKZJwSN

I Gave 100+ LLMs a Permanent Memory With One Python Package. hindsight-litellm adds persistent memory to any LLM provider via LiteLLM — OpenAI, Anthropic, Groq, Azure, Bedrock, Vertex AI, and 100+ more. Three lines of setup, and every LLM call automatically gets context from past conversations. The Problem: Stateless LLM Calls You build an app with an LLM. User talks to it on Monday. Comes back Tuesday. The LLM has no idea who they are. This is true for every provider. OpenAI, Anthropic, Groq, Azure — every API call is a blank slate. The LLM doesn't remember preferences, past conversations, or anything you've discussed before. Most teams work around this by stuffing the last N messages into the context window. But that's not memory — it's a sliding window that drops everything older than your token limit. The user's preferences from last week? Gone. The project context from last month? Gone. What if every LLM call automatically had the right context from past conversations? The Fix: Three Lines, Any Provider. Know more: https://lnkd.in/gmc8MXZv