Makers of Hindsight. Agent memory that lets your agents learn over time. https://xitter.cf/t.co/JkzXgOQg74
Boulder, CO
Joined December 2023
- Tweets1.4K
- Following332
- Followers1.5K
- Likes933
Vectorize retweeted
our competitors launched new features lately.
those show me how far ahead Hindsight is compared to them
- one just launched a coding memory benchmark (we did it 3 months ago)
- one just launched support for graph (we had it since the beginning)
- one just launched today and their main feature (and only one) is to generate skill for the agent (we launched it in April via Mental Models)
all our competitors are copying features from us and I couldn't be more flattered
November we will launch 2 very big milestones:
- cutting down costs and improve speed across every hindsight operation for everyone
- reinventing the paradigm for the interaction between agent and memory
Vectorize retweeted
toktok is the first library to have a @typesafeai jev tokenizer
github.com/vectorize-io/tokt…
Vectorize retweeted
Upgraded my OpenClaw to 2026.10.4, and one of the biggest surprises was Hindsight 1.0.0.
Turns out @Vectorizeio Hindsight, which I've been using as my agent's long-term memory, now integrates with OpenClaw's native memory provider API.
I've been running Hindsight for a while now. I even built my own MCP server for recall and reflect because I wanted my agent to actually make use of those memories.
Now OpenClaw can route memory.search and memory.get directly to Hindsight.
Which means I can finally start getting rid of some of my hand-rolled memory integration code. lol
What I really like is that Hindsight is now treated as a first-class memory provider, instead of relying entirely on custom plugin hooks and MCP tools.
As someone who has spent way too much time wiring AI tools together, that's a pretty nice upgrade 😂
Vectorize retweeted
Guys I'm so Hindsight pilled by @Vectorizeio
I have a memory bank for @RuneScape 3
If you played RS3 you'll know it's a huge game, and I'm too busy to read the wiki.
So instead I ask questions to my agent, specially about questing and where to get a certain item. And then hindsight saves it all.
Hindsight also tracks what I do, as I move on from quest to quest, it kinda creates these decision records, and it even tells me "you were here the last time", or "you got that item that other day ago it's probably in your bank".
If you're not using something like this in 2026 bro, this is like having a second brain. I love it.
This is super cheeky but I even have an @NousResearch Hermes bot for it! I call it Zaros which is the god of fate and control in Runescape.
I want so hard to make an RS3 plugin for this, but I don't have time!
Runescape 3 + Hindsight + Hermes is amazing!
Vectorize retweeted
51.3K ⭐ · Global Rank #496 · Hindsight by @Vectorizeio
🧠 Memory for AI agents that learn over time.
🎯 Need your support bot to recall past conversations?
📈 March momentum, a bigger September wave, and +2.9K stars this week.
star-history.com/?repos=vect…
Vectorize retweeted
🧰 Trending AI repo: vectorize-io/hindsight
Memory for AI agents that learns, not just logs chats.
→ Retain, recall, reflect: facts become beliefs
→ ★ 44.6k stars, +445 in a day, MIT
Why it matters: agents stop forgetting between sessions.
airadar08.github.io/repos/#v…
Vectorize retweeted
Hindsight: memory for AI agents that learns, not just recalls.
- Python, MIT, ~50.7k GitHub stars, ~6.1k forks
- 288 contributors, main, v1.0.0
- Retain, recall and reflect APIs, Python, TS and Go clients, MCP server
Best for: devs building agents that improve over time
Vectorize retweeted
Open-source AI projects worth watching this week:
1. FastMCP — MCP infrastructure
Python framework for building MCP servers and clients. Recent releases focus heavily on security, path containment, regex limits and auth reliability.
Best for: Production MCP deployments.
Watch: Version compatibility, proxy/auth behavior and security patches.
Source: github.com/PrefectHQ/fastmcp
2. Hindsight — Agent memory
Persistent agent memory combining semantic, BM25, graph and temporal retrieval. v1.0.0 shipped Oct 13 with very strong development activity.
Best for: Cross-session coding or agent memory.
Watch: Memory provenance, restore/export reliability and reported Windows/Hermes issues.
Source: github.com/vectorize-io/hind…
3. Substrate — Agent runtime / sandboxes
Kubernetes infrastructure for running stateful agents with snapshot, suspend and resume capabilities.
Best for: Large-scale agent infrastructure teams.
Watch: Breaking changes and runtime-state reliability.
Source: github.com/agent-substrate/s…
4. Bifrost — AI gateway
Central gateway for LLM, MCP and agent traffic, including A2A proxying, access control and streaming.
Best for: Multi-model or multi-provider AI platforms.
Watch: Credential concentration, permission handling and unverified performance claims.
Source: github.com/maximhq/bifrost
5. SGLang — Model serving
High-performance LLM/multimodal serving with advanced caching and prefill/decode optimizations.
Best for: Teams running their own GPU inference infrastructure.
Watch: Hardware-specific regressions and workload-specific benchmarks.
Source: github.com/sgl-project/sglan…
6. MinerU — Document ingestion
Converts complex PDFs/documents into structured Markdown and JSON, with improved worker recovery and async processing.
Best for: RAG/document pipelines.
Watch: Scientific/formula accuracy and additional commercial licensing terms.
Source: github.com/opendatalab/Miner…
7. Cua — Computer-use agents
Desktop automation stack with drivers, SDKs and sandbox infrastructure.
Best for: Computer-use and browser/desktop agents.
Watch: OS/input reliability, desktop security and mixed licensing.
Source: github.com/trycua/cua
8. Phoenix — AI observability & evaluation
Provides tracing, datasets, experiments and model evaluators, including sandboxed evaluation tooling.
Best for: Internal AI evaluation and observability.
Watch: Sensitive trace data and Elastic License restrictions.
Source: github.com/Arize-ai/phoenix
9. LightRAG — Graph RAG
Graph-enhanced retrieval with recent fixes improving filtered vector search in multi-workspace environments.
Best for: Graph + vector retrieval systems.
Watch: Important fixes may exist only on the main branch, not released packages.
Source: github.com/HKUDS/LightRAG
10. Pipecat — Voice agents
Framework for composable real-time voice/video AI pipelines, with active work on streaming, reconnects and transport reliability.
Best for: Voice-agent prototypes and production pilots.
Watch: Conversation-state, refusal handling and tool-call interruptions.
Source: github.com/pipecat-ai/pipeca…
Main trends:
- Agent permissions are becoming infrastructure-level features, especially across MCP, gateways and sandboxes.
- Agent memory quality depends on retrieval speed and quality, not storing large volumes of conversation history. Reliability and failure handling are becoming more important than impressive demos.
Recommended projects to test first:
FastMCP for MCP tooling and security.
Hindsight for persistent agent memory.
Bifrost for centralized model/agent routing and policy.
Vectorize retweeted
I’ll be upgrading as soon as I’m home. I love #hindsight
Vectorize retweeted
Better way is setting up a memory provider like Hindsight; was using Hermes for couple months with Hindsight setup as memory layer; I told Grok Bot about it it figured out how to read from/write to it, I can let it be shared across all my agents.
Vectorize retweeted
Who the f*ck open sourced an agent memory layer that learns from every run and put it under MIT?
hindsight by vectorize-io has 49.2k+ stars and the repo is full of things i haven't seen in other memory tools
it works with 3 verbs:
→ retain: an LLM pulls facts, dates, entities and relationships out of whatever the agent saw
→ recall: 4 searches run in parallel (semantic, keyword, graph, time range), then merge and cross-encoder rerank
→ reflect: the agent reasons over its own memories and builds new connections between them
it also stores beliefs, an observation is a belief with a proof count, showing how much evidence backs it, a mental model is a saved answer about your bank, and reading it is just a database read, no llm call or retrieval.
every agent gets its own bank. isolation is strict, nothing leaks between them here's setup:
→ docker run with ghcr[.]io/vectorize-io/hindsight:latest, API on 8888, UI on 9999
→ or pip install hindsight-api
→ for coding agents, npx [@]vectorize-io/hindsight-coding-agents install all
25+ LLM providers work, ollama and lmstudio included, so the whole thing can run on your machine. each bank also exposes an MCP endpoint at localhost.
there's a memory defense option that scans everything an agent tries to store against 45 patterns and blocks or redacts matches. that matters the moment your agent reads a random web page
the LongMemEval state-of-the-art claim is the team's own, and their README says Virginia Tech and the Washington Post reproduced it, they also admit it's overkill for a simple n8n flow.
Vectorize retweeted
we built a storage engine for agent memory, and it doesn't have a database.
why: agent memory only grows. an employee's agent writes ~68k memories a year. an autonomous agent running 500 tasks a day writes ~5M. a 10,000-person company with 1,000 agents: ~5.5B a year, and every one needs to be findable in under a second.
we run Hindsight Cloud for thousands of orgs, and at that scale Postgres stops keeping up: on a 210k-memory bank, one recall took 5.3s at p99.
so we built Memlake. everything lives in object storage, in a bucket you control. servers are just caches: lose one, you lose latency, never data. the protocol is model-checked in TLA+.
100M memories on 4 servers, <100ms p99, flat from 1 to 200 recalls/s. for 1,000 autonomous agents, ~$94k/month vs ~$2.46M on Postgres.
and if your banks stay small, Postgres is still great.
open-source Hindsight stays on it, and it's 1.0 today.
Vectorize retweeted
Hindsight is an agent memory system from Vectorize.io with three operations: retain extracts facts, entities, and temporal data through an LLM, recall fuses vector search, BM25, and graph links with cross-encoder reranking, and reflect answers questions that need more than lookup. Memory lives in isolated banks organized into world facts, experiences, observations, and mental models. Runs as a Docker service, embedded database, or hosted cloud on PostgreSQL with pgvector, with integrations for Claude Code, Codex, Cursor, LangGraph, and CrewAI.
github.com/vectorize-io/hind…
Vectorize retweeted
10 open-source repos for AI agent memory. 🧠🤖
Mem0 → github.com/mem0ai/mem0
Hindsight → github.com/vectorize-io/hind…
memU → github.com/NevaMind-AI/memU
Cognee → github.com/topoteretes/cogne…
Graphiti → github.com/getzep/graphiti
OpenViking → github.com/volcengine/OpenVi…
Vectorize retweeted
Compare Hindsight, Mem0, Graphiti, Letta, and Cognee to choose an AI agent memory architecture that improves recall, preserves context, and prevents errors.
#aiagentmemoryarchitecture #lettastatefulaiagents...Show more
Vectorize retweeted
The sovereign AI stack for the future:
Compute - an always-on box you control. At home (something like @voravault) or a confidential VM in the cloud. Reachable over your own overlay network (e.g. @Tailscale).
Data + memory - everything personal lives on that box. A memory layer like Hindsight (@Vectorizeio) makes it better over time. If you use external AI services, have them continually update your memory.
Inference - local for small/sensitive work, open-weight models in attested TEEs (e.g. @near_ai) for heavy lifting. Nobody reads your prompts.
Agent - an always-on runtime that acts for you (e.g. Hermes by @NousResearch).
Software - apps generated for you by AI, hosted on your server, thrown away when done. No subscriptions. Seamless interoperability, since they can all talk to each other.
Money - stablecoins to pay for inference and services.
Exit - open formats and open source. Encrypted backups. Restore anywhere, leave any time.
