Last updated 09-02-2026
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Vectorize
Vectorize builds Hindsight, a data science platform for open source agent memory aimed at teams shipping AI agents that need persistent, per-user context. Hindsight stores experiences, recalls them across sessions with four parallel search paths, and reflects on patterns so agents learn from failed tool calls instead of repeating mistakes. The core is MIT licensed, runs from a single Docker command, and ships a Python SDK, REST API, and built-in MCP server.
Most agent memory stacks stop at vector lookup or RAG-style retrieval. Hindsight runs dense vector search, BM25 keyword matching, graph traversal, and temporal causal search in parallel, then merges results with token budgets rather than a fixed top-K count. That gives predictable prompt size and API cost, which retrieval-only competitors typically skip. Vectorize reports 94.6% on LongMemEval and ranks first on the BEAM agent memory benchmark, scores it positions against flat vector stores.
Developers wiring memory into Claude Code, Cursor, or custom agents via MCP are the core audience, along with teams that want managed Hindsight Cloud with pay-as-you-go token billing. Customer logos on the site include NVIDIA, Groq, and Electronic Arts, and Vectorize cites 15,000+ developers building with Hindsight globally.
Four parallel retrieval strategies merge with token budgets instead of top-K limits
Parallel search returns relevant memories in under 100ms on the product homepage
Scores 94.6% on LongMemEval and ranks first on the BEAM agent memory benchmark
Hindsight Cloud bills Retain at $10 per million tokens and Reflect at $0.05 per call
MIT-licensed Docker deploy with Python SDK, REST API, and built-in MCP server
GitHub repository shows 22.1k stars for the open source Hindsight project
MIT-licensed self-hosted core with no usage limits or phone-home telemetry on your infrastructure
Four parallel retrieval strategies with token-budget merging score 94.6% on LongMemEval versus retrieval-only alternatives
One-command MCP setup via npx add-skill registers remember, recall, and reflect tools automatically
SOC2 Type 2 certified Hindsight Cloud with tag-based user isolation for production workloads
Hindsight Cloud token usage on Retain, Iris Extract, and long-term storage can add up for high-volume agents
Self-hosted deployments require running Docker and embedded PostgreSQL on your own infrastructure
Enterprise SSO, RBAC, and dedicated infrastructure require a sales conversation
Is Hindsight free to self-host?
Yes. Vectorize publishes Hindsight under the MIT license with no usage limits, telemetry, or restrictions. You can run it in production on your own infrastructure with a single Docker command at no license cost.
How does Hindsight Cloud pricing work?
Hindsight Cloud from Vectorize uses pay-as-you-go billing with no fixed monthly fee or per-seat pricing. Retain costs $10 per million tokens, Recall costs $0.75 per million tokens, and Reflect costs $0.05 per call, with free credits included to start.
What makes Hindsight different from RAG memory?
Vectorize positions Hindsight as learning memory, not a RAG pipeline wrapper. Hindsight runs four retrieval strategies in parallel, merges results with token budgets, and adds a reflection layer that turns failed tool calls and corrections into reusable experience.
Does Hindsight work with Claude Code and Cursor?
Yes. Vectorize documents a one-command MCP install via npx add-skill vectorize-io/hindsight that registers remember, recall, and reflect tools for any MCP-capable agent, including Claude Code and Cursor.
What benchmark scores does Hindsight claim?
Vectorize reports Hindsight at 94.6% on LongMemEval, a peer-reviewed agent memory benchmark, compared with 85.2% for Supermemory and 71.2% for Zep on the same published chart. Vectorize also ranks first on the BEAM agent memory benchmark.
Does Vectorize offer enterprise deployment?
Yes. Vectorize sells an Enterprise plan with bring-your-own-cloud or on-premises deployment, dedicated infrastructure, SSO and RBAC, custom SLA up to 99.95%, and up to 24x7 support with a 30-minute response SLA.
How fast is Hindsight memory recall?
Vectorize states that Hindsight parallel search returns the most relevant memories in under 100ms. The product page also cites 99.5% query reduction for batch entity resolution across hundreds of entities.