Hindsight

Hindsight™ is an agent memory system built to create smarter agents that learn over time. Most agent memory systems focus on recalling conversation history. Hindsight is focused on making agents that learn, not just remember.

It eliminates the shortcomings of alternative techniques such as RAG and knowledge graph and delivers state-of-the-art performance on long term memory tasks.

Hindsight is the most accurate agent memory system ever tested according to benchmark performance. It has achieved state-of-the-art performance on the LongMemEval benchmark, widely used to assess memory system performance across a variety of conversational AI scenarios. The current reported performance of Hindsight and other agent memory solutions as of January 2026 is shown here:

Hindsight is being used in production at Fortune 500 enterprises and by a growing number of AI startups.

🤖Using a coding agent?Install the Hindsight documentation skill for instant access to docs while you code:

npx skills add https://github.com/vectorize-io/hindsight --skill hindsight-docs

Works with Claude Code, Cursor, and other AI coding assistants.

export OPENAI_API_KEY=sk-xxx docker run -it --pull always --name hindsight --restart unless-stopped -p 8888:8888 -p 9999:9999 \ -e HINDSIGHT_API_LLM_API_KEY=$OPENAI_API_KEY \ -v hindsight-data:/home/hindsight/.pg0 \ ghcr.io/vectorize-io/hindsight:latest

export OPENAI_API_KEY=sk-xxx export HINDSIGHT_DB_PASSWORD=choose-a-password cd docker/docker-compose docker compose up

pip install hindsight-api export HINDSIGHT_API_LLM_API_KEY=sk-xxx hindsight-api

helm install hindsight oci://ghcr.io/vectorize-io/charts/hindsight \ --set api.llm.provider=openai \ --set api.llm.apiKey=sk-xxx \ --set postgresql.enabled=true

pip install hindsight-client -U # Python npm install @vectorize-io/hindsight-client # Node.js / TypeScript go get github.com/vectorize-io/hindsight/hindsight-clients/go # Go curl -fsSL https://hindsight.vectorize.io/get-cli | bash # CLI

from hindsight_client import Hindsight client = Hindsight(base_url="http://localhost:8888") # Retain: Store information client.retain(bank_id="my-bank", content="Alice works at Google as a software engineer") # Recall: Search memories client.recall(bank_id="my-bank", query="What does Alice do?") # Reflect: Generate disposition-aware response client.reflect(bank_id="my-bank", query="Tell me about Alice")

const { HindsightClient } = require('@vectorize-io/hindsight-client'); const main = async () => { const client = new HindsightClient({ baseUrl: 'http://localhost:8888' }); await client.retain('my-bank', 'Alice loves hiking in Yosemite'); const results = await client.recall('my-bank', 'What does Alice like?'); console.log(results); } main();

The easiest way to add memory to an existing agent is the LLM Wrapper. Swap your LLM client for a wrapped one — memories are then stored and retrieved automatically on every call, with no other changes to your code.

Theretainoperation is used to push new memories into Hindsight. It tells Hindsight toretainthe information you pass in as an input.

client.retain( bank_id="my-bank", content="Alice got promoted to senior engineer", context="career update", timestamp="2025-06-15T10:00:00Z", )

client.recall(bank_id="my-bank", query="What does Alice do?") client.recall(bank_id="my-bank", query="What happened in June?") # temporal

client.reflect(bank_id="my-bank", query="What should I know about Alice?")

Retained facts don't stay a flat pile. In the background, Hindsight consolidates related facts intoobservations— deduplicated beliefs the bank has built up over time. Each observation keeps its supporting evidence with exact quotes and a proof count, and isrefined*rather than overwritten when new evidence arrives, so new information strengthens, weakens or extends an existing belief instead of silently replacing it.

Amental modelis a standing answer to a question about a bank ("What are this user's preferences?"). You define the question once; Hindsight writes the answer, stores it, and rewrites it in the background as the bank learns more. Reading one is a database read — no retrieval, no LLM call — so an agent can boot with a page of settled knowledge instead of rediscovering it every session.

Knowledge pagesare mental models with the mechanics hidden: living documents a bank writes about itself, organized in folders like a wiki, searchable, and projectable onto disk as ordinary markdown. Supply a name and a question; every other decision is a default you can override.

Hindsight is built to support conversational AI agents as well as agents that are intended to perform tasks autonomously. The ideal use case for Hindsight are agents that require a blend of these features such as AI employees that need to handle open-ended tasks, change behavior based on user feedback, and learn to perform complex tasks to automate work at a level that approximates a human work. Hindsight can be used with simple AI workflows like those built with n8n and other similar tools, but may be overkill for such applications.

One of the simpler use cases you can use Hindsight for is to personalize AI chatbots and other conversational agents by storing and recalling memories associated with individual users.

The requirements for this use case usually look something like this:

Satisfying these requirements in Hindsight is straightforward. When new user inputs and tool calls are ingested into Hindsight using the retain operation, custom metadata can be used to enrich the new memories. Metadata provides a convenient way to isolate memories that need to be restricted to a given user. Once these are fed into the retain operation, any raw memories and mental models that get created can be filtered when retrieving relevant memories.

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