
Four retrieval strategies running in parallel. Token-budget optimization. Not RAG. Not a vector database wrapper. 94.6% on LongMemEval-S, peer-reviewed and independently reproducible.
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Integration
No schema design. No manual tagging. No migration. Your agent starts building memory from the first conversation.
# pip install hindsight-client
from hindsight_client import Hindsight
client = Hindsight(base_url="http://localhost:8888")
client.retain(bank_id="my-bank", content="Alice prefers Slack over email")
results = client.recall(bank_id="my-bank", query="How does Alice communicate?")Full REST API reference at hindsight.vectorize.io/api-reference. Frameworks, MCP servers and tools are in the Integrations Hub.
Real engineers, real answers. The Hindsight team and community are one message away.
Under the hood
All four run in parallel. Results merge with token budgets, not top-K. You get predictable context size, predictable cost, and the most relevant memories from four different angles.
Retrieval accuracy on every benchmark with a published comparison: Hindsight against the next-best system on that dataset. The research is peer-reviewed and the benchmark code is open source.
no published comparison
The same memory engine, API and MCP server, wherever your agents run.
MIT licensed
Hindsight 1.0 on Postgres, on your own infrastructure.
SOC 2 Type 2
Managed Hindsight, pay-as-you-go on usage. You focus on your agent.
Your cloud or ours
A private cloud, your own AWS, Google Cloud or Azure account, or on premises.
Open source, MIT licensed. Self-host or use Hindsight Cloud.
94.6%
LongMemEval-S retrieval accuracy · next best published system 74.0%