Price terms as published
Each line is the vendor’s own wording, split from the price capture checked on 2026-09-28. Nothing here is rounded, converted or re-worded — the full sentences stay in the review below. Lines that carry only a price continue the plan named on the line above, in the vendor’s own order.
| # | Plan / term | Checked |
|---|---|---|
| 1 | Free (MIT, self-hosted, single Docker command) | 2026-09-28 |
| 2 | Hindsight Cloud is pay-as-you-go on token usage with free starting credits — no fixed monthly or per-seat fee | 2026-09-28 |
One-liner: Hindsight is an open-source (MIT) memory system that makes AI
agents learn over time instead of just retrieving chat history: memories flow
through a retain operation that extracts facts, entities, temporal data and
relationships into four separate memory networks (world facts,
experiences, observations, opinions), retrieval runs across all of them via
recall, and reflect reasons across stored experience to answer questions
no single memory contains. One Docker command self-hosts the whole thing; the
managed cloud bills on token usage with no per-seat fee.
Verified facts (GitHub API + raw README + release feed + pricing page, 2026-09-28)
| Attribute | Value | Source |
|---|---|---|
| GitHub stars | 39,425 (5,208 forks, 88 watchers) | GitHub API + repo page, 2026-09-28 |
| Open issues vs PRs | 90 open issues + 83 open PRs (1,234 issues total ever) | GitHub search API, 2026-09-28 |
| License | MIT (Copyright 2025 Vectorize AI, Inc.) | LICENSE file via repo page, 2026-09-28 |
| Language / stack | Python; embedded PostgreSQL (pg0) or external Postgres; Oracle AI Database for enterprise; Helm chart for Kubernetes | README install matrix |
| Created / last push | 2025-10-30 / 2026-09-28 (day of this check) | GitHub API |
| Latest release | v0.10.1, published 2026-09-21 | GitHub Releases (HTML feed), 2026-09-28 |
| Memory architecture | Four logical networks — world facts, experiences, observations, opinions — separating objective knowledge from subjective belief | README + ACL 2026 demo paper |
| Operations | retain (LLM-extracts facts/entities/time/relationships → canonical entities + indexes), recall (multi-strategy retrieval), reflect (disposition-aware synthesis) |
README core concepts |
| Benchmark claim | State-of-the-art on LongMemEval; README reports 94.6% vs Mem0 49.0% (independent evaluation cited); live leaderboard published by vendor | README + vectorize.io comparison article, 2026-09-28 |
| Independent reproduction | Virginia Tech Sanghani Center for AI and Data Analytics and The Washington Post (per README; vendor-stated) | README “Memory Performance” section |
| Providers | 25+ LLM providers via one env var — hosted (OpenAI, Anthropic, Gemini, Groq, Bedrock, Vertex, MiniMax, DeepSeek…), local (Ollama, LM Studio, llama.cpp), and existing subscriptions with no API key (ChatGPT Plus/Pro, Claude Pro/Max, Cursor, GitHub Copilot) | README quick-start |
| Clients | Python / TypeScript / Go SDKs, CLI, REST; built-in MCP server; integrations for LangGraph, CrewAI and any framework | README + vectorize.io articles |
| Deploy paths | Docker (single command), pip bare metal, embedded no-server mode, Helm/K8s, external Postgres | README install matrix |
| Cloud pricing | Usage-based: Retain $10/M input tokens, Recall $0.75/M output tokens, Reflect $0.05/call, Storage $0.25/M tokens/month after first 30 days; free credits at signup; no monthly or seat fee | Pricing FAQ (vectorize.io) + HydraDB review, 2026-09-28 |
| Self-host vs cloud feature gap | README says none — all four networks + retain/recall/reflect + MCP in the MIT Docker image; cloud adds dashboard, backups, team features, 99.9% SLA | README + vectorize.io/pricing |
Why the four-network design matters
Most agent memory is one undifferentiated vector store: the agent “remembers”
facts, guesses, and user opinions with equal confidence. Hindsight’s split —
world (objective facts), experience (what happened), observation
(patterns extracted from experience), opinion (subjective beliefs) — gives
developers visibility into what the agent knows versus what it believes,
which is the core claim of the ACL 2026 system-demo paper (Latimer et al.,
pp. 275–285, San Diego). The consolidation behavior is real, not an
abstraction: a third-party practitioner write-up on code-review agents
describes reflect() surfacing lessons that “emerged from accumulated
retain() calls” the author never wrote explicitly.
The honest read on the benchmarks
94.6% on LongMemEval is a vendor-published number with a live leaderboard at the vendor’s own domain, and the README itself notes competitor scores are “self-reported by software vendors.” The independent-reproduction claim (a Virginia Tech center and The Washington Post) is also stated by the vendor. That is more evidence than most memory startups ship — an accepted peer venue paper plus named reproducers — but verify against your own recall workload before buying the superlative. The self-host path costs you nothing but a Docker volume, which makes that verification cheap.
Who should run this
- Teams building agents that do repeated real work in a domain (support, procurement, code review) where the agent should measurably improve.
- Anyone locked into a memory layer by a specific framework — Hindsight is framework-agnostic behind MCP/REST, so the memory survives a stack change.
- Local-first users: Ollama/llama.cpp providers mean the whole loop can run without an API bill.
Skip it if you only need single-chat history recall — context-window stuffing or a plain vector store is simpler; Hindsight’s machinery (extraction, consolidation, four networks) is designed for learning, not logging.
Sources
- GitHub API: stars/forks/created/pushed/license — 2026-09-28
- GitHub search API: 90 open issues / 83 open PRs / 1,234 issues ever — 2026-09-28
- GitHub Releases HTML feed: v0.10.1 2026-09-21 — 2026-09-28
- Raw README (main), fetched 2026-09-28
- ACL Anthology: “Hindsight: Structured Agent Memory that Retains, Recalls, and Reflects,” 64th ACL meeting system demos, 2026 — third-party venue
- vectorize.io/pricing FAQ + hindsight-vs-mem0 article — vendor pricing (cross-checked)
- HydraDB “Hindsight AI Review 2026” — third-party pricing cross-check
- Medium practitioner write-up (code-review agent) — third-party usage report