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RepoQuarry

Opencode vs Hindsight vs Ai-memory

Side by side, as alternatives to Mem0 Platform. Rows that differ are highlighted; the strongest value in a row is marked. Unknown means no source yet.

Opencodeanomalyco/opencode
Hindsightvectorize-io/hindsight
Ai-memoryakitaonrails/ai-memory
Mem0 Platformpaid product
Cost and coverage
Price
$0 software; you run it
$0 software; you run it
$0 software; you run it
Free Hobby tier (10,000 add and 1,000 retrieval requests a month); Starter $19/month, Pro $249/month, Enterprise custom source ↗
Long-term memory for chat assistants
No: No
Partial: Partly
Partial: Partly
Confirmed: Yes
Memory for coding agents
Partial: Partly
Partial: Partly
Partial: Partly
Confirmed: Yes
Knowledge graph memory
No: No
Partial: Partly
Partial: Partly
Confirmed: Yes
Jobs covered
1 of 3 jobs
3 of 3 jobs
3 of 3 jobs
All 3
Popularity and activity (from GitHub)
Stars
218,961Top
52,038
11,924
—
Gain this week
Unknown
Unknown
Unknown
—
Signal
Building history
Building history
Building history
—
Commits / 4 wk
986
455
1,156Top
—
Latest release
v1.19.3 · 2026-10-16
v1.0.0 · 2026-10-13
v3.1.2 · 2026-10-20Top
—
Commits / 12 wk
2,164
1,394
1,917
—
People committing / 12 mo
420+
100+
100+
—
Issues opened / closed, 30 days
3742 / 4694
473 / 382
195 / 196
—
Facts, with sources on each project page
Runs on my machine?Differs
Confirmed: Yes, CLI and desktop app
Confirmed: Yes, Docker, pip or embedded Python on your own machine
Confirmed: Yes, a single local binary and server; Docker image also available
Hosted by the vendor
Code licence
Confirmed: MIT
Confirmed: MIT
Confirmed: MIT
—
Model weights licenceDiffers
Partial: No single bundled model licence. OpenCode connects to models/providers; their individual terms apply.
Confirmed: No bundled LLM; licence depends on the model you connect
Partial: No single model licence. The default path makes no LLM calls; optional LLM and embedding models have separate provider/model terms.
—
Commercial useDiffers
Confirmed: Yes, MIT code
Confirmed: Yes, MIT code
Confirmed: Yes, MIT allows commercial use
—
Paid services needed?Differs
Partial: Needs a model provider: local models via Ollama, LM Studio or llama.cpp, or a paid API
Partial: Needs an LLM; local Ollama, LM Studio or llama.cpp work, hosted APIs and Hindsight Cloud are optional
Partial: None for ai-memory itself (zero-LLM default); it needs an agent CLI such as Claude Code, Codex or OpenCode, which may be paid
Subscription
HardwareDiffers
Unknown: Not checked yet
Confirmed: Full API image 1.5 GB RAM minimum (2 GB recommended), slim 512 MB; PostgreSQL 512 MB+; 2 vCPUs on CPU only is fine for development; GPU helps the reranker in production
Unknown: Not checked yet
—
Operating systemsDiffers
Confirmed: Windows, macOS and Linux (CLI and desktop app)
Confirmed: Linux (x86_64, ARM64), macOS (Apple Silicon; Intel via slim package) and Windows x86_64
Partial: Linux and macOS supported; Windows via WSL2 supported, native Windows experimental
—
Setup effortDiffers
Confirmed: Easy; one-line install script, npm or brew
Confirmed: Easy; one Docker command or pip install hindsight-all, plus an LLM provider setting
Confirmed: Easy; install script then ai-memory setup (PowerShell script on Windows)
—

Stars over time

Switch to Growth % to compare projects of different sizes fairly.

  • Opencode
  • Hindsight
  • Ai-memory
218,9610 2026-10-152026-10-21 Tracked daily begins 2026-10-15Opencode, 2026-10-19: 216,871 stars, trackedOpencode, 2026-10-20: 218,068 stars, trackedOpencode, 2026-10-21: 218,961 stars, trackedHindsight, 2026-10-15: 50,013 stars, trackedHindsight, 2026-10-16: 50,199 stars, trackedHindsight, 2026-10-17: 50,610 stars, trackedHindsight, 2026-10-18: 51,073 stars, trackedHindsight, 2026-10-19: 51,340 stars, trackedHindsight, 2026-10-20: 51,761 stars, trackedHindsight, 2026-10-21: 52,038 stars, trackedAi-memory, 2026-10-19: 10,687 stars, trackedAi-memory, 2026-10-20: 11,537 stars, trackedAi-memory, 2026-10-21: 11,924 stars, tracked
Focus a point, use arrow keys, or tap for its value.

Tracked daily since 15 Oct 2026. Dashed history: estimated from public records of when people starred the repo. Lines connect observations only; gaps contain no measured points.

Growth % starts at each project's first observed point in the shown range. Zero-star starts are unavailable.

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Numbers: GitHub, fetched nightly. Facts: read from each project's own documentation, with the source on its page. How we check. anomalyco/opencode ↗vectorize-io/hindsight ↗akitaonrails/ai-memory ↗

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