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OpenDream
Open, local-first memory for AI agents (with dreaming)
OpenDream is an open, local-first memory layer for AI agents. It captures agent activity, turns useful history into reviewable memory, retrieves only relevant context for the next task, and shows what was selected, skipped, or marked stale. Use it across Codex, Claude Code, Cursor, and other agent workflows without handing project memory to a hosted black box.
I built OpenDream because even though agent memory has slowly been improving, amnesia across agent sessions continues to be a productivity killer.
Agents don't just "forget" things, they fail predictably.
* agents half-remember old project decisions and fill in the gaps with hallucinations * outdated context gets leaked into new tasks * agents fail to see what previous agents already learned and require repeat prompting * memories become hidden context that is hard to inspect, trust, or correct
OpenDream makes agent memory open, local, portable, and reviewable.
It captures agent activity, turns useful history into saved memory, retrieves only the context that fits the next task, and shows what was selected, skipped, or marked stale.
A few things that make it different:
* Local-first by default * Open source * Built for multiple agents and tools * Source-linked memory * Reviewable memory changes * Context retrieval instead of dumping one giant memory file into every prompt
Codex is the most tested with OpenDream. Claude Code, Cursor, Copilot-style repo instructions, Hermes, OpenClaw, and custom runtimes are supported or experimental depending on what each host exposes through hooks, rules, generated context files, or CLI workflows.
I’d especially love feedback from people who use multiple agents on the same project.
What is one thing an agent should have remembered (or remembered incorrectly) that better memory could have helped with?
The local-first memory angle feels important. Agents are getting more useful across longer projects, but only if the memory is reviewable, scoped, and portable instead of hidden inside one tool.
About OpenDream on Product Hunt
“Open, local-first memory for AI agents (with dreaming)”
OpenDream was submitted on Product Hunt and earned 11 upvotes and 2 comments, placing #29 on the daily leaderboard. OpenDream is an open, local-first memory layer for AI agents. It captures agent activity, turns useful history into reviewable memory, retrieves only relevant context for the next task, and shows what was selected, skipped, or marked stale. Use it across Codex, Claude Code, Cursor, and other agent workflows without handing project memory to a hosted black box.
OpenDream was featured in Open Source (68.6k followers), Developer Tools (515.5k followers), Artificial Intelligence (473.1k followers) and GitHub (41.3k followers) on Product Hunt. Together, these topics include over 218.5k products, making this a competitive space to launch in.
Who hunted OpenDream?
OpenDream was hunted by Matt Shaffer. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how OpenDream stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Matt Shaffer
Hey Product Hunt-
I’m Matt, maker of OpenDream.
I built OpenDream because even though agent memory has slowly been improving, amnesia across agent sessions continues to be a productivity killer.
Agents don't just "forget" things, they fail predictably.
* agents half-remember old project decisions and fill in the gaps with hallucinations
* outdated context gets leaked into new tasks
* agents fail to see what previous agents already learned and require repeat prompting
* memories become hidden context that is hard to inspect, trust, or correct
OpenDream makes agent memory open, local, portable, and reviewable.
It captures agent activity, turns useful history into saved memory, retrieves only the context that fits the next task, and shows what was selected, skipped, or marked stale.
A few things that make it different:
* Local-first by default
* Open source
* Built for multiple agents and tools
* Source-linked memory
* Reviewable memory changes
* Context retrieval instead of dumping one giant memory file into every prompt
Codex is the most tested with OpenDream. Claude Code, Cursor, Copilot-style repo instructions, Hermes, OpenClaw, and custom runtimes are supported or experimental depending on what each host exposes through hooks, rules, generated context files, or CLI workflows.
I’d especially love feedback from people who use multiple agents on the same project.
What is one thing an agent should have remembered (or remembered incorrectly) that better memory could have helped with?