This product was not featured by Product Hunt yet. It will not be visible on their landing page and won't be ranked (cannot win product of the day regardless of upvotes).
Product upvotes vs the next 3
Waiting for data. Loading
Product comments vs the next 3
Waiting for data. Loading
Product upvote speed vs the next 3
Waiting for data. Loading
Product upvotes and comments
Waiting for data. Loading
Product vs the next 3
Loading
Talamus
Durable local-first memory for AI agents, with citations
Talamus gives coding and AI agents durable memory across sessions without sending your knowledge to a hosted service. It stores notes in plain Markdown, builds a local SQLite/FTS5 index, cites the source behind every recall, keeps bitemporal history, and routes corrections through review. It works as a Python library, CLI, MCP server, Docker image, and Gemini CLI extension. Apache-2.0, no account required, and the core works without an LLM call.
Hi Product Hunt — I built Talamus because agent context kept disappearing at the worst possible moment: between sessions, tools, and model providers. I wanted memory I could inspect, version, and correct, rather than another opaque hosted black box.
The design is deliberately local-first: Markdown remains the source of truth, SQLite/FTS5 makes recall fast, citations point back to exact files, temporal history preserves what was known when, and proposed corrections require review. The quick demo runs with --fake, so evaluating the core does not require an API key or paid model call.
I’d especially value feedback from people building coding agents or MCP workflows: which memory failure mode should we test next? The repository includes the runnable demo and committed benchmark artifacts.
About Talamus on Product Hunt
“Durable local-first memory for AI agents, with citations”
Talamus was submitted on Product Hunt and earned 3 upvotes and 1 comments, placing #118 on the daily leaderboard. Talamus gives coding and AI agents durable memory across sessions without sending your knowledge to a hosted service. It stores notes in plain Markdown, builds a local SQLite/FTS5 index, cites the source behind every recall, keeps bitemporal history, and routes corrections through review. It works as a Python library, CLI, MCP server, Docker image, and Gemini CLI extension. Apache-2.0, no account required, and the core works without an LLM call.
On the analytics side, Talamus competes within Open Source, Developer Tools, Artificial Intelligence and GitHub — topics that collectively have 1.1M followers on Product Hunt. The dashboard above tracks how Talamus performed against the three products that launched closest to it on the same day.
Who hunted Talamus?
Talamus was hunted by Giovanni Crapuzzi. 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.
For a complete overview of Talamus including community comment highlights and product details, visit the product overview.
Giovanni Crapuzzi