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StateFabric adds durable, inspectable state to AI agents by storing session events and rebuilding working context for each turn. It captures user messages, model outputs, and tool activity outside your runtime, so you can inspect histories, replay behaviour, and resume work without losing facts or open tasks. Use it alongside ADK or your own code via an HTTP API and adapters, and let automatic context compaction supply concise messages, summaries, and tasks to the next turn.
StateFabric a small infrastructure layer for AI agents that need more context than chat history.
Once an agent starts using tools, running for longer sessions between restarts, ‘keep the messages in memory’ doesn’t really cut it. You need to know:
- what happened?
- what state changed?
- which tools were called?
- what context should go into the next model turn?
StateFabric stores an append-only event log for agent runs, then derives working context from it.
Today it supports:
- Durable sessions and event storage
- User/model/tool event timelines
- Reconstructed session state from stored events
- Compacted model-facing context
- A dashboard for inspecting sessions, raw payloads, compaction artefacts, and usage
- Google ADK integration via @statefabric/adk
- Direct Node/REST usage via @statefabric/client (for custom runtimes)
It’s not trying to be an agent framework. Your runtime still owns prompts, tools, model calls, and business logic. StateFabric sits beside it as the state, replay, and context layer.
It's an early launch, so I’m deliberately starting with narrow integrations. ADK (JS) and custom node runtimes initially.
I’d love feedback from people building agents that need persistence, replay-ability, or better context management across long-running sessions.
The separation of session events from runtime logic is a really sharp design call, makes replay and inspection feel first-class rather than bolted on.
Tried replaying a few sessions and seeing the rebuilt context side by side was genuinely useful for catching where my agent drifted. The automatic compaction summaries kept things tight without me tweaking anything.
About StateFabric.dev on Product Hunt
“Add durable, inspectable state to your AI agents”
StateFabric.dev was submitted on Product Hunt and earned 4 upvotes and 3 comments, placing #150 on the daily leaderboard. StateFabric adds durable, inspectable state to AI agents by storing session events and rebuilding working context for each turn. It captures user messages, model outputs, and tool activity outside your runtime, so you can inspect histories, replay behaviour, and resume work without losing facts or open tasks. Use it alongside ADK or your own code via an HTTP API and adapters, and let automatic context compaction supply concise messages, summaries, and tasks to the next turn.
StateFabric.dev was featured in SaaS (43k followers), Developer Tools (515.4k followers) and Artificial Intelligence (473.1k followers) on Product Hunt. Together, these topics include over 229.4k products, making this a competitive space to launch in.
Who hunted StateFabric.dev?
StateFabric.dev was hunted by Jamie Gregory. 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.
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Jamie Gregory