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agent-coherence
Stop AI agents from silently corrupting shared state
Two AI agents (two sessions, or an agent and a script) read the same file and both write it back. The later write silently clobbers the first: no error, the work is just gone. agent-coherence is a drop-in guard that catches the stale read before the overwrite lands, so the stale writer is denied and re-reads first. It ships as an MCP server, a LangGraph store drop-in, and a Python library. Single-host today (2+ agents on one machine); cross-host is opt-in, demo-grade. Open source, TLA+-checked.
AI agents are increasingly used to read, process, and write back to shared state — project files, memory stores, configuration, knowledge bases. When multiple agents do this across sessions or concurrently, a silent failure mode emerges: an agent reads state at time T, does work, then writes back at T+N — unaware that another agent or session already modified that state in between. The result is a silent lost update: the second write overwrites the first, with no error, no warning, and no indication anything went wrong.
agent-coherence is a data-plane coordination layer that eliminates this class of failures. It tracks read generations and enforces a freshness guarantee: before any agent can commit a write to shared state, the system validates that the agent's read is still current. If the state has changed since the agent last read it, the write is denied and the agent is required to re-acquire — re-reading the latest state before proceeding. This prevents stale overwrites both across sequential sessions (temporal coherence) and within concurrent runs on the same host (optimistic concurrency control via compare-and-swap writes).
The solution is delivered as an open-source Python library with a pluggable adapter model, a first-party Claude Code plugin for enforcing coherence on project rule files, and an MCP server interface for tool-native agent integration. It targets development teams building production AI agent workflows where correctness of shared state is business-critical.
ran a quick test with two langgraph agents editing the same json file and it actually caught the stale write before the clobber, which is the exact thing i kept hitting. nice that it’s tiny to drop in.
how does it actually detect the stale read under the hood, like is it checking file mtimes or something fancier with the MCP session state?
About agent-coherence on Product Hunt
“Stop AI agents from silently corrupting shared state”
agent-coherence was submitted on Product Hunt and earned 5 upvotes and 5 comments, placing #127 on the daily leaderboard. Two AI agents (two sessions, or an agent and a script) read the same file and both write it back. The later write silently clobbers the first: no error, the work is just gone. agent-coherence is a drop-in guard that catches the stale read before the overwrite lands, so the stale writer is denied and re-reads first. It ships as an MCP server, a LangGraph store drop-in, and a Python library. Single-host today (2+ agents on one machine); cross-host is opt-in, demo-grade. Open source, TLA+-checked.
agent-coherence was featured in Open Source (68.6k followers), Developer Tools (515.4k followers), Artificial Intelligence (473.1k followers) and GitHub (41.3k followers) on Product Hunt. Together, these topics include over 218k products, making this a competitive space to launch in.
Who hunted agent-coherence?
agent-coherence was hunted by Vlad Parakhin. 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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Vlad Parakhin