Condensate

Condensate is infrastructure for multi-agent AI pipelines. When multiple agents run concurrently, they commonly overwrite each other's work, duplicate tasks, or accumulate stale context that degrades decisions over time. Condensate addresses this with a decoupled state and memory layer that sits across your agent swarm. Lock-safe shared state prevents race conditions so two agents don't act on the same task simultaneously. Cryptographically-signed Merkle-DAGs create a verifiable, tamper-evident record of every agent decision — useful for auditing and debugging nondeterministic systems. Context stays lean through active learning that decays irrelevant data, so agents work from a current, accurate picture rather than an ever-growing history. Condensate supports both REST API and Model Context Protocol (MCP) integration, making it compatible with most major agent frameworks.

Developers building multi-agent AI systems use Condensate as a shared state and memory layer to prevent context rot, race conditions, and task duplication.