We did not set out to build a memory middleware layer. We started out building a multi agent autonomous business marketing system to power a venture that we were attempting back in September 2025. When we moved past toy examples and tried deploying agents on long-running tasks, we hit a wall. Stuffing large codebases or weeks of conversational history into a prompt failed completely. Agents lost context. Their reasoning dropped. Latency and token costs spiked. We realized autonomous agents could not finish long-term projects without a persistent, structured memory. We tried standard RAG first. We threw a vector database at the problem. We quickly found that traditional RAG just retrieves disorganized text chunks based on proximity. It ignores logic. The agents still hallucinated because they lacked a strict ground truth. The real breaking point came during testing. We ran LoCoMo long-term memory benchmarks. We used fast, lightweight models to judge outputs and keep iteration loops fast. It was a complete mess. The judges rubber-stamped hallucinations with massive false-positive rates. The evaluation frameworks themselves were structurally flawed. The entire industry was trying to build memory on quicksand. We needed mathematical verifiability instead of semantic guessing. That realization changed everything. We stopped treating memory as just a storage problem. We started treating it as a state synchronization and concurrency problem, which is critical for enterprise control planes where multiple agents need to operate safely. When we tested multi-agent swarms, the agents constantly duplicated tasks and got trapped in infinite loops. They were isolated. We realized that agents need a lock-safe, shared brain to collaborate on complex architectures. We scrapped the standard vector approach. We built an extraction engine that parses text into cryptographically signed Merkle-DAGs. We gave the agents explicit, verifiable semantic graphs. Every state change is hashed and signed. The swarm finally had a unified, immutable truth to operate from without race conditions. We knew we could not let this infrastructure get trapped in a walled garden. Because our agent implementations live in multiple silos - some in Google ADK, a couple in OpenAI and some experimental versions inside AWS! Relying on a specific vendor means you cannot hot-swap models or run local LLMs without wiping the memory of your agents. We built Condensate to be completely decoupled. It acts as a model-agnostic middleware layer using standards like the Model Context Protocol. Developers keep total data sovereignty. What started as an attempt to automate our marketing and business operations evolved into a verifiable Remote Brain. Agents can finally collaborate and remember.

Condensate

Condensate is AI Memory built for Cognition

Condensate is AI Memory built for Cognition