Yugabyte bets on open-source memory infrastructure for AI agents
CEO: "Memory infrastructure for agents will be deeply embedded in production applications--that layer can’t be a proprietary black box."
Welcome to Forkable’s Open Profile column, where I go in-depth on key projects, companies, and figures from across the open source realm.
In this edition, I check in with Karthik Ranganathan, CEO and co-founder of Yugabyte, an open-source database company now pushing into AI infrastructure with the launch of Meko, a new “memory layer” for AI agents.
Meko reflects how infrastructure companies are reassessing their role in the agentic AI stack. As agents take on longer-running tasks across multiple systems, the problems companies keep hitting — shared context and coordination — are ones distributed systems engineers would recognise immediately.
"Agents are working from inconsistent views of what's true right now," Ranganathan told me. "This is because there is no way to enforce a shared truth."
Read the story in full below.
10 years in the making
When Karthik Ranganathan launched open-source database startup Yugabyte some 10 years ago, the company was focused on helping enterprises run PostgreSQL applications reliably — essentially, making sure apps and services stayed online and responsive even when spread across different data centers around the world.
AI agents, and the strain they would eventually place on data systems, weren’t part of the conversation.
Ranganathan arrived at Yugabyte after years building distributed database technology at Facebook, where he worked on projects including Apache Cassandra and HBase, before later joining cloud infrastructure company Nutanix. Yugabyte itself grew out of the cloud computing boom.
Now the company is turning its attention squarely towards AI agents. Last week, Yugabyte launched Meko, a “memory layer” designed to help AI agents store, share, and retain knowledge across tasks and between different agents.
The release comes as companies building agentic systems run into mounting difficulties around maintaining shared context, keeping information current, and tracking what agents have learned over time.
Many companies currently piece together agent memory using separate retrieval systems, databases, logging tools, and orchestration software. According to Ranganathan, that can create situations where different agents end up operating with different versions of the same information.
“Agents are working from inconsistent views of what's true right now,” Ranganathan explained to Forkable. “This is because there is no way to enforce a shared truth.”
As different agents interact with the same systems, one may update a piece of information while another continues working from an older version, causing errors and outdated assumptions to spread between agents over time.
“There’s no consistency model anywhere in a stitched stack,” Ranganathan continued.
Meko and the next layer of AI infrastructure
Meko, at its core, is a shared memory layer for AI agents. The software stores things like conversation history, agent decisions, shared knowledge, and longer-term memory in a single system that different agents can access. Yugabyte says the goal is to help agents retain context across tasks and sessions, while also giving teams a clearer record of how information moves between systems.
“Existing tools trace what agents did — they don't trace what agents learned, or how that learning propagated between agents,” Ranganathan said.
At the center of the platform is what Yugabyte calls a “context engine,” built around four main components: memory, knowledge, conversations, and “datapacks.” Together, those pieces are intended to handle persistent agent memory, shared organisational knowledge, conversational history, and reusable collections of structured data that agents can access across different tasks and workflows.
Ranganathan argues that many current AI tools can show prompts, tool calls, and latency, but struggle to explain how agents arrived at certain conclusions or how information spread between different systems over time. He also believes that growing regulatory pressure around AI accountability, including requirements around auditability, will place more scrutiny on those gaps as agentic systems take deeper root in enterprise software.
“None of these are fixable with better prompts or more retrieval,” Ranganathan continued. “They're infrastructure problems that need infrastructure solutions.”
The open source factor
For now, Meko is available by request only as a fully managed service, though Yugabyte says the intention is to release the underlying platform as open source. And the rationale for that, according to Ranganathan, is largely driven by the nature of the technology itself, and the belief that companies will be reluctant to build critical AI systems on top of closed memory infrastructure they cannot independently inspect or control.
“Memory infrastructure for agents will be deeply embedded in production applications — it holds the learned context, the conversation history, the audit trail of what agents knew and when,” Ranganathan explained. “That layer can’t be a proprietary black box. If we asked teams to commit their agent data to a closed system that they can’t self-host, audit, or extend, we’d lose them on day one. The data is too important to trust to a single vendor.”
The launch also reflects Yugabyte’s longer-running business model around open source infrastructure. The company has historically monetised through managed services built on top of its open source database technology.
Meko will ultimately follow a similar pattern, with Yugabyte making money through hosted and managed deployments aimed at enterprises that don’t want to operate the infrastructure themselves. Underneath, the system runs on YugabyteDB, the company’s PostgreSQL-compatible distributed database, which acts as the storage and coordination layer behind the memory system.
For Yugabyte, it’s also a bet on where enterprise software is heading — databases and data platforms angling for a foundational role in agentic systems, without the lock-in.
“If you’re building a multi-agent system where models will change, vendors will change, and you need an audit trail across the whole thing — that’s an infrastructure decision,” Ranganathan said. “And it shouldn’t be locked to whichever model you happened to start with.”