1. [Home](https://www.g2.com/) 2. ... 3. [AI Agent Builders Software](https://www.g2.com/categories/ai-agent-builders) 4. [Meko Discussions](https://www.g2.com/products/meko/discuss) [ ![Product Avatar Image](https://images.g2crowd.com/uploads/product/image/large_detail/large_detail_966e534d95bd319886d6fe9c92e4e771/meko.png "Product Avatar Image") ](/products/meko/reviews) [ Meko ](/products/meko/reviews) 0 ratings Meko is an agent-native data infrastructure, built on top of YugabyteDB, that enables multi-agent systems to share context. Instead of giving each agent its own isolated memory, Meko gives your entire system a memory that compounds. When one agent learns something from a conversation, a user interaction, or a data update, that learning is immediately available to every other agent in the system. Meko replaces the patchwork many teams have bolted together over time (Postgres here, pgvector there, a separate graph database for memory, an object store for conversations, an observability layer for traces) with a single unified data layer. Vector, relational data, graph, and search all live in a single distributed, Postgres-compatible database, exposed to any agentic framework via a single MCP endpoint. It works with Claude, Cursor, and Codex today, with more frameworks on the way. What Makes Meko Different? Compounding memory: One agent’s learning instantly becomes a system-wide advantage. Meko handles entity extraction, graph updates, and per-agent scoping automatically. Shared knowledge: Conversations, real-time data feeds, SQL tables, and documents are continuously processed into one knowledge layer that every agent can access and contribute to. Decision tracing and complete auditability: Every retrieval, memory update, and knowledge share is traced end to end, so you can see exactly what your system knows, how it came to know it, and what that learning cost in tokens and latency. Any agentic framework: Meko connects to any agentic framework, chat app, or coding harness via a single remote MCP endpoint and runs on a distributed, serverless hosted service for cost-controlled scaling and no extras to stitch together. Cost efficiency: Without shared memory, multi-agent systems can burn many more tokens than a standard chat interaction. Most of that is pure coordination overhead: agents re-fetching what a teammate already retrieved, and re-explaining context that should simply be shared state. Meko eliminates that redundancy at the infrastructure level. Because retrieval, embedding, and indexing all run server-side, none of it consumes your agents’ context windows, and once one agent learns something, no other agent has to spend tokens re-deriving it. Explore the agent-native data infrastructure for collective memory, shared knowledge, and decision traces. https://mekodata.ai/ Show More When users leave Meko reviews, G2 also collects common questions about the day-to-day use of Meko. These questions are then answered by our community of 850k professionals. Submit your question below and join in on the G2 Discussion. * * * ### 0.0 Nps Score ### All Meko Discussions Search Most CommentedMost HelpfulCommon QuestionsNewest All DiscussionsDiscussions with CommentsCommon QuestionsDiscussions without Comments FilterFilter Filter byExpand/Collapse Sort by Most Commented Most Helpful Common Questions Newest Filter by All Discussions Discussions with Comments Common Questions Discussions without Comments Sorry... There are no questions about Meko yet. ## Start a New Software Discussion Have a software question? Get answers from real users and experts [Start A Discussion](/products/meko/discussions/new) * * * ![Product Avatar Image](https://images.g2crowd.com/uploads/product/hd_favicon/793a6ff8150184ddb0e4efc73a39c3b7/meko.png "Product Avatar Image") ### Have you used Meko before? Answer a few questions to help the Meko community [ Yes ](javascript:void(0))[ Yes ](https://www.g2.com/login?context=product_review&return_to=https%3A%2F%2Fwww.g2.com%2Fproducts%2Fmeko%2Fdiscuss%3Fsmall_ask%3Dmeko) No