Project: Dakera
Self-host / deploy: https://github.com/dakera-ai/dakera-deploy
Website: https://dakera.ai
What it is: Dakera is an open-core, self-hosted memory server for AI agents — a single binary that gives agents persistent, decay-weighted long-term memory across sessions. It exposes a REST API and an MCP server, with vector + hybrid (semantic + full-text) search, knowledge-graph associations, and session tracking.
Why it fits Awesome-LLMOps: It ships as production infrastructure — Docker Compose, Kubernetes, and Helm charts, with HA cluster setup and monitoring (see dakera-deploy). It sits alongside the existing Vector search / serving entries as the retrieval/memory layer for agentic LLM apps, and is self-hostable so teams keep their agent memory on their own infra.
Suggested section: Vector search (or a dedicated Memory entry if you prefer).
Happy to match whatever format/section fits your contribution guidelines — thanks for maintaining this list!
Project: Dakera
Self-host / deploy: https://github.com/dakera-ai/dakera-deploy
Website: https://dakera.ai
What it is: Dakera is an open-core, self-hosted memory server for AI agents — a single binary that gives agents persistent, decay-weighted long-term memory across sessions. It exposes a REST API and an MCP server, with vector + hybrid (semantic + full-text) search, knowledge-graph associations, and session tracking.
Why it fits Awesome-LLMOps: It ships as production infrastructure — Docker Compose, Kubernetes, and Helm charts, with HA cluster setup and monitoring (see
dakera-deploy). It sits alongside the existing Vector search / serving entries as the retrieval/memory layer for agentic LLM apps, and is self-hostable so teams keep their agent memory on their own infra.Suggested section: Vector search (or a dedicated Memory entry if you prefer).
Happy to match whatever format/section fits your contribution guidelines — thanks for maintaining this list!