DAKERAGive every AI agent perfect memory
Dakera builds open-core memory infrastructure so AI agents can remember context across conversations, sessions, and teams — without sending data to third parties.
Why we're building Dakera
AI agents are getting smarter at reasoning, but they keep forgetting. Every time a conversation ends, the context is lost. Teams waste time re-explaining preferences. Customer support bots ask the same questions twice. Multi-agent systems can't share knowledge.
We believe memory is the missing layer in the AI stack. Not a simple key-value store — real memory that understands temporal relationships, supports hybrid retrieval, and builds knowledge graphs automatically.
Dakera is a single Rust binary that runs on your infrastructure. No external APIs, no embedding services, no databases to manage. Install, configure your AI client, and agents start remembering.
88.2% Recall@20 (LoCoMo) · 44 MB binary (+ ONNX models at startup) · No external APIs · Deploy in under 5 minutes
Read the Quickstart → Join Cloud WaitlistHow Dakera Works
A single binary handles the entire memory lifecycle — from ingestion through intelligent retrieval — with no external dependencies.
Self-hosted first
Your agent memory stays on your servers. No data leaves your infrastructure. Full control over storage, retention, and access.
Zero dependencies
One Rust binary. No Redis, no Postgres, no external embedding APIs. Dakera includes everything: storage, embeddings, retrieval, and knowledge graphs.
Open core
The memory engine is proprietary and free to self-host. SDKs for Python, JavaScript, Rust, and Go are MIT-licensed. Build on Dakera without vendor lock-in.
Production-grade
88.2% Recall@20 on LoCoMo — the standard long-context memory benchmark. Built for real workloads with concurrent agents and high throughput.
The team
Dakera is built by infrastructure engineers who've shipped ML pipelines, vector search systems, and distributed data stores at scale — and hit the same wall every time: agents that can't remember. We spent too many hours duct-taping Redis, Pinecone, and custom TTL scripts together and decided to build the thing we wished existed.
Our background is in low-level systems engineering: Rust, storage engines, and retrieval algorithms. Dakera's 88.2% LoCoMo Recall@20 comes from doing retrieval correctly at the engine level — hybrid BM25+vector fusion, importance decay, and on-device ONNX inference — not from bolting LLM reranking onto a vector store as an afterthought.
We believe the best infrastructure is invisible — it just works. Dakera should be as easy to set up as pulling a Docker image, and as reliable as the filesystem underneath it. No managed services, no opaque pricing, no data leaving your network.
Get in touch: via LinkedIn or the cloud waitlist.
Built with
Deploy in 5 minutes
One binary. No dependencies. Pull the Docker image, set your API key, and your agents start remembering.
Give your AI agents persistent memory
Self-host free today — or join the Dakera Cloud waitlist for managed hosting, SLA & founder pricing.