INTEGRATIONS

Framework Integrations

Drop-in Dakera memory for the AI frameworks you already use. Each integration is a thin, production-ready package — install with pip or npm, point it at your Dakera server, and your agents remember everything across sessions.

Early Access — Publishing pipelines are confirmed working. Packages install from GitHub while PyPI/npm listings are being finalized. Join the waitlist →

Python integrations

🦜

LangChain

Python · langchain-dakera

DakeraMemory for persistent conversation chains. DakeraVectorStore for server-side RAG — no local embedding model needed.

pip install langchain-dakera

Documentation → GitHub

🤝

CrewAI

Python · crewai-dakera

DakeraStorage as CrewAI's long-term memory backend. Your crews accumulate knowledge across every run — session-persistent, semantically recalled.

pip install crewai-dakera

Documentation → GitHub

🦙

LlamaIndex

Python · llamaindex-dakera

DakeraMemoryStore for agent memory. DakeraIndexStore replaces local vector indices — server-side embedding, no OpenAI API key needed for RAG.

pip install llamaindex-dakera

Documentation → GitHub

🤖

AutoGen

Python · autogen-dakera

DakeraMemory plugs directly into AutoGen's memory list. Agents and multi-agent teams share persistent, decay-weighted memory across sessions.

pip install autogen-dakera

Documentation → GitHub

JavaScript / TypeScript

🔗

LangChain.js

TypeScript · @dakera-ai/langchain

DakeraMemory and DakeraVectorStore for LangChain.js chains. Full TypeScript types, compatible with Node.js ≥ 20.

npm install @dakera-ai/langchain

Documentation → GitHub

How integrations work

Every integration is a thin adapter between the framework's memory or vector-store interface and the Dakera REST API. No embeddings run locally — the Dakera server handles them with its built-in ONNX inference engine.

Feature	What Dakera provides
Embedding	On-device ONNX model on the server — zero external API calls
Vector search	HNSW with IVF + SPFresh, BM25 hybrid reranking
Memory decay	Access-weighted importance, configurable half-life
Sessions	Per-session memory grouping and lifecycle management
Cross-agent network	Agents share knowledge via the cross-agent graph API

Prerequisites

All integrations require a running Dakera server. The fastest way to get one running:

docker run -d \
  --name dakera \
  -p 3300:3300 \
  -e DAKERA_ROOT_API_KEY=dk-mykey \
  ghcr.io/dakera-ai/dakera:latest

For persistent storage, see the Deployment guide. Then pick your framework above and follow the integration docs.