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.

Available Now — All packages are published on PyPI and npm. Install with pip or npm (see below). The server binary is available via Docker with no waitlist required.

MCP Clients

Connect Dakera to any MCP-compatible AI tool — zero code changes, 14 core memory tools available instantly (86+ via profiles for power users).

Cursor

MCP · Zero code changes

Persistent memory for AI coding — your assistant remembers project architecture, decisions, and debugging context across every session.

Add to .cursor/mcp.json

Documentation →

Claude Desktop

MCP · Zero code changes

Give Claude persistent cross-session memory — preferences, project context, and knowledge that survives restarts.

Add to claude_desktop_config.json

Documentation →

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

Governance

Persistent governance state for LLM applications — policy decisions, cost tracking, and delegation audit trails backed by Dakera's decay-weighted memory engine.

TealTiger

Python · JavaScript · Governance

DakeraCostStorage and DakeraDecisionStore for persistent governance state. Cost tracking, policy decisions, and delegation audit trails — all decay-weighted and semantically recalled.

pip install dakera[tealtiger]

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.