llamaindex-dakera

Drop-in LlamaIndex components backed by Dakera — persistent agent memory and server-side vector indexing with no local embedding model.

DakeraMemoryStore gives your LlamaIndex agents conversation memory that survives restarts. DakeraIndexStore replaces local vector indices with Dakera's server-side embedding engine — no OpenAI embeddings API needed for RAG.

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Quick Start

Step 1 — Run Dakera

Dakera is a self-hosted memory server. Spin it up with Docker:

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

For a production setup with persistent storage, use Docker Compose:

# Download and start
curl -sSfL https://raw.githubusercontent.com/Dakera-AI/dakera-deploy/main/docker-compose.yml \
  -o docker-compose.yml
DAKERA_API_KEY=dk-mykey docker compose up -d

# Verify it's running
curl http://localhost:3300/health

Full deployment guide: github.com/Dakera-AI/dakera-deploy

Step 2 — Install the integration

pip install llamaindex-dakera

Step 3 — Use it

from llama_index_dakera import DakeraMemoryStore, DakeraIndexStore

# Agent memory
memory = DakeraMemoryStore(
    api_url="http://localhost:3300",
    api_key="dk-mykey",
    agent_id="my-agent",
)

# RAG index — no local embedding model needed
vector_store = DakeraIndexStore(
    api_url="http://localhost:3300",
    api_key="dk-mykey",
    namespace="my-docs",
)

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Installation

pip install llamaindex-dakera

Requirements: Python ≥ 3.10, a running Dakera server (see Step 1 above)

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DakeraMemoryStore

Persistent conversation memory for LlamaIndex agents. Drop-in replacement for the default in-memory store.

Usage with a chat agent

from llama_index.core.agent import ReActAgent
from llama_index.core.memory import ChatMemoryBuffer
from llama_index.llms.openai import OpenAI
from llama_index_dakera import DakeraMemoryStore

store = DakeraMemoryStore(
    api_url="http://localhost:3300",
    api_key="dk-mykey",
    agent_id="react-agent",
)

memory = ChatMemoryBuffer.from_defaults(
    token_limit=3000,
    chat_store=store,
    chat_store_key="user-1",
)

agent = ReActAgent.from_tools(
    tools=[...],
    llm=OpenAI(model="gpt-4o"),
    memory=memory,
    verbose=True,
)

# First session
response = agent.chat("My project is called NeuralBridge.")
print(response)

# Later session — memory persists
response = agent.chat("What's the name of my project?")
print(response)  # "Your project is called NeuralBridge."

Options

Parameter	Type	Default	Description
api_url	str	—	Dakera server URL
api_key	str	""	Dakera API key
agent_id	str	—	Namespace for this agent's memories
top_k	int	5	Memories to retrieve per query
min_importance	float	0.0	Minimum importance for recall

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DakeraIndexStore

Server-side embedded vector store for RAG. Dakera embeds documents on the server — no local model, no OpenAI embeddings API needed.

Indexing documents

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, StorageContext
from llama_index_dakera import DakeraIndexStore

# Load documents
documents = SimpleDirectoryReader("./docs").load_data()

# Create index backed by Dakera
vector_store = DakeraIndexStore(
    api_url="http://localhost:3300",
    api_key="dk-mykey",
    namespace="product-docs",
)
storage_context = StorageContext.from_defaults(vector_store=vector_store)

index = VectorStoreIndex.from_documents(
    documents,
    storage_context=storage_context,
)

Querying

query_engine = index.as_query_engine(similarity_top_k=4)
response = query_engine.query("How does the billing work?")
print(response)

Chat with your documents

from llama_index.core.chat_engine import CondensePlusContextChatEngine
from llama_index_dakera import DakeraIndexStore, DakeraMemoryStore

vector_store = DakeraIndexStore(
    api_url="http://localhost:3300",
    api_key="dk-mykey",
    namespace="product-docs",
)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_defaults(storage_context=storage_context)

memory_store = DakeraMemoryStore(
    api_url="http://localhost:3300",
    api_key="dk-mykey",
    agent_id="doc-chat",
)

chat_engine = CondensePlusContextChatEngine.from_defaults(
    retriever=index.as_retriever(similarity_top_k=4),
    memory=ChatMemoryBuffer.from_defaults(chat_store=memory_store),
)

response = chat_engine.chat("What are the pricing tiers?")
print(response)

Options

Parameter	Type	Default	Description
api_url	str	—	Dakera server URL
api_key	str	""	Dakera API key
namespace	str	—	Vector namespace to read/write
embedding_model	str	namespace default	Server-side embedding model override

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Related packages

Package	Framework	Language
crewai-dakera	CrewAI	Python
langchain-dakera	LangChain	Python
autogen-dakera	AutoGen	Python
@dakera-ai/langchain	LangChain.js	TypeScript

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Links

• Dakera Server — self-hosted memory server
• Dakera Python SDK — low-level API client
• Integration guide — full setup walkthrough
• All integrations

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License

MIT © Dakera AI

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dakera.ai · Documentation · Request Early Access