crewai-dakera 0.2.0

CrewAI integration for the Dakera AI memory platform

crewai-dakera

Persistent, semantically-recalled memory for CrewAI agents, powered by Dakera.

Your CrewAI crews remember everything — across sessions, across restarts. Dakera handles embedding, storage, and retrieval server-side with no local model required.

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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 crewai-dakera

Step 3 — Add memory to your crew

from crewai import Crew, Agent, Task
from crewai.memory import LongTermMemory
from crewai_dakera import DakeraStorage

storage = DakeraStorage(
    api_url="http://localhost:3300",
    api_key="dk-mykey",
    agent_id="my-crew",
)

crew = Crew(
    agents=[...],
    tasks=[...],
    memory=True,
    long_term_memory=LongTermMemory(storage=storage),
)

result = crew.kickoff(inputs={"topic": "AI trends"})

Your crew now persists everything it learns across runs.

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Installation

# Core + integration
pip install crewai-dakera

# With CrewAI (if not already installed)
pip install "crewai-dakera[crewai]"

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

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Configuration

Parameter	Type	Default	Description
api_url	str	—	Dakera server URL (e.g. http://localhost:3300)
api_key	str	""	API key set via DAKERA_ROOT_API_KEY
agent_id	str	—	Logical identifier for this crew's memory
min_importance	float	0.0	Minimum importance score for recalled memories
top_k	int	5	Number of memories to surface per turn

Use environment variables to avoid hardcoding credentials:

import os
from crewai_dakera import DakeraStorage

storage = DakeraStorage(
    api_url=os.environ["DAKERA_URL"],
    api_key=os.environ["DAKERA_API_KEY"],
    agent_id="research-crew",
)

---

Examples

Research crew with persistent memory

from crewai import Agent, Task, Crew, Process
from crewai.memory import LongTermMemory, ShortTermMemory, EntityMemory
from crewai_dakera import DakeraStorage

dakera = DakeraStorage(
    api_url="http://localhost:3300",
    api_key="dk-mykey",
    agent_id="research-crew",
)

researcher = Agent(
    role="Senior Researcher",
    goal="Uncover groundbreaking insights in {topic}",
    backstory="An expert researcher with decades of experience.",
    verbose=True,
)

writer = Agent(
    role="Content Writer",
    goal="Craft compelling reports based on research findings",
    backstory="A skilled writer who turns complex ideas into clear prose.",
    verbose=True,
)

research_task = Task(
    description="Research the latest developments in {topic}",
    expected_output="A detailed research report",
    agent=researcher,
)

write_task = Task(
    description="Write a blog post based on the research",
    expected_output="A polished 500-word article",
    agent=writer,
)

crew = Crew(
    agents=[researcher, writer],
    tasks=[research_task, write_task],
    process=Process.sequential,
    memory=True,
    long_term_memory=LongTermMemory(storage=dakera),
    verbose=True,
)

# First run — learns and stores findings
result = crew.kickoff(inputs={"topic": "quantum computing"})
print(result.raw)

# Second run — recalls prior research automatically
result = crew.kickoff(inputs={"topic": "quantum computing advances"})
print(result.raw)

Custom importance scoring

storage = DakeraStorage(
    api_url="http://localhost:3300",
    api_key="dk-mykey",
    agent_id="my-crew",
    min_importance=0.6,  # only surface high-quality memories
    top_k=10,
)

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How it works

1. After each task, CrewAI calls DakeraStorage.save() with the result
2. Dakera embeds the content server-side (no local model needed) and stores it with a semantic vector
3. Before the next task, CrewAI calls DakeraStorage.search() — Dakera performs hybrid search (vector + BM25) and returns the most relevant past memories
4. Memories decay gracefully over time based on access patterns — frequently-accessed memories stay prominent

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Related packages

Package	Framework	Language
langchain-dakera	LangChain	Python
llamaindex-dakera	LlamaIndex	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