CrewAI

Persistent memory for AI agent crews via CrewAI. Give your crews long-term memory with fact extraction, entity tracking, and temporal awareness.

Features

• Drop-in Storage Backend - Implements CrewAI's Storage interface for ExternalMemory
• Automatic Memory Flow - CrewAI automatically stores task outputs and retrieves relevant memories
• Per-Agent Banks - Optionally give each agent its own isolated memory bank
• Reflect Tool - Agents can explicitly reason over memories with disposition-aware synthesis
• Simple Configuration - Configure once, use everywhere

Installation

pip install atulya-crewai

Quick Start

from atulya_crewai import configure, AtulyaStorage
from crewai.memory.external.external_memory import ExternalMemory
from crewai import Agent, Crew, Task

configure(atulya_api_url="http://localhost:8888")

crew = Crew(
    agents=[Agent(role="Researcher", goal="Find information", backstory="...")],
    tasks=[Task(description="Research AI trends", expected_output="Report")],
    external_memory=ExternalMemory(
        storage=AtulyaStorage(bank_id="my-crew")
    ),
)

crew.kickoff()

That's it. CrewAI will automatically:

• Query memories at the start of each task
• Store task outputs to Atulya after each task completes

Memories persist across crew runs, so your crew learns over time.

How It Works

The integration maps CrewAI's 3-method Storage interface to Atulya's API:

CrewAI	Atulya	What happens
save(value, metadata, agent)	retain(bank_id, content, ...)	Task output is stored. Atulya extracts facts, entities, and relationships from the raw text.
search(query, limit)	recall(bank_id, query, ...)	CrewAI constructs a query from the task description. Atulya runs semantic search, BM25, graph traversal, and reranking.
reset()	delete_bank(bank_id)	Wipes the bank and optionally recreates it with its original mission.

CrewAI calls search() automatically at the start of each task and save() after each task completes.

Configuration Options

from atulya_crewai import configure

configure(
    atulya_api_url="http://localhost:8888",  # Atulya API URL
    api_key="your-api-key",                     # Or set ATULYA_API_KEY env var
    budget="mid",                               # Recall budget: "low", "mid", "high"
    max_tokens=4096,                            # Max tokens for recall results
    tags=["env:prod"],                          # Tags for stored memories
    recall_tags=["scope:global"],               # Tags to filter recall
    recall_tags_match="any",                    # Tag match: any/all/any_strict/all_strict
    verbose=True,                               # Enable logging
)

Per-Storage Overrides

Constructor arguments override global configuration:

storage = AtulyaStorage(
    bank_id="my-crew",
    budget="high",
    max_tokens=8192,
    tags=["team:alpha"],
)

Bank Missions

Set a mission to guide how Atulya processes and organizes memories:

storage = AtulyaStorage(
    bank_id="my-crew",
    mission="Track software architecture decisions, technical debt, and team preferences.",
)

Per-Agent Memory Banks

Give each agent its own isolated memory bank:

storage = AtulyaStorage(
    bank_id="my-crew",
    per_agent_banks=True,
    # Researcher -> "my-crew-researcher"
    # Writer     -> "my-crew-writer"
)

Or use a custom bank resolver for full control:

storage = AtulyaStorage(
    bank_id="my-crew",
    bank_resolver=lambda base, agent: f"{base}-{agent.lower()}" if agent else base,
)

info

When per_agent_banks=True, the automatic search() at task start queries the base bank (shared context), since CrewAI's search() method does not receive the agent parameter. For per-agent search isolation, create separate AtulyaStorage instances per agent.

Reflect Tool

CrewAI's storage interface only supports save/search/reset. To give agents access to Atulya's reflect (disposition-aware memory synthesis), add it as a tool:

from atulya_crewai import AtulyaReflectTool

reflect_tool = AtulyaReflectTool(
    bank_id="my-crew",
    budget="mid",
    reflect_context="You are helping a software team track decisions.",
)

agent = Agent(
    role="Analyst",
    goal="Analyze project history",
    backstory="...",
    tools=[reflect_tool],
)

When the agent calls this tool, it gets a synthesized, contextual answer based on all relevant memories rather than raw fact snippets.

Full Example

A research crew that remembers findings across runs:

from atulya_crewai import configure, AtulyaStorage, AtulyaReflectTool
from crewai.memory.external.external_memory import ExternalMemory
from crewai import Agent, Crew, Task

configure(atulya_api_url="http://localhost:8888")

storage = AtulyaStorage(
    bank_id="research-crew",
    mission="Track technology research findings and comparisons.",
)

reflect_tool = AtulyaReflectTool(bank_id="research-crew", budget="mid")

researcher = Agent(
    role="Researcher",
    goal="Research topics, building on prior knowledge.",
    backstory="Before starting, use atulya_reflect to check what you already know.",
    tools=[reflect_tool],
)

writer = Agent(
    role="Writer",
    goal="Write summaries incorporating prior findings.",
    backstory="Use atulya_reflect to recall prior research.",
    tools=[reflect_tool],
)

crew = Crew(
    agents=[researcher, writer],
    tasks=[
        Task(description="Research the benefits of Rust", expected_output="Analysis", agent=researcher),
        Task(description="Write an executive summary", expected_output="Summary", agent=writer),
    ],
    external_memory=ExternalMemory(storage=storage),
)

# Run 1: researches Rust, stores findings
crew.kickoff()

# Run 2: recalls Rust research when comparing with Go
crew.tasks[0].description = "Compare Rust with Go"
crew.kickoff()

API Reference

Configuration

Function	Description
configure(...)	Set global connection and default settings
get_config()	Get current configuration
reset_config()	Reset configuration to None

Storage

Parameter	Default	Description
bank_id	required	Atulya memory bank ID
atulya_api_url	from config	Override API URL
api_key	from config	Override API key
budget	"mid"	Recall budget (low/mid/high)
max_tokens	4096	Max tokens for recall results
tags	None	Tags applied when storing
recall_tags	None	Tags to filter when searching
recall_tags_match	"any"	Tag matching mode
per_agent_banks	False	Give each agent its own bank
bank_resolver	None	Custom (bank_id, agent) -> bank_id
mission	None	Bank mission for memory organization
verbose	False	Enable verbose logging

Reflect Tool

Parameter	Default	Description
bank_id	required	Atulya memory bank ID
budget	"mid"	Reflect budget (low/mid/high)
reflect_context	None	Additional context for reasoning
atulya_api_url	from config	Override API URL
api_key	from config	Override API key

Requirements

• Python >= 3.10
• crewai >= 0.86.0
• A running Atulya API server