atulya-crewai

Persistent memory for AI agent crews via Atulya. Give your CrewAI 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

# Step 1: Configure connection
configure(atulya_api_url="http://localhost:8888")

# Step 2: Create crew with Atulya-backed memory
crew = Crew(
    agents=[
        Agent(role="Researcher", goal="Find information", backstory="..."),
        Agent(role="Writer", goal="Write reports", 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.

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,
)

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 — not just raw facts.

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.",
)

Configuration

Global Configuration

from atulya_crewai import configure

configure(
    atulya_api_url="http://localhost:8888",  # Default: production API
    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 mode: 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",       # Override global budget
    max_tokens=8192,     # Override global max_tokens
    tags=["team:alpha"], # Override global tags
)

Examples

See the CrewAI memory example in the Atulya Cookbook for a complete working demo with a Researcher + Writer crew.

Configuration Reference