Memorizz

Experimental software

Memorizz is an educational/experimental framework. APIs may change and the project has not undergone security hardening for production workloads.

Memorizz is a Python framework for building memory-augmented AI agents. It provides:

• multiple memory systems (episodic, semantic, procedural, short-term, shared)
• pluggable storage providers (Oracle, MongoDB, filesystem)
• agent builders and application modes (assistant, workflow, deep_research)
• optional internet access, sandbox code execution, and local web UI

Key Capabilities

• Persistent memory across sessions and conversations
• Semantic retrieval with embeddings + vector search
• Entity memory tools for profile-style facts (entity_memory_lookup / entity_memory_upsert)
• Tool calling with automatic function registration
• Semantic cache to reduce repeat LLM calls
• Multi-agent orchestration with shared blackboard memory
• Context-window telemetry via get_context_window_stats()

Installation

Base install:

pip install memorizz

Common extras:

pip install "memorizz[oracle]"          # Oracle provider
pip install "memorizz[mongodb]"         # MongoDB provider
pip install "memorizz[filesystem]"      # Local filesystem + FAISS
pip install "memorizz[sandbox-e2b]"     # E2B sandbox
pip install "memorizz[sandbox-daytona]" # Daytona sandbox
pip install "memorizz[ui]"              # Local web UI
pip install "memorizz[all]"             # Everything

Quick Start (Filesystem Provider)

import os
from pathlib import Path

from memorizz.memagent.builders import MemAgentBuilder
from memorizz.memory_provider import FileSystemConfig, FileSystemProvider

os.environ["OPENAI_API_KEY"] = "your-openai-api-key"

provider = FileSystemProvider(
    FileSystemConfig(
        root_path=Path("~/.memorizz").expanduser(),
        embedding_provider="openai",
        embedding_config={"model": "text-embedding-3-small"},
    )
)

agent = (
    MemAgentBuilder()
    .with_instruction("You are a helpful assistant with persistent memory.")
    .with_memory_provider(provider)
    .with_llm_config(
        {
            "provider": "openai",
            "model": "gpt-4o-mini",
            "api_key": os.environ["OPENAI_API_KEY"],
        }
    )
    .with_semantic_cache(enabled=True, threshold=0.85)
    .build()
)

print(agent.run("Hi, my name is Leah and I work on payments systems."))
print(agent.run("What did I tell you about my work?"))

stats = agent.get_context_window_stats()
print(stats)

Oracle Setup (Optional)

If you want Oracle AI Database as the backing store:

./install_oracle.sh
memorizz setup-oracle

Then configure ORACLE_USER, ORACLE_PASSWORD, ORACLE_DSN, and your LLM credentials. Full setup details are in SETUP.md.

For multi-client consistency (UI + notebooks), you can set shared embedding defaults:

export MEMORIZZ_DEFAULT_EMBEDDING_PROVIDER=openai
export MEMORIZZ_DEFAULT_EMBEDDING_MODEL=text-embedding-3-small
export MEMORIZZ_DEFAULT_EMBEDDING_DIMENSIONS=1536

Application Modes

ApplicationMode presets automatically enable different memory stacks:

• assistant: conversation, long-term, personas, entity memory, short-term, summaries
• workflow: workflow memory, toolbox, long-term, short-term, summaries
• deep_research: toolbox, shared memory, long-term, short-term, summaries

Example:

import os

from memorizz.enums import ApplicationMode
from memorizz.memagent.builders import MemAgentBuilder

llm_config = {
    "provider": "openai",
    "model": "gpt-4o-mini",
    "api_key": os.environ["OPENAI_API_KEY"],
}

agent = (
    MemAgentBuilder()
    .with_application_mode(ApplicationMode.DEEP_RESEARCH)
    .with_memory_provider(provider)
    .with_llm_config(llm_config)
    .build()
)

Internet Access (Deep Research)

Deep Research agents can attach internet providers and expose internet_search / open_web_page tools.

import os

from memorizz.internet_access import TavilyProvider
from memorizz.memagent.builders import create_deep_research_agent

llm_config = {
    "provider": "openai",
    "model": "gpt-4o-mini",
    "api_key": os.environ["OPENAI_API_KEY"],
}

internet_provider = TavilyProvider(api_key=os.environ["TAVILY_API_KEY"])

agent = (
    create_deep_research_agent(internet_provider=internet_provider)
    .with_memory_provider(provider)
    .with_llm_config(llm_config)
    .build()
)

results = agent.search_internet("latest vector database benchmark")

Sandbox Code Execution

Attach a sandbox provider to enable execute_code, sandbox_write_file, and sandbox_read_file tools.

import os

from memorizz.memagent import MemAgent

llm_config = {
    "provider": "openai",
    "model": "gpt-4o-mini",
    "api_key": os.environ["OPENAI_API_KEY"],
}

agent = MemAgent(
    llm_config=llm_config,
    memory_provider=provider,
    sandbox_provider="e2b",  # or "daytona" / "graalpy"
)

print(agent.execute_code("print(2 ** 16)"))

Multi-Agent Deep Research Workflow

from memorizz.memagent.orchestrators import DeepResearchWorkflow

workflow = DeepResearchWorkflow.from_config(
    memory_provider=provider,
    delegate_instructions=[
        "Financial researcher: collect metrics and citations.",
        "Risk analyst: identify key downside scenarios.",
    ],
)

report = workflow.run("Analyze the last 3 years of cloud infrastructure trends.")
print(report)

CLI

After installation, the memorizz command exposes:

memorizz run local                  # start local web UI (requires [ui])
memorizz install-oracle             # start Oracle container helper
memorizz setup-oracle               # initialize Oracle schema/user

Examples

• examples/single_agent/memagent_local_oracle.ipynb
• examples/single_agent/memagent_remote_oracle.ipynb
• examples/deep_research/deep_research_memagent.ipynb
• examples/sandbox/memagent_e2b_sandbox.ipynb
• examples/sandbox/memagent_daytona_sandbox.ipynb
• examples/sandbox/memagent_graalpy_sandbox.ipynb

Documentation

• Docs source: docs/
• Local preview: make docs-serve (or mkdocs serve)
• Architecture notes: src/memorizz/MEMORY_ARCHITECTURE.md

License

PolyForm Noncommercial 1.0.0. See LICENSE and NOTICE.