Memorizz
The intelligence plane for memory-first agents.
Memorizz is a Python framework that connects persistent memory, models, prompts, and reasoning so agents can recall what matters, reuse experience, and make better decisions across sessions.
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Memory management
Memory is the foundation: retain information, retrieve relevant context, and refine what an agent knows over time.
| Memory | What it gives your agents |
|---|---|
| Episodic | Conversation history, past interactions, and summaries. |
| Semantic | Knowledge bases, entity facts, preferences, and personas. |
| Procedural | Registered tools, recorded workflows, and reusable skills. |
| Short-term | Working context and semantic caching. |
| Shared | A shared workspace for agent coordination and handoffs. |
- Persist and isolate: scope memory by agent, user, and conversation. Start with local filesystem storage, or use MongoDB, Oracle, or Notion with separate vector storage through the same provider contract.
- Ingest and retrieve: turn files and folders into a knowledge base with configurable chunking, embeddings, and semantic or hybrid search.
- Manage context: deduplicate retrieved memories, summarize and compact history, inspect recall decisions, and control retention and forgetting.
Explore memory concepts and scope, storage providers, and context efficiency.
Agent framework
Build persistent agents with MemAgent and MemAgentBuilder. Choose
assistant, workflow, or deep_research modes and connect cloud or local
model providers.
- Tools and integrations: Python functions, MCP servers, internet search, governed browser control, sandboxes, and external skills.
- Coordination: delegate work to specialist agents, share memory, and run scheduled automations.
- Interfaces: use the Python SDK, interactive CLI, local web UI, or expose Memorizz as an MCP server.
Start with the SDK quickstart or the CLI guide.
Harness
MetaHarness runs tasks through Codex, Claude Code, OpenHands, or native
MemAgent workers. It carries memory scope and learning evidence across
harnesses while managing permissions, approvals, workspace isolation, budgets,
cancellation, and host-side verification.
Use an external harness for an entire task or let a MemAgent call one as a
specialist tool. Inspect installed adapters with memorizz harness doctor.
See the MetaHarness guide and hands-on examples.
Continual learning and the intelligence plane
The intelligence plane is the model layer and everything that helps it think: the LLM, prompts, reasoning strategies such as planning, reflection, and tool selection, plus the memory and retrieval that supply context. Its job is decision quality. A bad plan or a hallucinated tool call is an intelligence plane problem.
Memorizz makes memory the foundation of this plane. Its opt-in continual learning loop captures tool workflows and their outcomes, distills repeated successful procedures into reusable skills, retrieves relevant active skills, and monitors their performance. Shadow evaluation and review support skill activation; skills that stop working can be demoted.
The learning control plane adds traceable outcome evidence, bounded retrieval, and reversible forgetting. Together, these capabilities help agents retain, recall, reuse, and refine experience.
Read about continual learning, the learning control plane, and evaluation methods.
Quick start
Requires Python 3.10+. Install and launch the CLI with a cloud model:
python -m pip install memorizz
export OPENAI_API_KEY="your-key"
memorizz
For a fully local setup, use Ollama.
The installation guide covers uv,
pipx, npm, Homebrew, and optional integrations.
Save credentials without putting them in shell history, and choose memory storage independently from the model:
memorizz config set OPENAI_API_KEY # hidden prompt
memorizz memory configure # guided memory-provider setup
memorizz notion connect # Notion library + separate vector provider
memorizz config path # actual save target and precedence
These commands save defaults for the next launch; they do not switch a running agent or migrate memory. See CLI configuration.
Build a memory-first agent in Python using the same environment:
from memorizz import MemAgentBuilder
agent = (
MemAgentBuilder()
.with_instruction("Remember useful context and use it to help the user.")
.with_llm_config({"provider": "openai", "model": "gpt-4o-mini"})
.with_memory_ids("my-assistant")
.build_and_save()
)
scope = {"memory_id": "my-assistant", "user_id": "alice", "thread_id": "intro"}
with agent.lifecycle(close_memory_provider=True):
agent.run("I work on payments systems.", **scope)
print(agent.run("What did I tell you about my work?", **scope))
Memory persists under ~/.memorizz/memory by default. Save agent.agent_id to
restore the agent later.
Explore
- Memory-first tutorials
- Persistent notes and searchable history: filesystem and Oracle notebooks
- MCP connectivity and MCP server
- Automations and multi-user applications
- Observability and evaluation suite
- Usage, cost and memory analytics
- Account-owned persona evolution
- API reference, configuration, and troubleshooting
License
Source available under PolyForm Noncommercial 1.0.0. See NOTICE. Memorizz is experimental: APIs may change and it has not undergone security hardening for production workloads.
1 maintainer
Metadata
1 maintainer
Release files for memorizz 0.12.0
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| memorizz-0.12.0.tar.gz | 2.4 MB | Details |
Built distribution (wheel)
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| memorizz-0.12.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 4.0 MB
Release files / memorizz-0.12.0.tar.gz
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