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Choose Your Path

Memorizz exposes one agent and memory model through four form factors. Choose the interface that owns your deployment; saved agents remain available to the other interfaces when they use the same provider and scope.

Choose an interface

Interface Best for Process model Guide
Python SDK Product integration and custom orchestration In your application SDK quickstart
CLI Local development, scripting, and operations Interactive or one-shot terminal CLI guide
Local UI Configuration, playground use, and trace inspection Local FastAPI server UI guide
MCP server Claude Desktop, IDEs, and other MCP hosts Headless stdio or HTTP server MCP server

All four can run headlessly. Only the browser UI requires an HTTP client; the server itself does not require a desktop or display.

Choose a memory provider

Provider Choose it when Operational tradeoff
Filesystem Developing locally, evaluating, or running one process Zero configuration; coordinate writes if several processes share a directory
MongoDB You already operate MongoDB or need document-native scale Requires indexes, backups, and tenant-aware query review
Oracle AI Database You need relational governance plus native vector operations Requires schema provisioning, preflight, and an explicit vector-index policy
Custom Storage must live in an existing platform You own conformance, tenant isolation, atomicity, and lifecycle behavior

The filesystem provider is the default. Explicitly select a backend before production and test it with the same scopes and retrieval settings used by the application.

Choose an agent mode

Mode Default emphasis Typical use
assistant Conversation, entities, knowledge, summaries Stateful support or personal assistants
workflow Tools, workflow traces, learned procedures Repeatable operational tasks
deep_research Tools, knowledge, shared coordination Multi-step investigation and synthesis

Modes select sensible memory defaults; they do not grant authority. Add tools, MCP, browser control, internet access, or code execution explicitly and apply the corresponding host policy.

For repository and terminal work, a MemAgent can either delegate to an external harness or run its complete turn on Codex, Claude Code, or an isolated OpenHands worker. See the memory-first meta-harness guide.

  1. Install the base package and only the extras you need.
  2. Build one agent and make memory_id, user_id, and thread_id explicit.
  3. Add external authority through governed tools.
  4. Enable caching, summaries, or continual learning only after defining freshness, evaluation, and rollback requirements.
  5. Add observability and a capability/preflight check to deployment startup.
  6. Exercise both happy paths and isolation/failure paths against the real provider before accepting traffic.

Requirements

  • CPython 3.10, 3.11, or 3.12.
  • An LLM provider for model-backed runs. OpenAI and Ollama clients ship in the base install; other providers use optional extras.
  • An embedding provider only when semantic retrieval or semantic caching is required. Exact persistence and retrieval remain available without one.

Continue with Core Concepts for the runtime mental model, or go directly to the Python SDK quickstart.