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You can build an email agent that runs model inference and keeps its conversation memory on your computer, while using Skillware’s Gmail handler for mail operations. That does not make the whole system offline or automatically private: fetching and sending email still use the network, and local files remain exposed to anyone or anything with access to the machine. The safest design lets the model propose actions but makes deterministic code and a human approval step control whether a message is sent.
How the agent works
The system is a loop rather than a single model prompt. Ollama runs a locally selected language model. SQLite stores conversation turns and locally generated embeddings. The application retrieves a small set of relevant past context, combines it with recent conversation history and persona instructions, and asks the model what to do. Skillware’s Gmail handler exposes mail operations; the application translates the handler’s manifest into the tool format expected by the model and executes proposed calls through the handler.
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A typical turn follows this sequence:
- Load the persona and behavioral rules, plus the Skillware Gmail handler.
- Retrieve relevant stored memories and a bounded window of recent messages.
- Send that context and the available tool definitions to Ollama.
- Inspect any proposed mail action. Validate the action and require explicit approval before sending or replying.
- Run an approved action through the deterministic handler, return its result to the model, and save the exchange and its embedding.
Persona instructions and local contact mappings are configuration, not safeguards. They can help the model behave consistently, but code should enforce which actions are allowed and when confirmation is required.
What “private” means here
With a local model, inference can happen on your computer rather than sending prompts and responses to an inference provider. Ollama’s privacy policy, last updated in March 2026, distinguishes local processing from its cloud-hosted models: it says locally processed prompts and responses are not received by Ollama, while cloud-model prompts and responses are handled transiently. Check the policy and your model configuration before relying on that boundary.
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Email itself still travels through your mail provider. The application also stores text and vectors in local files, which are not automatically encrypted or governed by a retention policy just because they are in SQLite. Back up, restrict access to, and periodically review the database. Do not use a cloud-model adapter if your goal is to keep inference local.
Install Ollama and choose models
Install Ollama for your operating system, then download a model that your computer can run. The tutorial’s example uses llama3.2 for inference and nomic-embed-text for local embeddings. Its description of the embedding model uses 768 dimensions. These are example choices, not a current compatibility guarantee or a benchmark-backed recommendation; tool-call behavior, context handling, model availability, and resource needs can vary with model and Ollama versions.
Start with a model small enough to run comfortably on your hardware, then check whether it reliably follows tool schemas and handles the context you need. The tutorial’s RAM and model-size figures are article-era estimates, not dependable specifications for every current build or machine. Verify current model details and test performance locally rather than treating those figures as minimum requirements.
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Create a virtual environment for the application and install the dependencies used in the tutorial:
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python -m venv .venv
# Activate the environment for your operating system, then:
pip install skillware ollama pyyaml python-dotenvKeep mailbox credentials in a local .env file and contact mappings in a YAML address book; keep persona and behavior settings in JSON. Do not put passwords in prompts, SQLite conversation records, source control, or logs. The tutorial describes its app password as being held in .env and not sent to the model; that is a design claim, not an independent verification of runtime behavior. Protect the file with restrictive permissions and exclude it from version control.
Use a dedicated mailbox for the agent rather than connecting a primary personal or work inbox. A separate account limits the impact of an incorrect recipient, mistaken draft, or model misunderstanding.
Gmail authentication is conditional
The tutorial uses IMAP and a Google app password, but Google’s current guidance differs from older setup instructions. Personal Gmail IMAP access is always on starting January 2025, so there is no need to enable an IMAP toggle. Google recommends “Sign in with Google” where a client supports it and says app passwords are not recommended and unnecessary in most cases. App passwords require 2-Step Verification; they may be unavailable for accounts that use only security keys, managed work or school accounts, or Advanced Protection, and Google revokes them after an account password change. See Google Account Help: Sign in with app passwords.
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Store and retrieve memory with SQLite
The sample uses Python’s built-in sqlite3 module to store conversation turns and embedding vectors. It generates embeddings locally, computes cosine similarity in Python, and supplies a small number of retrieved memories alongside a bounded recent-history window. This gives the model selected continuity without placing the entire conversation history into every prompt.
Memory retrieval is not guaranteed recall. Its usefulness depends on which text you store, the embedding model, the similarity threshold, and how many results you select. Review retrieved content for relevance and avoid storing sensitive messages unless they are necessary. A database containing text and vectors is still a local data store: SQLite alone does not provide encryption, correct recall, or an automatic retention policy.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Build a controlled tool loop
Skillware’s Gmail handler provides the deterministic operations; the model should not get unrestricted access to mail. Convert the handler’s manifest into a tool definition the chosen Ollama model accepts, pass that schema with the prompt and history, and process returned tool calls in application code. Validate the action type and recipient before invoking the handler. Treat tool-call output as a proposal, not authorization.
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For sending or replying, show the parsed recipient, subject, and complete message body, then require a distinct human approval action before execution. Start with read-only or draft-only behavior, test with a disposable mailbox, and only enable sending after the review path works as intended. Log action metadata for debugging without recording credentials or unnecessary message contents.
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Protect the agent from email content
Every incoming message and attachment is untrusted input. A message can contain instructions intended to manipulate the model; it must not override the agent’s rules or authorize mail actions. Skillware documentation surfaced an untrusted-content marker, and the tutorial describes prompt guidance for untrusted content. These measures can help distinguish quoted mail from system instructions, but neither a label nor prompt wording proves resistance to prompt injection.
- Keep tool permissions narrow and enforce them in code, not only in the system prompt.
- Validate recipients and action types against application rules before any handler call.
- Require explicit approval for every send or reply; do not treat a model’s statement that the user approved as approval.
- Keep secrets out of model context, stored conversation text, and diagnostic logs.
- Review drafts and attachments yourself, and test failure cases with a disposable account.
What to verify before relying on it
The tutorial describes an architecture and sample code; it does not establish that the implementation has been independently audited or tested. Before using the agent beyond experiments, check that the model is running locally, credentials are not exposed to prompts or logs, stored data is access-controlled, and a failed or ambiguous approval cannot fall through to sending. Confirm that the handler’s available operations and authentication method match your account, then test recipient validation, draft previews, and rejection paths with non-sensitive email.
Local inference reduces exposure to an inference provider when local models are used, but it does not eliminate network exposure through mail transport, risks from local files, or the consequences of incorrect tool execution. Treat the model as a drafting and decision-support component; keep consequential authority in the application and with the human reviewer.
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