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You can build an email agent that runs model inference on your own computer, remembers selected context in a local SQLite database, and uses Skillware’s Gmail handler for mail operations. “Local” applies to inference and stored memory—not to email delivery: fetching and sending messages still requires a network connection to your mail provider. Keep the model in a proposing role and require a separate, deterministic approval gate before any message is sent.
How the local email agent works
The design has four main parts: Ollama runs a language model; SQLite stores conversation turns and locally generated embeddings; retrieval selects useful prior context for a new request; and Skillware’s Gmail handler exposes mail operations through IMAP and SMTP. A persona, behavior rules, and a local address book provide configuration. The model can request a tool, but application code should validate the request and a person should approve consequential actions.
- Load configuration. Read the persona, behavioral rules, contact mappings, and mailbox configuration. Keep credentials out of prompts and conversation history.
- Retrieve context. Search stored conversation memories and combine the most relevant results with a bounded window of recent turns.
- Ask Ollama. Send the request, selected context, and tool definitions to the locally running model. The tutorial’s sample uses
llama3.2for inference andnomic-embed-textfor embeddings. - Validate and execute tools. Convert Skillware’s manifest into the model’s tool schema, then have deterministic application code validate any proposed action. Preview message details and require explicit approval for sending or replying.
- Save the exchange. Store the conversation and its locally generated embedding so future requests can retrieve relevant context.
The architecture and examples are described in Ross Peili’s 2025 tutorial for ARPA Hellenic Logical Systems. The article describes a sample implementation; it does not establish that the implementation has been independently audited or tested.
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With a local model, inference can stay on the computer rather than sending prompts and responses to an inference provider. Ollama’s privacy policy, updated in March 2026, says it does not receive prompts and responses processed locally, while distinguishing its cloud-hosted models, which handle prompts and responses transiently. Choosing a cloud-hosted model or optional cloud adapter changes that boundary.
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Email itself still travels through the provider’s network when the agent fetches or sends it. The local database and configuration files also remain exposed to anyone or anything with access to the computer. SQLite is a storage format, not a guarantee of encryption, controlled retention, or correct memory recall.
- Use a dedicated agent-only mailbox rather than granting access to a primary personal or work inbox.
- Keep app passwords and other credentials in a protected secret store or environment configuration—not in prompts, persona files, database rows, or logs.
- Start in read-only or draft-only mode. Before enabling send or reply, inspect the parsed recipients and full message body, and require an explicit human approval action.
- Test with a disposable mailbox. Log action metadata needed for troubleshooting, but never log secrets.
Install Ollama and choose models for your hardware
Install Ollama for your operating system, then download the model you intend to use. The tutorial’s sample uses llama3.2, described there as a compact 3B model with tool-calling support, and nomic-embed-text for local embeddings. Its example describes the embeddings as 768-dimensional. Treat those details as the tutorial’s sample specifications, not as a performance guarantee or a current hardware recommendation: available models, context handling, resource requirements, and tool-call behavior can vary with Ollama and model versions.
ollama pull llama3.2
ollama pull nomic-embed-textChoose an inference model by testing the tasks you actually need—such as finding messages, drafting, and producing valid tool calls—on your own machine. Consider latency, available memory, the context size your workflow needs, and model availability and licensing. The tutorial does not provide a systematic comparison of alternatives, so its example model should not be read as a benchmark-backed winner.
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Set up Python and mailbox configuration
Create an isolated Python environment and install the dependencies used by the tutorial: skillware, ollama, pyyaml, and python-dotenv. The exact install commands and package compatibility can depend on the current releases and the tutorial’s code; verify the package instructions for the versions you install.
The sample configuration uses a .env file for the Gmail address and app password, a YAML address book for resolving contacts, and JSON for persona and behavioral instructions. Do not copy credentials into model-visible text or store them in conversation memory. The tutorial says its app password stays in .env and is not sent to the model; that is a design claim about the example, not independent verification of runtime behavior.
Gmail authentication is account-dependent
The tutorial’s IMAP/app-password route is not the universal current default. Google says personal Gmail IMAP access is always on starting January 2025, so there is no longer an IMAP setting to toggle. Google recommends “Sign in with Google” when a mail client supports it and says, “App passwords aren’t recommended and are unnecessary in most cases.” See Google Account Help: Sign in with app passwords (accessed October 7, 2026).
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App passwords require 2-Step Verification. Google says they may not be available for accounts using only security keys, managed work or school accounts, or Advanced Protection; changing the Google Account password revokes existing app passwords. If the selected Skillware handler only accepts an app password, first verify that your account permits one and check for an OAuth-capable alternative before connecting a sensitive mailbox.
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Store conversation memory in SQLite
The tutorial uses Python’s built-in sqlite3 module to store conversation turns and embedding vectors. It generates embeddings locally with nomic-embed-text, computes cosine similarity in Python, and retrieves a small number of relevant memories alongside a recent-history window.
This approach gives the agent a searchable record of selected prior context without requiring a separate vector database. Its usefulness depends on what text you store, how the embedding model represents it, the similarity threshold, and how much retrieved material you include in the prompt. Irrelevant or stale memories can mislead the model; retrieval is not guaranteed recall, and the database does not automatically expire or encrypt data.
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Choose what to retain deliberately. Avoid saving unnecessary sensitive content, define a retention and deletion policy, and protect the database file with the operating system’s access controls or suitable disk encryption. A recent-history limit bounds the immediate chat context, but does not by itself remove older rows from SQLite.
Connect Skillware tools without handing the model authority
Skillware’s Gmail handler provides deterministic mail operations over IMAP and SMTP. The tutorial’s loop loads the office/gmail_handler skill, converts its manifest to the model’s tool schema, sends the request and context to Ollama, then handles proposed tool calls in application code. Skillware’s documentation excerpt also emphasizes treating inbound messages and attachments as untrusted content; that is a sound boundary for this design.
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- Mark email content as untrusted data in the prompt and instruct the model not to follow instructions found inside messages or attachments.
- Validate the requested operation, recipient, and message fields in deterministic code rather than trusting the model’s interpretation.
- For send and reply, display the exact recipients and complete body, then wait for a separate explicit approval action before execution.
- Return tool results to the model only as data, and save the interaction according to your retention policy.
Prompt wording and an “untrusted content” marker are defense layers, not proof against prompt injection. A malicious message can still try to manipulate the model. The approval gate must be enforced by code outside the model’s control; do not treat a model-generated claim that approval was granted as approval.
Operational checks before connecting a real inbox
- Confirm which Ollama model is running locally and whether any cloud model or adapter is enabled.
- Test memory retrieval with expected and unrelated requests; confirm that the agent receives only the intended context.
- Exercise tool calls against a disposable account, including malformed recipients and unexpected message content.
- Verify that no send or reply can execute without the approval interface and that the preview matches the final action.
- Check file permissions and backups for the SQLite database, configuration files, and credential storage.
- Confirm that logs contain useful action metadata but no passwords, tokens, or unnecessary message contents.
Limits to keep in mind
A local model reduces exposure to an inference provider when inference is genuinely local, but it cannot make the mail provider, the machine, or the agent’s decisions risk-free. Tool-call behavior can vary by model and version; memory can retrieve the wrong context; and prompt defenses cannot make hostile inbound email trustworthy. Keep the agent’s permissions narrow and make the application—not the model—the final authority over mail actions.
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