「TL;DR」
This guide breaks down an open-source Email Agent prototype that integrates IMAP synchronization, a local SQLite cache, a lightweight Bun backend with WebSocket streaming, and an LLM-driven agent that calls tools (e.g., search_emails) to retrieve and act on mailbox data. The design emphasizes low latency, local data control, clear tool interfaces, and a pragmatic path from prototype to production.

Executive summary

Modern knowledge workers need AI assistance for routine email tasks — triage, summarization, and drafting — but often cannot or will not send their entire mailbox to a third-party cloud service. The Email Agent prototype we analyze here (Thariq’s open-source project) demonstrates a pragmatic alternative: keep primary data local, expose small, auditable tool interfaces to an LLM, and deliver a streaming, low-latency UI that feels responsive.

This document is a technical blueprint for engineers and technical product leads who want to:

  • understand the architecture and rationale behind a local-first email assistant;
  • implement the core components (IMAP sync → SQLite → Bun → WebSocket → LLM tool calls); and
  • make informed decisions about production hardening, security, and scaling.

All recommendations and analysis below are grounded in the prototype’s implementation and design choices.

System overview — four layers

At a high level the system decomposes into four cooperating layers:

  1. 「Data layer — IMAP synchronization + local SQLite cache.」 Mail metadata and short snippets are stored locally for fast retrieval and privacy control.
  2. 「Backend layer — Bun server + WebSocket streaming.」 A lightweight server orchestrates syncs, executes tool calls, and forwards streaming model outputs to the client.
  3. 「AI layer — the agent + tool calling.」 The LLM (via Claude Code SDK in the prototype) decides when to call defined tools (for example search_emails) and merges structured tool outputs into natural language answers or drafts.
  4. 「Frontend layer — minimal email UI + streaming chat pane.」 The client renders messages and model output in real time, with controls for adoption, editing, and sending drafts.

This decomposition separates retrieval/IO responsibilities (tools) from reasoning and generation (the model), yielding a system that is auditable, testable, and easier to secure.

Data layer: IMAP → SQLite (design goals and implementation)

Goals

  • Fast, deterministic retrieval for common queries.
  • Minimal exposure of sensitive content.
  • Simple, auditable synchronization and deduplication.

Recommended minimal schema

Use a compact schema that captures the metadata and the essential context the agent needs. A minimal emails table includes:

  • uid (primary key — IMAP UID or Message-ID)
  • thread_id
  • from
  • to (or a separate recipients table if needed)
  • subject
  • date (ISO8601)
  • snippet (first N characters / summary)
  • flags (read/seen/labels)
  • attachments_meta (JSON)
  • fetched_at (timestamp)

A sync_log table should record each sync run (start, end, counts, errors). For multi-account setups, an accounts table stores per-account metadata and a secure reference to credentials.

Sync strategy (practical)

  • 「Incremental by UID/Message-ID.」 Use the IMAP UID (or Message-ID) as an idempotent primary key to avoid duplicates.
  • 「Head-first fetch.」 Fetch headers and a short snippet quickly; defer full body pulls to on-demand workflows or asynchronous workers to save bandwidth and local storage.
  • 「Backoff and rate limits.」 Respect provider rate limits (Gmail/Exchange); implement exponential backoff for transient errors.
  • 「Selective retention.」 Keep only the metadata and an excerpt by default; allow users to opt in to full-body storage for selected messages.
  • 「Backup.」 Periodically snapshot the SQLite file to an encrypted backup (or push to a secure object store).

Why SQLite?

SQLite is a pragmatic choice for single-user or small-team prototypes: no DB server needed, ACID semantics, easy local backups. If team or scale requirements arise, plan migration paths (Postgres, vector DB for embeddings, or a managed store).

Backend layer: Bun + WebSocket (responsibilities and patterns)

Why Bun?

Bun is used in the prototype for rapid TypeScript-first iteration: quick startup, good TypeScript ergonomics, and a small footprint. The backend responsibilities include:

  • orchestrating IMAP sync runs (cron or on-demand triggers),
  • exposing tool endpoints (e.g., search_emails) that the agent can call,
  • running LLM sessions and streaming partial outputs to connected clients, and
  • handling draft send requests (SMTP) with necessary confirmations.

WebSocket streaming pattern

A robust streaming pattern is essential to achieve low perceived latency:

  1. Client opens a WebSocket and authenticates.
  2. Client sends a natural language instruction.
  3. Server invokes the model session. As the model streams tokens, server forwards them as partial messages.
  4. If the model requests a tool call, server performs the tool (e.g., run SQL against SQLite) and sends the tool_response back to the model, then the model continues streaming.
  5. On finalization, server sends a final message including references (email UIDs) to support follow-up actions.

Key details: carry sequence numbers per chunk, tag messages with session and request IDs, and buffer recent messages for reconnection scenarios.

Concurrency & resource management

  • Use a worker pool for model invocations and cap concurrent calls.
  • Queue excess requests and provide status endpoints.
  • Log tool calls, model latencies, and errors for observability.

Error handling

  • Return structured errors to the model when tool calls fail, so the model can choose to retry, escalate, or ask the user for clarification.
  • Implement circuit breakers around external systems (IMAP, SMTP, model endpoints).

AI layer: agent design and tool calling

Core idea: tools as deterministic capabilities

Instead of embedding retrieval logic inside prompts (which is brittle), define explicit tools the model may call. Tools return structured JSON; the model consumes that and generates the next response. This design provides:

  • 「Determinism」 — tool outputs are auditable.
  • 「Separation of concerns」 — retrieval and IO are handled by code, reasoning/generation by the model.
  • 「Security controls」 — tools can redact or limit the fields they return.

Typical tool: search_emails

A concise contract example:

Input:

{
  "query": "string",
  "start_date": "string (ISO8601) optional",
  "end_date": "string (ISO8601) optional",
  "sender": "string optional",
  "limit": 10
}

Output:

{
  "results": [
    {
      "uid": "string",
      "thread_id": "string",
      "from": "string",
      "subject": "string",
      "date": "string",
      "snippet": "string"
    }
  ]
}

Design guidelines:

  • Limit returned fields to metadata and snippets; require explicit user consent to release full bodies.
  • Make outputs predictable (consistent shapes) so models can reliably parse them.
  • Provide examples in the system prompt that demonstrate tool usage and expected responses.

Conversation flows

  • 「Search & summarize」: Model sees user intent → calls search_emails → receives results → produces a concise summary and suggested actions (reply drafts, follow-ups).
  • 「Draft reply」: Model fetches thread context via search_emails using a UID, then generates a draft. The UI allows the user to adopt, edit, or send the draft.
  • 「Follow-ups」: Keep references (UIDs) in context so the model can perform stateful follow-up actions like “send a follow-up in 3 days”.

Context management

  • Maintain a sliding window of recent interactions and tool outputs.
  • Persist session metadata and tool results for auditability and to support resuming after disconnects.
  • Avoid overloading the model with excessive raw text — summarize or provide snippets when feasible.

Frontend: UX patterns and engineering considerations

Minimal but effective UI

  • 「Left pane」: folders and thread list (subject, sender, snippet, timestamp).
  • 「Center」: thread viewer with messages expandable to full view.
  • 「Right / bottom」: AI chat pane — input, streaming response area, and action buttons (Summarize, Draft Reply, Send).

Streaming UX

  • Render partial tokens as they arrive to emulate natural typing.
  • Provide explicit UI states for “searching”, “fetching thread”, or “generating draft”.
  • Allow inline acceptance or inline editing of drafts: e.g., “Adopt draft”, “Edit and send”, “Regenerate”.

Safety and confirmation

  • For send actions, require a clear confirmation modal that displays recipients and full content. Include an option to redact or exclude attachments.
  • Surface which data came from local storage vs. what was reconstructed by the model (use UID references).

Security, privacy and compliance (mandatory engineering work)

Principles

  • 「Least privilege」 for all credentials and access.
  • 「Transparency」: present users with what data is used and where it’s transmitted.
  • 「Auditability」: log model requests, tool invocations, and send actions.

Practical controls

  • 「Credential storage」: store IMAP/SMTP credentials in OS keychain or an encrypted file with a passphrase.
  • 「Transmission policy」: by default only send snippets/metadata to external model endpoints; the full body is transmitted only on explicit user consent.
  • 「Redaction」: implement redaction rules to remove PII or sensitive fields from tool outputs unless explicitly permitted.
  • 「Audit log」: store an immutable log of model invocations and send actions, with references to UIDs and timestamps.
  • 「Data retention」: keep clear policies and UI controls for retention and deletion of local cache.
  • 「Access controls」: for multi-user deployments, implement RBAC and tenant isolation.

These measures ensure the system can be operated without undermining privacy guarantees while remaining functional and debuggable.

Production hardening checklist

Priority tasks before considered production-ready:

  1. 「Credential encryption & secret rotation.」 Use a KMS or OS keychain; plan for rotation.
  2. 「Auditability.」 Record model requests, tool inputs/outputs, and SMTP sends with UIDs.
  3. 「Monitoring.」 Instrument sync latency, model latency, WebSocket health, and error rates.
  4. 「Data backup & restore.」 Snapshot SQLite (or migrate to a managed DB) and test restores.
  5. 「Scaling plan.」 Prepare for migration to Postgres or a vector store for semantic search and concurrency.
  6. 「RBAC & multi-account support.」 If used by teams, implement per-account isolation and access rules.
  7. 「Testing.」 Unit tests for sync idempotency, schema contracts for tools, and end-to-end tests simulating IMAP provider behaviors.
  8. 「Operational playbooks.」 Runbooks for sync failures, model outages, and credential compromise.

Performance and scaling considerations

Where bottlenecks appear

  • 「IMAP sync」 on large mailboxes (initial bulk ingest). Mitigate via batched, incremental imports and selective full-body fetching.
  • 「SQLite limitations」 under concurrent read/write loads. For read-heavy patterns, consider read replicas or a move to Postgres.
  • 「Model latency and concurrency.」 Use a worker pool, and expose a queue with backpressure. Precompute summaries for hot threads where appropriate.

Semantic search and vectors

If you require semantic, “similar message” retrieval, introduce a vectorization pipeline:

  1. selectively vectorize snippets or user-consented full bodies,
  2. store vectors in a vector DB (Milvus, Faiss, Pinecone), and
  3. use nearest neighbor retrieval as a tool that the model can call.

Note the tradeoffs: vectors increase storage and raise privacy questions because dense representations can be reversed in some circumstances — apply appropriate consent and access controls.

Risks and mitigations

Risk: model hallucination about message contents

「Mitigation:」 Always include UIDs and tool output excerpts in model responses when making factual claims. If evidence is lacking, have the model explicitly state that it found no direct evidence.

Risk: accidental data exposure via tool outputs

「Mitigation:」 restrict tool outputs to snippets by default and require explicit elevated consent for full bodies. Log all extractions.

Risk: synchronization drift / duplicates

「Mitigation:」 base idempotency on IMAP UID/Message-ID and maintain a sync log with checkpoints.

Risk: provider limits (Gmail/Exchange)

「Mitigation:」 respect rate limits, batch operations, and implement exponential backoff.

Risk: scaling issues with Bun/SQLite

「Mitigation:」 use Bun for prototype and consider migrating the backend to a platform with well-understood operational tooling for production (Node.js + Postgres or a managed stack).

Implementation roadmap — step-by-step

Phase A — prototype (goal: working demo)

  1. Implement incremental IMAP sync script that writes headers and snippets to SQLite (UID as primary key).
  2. Stand up a Bun server serving a static frontend and exposing a WebSocket endpoint.
  3. Implement a search_emails tool that queries SQLite and returns structured JSON.
  4. Integrate the LLM SDK and enable streaming output to WebSocket.
  5. Implement a minimal UI to display streaming responses and allow sending (stubbed) drafts.

Phase B — harden (goal: robust, secure single-user deployment)

  1. Replace plaintext credential storage with OS keychain or encrypted storage.
  2. Add audit logs and local encrypted backups.
  3. Add worker pool, concurrency limits, and queueing.
  4. Add end-to-end tests: sync idempotency, tool contracts, and error injections.

Phase C — scale (goal: multi-user, production)

  1. Migrate SQLite to a managed DB (Postgres) for concurrency.
  2. Introduce vector DB for semantic search if needed.
  3. Implement multi-tenant isolation, RBAC, and policy controls.
  4. Add monitoring and alerting dashboards; codify runbooks.

Example operational scenarios

Scenario 1 — “Summarize my manager’s threads from last week”

Flow:

  1. User instructs agent: “Summarize threads with [manager] last week.”
  2. Model calls search_emails(query='manager', start_date=YYYY-MM-DD, end_date=YYYY-MM-DD).
  3. Backend returns structured results (UIDs + snippets).
  4. Model synthesizes a concise summary and suggests action items; the UI displays the summary with links to UIDs for inspection.

Scenario 2 — “Draft reply to thread UID 12345”

Flow:

  1. Agent fetches full context for UID 12345 (subject to consent).
  2. Model generates a draft and streams it to the UI.
  3. User edits or accepts; on send, server performs SMTP send with an audit log entry.

These flows illustrate how tool calling keeps retrieval precise and auditable while leaving generation to the model.

Conclusion and recommended next steps

Thariq’s Email Agent presents a disciplined, practical approach to building an AI-assisted email workflow that preserves user control over data. Its core strengths:

  • 「Local-first data architecture」 (IMAP → SQLite) for fast retrieval and privacy.
  • 「Agent + tool pattern」 that separates deterministic IO from generative reasoning.
  • 「Streaming UX」 via WebSocket that reduces perceived latency and improves interactivity.

If you are starting an implementation path, choose one of these immediate actions:

  • build a minimal prototype (IMAP sync + SQLite + search_emails) to validate retrieval and tool contracts; or
  • implement the streaming backend and a simple UI to test perceived latency and UX; or
  • harden credential storage and audit logging if you plan to run the prototype on sensitive mailboxes.

If you want, I can immediately generate any of the following, ready to use:

  • a runnable prototype (sync script, Bun server scaffold, SQLite schema, and search_emails SQL example),
  • concrete code snippets for IMAP incremental sync and tool-response serialization, or
  • a production checklist tailored to your deployment environment.