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DeepTutor — Agent-Native Architecture

Overview

DeepTutor is an agent-native intelligent learning companion organized around a two-layer plugin model — single-shot Tools invoked by the LLM, and multi-stage Capabilities that take over a turn — exposed through three entry points: CLI, WebSocket API, and Python SDK. All three enter the durable turn application service before the shared turn engine routes a normalized context to the selected capability.

Architecture

Entry Points:  CLI (Typer)  |  WebSocket /ws  |  Python SDK
                    ↓                   ↓                   ↓
              ┌─────────────────────────────────────────────────┐
              │          TurnApplicationService                 │
              │   persists, coordinates, and replays turns      │
              └──────────────────────┬──────────────────────────┘
                                     ↓
              ┌─────────────────────────────────────────────────┐
              │       TurnEngine → ChatOrchestrator              │
              │   routes UnifiedContext → selected Capability    │
              │   (defaults to `chat`)                           │
              └──────────┬──────────────┬───────────────────────┘
                         │              │
              ┌──────────▼──┐  ┌────────▼──────────┐
              │ ToolRegistry │  │ CapabilityRegistry │
              │  (Level 1)   │  │   (Level 2)        │
              └──────────────┘  └────────────────────┘

TurnApplicationService owns durable turn state and replay through the session store and runtime coordinator. Each execution uses a per-turn StreamBus; the orchestrator emits events, the turn runtime persists them, and adapters replay them to consumers. Runtime settings live in data/user/settings/*.json — project-root .env files are intentionally ignored.

Level 1 — Tools

Single-function tools the LLM picks on demand. Seven user-toggleable tools surface in /settings/tools:

Tool Description
brainstorm Breadth-first idea exploration with rationale
web_search Web search with citations
paper_search arXiv preprint search
reason Dedicated deep-reasoning LLM call
geogebra_analysis Analyze math images into GeoGebra commands
imagegen Generate images
videogen Generate videos

USER_TOGGLEABLE_TOOL_NAMES in deeptutor/tools/builtin/__init__.py is the authoritative toggle list. Other built-ins are context-gated or capability-owned: CONFIGURABLE_BUILTIN_TOOL_NAMES declares the context-gated surface, while deeptutor/agents/_shared/tool_composition.py owns the mount rules (ToolMountFlags) and the always-available workspace tools. Examples include rag, memory and notebook tools, read_skill, deferred MCP/CLI tools, exec, ask_user, and mastery navigation. --tool selects from the user-toggleable whitelist; it does not bypass context or capability gates.

Level 2 — Capabilities

Multi-stage pipelines that own the turn:

Capability Stages
chat exploring → responding (single agentic loop, default)
ask_questions responding (chat loop forced through ask_user)
deep_solve responding (chat loop + solve planning tools)
deep_question ideation → generation
deep_research rephrasing → decomposing → researching → reporting
visualize analyzing → generating → reviewing (SVG / Chart.js / Mermaid / HTML; or routes to Manim sub-stages via render_type)
math_animator concept_analysis → concept_design → code_generation → code_retry → summary → render_output
mastery_path responding (Guided Learning — chat loop + mastery tools, gated per topic type)
immersive_reading responding (document- or timestamp-grounded media reading loop)
course_study responding (course-state sensing and hand-off loop)

All capabilities converge on emit_capability_result() in deeptutor/capabilities/_shared.py so every turn emits the same envelope (response payload + cost_summary from UsageTracker). Status copy and prompts are i18n'd via capabilities/prompts/{en,zh}/<name>.yaml.

CLI Usage

# Install
pip install deeptutor      # Full app (CLI + Web/API + packaged Web assets)
pip install deeptutor-cli  # CLI-only

# Run any capability
deeptutor run chat "Explain Fourier transform"
deeptutor run deep_solve "Solve x^2=4" -t rag --kb my-kb
deeptutor run visualize "Animate sine wave" --config render_mode=manim_video

# Interactive REPL
deeptutor chat
# (inside the REPL: /regenerate or /retry re-runs the last user message)

# Partners (IM-connected companions)
deeptutor partner list

# Knowledge bases, memory, server
deeptutor kb list
deeptutor kb create my-kb --doc textbook.pdf
deeptutor memory show
deeptutor serve --port 8001       # API server only
deeptutor start                   # backend + frontend together

Key Files

Path Purpose
deeptutor/runtime/orchestrator.py ChatOrchestrator — unified entry
deeptutor/runtime/launcher.py Backend + frontend lifecycle / port discovery
deeptutor/runtime/registry/ Tool + Capability registries
deeptutor/runtime/bootstrap/builtin_capabilities.py Built-in capability class paths
deeptutor/services/config/runtime_settings.py JSON settings + process-env overrides
deeptutor/services/subagent/ Local/remote agent connectors; register each backend in registry.py and its model options in models.py (Grok CLI uses native streaming-json)
deeptutor/core/stream.py, deeptutor/runtime/stream_bus.py StreamEvent protocol + async fan-out
deeptutor/core/tool_protocol.py BaseTool + ToolDefinition
deeptutor/core/capability_protocol.py TurnCapability + CapabilityManifest
deeptutor/core/context.py UnifiedContext dataclass
deeptutor/tools/builtin/__init__.py All built-in tool wrappers
deeptutor/capabilities/ Built-in capability implementations
deeptutor/app.py DeepTutorApp — Python SDK facade
deeptutor_cli/main.py Typer CLI entry point
deeptutor/api/routers/unified_ws.py Unified WebSocket endpoint

Dependency Layers

Public install paths and source extras are defined in pyproject.toml. Requirements files mirror the same dependency groups for Docker/CI installs.

pip install deeptutor      — Full app (CLI + Web/API + packaged Web assets)
pip install deeptutor-cli  — CLI-only (LLM + RAG + providers + document parsing)
pip install -e .           — Source install for development

Source extras (.[ extra ], defined in pyproject.toml):
.[cli]            — CLI-only dependency set
.[server]         — Web/API server dependencies
.[partners]       — Partner channel SDKs  (legacy alias: .[tutorbot])
.[matrix]         — Matrix channel for Partners (matrix-nio; needs libolm)
.[matrix-e2e]     — Matrix with end-to-end encryption (matrix-nio[e2e])
.[math-animator]  — Manim addon (powers `visualize` Manim renders + `deeptutor run math_animator`)
.[dev]            — Test / lint tooling
.[all]            — Everything above