WorkDiary Agent

A LangGraph-based multi-node agent that turns a rough, spoken-style work description into a polished, manager-friendly daily report — in under 5 minutes.

The agent structures your input, pulls in today's git commits as context, selects the right report template, drafts and refines the content from a manager's perspective, then pauses for your review. You can accept, edit, or request revisions (up to 3 rounds) before the report is saved and exported as a markdown file.

Built as a portfolio project demonstrating: LangGraph state machine design, Human-in-the-Loop interrupt/resume with SQLite persistence, multi-node Pydantic structured outputs, and Streamlit UI integration.

---

Demo

User input: "今天修了个 bug，跑了个数据，开了两个会"

→ extract:  { tasks: ["修复登录bug"], outputs: ["数据报告"], blockers: [] }
→ enrich:   git log pulled: "fix(auth): resolve token expiry edge case"
→ template: 技术型
→ draft:    structured first draft
→ polish:   "修复登录模块 Token 过期边界问题，影响约 3% DAU 的登录成功率..."

[HITL pause — user reviews in Streamlit]

User: "加上数据影响的百分比"
→ revise → polish (round 2)

User: approve

→ saved to history.db
→ exported to exports/daily_report_2026-05-06.md

---

Prerequisites

• Python 3.10+
• An LLM API key — pick one backend:
  • SiliconFlow (domestic, free credits, recommended for China) — https://cloud.siliconflow.cn/
  • Anthropic (direct API key or corporate proxy)

---

Setup

1. Clone the repo

git clone https://github.com/xyma2003/workdiary-agent.git
cd workdiary-agent

2. Create a virtual environment

# Option A: venv
python3 -m venv .venv
source .venv/bin/activate       # macOS/Linux
# .venv\Scripts\activate        # Windows

# Option B: conda
conda create -n workdiary python=3.10
conda activate workdiary

3. Install dependencies

pip install -r requirements.txt

4. Configure API credentials

The app supports two LLM backends: SiliconFlow/OpenAI-compatible (default, domestic-friendly) and Anthropic (overseas).

Option A — SiliconFlow / OpenAI-compatible (default, recommended for domestic use):

# .env
OPENAI_API_KEY=sk-...                    # SiliconFlow API key
OPENAI_API_BASE=https://api.siliconflow.cn/v1
OPENAI_MODEL=Qwen/Qwen3-32B              # or deepseek-ai/DeepSeek-V3

When OPENAI_API_KEY is set, make_llm() in utils.py returns a ChatOpenAI routed through OPENAI_API_BASE. Get a SiliconFlow key at https://cloud.siliconflow.cn/ (free credits available).

Option B — Direct Anthropic API key (overseas):

# .env
ANTHROPIC_API_KEY=sk-ant-api03-...

Option C — Corporate/internal proxy:

# .env
ANTHROPIC_BASE_URL=https://your-proxy-base-url
ANTHROPIC_AUTH_TOKEN=your-auth-token
# Optional: extra headers required by your proxy, newline-separated "Key: Value" pairs
ANTHROPIC_CUSTOM_HEADERS=X-Custom-Header: value

If OPENAI_API_KEY is set, Option A takes precedence. Otherwise falls back to Anthropic (Option B or C). The app auto-loads .env via python-dotenv (with override=True, so .env wins over stale shell vars). No manual export needed.

5. Run

streamlit run app.py

Open http://localhost:8501 in your browser.

---

Usage

1. Enter a rough description of your day (口语化输入, any style)
2. Optionally paste a git repo path to pull in today's commits as context
3. Optionally paste raw data/metrics for the agent to extract and include
4. Click 生成日报 — the agent runs through all nodes and pauses for your review
5. Read the draft, edit inline if needed, then 接受 or 修改（up to 3 revision rounds）
6. The final report is saved to history and exported as a markdown file in exports/
7. Past reports appear in the 历史记录 sidebar tab

---

How It Works

User input
    │
    ▼
extract ── structured extraction (tasks / outputs / blockers / progress)
    │
    ▼
enrich ── git log (today's commits) + LLM extraction of data metrics
    │
    ▼
route_template ── TemplateRouterAgent subgraph (技术型 / 业务型 / 混合型)
    │
    ▼
draft ── template-specific first draft
    │
    ▼
polish ── rewrite from manager's perspective, emphasise outcomes
    │
    ▼
review ── interrupt() → Streamlit HITL pause
    │
  ┌─┴──────────────┐
approve           revise (up to 3×)
  │                 │
save            revise_node → polish (loop)
  │
exports/ + history.db

Key design decisions:

Decision	Rationale
"Manager's perspective" as a separate polish node	Decouples tone/framing from content generation; polish can be reused across templates
TemplateRouterAgent as a subgraph	Two-step chain-of-thought (analyse → decide) improves classification accuracy; fully isolated from main graph
interrupt() inside review node	Gives fine-grained control (pause mid-node with context payload); more flexible than compile-level interrupt_before
Two SQLite files	graph_state.db is owned exclusively by LangGraph's SqliteSaver; mixing app data into it breaks serialisation
Revision limit (3×)	Prevents infinite HITL loop; enforced by three independent guards (safe field access, conditional edge, approve shortcut)

---

Project Structure

workdiary-agent/
├── app.py                  # Streamlit UI — generation page + history page
├── requirements.txt
├── workdiary_agent/
│   ├── graph.py            # StateGraph assembly, conditional edges, checkpointer init
│   ├── state.py            # AgentState TypedDict + StructuredInfo Pydantic model
│   ├── utils.py            # make_llm() factory + validate_repo_path()
│   ├── nodes/
│   │   ├── extract.py      # Structured extraction via with_structured_output
│   │   ├── enrich.py       # Git log reader + LLM metric extraction
│   │   ├── route_template.py  # Calls TemplateRouterAgent subgraph
│   │   ├── draft.py        # Template-specific first draft (3 templates)
│   │   ├── polish.py       # Manager-perspective rewrite (accepts revision feedback)
│   │   ├── review.py       # HITL interrupt node
│   │   ├── revise.py       # Increments revision_count
│   │   └── save.py         # Persist to history.db + export markdown
│   ├── router/
│   │   └── agent.py        # TemplateRouterAgent subgraph (analyse → decide)
│   └── storage/
│       ├── sqlite.py       # history.db read/write API
│       └── export.py       # Markdown file export to exports/
├── scripts/                # Manual integration test scripts
│   ├── test_hitl_cycle.py
│   └── test_skeleton.py
├── tests/                  # pytest unit tests (5 phases)
│   ├── test_graph_skeleton.py
│   ├── test_phase02_llm_nodes.py
│   ├── test_phase03_enrichment.py
│   ├── test_phase04_hitl.py
│   └── test_phase05_storage.py
└── exports/                # Auto-created; exported markdown reports

---

Running Tests

# Fast unit tests only (mocked LLM calls, ~10 seconds)
python -m pytest tests/ -m "not integration" -v

# Full suite including live LLM calls (~3–5 minutes)
python -m pytest tests/ -v

---

Tech Stack

Component	Library	Version
Agent orchestration	LangGraph	1.1.9
LLM (default)	SiliconFlow / OpenAI-compatible via langchain-openai	—
LLM (alt)	Claude via langchain-anthropic	1.4.1
Structured outputs	Pydantic	2.x
HITL persistence	SQLite (langgraph-checkpoint-sqlite)	3.0.3
UI	Streamlit	1.56.0
Git context	GitPython	3.1.47

---

Resume Bullet

Built a LangGraph agent that converts unstructured daily work notes into polished manager-facing reports, featuring a Human-in-the-Loop review loop with full state persistence across Streamlit reruns, a two-step template classification subgraph, and git commit enrichment. (LangGraph · Claude API · Pydantic · Streamlit · SQLite)