job-hunt-agent 0.1.0

Job hunting + tailored CV generation agent built with LangChain deepagents

💼 Job Hunt Agent

An end-to-end job search + tailored CV generation agent built with LangChain deepagents:

1. builds your master profile (profile/profile.md) from an uploaded CV,
2. searches postings on LinkedIn/Indeed and more (JobSpy scraper — no API key needed; optional JSearch API),
3. ranks them against your profile with honest fit scores,
4. for every job you pick, a cv-tailor subagent writes a job-specific CV + cover letter (XYZ-formula bullets, ATS-friendly) into applications/,
5. starts an application only with your approval (human-in-the-loop interrupt).

What is deepagents and how is it used here?

Deepagents is an agent harness that ships with planning (write_todos), a virtual file system, subagent delegation (task tool) and human approval (interrupts). The mapping in this project:

Deepagents feature	Here
create_deep_agent(tools=...)	search_jobs, get_job_details, read_profile, submit_application
subagents=[...]	cv-tailor: writes the CV/cover letter in an isolated context
interrupt_on={...} + checkpointer	submit_application never runs without human approval
write_todos (built-in)	The agent plans multi-job tasks itself
Model	Anthropic, OpenRouter or OpenAI — whichever key you set

Setup

git clone https://github.com/EceDalpolat/job-hunt-agent && cd job-hunt-agent
uv sync                      # or: pip install -e .
cp .env.example .env         # fill in ONE of the API keys (see below)

• For the model, set one of ANTHROPIC_API_KEY, OPENROUTER_API_KEY or OPENAI_API_KEY (priority in that order). Override the default model with JOB_AGENT_MODEL — e.g. openai/gpt-5.1 on OpenRouter.
• Job search needs no key: JobSpy scrapes LinkedIn + Indeed directly. LinkedIn rate-limits aggressively; if it blocks you, Indeed results keep coming. Setting RAPIDAPI_KEY switches to the JSearch API instead. If both fail, the agent falls back to 6 bundled sample postings.

Create your profile

uv run job-agent-profile path/to/your-cv.pdf --notes "linkedin.com/in/you, target: AI engineer roles"

This extracts the text from your CV (pdf/docx/md/txt), normalizes it with the LLM into profile/profile.md, and that file becomes the single source of truth — the agent never puts anything on a CV that is not in it. Review it before applying anywhere. See profile/profile.example.md for the expected shape. (profile/profile.md is gitignored — your personal data stays local.)

Run

uv run job-agent                          # terminal CLI
uv run streamlit run job_agent/ui.py     # web UI

In the Streamlit UI: upload your CV in the sidebar to build the profile, chat to search postings ("find AI/LLM engineer jobs in Istanbul, rank the top 5"), say "prepare CVs for 1 and 3", approve applications with ✅/❌ buttons, download the generated CV/cover letters from the sidebar, and watch live token/cost metrics.

Example CLI session:

👤 > find python backend jobs that fit me
🤖 (reads your profile, searches postings, returns a fit-scored list)

👤 > I want to apply to 1 and 3
🤖 (cv-tailor subagent writes a CV + cover letter per job into
    applications/2026-07-06-healthtech-labs-senior-backend.../)

⚠️  Pending approval:
   Tool : submit_application
   Args : {'job_id': 'mock-001', ...}
   Approve? [y/n]: y
🤖 Application logged, apply page opened in your browser.
💰 7 LLM calls • 41,203 in + 2,841 out tokens • ~$0.16

Architecture

                    ┌─────────────────────────────────────────────┐
 Entry points       │  main.py (CLI)         ui.py (Streamlit)    │
                    │  setup_logging() + UsageTracker + thread_id │
                    └────────────────┬────────────────────────────┘
                                     │ agent.invoke(message, callbacks=[tracker])
                    ┌────────────────▼────────────────────────────┐
 Orchestration      │  agent.py → build_agent()                   │
                    │  _resolve_model(): ANTHROPIC > OPENROUTER > │
                    │                    OPENAI (from config.py)  │
                    │  system prompt: prompts/system.md           │
                    └───────┬──────────────────────┬──────────────┘
                            │ tool calls           │ task (subagent)
                ┌───────────▼──────────┐  ┌────────▼───────────────┐
 Tools          │ tools.py             │  │ cv-tailor subagent     │
                │ search_jobs:         │  │ prompt: cv_tailor.md   │
                │  JSearch>JobSpy>mock │  │ reads profile + the    │
                │ get_job_details      │  │ references/ guides,    │
                │ read_profile         │  │ writes XYZ-format CV,  │
                │ submit_application ←─┼──│ save_application_pkg   │
                │  (human approval!)   │  └────────────────────────┘
                └───────────┬──────────┘
                ┌───────────▼──────────────────────────────────────┐
 Data & output  │ schemas.py (Job, UsageRecord)   config.py (paths)│
                │ applications/  logs/job_agent.log  logs/usage.jsonl│
                └──────────────────────────────────────────────────┘

Flow of one "apply to 1 and 3" turn:

1. main.py/ui.py passes the user message to agent.invoke(...); the UsageTracker callback captures the tokens of every LLM call (main agent AND subagents).
2. The main agent (prompt: prompts/system.md) calls search_jobs — tools.py tries JSearch API → JobSpy scraper → sample data in order, normalizes results to schemas.Job and caches them in _JOB_CACHE.
3. For each selected job the main agent spawns the cv-tailor subagent via the task tool (prompt: prompts/cv_tailor.md). The subagent reads the profile and the writing guides in references/, writes the CV/cover letter and saves them with save_application_package under applications/.
4. submit_application pauses on interrupt_on; y/n in the CLI or ✅/❌ in the UI resumes the flow with Command(resume={"decisions": ...}).
5. Every step is logged to logs/job_agent.log; every LLM call's tokens/cost go to logs/usage.jsonl (rates in config.PRICING — approximate, keep updated).

File map

job_agent/
├── config.py          # paths, model defaults, pricing table (single place)
├── schemas.py         # Pydantic: Job, UsageRecord, ApplicationLogEntry
├── prompts/           # system.md, cv_tailor.md, profile_builder.md (out of code)
├── logging_conf.py    # logs/job_agent.log (rotating) + console warnings
├── usage.py           # UsageTracker callback → tokens/cost → usage.jsonl
├── agent.py           # deep agent wiring, model selection, subagent, HITL
├── tools.py           # search (JSearch>JobSpy>sample), profile, references, save, apply
├── profile_builder.py # CV file → LLM → profile/profile.md
├── main.py            # interactive CLI
├── ui.py              # Streamlit chat UI (approvals, downloads, cost metrics)
└── sample_jobs.py     # sample postings (last-resort fallback)
profile/               # profile.md (gitignored) + profile.example.md
references/            # CV writing guides (XYZ formula, Harvard guide, ...)
logs/                  # job_agent.log + usage.jsonl (gitignored)
applications/          # generated packages (gitignored)

Honesty notes

• No automated form submission: neither the job boards' APIs nor their terms of service allow auto-filling application forms. The agent prepares the package, logs the application and opens the apply page; you press the final "Submit".
• No fabrication on CVs: the subagent is instructed to use only the real facts in profile/profile.md — still, read everything before sending it.