Job Application Agent

A powerful, multi-agent automation system for job applications, leveraging Crew AI to orchestrate specialized agents that extract, analyze, and summarize job postings, tailor resumes, and prepare interview materials. Semantic search over your GitHub projects runs on a Weaviate vector store with Voyage AI embeddings and reranking, and the system features custom tools for LinkedIn and GitHub data extraction plus Serper-backed web research.

This project also includes a Full-Stack Web App version with a FastAPI backend (using Google Cloud SQL MySQL and GCS storage) and a React + Vite frontend that displays live agent execution progress via Server-Sent Events (SSE).

The final Word (.docx) resume is rendered by a separate live microservice deployed on AWS (ECS Fargate behind an ALB) — its source is available at arijitde92/resume_docx_generator. This backend calls that service over HTTP and converts the returned .docx to PDF locally (LibreOffice headless) for the in-browser preview. See System Design for the full picture.

📦 Project Structure

Job_Application_Agent/
├── backend/ # FastAPI backend + CrewAI pipeline (uv-managed)
│ ├── app/
│ │ ├── api/ # Route handlers (api/v1) + shared deps
│ │ ├── core/ # config, security, database, logging
│ │ ├── services/ # GCS, crew runner, CrewAI pipeline, extractors,
│ │ │ # vector_store (Weaviate + Voyage)
│ │ ├── models/ # SQLAlchemy ORM models
│ │ ├── schemas/ # Pydantic schemas
│ │ └── main.py # App init + CORS + routers
│ ├── credentials/ # GCP keys (gitignored)
│ ├── pyproject.toml # Dependencies (uv)
│ ├── Dockerfile # (empty for now)
│ └── README.md # Backend setup & run instructions
├── frontend/ # React + Vite (JavaScript) frontend
│ ├── src/
│ │ ├── components/ pages/ context/ hooks/ services/ assets/
│ │ └── main.jsx App.jsx
│ ├── Dockerfile # (empty for now)
│ └── package.json
├── .github/workflows/ # CI/CD (deploy.yml — empty for now)
├── docker-compose.yml # (empty for now)
└── README.md # This file

✅ Prerequisites

Requirement	Version / Notes
Python	3.11+ (3.12 recommended). You don't need to install it manually — `uv` will fetch a matching interpreter.
uv	The Python package & project manager used here. Install: `curl -LsSf https://astral.sh/uv/install.sh \| sh` (then restart your shell so `~/.local/bin` is on `PATH`).
Node.js	18+ (with `npm`), for the React frontend. Download.
Google Cloud account	A GCP project with the Cloud SQL Admin and Cloud Storage APIs enabled, plus a service-account JSON key. BigQuery and Vertex AI are no longer required — the vector store moved off GCP. See backend/docs/GCP_Setup.md.
Cloud SQL (MySQL)	A Cloud SQL MySQL instance (the web app stores users/jobs there).
Cloud SQL Auth Proxy	`cloud-sql-proxy` binary on `PATH` (the `uv run db-proxy` / `uv run dev` commands invoke it). See Installation to install
Weaviate Cloud	A cluster for the GitHub repo vector store (the free sandbox tier is enough to start).
LibreOffice	Used headless to convert the generated .docx resume to PDF for the in-browser preview: `sudo apt-get install -y libreoffice-writer fonts-liberation`. Optional — without it the .docx is still generated, the preview just falls back to Markdown. See backend/README.md.
API keys	GitHub PAT, Bright Data, Gemini, Anthropic, Z.ai, Weaviate, Voyage AI, and Serper. See API Keys & Cloud Setup.

All uv run commands are run from the backend/ directory — the project and its custom commands are defined in backend/pyproject.toml. There is no root-level pyproject.toml, so uv run dev from the repo root will not work.

⚡ Quick Start

git clone https://github.com/arijitde92/Job_Application_Agent.git
cd Job_Application_Agent

1. Backend (uv)

cd backend
cp .env.example .env # then fill in secrets (see below)
uv sync # creates backend/.venv and installs everything

Place your GCP service-account JSON key under backend/credentials/ and point GOOGLE_APPLICATION_CREDENTIALS in backend/.env at it (an absolute path is safest).

2. Frontend

cd ../frontend
cp .env.example .env
npm install

3. Run everything (from `backend/`)

cd ../backend
uv run dev # Cloud SQL proxy + API + frontend, together

Then open http://localhost:5173. The API is at http://localhost:8000 (docs at http://localhost:8000/docs).

Prefer separate terminals? Run them individually:

uv run db-proxy # Cloud SQL Auth Proxy → 127.0.0.1:3306
uv run api # FastAPI dev server → http://localhost:8000
uv run frontend # Vite dev server → http://localhost:5173

See backend/README.md for the full command list, env-var overrides, and the standalone crew CLI.

🚀 Features

Multi-Agent Orchestration: Uses Crew AI to coordinate agents for GitHub analysis, resume parsing, profiling, resume tailoring, and interview preparation.
Custom Tools: Includes LinkedIn job extractor, GitHub repo summarizer, and Serper web search, built using Crew AI's extensible tool system.
Weaviate + Voyage AI: Stores and semantically searches GitHub project data in a Weaviate vector store, using Voyage AI's code-specialised voyage-code-3 embeddings and a rerank-2.5-lite second-stage reranker.
Structured Resume Parsing: A dedicated Resume Analyzer agent extracts the resume into a validated JSON schema, deriving years of experience with a deterministic tool rather than LLM arithmetic.
Automated Resume Tailoring: Aligns your resume with job requirements and optimizes for ATS.
Polished DOCX + PDF Output: The Resume Strategist sends the tailored content to a live document-generation microservice — a separate FastAPI service running on AWS (ECS Fargate behind an ALB) whose source lives at arijitde92/resume_docx_generator — which renders a professional Word (.docx) resume. The backend then converts that .docx to PDF locally (LibreOffice headless) for in-browser viewing. Up to 3 attempts; on failure the pipeline gracefully falls back to the Markdown resume.
Interview Prep: Researches the employer on the web, then generates tailored interview questions and talking points.
Purpose-Matched LLMs: Each agent runs on the model that suits its job — Gemini for synthesis, GLM-5.2 for repo analysis, Claude Sonnet 5 for structured extraction.

🧑‍💻 Agents & Tools

Crew AI Multi-Agent System

Crew AI (Homepage): The backbone of the system, enabling modular, collaborative agent workflows.

Agents: (each runs on the LLM best suited to its task — see backend/app/services/crew/agents.py)

Agent	Role	LLM
GitHub Project Summarizer	Indexes your repos and picks the ones most relevant to the job.	GLM-5.2 (Z.ai)
Resume Analyzer	Extracts the resume into validated structured JSON.	Claude Sonnet 5
Profiler	Compiles a comprehensive personal/professional profile.	Gemini
Resume Strategist	Tailors your resume for each job.	Gemini
Interview Preparer	Researches the employer via web search, then prepares interview questions and talking points.	Gemini

The GitHub summary and resume analysis tasks run in parallel; profiling waits on both. When no GitHub profile is supplied, that agent and its task are skipped entirely and the rest run under explicit grounding rules so nothing is fabricated.

Crew AI Tools Used

FileReadTool: Reads and processes resume files.
ScrapeWebsiteTool: Scrapes web content for job and company info.
MDXSearchTool: Performs semantic search on resume content.
SerperDevTool: Web search for the Resume Strategist.

Custom Tools

LinkedIn Job Extractor: Scrapes and parses job details from LinkedIn job postings.
GitHub Repos Extractor: Recursively fetches and summarizes public GitHub repositories for a user.
Web Search (web_search): Serper-backed Google search used by the Interview Preparer to research the employer's interview process, role-specific questions, and recent company news. Returns a compact ranked digest (featured answer + title/URL/snippet) rather than Serper's raw JSON. It never raises: if the key is missing or the API fails it returns an ERROR: ... observation and the agent falls back to the resume and job details alone, so a search outage cannot sink a run whose resume tailoring has already completed.
Weaviate/Voyage AI Integration: Custom logic to chunk, embed, upsert, and search GitHub repo content — one shared Weaviate collection, with every search scoped to the applicant's own repositories.
Resume Tools: A calculate_yoe tool for deterministic years-of-experience arithmetic and a per-run save_parsed_resume tool that validates the JSON against the schema before persisting it.
DOCX Generator (generate_resume_docx): A per-run tool for the Resume Strategist that POSTs the tailored resume JSON to the live document-generation microservice (POST /api/v1/resume/generate on arijitde92/resume_docx_generator) and saves the returned Word (.docx) resume. The tool wraps the agent's content in the service's metadata envelope deterministically and enforces a 3-attempt retry cap, after which the run falls back to Markdown output.

🏗️ Architecture

Python 3.11+ with uv for dependency & environment management (see backend/pyproject.toml)
FastAPI backend (async SQLAlchemy + Cloud SQL MySQL, JWT/Google OAuth auth)
React + Vite frontend with live progress via Server-Sent Events (SSE)
Crew AI for agent orchestration, across Gemini, GLM-5.2 (Z.ai) and Claude Sonnet 5 depending on the agent
Weaviate Cloud as the vector store for GitHub repo embeddings
Voyage AI for code-specialised embeddings (voyage-code-3) and reranking (rerank-2.5-lite)
Google Cloud Storage for resume & generated-output storage
Resume DOCX microservice — a standalone FastAPI service on AWS ECS Fargate that renders the tailored resume JSON into a Word document (source); the backend converts the returned .docx to PDF with LibreOffice headless
Bright Data MCP for LinkedIn job scraping; LangChain for document loading and chunking

🧭 System Design

Component architecture

flowchart LR
User(["👤 User (browser)"])
subgraph Frontend["React + Vite frontend :5173"]
UI["Pages & components<br/>Home · Job list · Resume preview"]
SSE["SSE client<br/>live agent progress"]
end
subgraph Backend["FastAPI backend :8000"]
API["API routers<br/>/api/auth · /api/resumes<br/>/api/github · /api/jobs"]
Runner["crew_runner<br/>ThreadPoolExecutor (4 jobs)<br/>+ progress store"]
Crew["CrewAI pipeline<br/>agents · tasks · tools"]
PDF["docx_to_pdf<br/>LibreOffice headless"]
end
subgraph GCP["Google Cloud"]
SQL[("Cloud SQL MySQL<br/>users · resumes · jobs")]
GCS[("Cloud Storage<br/>resumes & outputs")]
end
subgraph External["External services"]
BD["Bright Data MCP<br/>LinkedIn scraping"]
GH["GitHub API"]
WV[("Weaviate Cloud<br/>GithubRepoData")]
VOY["Voyage AI<br/>voyage-code-3 · rerank-2.5-lite"]
SERP["Serper<br/>web search"]
LLM["LLMs<br/>Gemini · GLM-5.2 · Claude Sonnet 5"]
DOCX["Resume DOCX microservice<br/>FastAPI on AWS ECS Fargate + ALB<br/>POST /api/v1/resume/generate"]
end
User --> UI
UI -->|"REST + JWT / Google OAuth"| API
SSE <-->|"text/event-stream"| API
API --> Runner --> Crew
API <--> SQL
API <--> GCS
Runner <--> GCS
Crew --> BD
Crew --> GH
Crew <--> WV
WV -.->|"embed & rerank"| VOY
Crew --> SERP
Crew --> LLM
Crew -->|"tailored resume JSON"| DOCX
DOCX -->|".docx bytes"| Crew
Crew --> PDF
PDF -->|".docx + .pdf"| GCS
classDef ext fill:#fff4e6,stroke:#e8a33d,color:#000
classDef svc fill:#e6f0ff,stroke:#4a7fd4,color:#000
class BD,GH,WV,VOY,SERP,LLM,DOCX ext
class SQL,GCS svc

Agent pipeline (one job run)

flowchart TD
Start(["Job submitted<br/>LinkedIn URL + resume + GitHub URL"]) --> Extract["Extract job details<br/>Bright Data MCP → JobDetails JSON"]
Extract --> Parse["Parse resume file<br/>.pdf / .docx / .md → text"]
Parse --> A1["GitHub Project Summarizer<br/><i>GLM-5.2</i><br/>index repos → Weaviate,<br/>retrieve + rerank relevant projects"]
Parse --> A2["Resume Analyzer<br/><i>Claude Sonnet 5</i><br/>resume → validated JSON<br/>(calculate_yoe · save_parsed_resume)"]
A1 --> A3
A2 --> A3
A3["Profiler<br/><i>Gemini</i><br/>compile personal & professional profile"]
A3 --> A4["Resume Strategist<br/><i>Gemini</i><br/>tailor resume · optimise for ATS"]
A4 --> Tool{"generate_resume_docx<br/>≤ 3 attempts"}
Tool -->|"success"| Conv["LibreOffice headless<br/>.docx → .pdf"]
Tool -->|"all attempts failed<br/>or LibreOffice missing"| MD["Markdown resume fallback"]
Conv --> A5
MD --> A5
A5["Interview Preparer<br/><i>Gemini</i><br/>web_search company research →<br/>questions & talking points"]
A5 --> Store["Upload outputs to GCS<br/>update job row in Cloud SQL"]
Store --> Done(["Job completed → SSE 'done'"])
subgraph Parallel["run in parallel"]
A1
A2
end
style Parallel fill:#f4f4f8,stroke:#bbb

The GitHub agent and its task are skipped entirely when no GitHub profile is supplied; the remaining agents then run under explicit grounding rules.

Request lifecycle

sequenceDiagram
autonumber
actor U as User
participant FE as React frontend
participant API as FastAPI (jobs router)
participant DB as Cloud SQL
participant CR as crew_runner (thread pool)
participant CW as CrewAI agents
participant MS as DOCX microservice (AWS)
participant GCS as Cloud Storage
U->>FE: Submit job URL + select resume (+ GitHub profile)
FE->>API: POST /api/jobs/tailor
API->>DB: INSERT job (status=pending)
API-->>FE: 201 { job_id }
API->>CR: asyncio.create_task(run_crew_for_job)
CR->>GCS: Download stored resume bytes
FE->>API: GET /api/jobs/{job_id}/progress (SSE)
loop Each pipeline step
CR->>CW: Run next agent/task
CW-->>CR: Step result
CR->>CR: progress_store[job_id] = step
API-->>FE: event: progress { current_step }
end
CW->>MS: POST /api/v1/resume/generate (resume JSON)
MS-->>CW: .docx bytes
CW->>CR: docx path via ResumeDocxContext.state
CR->>CR: convert_docx_to_pdf (LibreOffice)
CR->>GCS: Upload .docx, .pdf, interview materials
CR->>DB: UPDATE job (status=completed, GCS paths)
API-->>FE: event: done
FE->>API: GET tailored resume / interview materials
API-->>U: PDF preview + .docx download

⚙️ Configuration

All backend configuration lives in backend/.env (copy it from backend/.env.example). Key groups:

# ── GCP ──────────────────────────────────────────────────────────GOOGLE_APPLICATION_CREDENTIALS=/abs/path/to/backend/credentials/service-account.jsonGCP_PROJECT_ID=your-project-idGCP_LOCATION=asia-south2# ── Vector store (Weaviate Cloud + Voyage AI — not GCP) ──────────WEAVIATE_URL=...# cluster REST endpoint; scheme optionalWEAVIATE_API_KEY=...WEAVIATE_COLLECTION_NAME=GithubRepoDataVOYAGE_API_KEY=...# embeddings + rerankingVOYAGE_EMBED_MODEL=voyage-code-3VOYAGE_EMBED_DIMENSION=1024VOYAGE_RERANK_MODEL=rerank-2.5-lite# ── API keys ─────────────────────────────────────────────────────GITHUB_PERSONAL_ACCESS_TOKEN=...BRIGHT_DATA_API_KEY=...# LinkedIn scraping (Bright Data MCP)GEMINI_API_KEY=...# Profiler / Strategist / Interview PreparerANTHROPIC_API_KEY=...# Resume Analyzer (Claude Sonnet 5)ZAI_API_KEY=...# GitHub Project Summarizer (GLM-5.2)ZAI_BASE_URL=# optional override; blank = https://api.z.ai/api/paas/v4SERPER_API_KEY=...# web search (Interview Preparer research)# ── Resume DOCX generation microservice ──────────────────────────# Live service on AWS (ECS Fargate + ALB). Source & self-hosting instructions:# https://github.com/arijitde92/resume_docx_generatorRESUME_DOCX_SERVICE_URL=http://resume-service-alb-1115872566.us-east-2.elb.amazonaws.com/api/v1/resume/generateRESUME_DOCX_TIMEOUT_SECONDS=120# ── Cloud SQL (MySQL) ────────────────────────────────────────────MYSQL_HOST=127.0.0.1# 127.0.0.1 when using the Cloud SQL Auth ProxyMYSQL_PORT=3306MYSQL_USER=...MYSQL_PASSWORD=...MYSQL_DATABASE=job_applier# ── GCS / Auth ───────────────────────────────────────────────────GCS_BUCKET_NAME=your-resumes-bucketJWT_SECRET_KEY=# generate: python -c "import secrets; print(secrets.token_urlsafe(32))"GOOGLE_OAUTH_CLIENT_ID=# optional, for "Sign in with Google"GOOGLE_OAUTH_CLIENT_SECRET=

The frontend has its own frontend/.env (copy from frontend/.env.example) for VITE_* variables.

Never commit .env or service-account keys. They are gitignored. The db-proxy connection name is derived from GCP_PROJECT_ID, GCP_LOCATION, and instance job-applier-mysql by default — override with CLOUD_SQL_INSTANCE if yours differs.

For step-by-step GCP provisioning (project, Cloud SQL instance, GCS bucket, service account), see backend/docs/GCP_Setup.md.

🖥️ Standalone Crew CLI (no web app)

You can run the agent pipeline directly, without the frontend/DB, from backend/:

cd backend
uv run crew \
--url https://www.linkedin.com/jobs/view/<id>/ \
--github https://github.com/<user> \
--resume sample_data/Arijit_De_Resume.md \
--name "Your Name"

This extracts the job, indexes the GitHub repos into Weaviate, parses and tailors the resume, and writes interview materials — same pipeline the web app drives.

🔑 API Keys & Cloud Setup

All keys go in backend/.env.

GitHub:Create a Personal Access Token → GITHUB_PERSONAL_ACCESS_TOKEN.
Google Cloud:
- Enable the Cloud SQL Admin and Cloud Storage APIs. BigQuery and Vertex AI are no longer used.
- Create a service account with the relevant roles (Cloud SQL Client, Storage Object Admin).
- Download the JSON key into backend/credentials/ and point GOOGLE_APPLICATION_CREDENTIALS at it.
Weaviate: Create a cluster at Weaviate Cloud → WEAVIATE_URL + WEAVIATE_API_KEY (GitHub repo vector store).
Voyage AI:Get an API key → VOYAGE_API_KEY (embeddings + reranking for the GitHub RAG pipeline).
Gemini:Get a Gemini API key → GEMINI_API_KEY (Profiler, Resume Strategist, Interview Preparer, and the RAGAS judge).
Anthropic:Get an API key → ANTHROPIC_API_KEY (the Resume Analyzer runs on Claude Sonnet 5).
Z.ai:Get an API key → ZAI_API_KEY (the GitHub Project Summarizer runs on GLM-5.2).
Bright Data:Get an API key for the MCP scraper → BRIGHT_DATA_API_KEY (LinkedIn job extraction).
Serper:Get a Serper API key → SERPER_API_KEY (the web_search tool). Strictly optional — without it the crew still runs end to end, but the Interview Preparer loses its company research and works from the resume and job details alone.

📚 How it Works

Job Extraction: The job posting is scraped once via the Bright Data MCP LinkedIn extractor and parsed into structured JobDetails, which are injected into every downstream task.
GitHub Analysis & Resume Parsing (in parallel): The GitHub Project Summarizer chunks and indexes your public repos into Weaviate with Voyage embeddings, then retrieves and reranks the projects most relevant to the job. Meanwhile the Resume Analyzer extracts your resume into validated structured JSON.
Profile Compilation: The Profiler agent creates a comprehensive profile using the parsed resume, GitHub summaries, and job requirements.
Resume Tailoring: The Resume Strategist aligns your resume with the job description, then renders it as a polished Word (.docx) resume via the live document-generation microservice (generate_resume_docx tool, max 3 attempts) — the .docx is converted to PDF with LibreOffice for the in-browser preview, and the Markdown version remains the fallback when the service or conversion is unavailable. The microservice is a separate FastAPI app deployed on AWS ECS Fargate; its source is at arijitde92/resume_docx_generator.
Interview Prep: The Interview Preparer researches the company's interview process and role-specific questions with the web_search tool, then generates custom interview questions and talking points grounded in your tailored resume — citing sources for anything it found on the web.

🛠️ Extending the System

Add new agents or tools by following the Crew AI documentation.
Integrate additional data sources or cloud services as needed.

📄 License

MIT License

🤝 Acknowledgements

Crew AI
resume_docx_generator — the companion .docx rendering microservice
Weaviate
Voyage AI
LangChain
BeautifulSoup

Job Application Agent

A powerful, multi-agent automation system for job applications, leveraging Crew AI to orchestrate specialized agents that extract, analyze, and summarize job postings, tailor resumes, and prepare interview materials. Semantic search over your GitHub projects runs on a Weaviate vector store with Voyage AI embeddings and reranking, and the system features custom tools for LinkedIn and GitHub data extraction plus Serper-backed web research.

This project also includes a Full-Stack Web App version with a FastAPI backend (using Google Cloud SQL MySQL and GCS storage) and a React + Vite frontend that displays live agent execution progress via Server-Sent Events (SSE).

The final Word (.docx) resume is rendered by a separate live microservice deployed on AWS (ECS Fargate behind an ALB) — its source is available at arijitde92/resume_docx_generator. This backend calls that service over HTTP and converts the returned .docx to PDF locally (LibreOffice headless) for the in-browser preview. See System Design for the full picture.

📦 Project Structure

Job_Application_Agent/
├── backend/ # FastAPI backend + CrewAI pipeline (uv-managed)
│ ├── app/
│ │ ├── api/ # Route handlers (api/v1) + shared deps
│ │ ├── core/ # config, security, database, logging
│ │ ├── services/ # GCS, crew runner, CrewAI pipeline, extractors,
│ │ │ # vector_store (Weaviate + Voyage)
│ │ ├── models/ # SQLAlchemy ORM models
│ │ ├── schemas/ # Pydantic schemas
│ │ └── main.py # App init + CORS + routers
│ ├── credentials/ # GCP keys (gitignored)
│ ├── pyproject.toml # Dependencies (uv)
│ ├── Dockerfile # (empty for now)
│ └── README.md # Backend setup & run instructions
├── frontend/ # React + Vite (JavaScript) frontend
│ ├── src/
│ │ ├── components/ pages/ context/ hooks/ services/ assets/
│ │ └── main.jsx App.jsx
│ ├── Dockerfile # (empty for now)
│ └── package.json
├── .github/workflows/ # CI/CD (deploy.yml — empty for now)
├── docker-compose.yml # (empty for now)
└── README.md # This file

✅ Prerequisites

Requirement	Version / Notes
Python	3.11+ (3.12 recommended). You don't need to install it manually — `uv` will fetch a matching interpreter.
uv	The Python package & project manager used here. Install: `curl -LsSf https://astral.sh/uv/install.sh \| sh` (then restart your shell so `~/.local/bin` is on `PATH`).
Node.js	18+ (with `npm`), for the React frontend. Download.
Google Cloud account	A GCP project with the Cloud SQL Admin and Cloud Storage APIs enabled, plus a service-account JSON key. BigQuery and Vertex AI are no longer required — the vector store moved off GCP. See backend/docs/GCP_Setup.md.
Cloud SQL (MySQL)	A Cloud SQL MySQL instance (the web app stores users/jobs there).
Cloud SQL Auth Proxy	`cloud-sql-proxy` binary on `PATH` (the `uv run db-proxy` / `uv run dev` commands invoke it). See Installation to install
Weaviate Cloud	A cluster for the GitHub repo vector store (the free sandbox tier is enough to start).
LibreOffice	Used headless to convert the generated .docx resume to PDF for the in-browser preview: `sudo apt-get install -y libreoffice-writer fonts-liberation`. Optional — without it the .docx is still generated, the preview just falls back to Markdown. See backend/README.md.
API keys	GitHub PAT, Bright Data, Gemini, Anthropic, Z.ai, Weaviate, Voyage AI, and Serper. See API Keys & Cloud Setup.

All uv run commands are run from the backend/ directory — the project and its custom commands are defined in backend/pyproject.toml. There is no root-level pyproject.toml, so uv run dev from the repo root will not work.

⚡ Quick Start

git clone https://github.com/arijitde92/Job_Application_Agent.git
cd Job_Application_Agent

1. Backend (uv)

cd backend
cp .env.example .env # then fill in secrets (see below)
uv sync # creates backend/.venv and installs everything

Place your GCP service-account JSON key under backend/credentials/ and point GOOGLE_APPLICATION_CREDENTIALS in backend/.env at it (an absolute path is safest).

2. Frontend

cd ../frontend
cp .env.example .env
npm install

3. Run everything (from `backend/`)

cd ../backend
uv run dev # Cloud SQL proxy + API + frontend, together

Then open http://localhost:5173. The API is at http://localhost:8000 (docs at http://localhost:8000/docs).

Prefer separate terminals? Run them individually:

uv run db-proxy # Cloud SQL Auth Proxy → 127.0.0.1:3306
uv run api # FastAPI dev server → http://localhost:8000
uv run frontend # Vite dev server → http://localhost:5173

See backend/README.md for the full command list, env-var overrides, and the standalone crew CLI.

🚀 Features

Multi-Agent Orchestration: Uses Crew AI to coordinate agents for GitHub analysis, resume parsing, profiling, resume tailoring, and interview preparation.
Custom Tools: Includes LinkedIn job extractor, GitHub repo summarizer, and Serper web search, built using Crew AI's extensible tool system.
Weaviate + Voyage AI: Stores and semantically searches GitHub project data in a Weaviate vector store, using Voyage AI's code-specialised voyage-code-3 embeddings and a rerank-2.5-lite second-stage reranker.
Structured Resume Parsing: A dedicated Resume Analyzer agent extracts the resume into a validated JSON schema, deriving years of experience with a deterministic tool rather than LLM arithmetic.
Automated Resume Tailoring: Aligns your resume with job requirements and optimizes for ATS.
Polished DOCX + PDF Output: The Resume Strategist sends the tailored content to a live document-generation microservice — a separate FastAPI service running on AWS (ECS Fargate behind an ALB) whose source lives at arijitde92/resume_docx_generator — which renders a professional Word (.docx) resume. The backend then converts that .docx to PDF locally (LibreOffice headless) for in-browser viewing. Up to 3 attempts; on failure the pipeline gracefully falls back to the Markdown resume.
Interview Prep: Researches the employer on the web, then generates tailored interview questions and talking points.
Purpose-Matched LLMs: Each agent runs on the model that suits its job — Gemini for synthesis, GLM-5.2 for repo analysis, Claude Sonnet 5 for structured extraction.

🧑‍💻 Agents & Tools

Crew AI Multi-Agent System

Crew AI (Homepage): The backbone of the system, enabling modular, collaborative agent workflows.

Agents: (each runs on the LLM best suited to its task — see backend/app/services/crew/agents.py)

Agent	Role	LLM
GitHub Project Summarizer	Indexes your repos and picks the ones most relevant to the job.	GLM-5.2 (Z.ai)
Resume Analyzer	Extracts the resume into validated structured JSON.	Claude Sonnet 5
Profiler	Compiles a comprehensive personal/professional profile.	Gemini
Resume Strategist	Tailors your resume for each job.	Gemini
Interview Preparer	Researches the employer via web search, then prepares interview questions and talking points.	Gemini

The GitHub summary and resume analysis tasks run in parallel; profiling waits on both. When no GitHub profile is supplied, that agent and its task are skipped entirely and the rest run under explicit grounding rules so nothing is fabricated.

Crew AI Tools Used

FileReadTool: Reads and processes resume files.
ScrapeWebsiteTool: Scrapes web content for job and company info.
MDXSearchTool: Performs semantic search on resume content.
SerperDevTool: Web search for the Resume Strategist.

Custom Tools

LinkedIn Job Extractor: Scrapes and parses job details from LinkedIn job postings.
GitHub Repos Extractor: Recursively fetches and summarizes public GitHub repositories for a user.
Web Search (web_search): Serper-backed Google search used by the Interview Preparer to research the employer's interview process, role-specific questions, and recent company news. Returns a compact ranked digest (featured answer + title/URL/snippet) rather than Serper's raw JSON. It never raises: if the key is missing or the API fails it returns an ERROR: ... observation and the agent falls back to the resume and job details alone, so a search outage cannot sink a run whose resume tailoring has already completed.
Weaviate/Voyage AI Integration: Custom logic to chunk, embed, upsert, and search GitHub repo content — one shared Weaviate collection, with every search scoped to the applicant's own repositories.
Resume Tools: A calculate_yoe tool for deterministic years-of-experience arithmetic and a per-run save_parsed_resume tool that validates the JSON against the schema before persisting it.
DOCX Generator (generate_resume_docx): A per-run tool for the Resume Strategist that POSTs the tailored resume JSON to the live document-generation microservice (POST /api/v1/resume/generate on arijitde92/resume_docx_generator) and saves the returned Word (.docx) resume. The tool wraps the agent's content in the service's metadata envelope deterministically and enforces a 3-attempt retry cap, after which the run falls back to Markdown output.

🏗️ Architecture

Python 3.11+ with uv for dependency & environment management (see backend/pyproject.toml)
FastAPI backend (async SQLAlchemy + Cloud SQL MySQL, JWT/Google OAuth auth)
React + Vite frontend with live progress via Server-Sent Events (SSE)
Crew AI for agent orchestration, across Gemini, GLM-5.2 (Z.ai) and Claude Sonnet 5 depending on the agent
Weaviate Cloud as the vector store for GitHub repo embeddings
Voyage AI for code-specialised embeddings (voyage-code-3) and reranking (rerank-2.5-lite)
Google Cloud Storage for resume & generated-output storage
Resume DOCX microservice — a standalone FastAPI service on AWS ECS Fargate that renders the tailored resume JSON into a Word document (source); the backend converts the returned .docx to PDF with LibreOffice headless
Bright Data MCP for LinkedIn job scraping; LangChain for document loading and chunking

🧭 System Design

Component architecture

flowchart LR
User(["👤 User (browser)"])
subgraph Frontend["React + Vite frontend :5173"]
UI["Pages & components<br/>Home · Job list · Resume preview"]
SSE["SSE client<br/>live agent progress"]
end
subgraph Backend["FastAPI backend :8000"]
API["API routers<br/>/api/auth · /api/resumes<br/>/api/github · /api/jobs"]
Runner["crew_runner<br/>ThreadPoolExecutor (4 jobs)<br/>+ progress store"]
Crew["CrewAI pipeline<br/>agents · tasks · tools"]
PDF["docx_to_pdf<br/>LibreOffice headless"]
end
subgraph GCP["Google Cloud"]
SQL[("Cloud SQL MySQL<br/>users · resumes · jobs")]
GCS[("Cloud Storage<br/>resumes & outputs")]
end
subgraph External["External services"]
BD["Bright Data MCP<br/>LinkedIn scraping"]
GH["GitHub API"]
WV[("Weaviate Cloud<br/>GithubRepoData")]
VOY["Voyage AI<br/>voyage-code-3 · rerank-2.5-lite"]
SERP["Serper<br/>web search"]
LLM["LLMs<br/>Gemini · GLM-5.2 · Claude Sonnet 5"]
DOCX["Resume DOCX microservice<br/>FastAPI on AWS ECS Fargate + ALB<br/>POST /api/v1/resume/generate"]
end
User --> UI
UI -->|"REST + JWT / Google OAuth"| API
SSE <-->|"text/event-stream"| API
API --> Runner --> Crew
API <--> SQL
API <--> GCS
Runner <--> GCS
Crew --> BD
Crew --> GH
Crew <--> WV
WV -.->|"embed & rerank"| VOY
Crew --> SERP
Crew --> LLM
Crew -->|"tailored resume JSON"| DOCX
DOCX -->|".docx bytes"| Crew
Crew --> PDF
PDF -->|".docx + .pdf"| GCS
classDef ext fill:#fff4e6,stroke:#e8a33d,color:#000
classDef svc fill:#e6f0ff,stroke:#4a7fd4,color:#000
class BD,GH,WV,VOY,SERP,LLM,DOCX ext
class SQL,GCS svc

Agent pipeline (one job run)

flowchart TD
Start(["Job submitted<br/>LinkedIn URL + resume + GitHub URL"]) --> Extract["Extract job details<br/>Bright Data MCP → JobDetails JSON"]
Extract --> Parse["Parse resume file<br/>.pdf / .docx / .md → text"]
Parse --> A1["GitHub Project Summarizer<br/><i>GLM-5.2</i><br/>index repos → Weaviate,<br/>retrieve + rerank relevant projects"]
Parse --> A2["Resume Analyzer<br/><i>Claude Sonnet 5</i><br/>resume → validated JSON<br/>(calculate_yoe · save_parsed_resume)"]
A1 --> A3
A2 --> A3
A3["Profiler<br/><i>Gemini</i><br/>compile personal & professional profile"]
A3 --> A4["Resume Strategist<br/><i>Gemini</i><br/>tailor resume · optimise for ATS"]
A4 --> Tool{"generate_resume_docx<br/>≤ 3 attempts"}
Tool -->|"success"| Conv["LibreOffice headless<br/>.docx → .pdf"]
Tool -->|"all attempts failed<br/>or LibreOffice missing"| MD["Markdown resume fallback"]
Conv --> A5
MD --> A5
A5["Interview Preparer<br/><i>Gemini</i><br/>web_search company research →<br/>questions & talking points"]
A5 --> Store["Upload outputs to GCS<br/>update job row in Cloud SQL"]
Store --> Done(["Job completed → SSE 'done'"])
subgraph Parallel["run in parallel"]
A1
A2
end
style Parallel fill:#f4f4f8,stroke:#bbb

The GitHub agent and its task are skipped entirely when no GitHub profile is supplied; the remaining agents then run under explicit grounding rules.

Request lifecycle

sequenceDiagram
autonumber
actor U as User
participant FE as React frontend
participant API as FastAPI (jobs router)
participant DB as Cloud SQL
participant CR as crew_runner (thread pool)
participant CW as CrewAI agents
participant MS as DOCX microservice (AWS)
participant GCS as Cloud Storage
U->>FE: Submit job URL + select resume (+ GitHub profile)
FE->>API: POST /api/jobs/tailor
API->>DB: INSERT job (status=pending)
API-->>FE: 201 { job_id }
API->>CR: asyncio.create_task(run_crew_for_job)
CR->>GCS: Download stored resume bytes
FE->>API: GET /api/jobs/{job_id}/progress (SSE)
loop Each pipeline step
CR->>CW: Run next agent/task
CW-->>CR: Step result
CR->>CR: progress_store[job_id] = step
API-->>FE: event: progress { current_step }
end
CW->>MS: POST /api/v1/resume/generate (resume JSON)
MS-->>CW: .docx bytes
CW->>CR: docx path via ResumeDocxContext.state
CR->>CR: convert_docx_to_pdf (LibreOffice)
CR->>GCS: Upload .docx, .pdf, interview materials
CR->>DB: UPDATE job (status=completed, GCS paths)
API-->>FE: event: done
FE->>API: GET tailored resume / interview materials
API-->>U: PDF preview + .docx download

⚙️ Configuration

All backend configuration lives in backend/.env (copy it from backend/.env.example). Key groups:

# ── GCP ──────────────────────────────────────────────────────────GOOGLE_APPLICATION_CREDENTIALS=/abs/path/to/backend/credentials/service-account.jsonGCP_PROJECT_ID=your-project-idGCP_LOCATION=asia-south2# ── Vector store (Weaviate Cloud + Voyage AI — not GCP) ──────────WEAVIATE_URL=...# cluster REST endpoint; scheme optionalWEAVIATE_API_KEY=...WEAVIATE_COLLECTION_NAME=GithubRepoDataVOYAGE_API_KEY=...# embeddings + rerankingVOYAGE_EMBED_MODEL=voyage-code-3VOYAGE_EMBED_DIMENSION=1024VOYAGE_RERANK_MODEL=rerank-2.5-lite# ── API keys ─────────────────────────────────────────────────────GITHUB_PERSONAL_ACCESS_TOKEN=...BRIGHT_DATA_API_KEY=...# LinkedIn scraping (Bright Data MCP)GEMINI_API_KEY=...# Profiler / Strategist / Interview PreparerANTHROPIC_API_KEY=...# Resume Analyzer (Claude Sonnet 5)ZAI_API_KEY=...# GitHub Project Summarizer (GLM-5.2)ZAI_BASE_URL=# optional override; blank = https://api.z.ai/api/paas/v4SERPER_API_KEY=...# web search (Interview Preparer research)# ── Resume DOCX generation microservice ──────────────────────────# Live service on AWS (ECS Fargate + ALB). Source & self-hosting instructions:# https://github.com/arijitde92/resume_docx_generatorRESUME_DOCX_SERVICE_URL=http://resume-service-alb-1115872566.us-east-2.elb.amazonaws.com/api/v1/resume/generateRESUME_DOCX_TIMEOUT_SECONDS=120# ── Cloud SQL (MySQL) ────────────────────────────────────────────MYSQL_HOST=127.0.0.1# 127.0.0.1 when using the Cloud SQL Auth ProxyMYSQL_PORT=3306MYSQL_USER=...MYSQL_PASSWORD=...MYSQL_DATABASE=job_applier# ── GCS / Auth ───────────────────────────────────────────────────GCS_BUCKET_NAME=your-resumes-bucketJWT_SECRET_KEY=# generate: python -c "import secrets; print(secrets.token_urlsafe(32))"GOOGLE_OAUTH_CLIENT_ID=# optional, for "Sign in with Google"GOOGLE_OAUTH_CLIENT_SECRET=

The frontend has its own frontend/.env (copy from frontend/.env.example) for VITE_* variables.

Never commit .env or service-account keys. They are gitignored. The db-proxy connection name is derived from GCP_PROJECT_ID, GCP_LOCATION, and instance job-applier-mysql by default — override with CLOUD_SQL_INSTANCE if yours differs.

For step-by-step GCP provisioning (project, Cloud SQL instance, GCS bucket, service account), see backend/docs/GCP_Setup.md.

🖥️ Standalone Crew CLI (no web app)

You can run the agent pipeline directly, without the frontend/DB, from backend/:

cd backend
uv run crew \
--url https://www.linkedin.com/jobs/view/<id>/ \
--github https://github.com/<user> \
--resume sample_data/Arijit_De_Resume.md \
--name "Your Name"

This extracts the job, indexes the GitHub repos into Weaviate, parses and tailors the resume, and writes interview materials — same pipeline the web app drives.

🔑 API Keys & Cloud Setup

All keys go in backend/.env.

GitHub:Create a Personal Access Token → GITHUB_PERSONAL_ACCESS_TOKEN.
Google Cloud:
- Enable the Cloud SQL Admin and Cloud Storage APIs. BigQuery and Vertex AI are no longer used.
- Create a service account with the relevant roles (Cloud SQL Client, Storage Object Admin).
- Download the JSON key into backend/credentials/ and point GOOGLE_APPLICATION_CREDENTIALS at it.
Weaviate: Create a cluster at Weaviate Cloud → WEAVIATE_URL + WEAVIATE_API_KEY (GitHub repo vector store).
Voyage AI:Get an API key → VOYAGE_API_KEY (embeddings + reranking for the GitHub RAG pipeline).
Gemini:Get a Gemini API key → GEMINI_API_KEY (Profiler, Resume Strategist, Interview Preparer, and the RAGAS judge).
Anthropic:Get an API key → ANTHROPIC_API_KEY (the Resume Analyzer runs on Claude Sonnet 5).
Z.ai:Get an API key → ZAI_API_KEY (the GitHub Project Summarizer runs on GLM-5.2).
Bright Data:Get an API key for the MCP scraper → BRIGHT_DATA_API_KEY (LinkedIn job extraction).
Serper:Get a Serper API key → SERPER_API_KEY (the web_search tool). Strictly optional — without it the crew still runs end to end, but the Interview Preparer loses its company research and works from the resume and job details alone.

📚 How it Works

Job Extraction: The job posting is scraped once via the Bright Data MCP LinkedIn extractor and parsed into structured JobDetails, which are injected into every downstream task.
GitHub Analysis & Resume Parsing (in parallel): The GitHub Project Summarizer chunks and indexes your public repos into Weaviate with Voyage embeddings, then retrieves and reranks the projects most relevant to the job. Meanwhile the Resume Analyzer extracts your resume into validated structured JSON.
Profile Compilation: The Profiler agent creates a comprehensive profile using the parsed resume, GitHub summaries, and job requirements.
Resume Tailoring: The Resume Strategist aligns your resume with the job description, then renders it as a polished Word (.docx) resume via the live document-generation microservice (generate_resume_docx tool, max 3 attempts) — the .docx is converted to PDF with LibreOffice for the in-browser preview, and the Markdown version remains the fallback when the service or conversion is unavailable. The microservice is a separate FastAPI app deployed on AWS ECS Fargate; its source is at arijitde92/resume_docx_generator.
Interview Prep: The Interview Preparer researches the company's interview process and role-specific questions with the web_search tool, then generates custom interview questions and talking points grounded in your tailored resume — citing sources for anything it found on the web.

🛠️ Extending the System

Add new agents or tools by following the Crew AI documentation.
Integrate additional data sources or cloud services as needed.

📄 License

MIT License

🤝 Acknowledgements

Crew AI
resume_docx_generator — the companion .docx rendering microservice
Weaviate
Voyage AI
LangChain
BeautifulSoup

Job Application Agent

A powerful, multi-agent automation system for job applications, leveraging Crew AI to orchestrate specialized agents that extract, analyze, and summarize job postings, tailor resumes, and prepare interview materials. Semantic search over your GitHub projects runs on a Weaviate vector store with Voyage AI embeddings and reranking, and the system features custom tools for LinkedIn and GitHub data extraction plus Serper-backed web research.

This project also includes a Full-Stack Web App version with a FastAPI backend (using Google Cloud SQL MySQL and GCS storage) and a React + Vite frontend that displays live agent execution progress via Server-Sent Events (SSE).

The final Word (.docx) resume is rendered by a separate live microservice deployed on AWS (ECS Fargate behind an ALB) — its source is available at arijitde92/resume_docx_generator. This backend calls that service over HTTP and converts the returned .docx to PDF locally (LibreOffice headless) for the in-browser preview. See System Design for the full picture.

📦 Project Structure

Job_Application_Agent/
├── backend/ # FastAPI backend + CrewAI pipeline (uv-managed)
│ ├── app/
│ │ ├── api/ # Route handlers (api/v1) + shared deps
│ │ ├── core/ # config, security, database, logging
│ │ ├── services/ # GCS, crew runner, CrewAI pipeline, extractors,
│ │ │ # vector_store (Weaviate + Voyage)
│ │ ├── models/ # SQLAlchemy ORM models
│ │ ├── schemas/ # Pydantic schemas
│ │ └── main.py # App init + CORS + routers
│ ├── credentials/ # GCP keys (gitignored)
│ ├── pyproject.toml # Dependencies (uv)
│ ├── Dockerfile # (empty for now)
│ └── README.md # Backend setup & run instructions
├── frontend/ # React + Vite (JavaScript) frontend
│ ├── src/
│ │ ├── components/ pages/ context/ hooks/ services/ assets/
│ │ └── main.jsx App.jsx
│ ├── Dockerfile # (empty for now)
│ └── package.json
├── .github/workflows/ # CI/CD (deploy.yml — empty for now)
├── docker-compose.yml # (empty for now)
└── README.md # This file

✅ Prerequisites

Requirement	Version / Notes
Python	3.11+ (3.12 recommended). You don't need to install it manually — `uv` will fetch a matching interpreter.
uv	The Python package & project manager used here. Install: `curl -LsSf https://astral.sh/uv/install.sh \| sh` (then restart your shell so `~/.local/bin` is on `PATH`).
Node.js	18+ (with `npm`), for the React frontend. Download.
Google Cloud account	A GCP project with the Cloud SQL Admin and Cloud Storage APIs enabled, plus a service-account JSON key. BigQuery and Vertex AI are no longer required — the vector store moved off GCP. See backend/docs/GCP_Setup.md.
Cloud SQL (MySQL)	A Cloud SQL MySQL instance (the web app stores users/jobs there).
Cloud SQL Auth Proxy	`cloud-sql-proxy` binary on `PATH` (the `uv run db-proxy` / `uv run dev` commands invoke it). See Installation to install
Weaviate Cloud	A cluster for the GitHub repo vector store (the free sandbox tier is enough to start).
LibreOffice	Used headless to convert the generated .docx resume to PDF for the in-browser preview: `sudo apt-get install -y libreoffice-writer fonts-liberation`. Optional — without it the .docx is still generated, the preview just falls back to Markdown. See backend/README.md.
API keys	GitHub PAT, Bright Data, Gemini, Anthropic, Z.ai, Weaviate, Voyage AI, and Serper. See API Keys & Cloud Setup.

All uv run commands are run from the backend/ directory — the project and its custom commands are defined in backend/pyproject.toml. There is no root-level pyproject.toml, so uv run dev from the repo root will not work.

⚡ Quick Start

git clone https://github.com/arijitde92/Job_Application_Agent.git
cd Job_Application_Agent

1. Backend (uv)

cd backend
cp .env.example .env # then fill in secrets (see below)
uv sync # creates backend/.venv and installs everything

Place your GCP service-account JSON key under backend/credentials/ and point GOOGLE_APPLICATION_CREDENTIALS in backend/.env at it (an absolute path is safest).

2. Frontend

cd ../frontend
cp .env.example .env
npm install

3. Run everything (from `backend/`)

cd ../backend
uv run dev # Cloud SQL proxy + API + frontend, together

Then open http://localhost:5173. The API is at http://localhost:8000 (docs at http://localhost:8000/docs).

Prefer separate terminals? Run them individually:

uv run db-proxy # Cloud SQL Auth Proxy → 127.0.0.1:3306
uv run api # FastAPI dev server → http://localhost:8000
uv run frontend # Vite dev server → http://localhost:5173

See backend/README.md for the full command list, env-var overrides, and the standalone crew CLI.

🚀 Features

Multi-Agent Orchestration: Uses Crew AI to coordinate agents for GitHub analysis, resume parsing, profiling, resume tailoring, and interview preparation.
Custom Tools: Includes LinkedIn job extractor, GitHub repo summarizer, and Serper web search, built using Crew AI's extensible tool system.
Weaviate + Voyage AI: Stores and semantically searches GitHub project data in a Weaviate vector store, using Voyage AI's code-specialised voyage-code-3 embeddings and a rerank-2.5-lite second-stage reranker.
Structured Resume Parsing: A dedicated Resume Analyzer agent extracts the resume into a validated JSON schema, deriving years of experience with a deterministic tool rather than LLM arithmetic.
Automated Resume Tailoring: Aligns your resume with job requirements and optimizes for ATS.
Polished DOCX + PDF Output: The Resume Strategist sends the tailored content to a live document-generation microservice — a separate FastAPI service running on AWS (ECS Fargate behind an ALB) whose source lives at arijitde92/resume_docx_generator — which renders a professional Word (.docx) resume. The backend then converts that .docx to PDF locally (LibreOffice headless) for in-browser viewing. Up to 3 attempts; on failure the pipeline gracefully falls back to the Markdown resume.
Interview Prep: Researches the employer on the web, then generates tailored interview questions and talking points.
Purpose-Matched LLMs: Each agent runs on the model that suits its job — Gemini for synthesis, GLM-5.2 for repo analysis, Claude Sonnet 5 for structured extraction.

🧑‍💻 Agents & Tools

Crew AI Multi-Agent System

Crew AI (Homepage): The backbone of the system, enabling modular, collaborative agent workflows.

Agents: (each runs on the LLM best suited to its task — see backend/app/services/crew/agents.py)

Agent	Role	LLM
GitHub Project Summarizer	Indexes your repos and picks the ones most relevant to the job.	GLM-5.2 (Z.ai)
Resume Analyzer	Extracts the resume into validated structured JSON.	Claude Sonnet 5
Profiler	Compiles a comprehensive personal/professional profile.	Gemini
Resume Strategist	Tailors your resume for each job.	Gemini
Interview Preparer	Researches the employer via web search, then prepares interview questions and talking points.	Gemini

The GitHub summary and resume analysis tasks run in parallel; profiling waits on both. When no GitHub profile is supplied, that agent and its task are skipped entirely and the rest run under explicit grounding rules so nothing is fabricated.

Crew AI Tools Used

FileReadTool: Reads and processes resume files.
ScrapeWebsiteTool: Scrapes web content for job and company info.
MDXSearchTool: Performs semantic search on resume content.
SerperDevTool: Web search for the Resume Strategist.

Custom Tools

LinkedIn Job Extractor: Scrapes and parses job details from LinkedIn job postings.
GitHub Repos Extractor: Recursively fetches and summarizes public GitHub repositories for a user.
Web Search (web_search): Serper-backed Google search used by the Interview Preparer to research the employer's interview process, role-specific questions, and recent company news. Returns a compact ranked digest (featured answer + title/URL/snippet) rather than Serper's raw JSON. It never raises: if the key is missing or the API fails it returns an ERROR: ... observation and the agent falls back to the resume and job details alone, so a search outage cannot sink a run whose resume tailoring has already completed.
Weaviate/Voyage AI Integration: Custom logic to chunk, embed, upsert, and search GitHub repo content — one shared Weaviate collection, with every search scoped to the applicant's own repositories.
Resume Tools: A calculate_yoe tool for deterministic years-of-experience arithmetic and a per-run save_parsed_resume tool that validates the JSON against the schema before persisting it.
DOCX Generator (generate_resume_docx): A per-run tool for the Resume Strategist that POSTs the tailored resume JSON to the live document-generation microservice (POST /api/v1/resume/generate on arijitde92/resume_docx_generator) and saves the returned Word (.docx) resume. The tool wraps the agent's content in the service's metadata envelope deterministically and enforces a 3-attempt retry cap, after which the run falls back to Markdown output.

🏗️ Architecture

Python 3.11+ with uv for dependency & environment management (see backend/pyproject.toml)
FastAPI backend (async SQLAlchemy + Cloud SQL MySQL, JWT/Google OAuth auth)
React + Vite frontend with live progress via Server-Sent Events (SSE)
Crew AI for agent orchestration, across Gemini, GLM-5.2 (Z.ai) and Claude Sonnet 5 depending on the agent
Weaviate Cloud as the vector store for GitHub repo embeddings
Voyage AI for code-specialised embeddings (voyage-code-3) and reranking (rerank-2.5-lite)
Google Cloud Storage for resume & generated-output storage
Resume DOCX microservice — a standalone FastAPI service on AWS ECS Fargate that renders the tailored resume JSON into a Word document (source); the backend converts the returned .docx to PDF with LibreOffice headless
Bright Data MCP for LinkedIn job scraping; LangChain for document loading and chunking

🧭 System Design

Component architecture

flowchart LR
User(["👤 User (browser)"])
subgraph Frontend["React + Vite frontend :5173"]
UI["Pages & components<br/>Home · Job list · Resume preview"]
SSE["SSE client<br/>live agent progress"]
end
subgraph Backend["FastAPI backend :8000"]
API["API routers<br/>/api/auth · /api/resumes<br/>/api/github · /api/jobs"]
Runner["crew_runner<br/>ThreadPoolExecutor (4 jobs)<br/>+ progress store"]
Crew["CrewAI pipeline<br/>agents · tasks · tools"]
PDF["docx_to_pdf<br/>LibreOffice headless"]
end
subgraph GCP["Google Cloud"]
SQL[("Cloud SQL MySQL<br/>users · resumes · jobs")]
GCS[("Cloud Storage<br/>resumes & outputs")]
end
subgraph External["External services"]
BD["Bright Data MCP<br/>LinkedIn scraping"]
GH["GitHub API"]
WV[("Weaviate Cloud<br/>GithubRepoData")]
VOY["Voyage AI<br/>voyage-code-3 · rerank-2.5-lite"]
SERP["Serper<br/>web search"]
LLM["LLMs<br/>Gemini · GLM-5.2 · Claude Sonnet 5"]
DOCX["Resume DOCX microservice<br/>FastAPI on AWS ECS Fargate + ALB<br/>POST /api/v1/resume/generate"]
end
User --> UI
UI -->|"REST + JWT / Google OAuth"| API
SSE <-->|"text/event-stream"| API
API --> Runner --> Crew
API <--> SQL
API <--> GCS
Runner <--> GCS
Crew --> BD
Crew --> GH
Crew <--> WV
WV -.->|"embed & rerank"| VOY
Crew --> SERP
Crew --> LLM
Crew -->|"tailored resume JSON"| DOCX
DOCX -->|".docx bytes"| Crew
Crew --> PDF
PDF -->|".docx + .pdf"| GCS
classDef ext fill:#fff4e6,stroke:#e8a33d,color:#000
classDef svc fill:#e6f0ff,stroke:#4a7fd4,color:#000
class BD,GH,WV,VOY,SERP,LLM,DOCX ext
class SQL,GCS svc

Agent pipeline (one job run)

flowchart TD
Start(["Job submitted<br/>LinkedIn URL + resume + GitHub URL"]) --> Extract["Extract job details<br/>Bright Data MCP → JobDetails JSON"]
Extract --> Parse["Parse resume file<br/>.pdf / .docx / .md → text"]
Parse --> A1["GitHub Project Summarizer<br/><i>GLM-5.2</i><br/>index repos → Weaviate,<br/>retrieve + rerank relevant projects"]
Parse --> A2["Resume Analyzer<br/><i>Claude Sonnet 5</i><br/>resume → validated JSON<br/>(calculate_yoe · save_parsed_resume)"]
A1 --> A3
A2 --> A3
A3["Profiler<br/><i>Gemini</i><br/>compile personal & professional profile"]
A3 --> A4["Resume Strategist<br/><i>Gemini</i><br/>tailor resume · optimise for ATS"]
A4 --> Tool{"generate_resume_docx<br/>≤ 3 attempts"}
Tool -->|"success"| Conv["LibreOffice headless<br/>.docx → .pdf"]
Tool -->|"all attempts failed<br/>or LibreOffice missing"| MD["Markdown resume fallback"]
Conv --> A5
MD --> A5
A5["Interview Preparer<br/><i>Gemini</i><br/>web_search company research →<br/>questions & talking points"]
A5 --> Store["Upload outputs to GCS<br/>update job row in Cloud SQL"]
Store --> Done(["Job completed → SSE 'done'"])
subgraph Parallel["run in parallel"]
A1
A2
end
style Parallel fill:#f4f4f8,stroke:#bbb

The GitHub agent and its task are skipped entirely when no GitHub profile is supplied; the remaining agents then run under explicit grounding rules.

Request lifecycle

sequenceDiagram
autonumber
actor U as User
participant FE as React frontend
participant API as FastAPI (jobs router)
participant DB as Cloud SQL
participant CR as crew_runner (thread pool)
participant CW as CrewAI agents
participant MS as DOCX microservice (AWS)
participant GCS as Cloud Storage
U->>FE: Submit job URL + select resume (+ GitHub profile)
FE->>API: POST /api/jobs/tailor
API->>DB: INSERT job (status=pending)
API-->>FE: 201 { job_id }
API->>CR: asyncio.create_task(run_crew_for_job)
CR->>GCS: Download stored resume bytes
FE->>API: GET /api/jobs/{job_id}/progress (SSE)
loop Each pipeline step
CR->>CW: Run next agent/task
CW-->>CR: Step result
CR->>CR: progress_store[job_id] = step
API-->>FE: event: progress { current_step }
end
CW->>MS: POST /api/v1/resume/generate (resume JSON)
MS-->>CW: .docx bytes
CW->>CR: docx path via ResumeDocxContext.state
CR->>CR: convert_docx_to_pdf (LibreOffice)
CR->>GCS: Upload .docx, .pdf, interview materials
CR->>DB: UPDATE job (status=completed, GCS paths)
API-->>FE: event: done
FE->>API: GET tailored resume / interview materials
API-->>U: PDF preview + .docx download

⚙️ Configuration

All backend configuration lives in backend/.env (copy it from backend/.env.example). Key groups:

# ── GCP ──────────────────────────────────────────────────────────GOOGLE_APPLICATION_CREDENTIALS=/abs/path/to/backend/credentials/service-account.jsonGCP_PROJECT_ID=your-project-idGCP_LOCATION=asia-south2# ── Vector store (Weaviate Cloud + Voyage AI — not GCP) ──────────WEAVIATE_URL=...# cluster REST endpoint; scheme optionalWEAVIATE_API_KEY=...WEAVIATE_COLLECTION_NAME=GithubRepoDataVOYAGE_API_KEY=...# embeddings + rerankingVOYAGE_EMBED_MODEL=voyage-code-3VOYAGE_EMBED_DIMENSION=1024VOYAGE_RERANK_MODEL=rerank-2.5-lite# ── API keys ─────────────────────────────────────────────────────GITHUB_PERSONAL_ACCESS_TOKEN=...BRIGHT_DATA_API_KEY=...# LinkedIn scraping (Bright Data MCP)GEMINI_API_KEY=...# Profiler / Strategist / Interview PreparerANTHROPIC_API_KEY=...# Resume Analyzer (Claude Sonnet 5)ZAI_API_KEY=...# GitHub Project Summarizer (GLM-5.2)ZAI_BASE_URL=# optional override; blank = https://api.z.ai/api/paas/v4SERPER_API_KEY=...# web search (Interview Preparer research)# ── Resume DOCX generation microservice ──────────────────────────# Live service on AWS (ECS Fargate + ALB). Source & self-hosting instructions:# https://github.com/arijitde92/resume_docx_generatorRESUME_DOCX_SERVICE_URL=http://resume-service-alb-1115872566.us-east-2.elb.amazonaws.com/api/v1/resume/generateRESUME_DOCX_TIMEOUT_SECONDS=120# ── Cloud SQL (MySQL) ────────────────────────────────────────────MYSQL_HOST=127.0.0.1# 127.0.0.1 when using the Cloud SQL Auth ProxyMYSQL_PORT=3306MYSQL_USER=...MYSQL_PASSWORD=...MYSQL_DATABASE=job_applier# ── GCS / Auth ───────────────────────────────────────────────────GCS_BUCKET_NAME=your-resumes-bucketJWT_SECRET_KEY=# generate: python -c "import secrets; print(secrets.token_urlsafe(32))"GOOGLE_OAUTH_CLIENT_ID=# optional, for "Sign in with Google"GOOGLE_OAUTH_CLIENT_SECRET=

The frontend has its own frontend/.env (copy from frontend/.env.example) for VITE_* variables.

Never commit .env or service-account keys. They are gitignored. The db-proxy connection name is derived from GCP_PROJECT_ID, GCP_LOCATION, and instance job-applier-mysql by default — override with CLOUD_SQL_INSTANCE if yours differs.

For step-by-step GCP provisioning (project, Cloud SQL instance, GCS bucket, service account), see backend/docs/GCP_Setup.md.

🖥️ Standalone Crew CLI (no web app)

You can run the agent pipeline directly, without the frontend/DB, from backend/:

cd backend
uv run crew \
--url https://www.linkedin.com/jobs/view/<id>/ \
--github https://github.com/<user> \
--resume sample_data/Arijit_De_Resume.md \
--name "Your Name"

This extracts the job, indexes the GitHub repos into Weaviate, parses and tailors the resume, and writes interview materials — same pipeline the web app drives.

🔑 API Keys & Cloud Setup

All keys go in backend/.env.

GitHub:Create a Personal Access Token → GITHUB_PERSONAL_ACCESS_TOKEN.
Google Cloud:
- Enable the Cloud SQL Admin and Cloud Storage APIs. BigQuery and Vertex AI are no longer used.
- Create a service account with the relevant roles (Cloud SQL Client, Storage Object Admin).
- Download the JSON key into backend/credentials/ and point GOOGLE_APPLICATION_CREDENTIALS at it.
Weaviate: Create a cluster at Weaviate Cloud → WEAVIATE_URL + WEAVIATE_API_KEY (GitHub repo vector store).
Voyage AI:Get an API key → VOYAGE_API_KEY (embeddings + reranking for the GitHub RAG pipeline).
Gemini:Get a Gemini API key → GEMINI_API_KEY (Profiler, Resume Strategist, Interview Preparer, and the RAGAS judge).
Anthropic:Get an API key → ANTHROPIC_API_KEY (the Resume Analyzer runs on Claude Sonnet 5).
Z.ai:Get an API key → ZAI_API_KEY (the GitHub Project Summarizer runs on GLM-5.2).
Bright Data:Get an API key for the MCP scraper → BRIGHT_DATA_API_KEY (LinkedIn job extraction).
Serper:Get a Serper API key → SERPER_API_KEY (the web_search tool). Strictly optional — without it the crew still runs end to end, but the Interview Preparer loses its company research and works from the resume and job details alone.

📚 How it Works

Job Extraction: The job posting is scraped once via the Bright Data MCP LinkedIn extractor and parsed into structured JobDetails, which are injected into every downstream task.
GitHub Analysis & Resume Parsing (in parallel): The GitHub Project Summarizer chunks and indexes your public repos into Weaviate with Voyage embeddings, then retrieves and reranks the projects most relevant to the job. Meanwhile the Resume Analyzer extracts your resume into validated structured JSON.
Profile Compilation: The Profiler agent creates a comprehensive profile using the parsed resume, GitHub summaries, and job requirements.
Resume Tailoring: The Resume Strategist aligns your resume with the job description, then renders it as a polished Word (.docx) resume via the live document-generation microservice (generate_resume_docx tool, max 3 attempts) — the .docx is converted to PDF with LibreOffice for the in-browser preview, and the Markdown version remains the fallback when the service or conversion is unavailable. The microservice is a separate FastAPI app deployed on AWS ECS Fargate; its source is at arijitde92/resume_docx_generator.
Interview Prep: The Interview Preparer researches the company's interview process and role-specific questions with the web_search tool, then generates custom interview questions and talking points grounded in your tailored resume — citing sources for anything it found on the web.

🛠️ Extending the System

Add new agents or tools by following the Crew AI documentation.
Integrate additional data sources or cloud services as needed.

📄 License

MIT License

🤝 Acknowledgements

Crew AI
resume_docx_generator — the companion .docx rendering microservice
Weaviate
Voyage AI
LangChain
BeautifulSoup

Job Application Agent

A powerful, multi-agent automation system for job applications, leveraging Crew AI to orchestrate specialized agents that extract, analyze, and summarize job postings, tailor resumes, and prepare interview materials. Semantic search over your GitHub projects runs on a Weaviate vector store with Voyage AI embeddings and reranking, and the system features custom tools for LinkedIn and GitHub data extraction plus Serper-backed web research.

This project also includes a Full-Stack Web App version with a FastAPI backend (using Google Cloud SQL MySQL and GCS storage) and a React + Vite frontend that displays live agent execution progress via Server-Sent Events (SSE).

The final Word (.docx) resume is rendered by a separate live microservice deployed on AWS (ECS Fargate behind an ALB) — its source is available at arijitde92/resume_docx_generator. This backend calls that service over HTTP and converts the returned .docx to PDF locally (LibreOffice headless) for the in-browser preview. See System Design for the full picture.

📦 Project Structure

Job_Application_Agent/
├── backend/ # FastAPI backend + CrewAI pipeline (uv-managed)
│ ├── app/
│ │ ├── api/ # Route handlers (api/v1) + shared deps
│ │ ├── core/ # config, security, database, logging
│ │ ├── services/ # GCS, crew runner, CrewAI pipeline, extractors,
│ │ │ # vector_store (Weaviate + Voyage)
│ │ ├── models/ # SQLAlchemy ORM models
│ │ ├── schemas/ # Pydantic schemas
│ │ └── main.py # App init + CORS + routers
│ ├── credentials/ # GCP keys (gitignored)
│ ├── pyproject.toml # Dependencies (uv)
│ ├── Dockerfile # (empty for now)
│ └── README.md # Backend setup & run instructions
├── frontend/ # React + Vite (JavaScript) frontend
│ ├── src/
│ │ ├── components/ pages/ context/ hooks/ services/ assets/
│ │ └── main.jsx App.jsx
│ ├── Dockerfile # (empty for now)
│ └── package.json
├── .github/workflows/ # CI/CD (deploy.yml — empty for now)
├── docker-compose.yml # (empty for now)
└── README.md # This file

✅ Prerequisites

Requirement	Version / Notes
Python	3.11+ (3.12 recommended). You don't need to install it manually — `uv` will fetch a matching interpreter.
uv	The Python package & project manager used here. Install: `curl -LsSf https://astral.sh/uv/install.sh \| sh` (then restart your shell so `~/.local/bin` is on `PATH`).
Node.js	18+ (with `npm`), for the React frontend. Download.
Google Cloud account	A GCP project with the Cloud SQL Admin and Cloud Storage APIs enabled, plus a service-account JSON key. BigQuery and Vertex AI are no longer required — the vector store moved off GCP. See backend/docs/GCP_Setup.md.
Cloud SQL (MySQL)	A Cloud SQL MySQL instance (the web app stores users/jobs there).
Cloud SQL Auth Proxy	`cloud-sql-proxy` binary on `PATH` (the `uv run db-proxy` / `uv run dev` commands invoke it). See Installation to install
Weaviate Cloud	A cluster for the GitHub repo vector store (the free sandbox tier is enough to start).
LibreOffice	Used headless to convert the generated .docx resume to PDF for the in-browser preview: `sudo apt-get install -y libreoffice-writer fonts-liberation`. Optional — without it the .docx is still generated, the preview just falls back to Markdown. See backend/README.md.
API keys	GitHub PAT, Bright Data, Gemini, Anthropic, Z.ai, Weaviate, Voyage AI, and Serper. See API Keys & Cloud Setup.

All uv run commands are run from the backend/ directory — the project and its custom commands are defined in backend/pyproject.toml. There is no root-level pyproject.toml, so uv run dev from the repo root will not work.

⚡ Quick Start

git clone https://github.com/arijitde92/Job_Application_Agent.git
cd Job_Application_Agent

1. Backend (uv)

cd backend
cp .env.example .env # then fill in secrets (see below)
uv sync # creates backend/.venv and installs everything

Place your GCP service-account JSON key under backend/credentials/ and point GOOGLE_APPLICATION_CREDENTIALS in backend/.env at it (an absolute path is safest).

2. Frontend

cd ../frontend
cp .env.example .env
npm install

3. Run everything (from `backend/`)

cd ../backend
uv run dev # Cloud SQL proxy + API + frontend, together

Then open http://localhost:5173. The API is at http://localhost:8000 (docs at http://localhost:8000/docs).

Prefer separate terminals? Run them individually:

uv run db-proxy # Cloud SQL Auth Proxy → 127.0.0.1:3306
uv run api # FastAPI dev server → http://localhost:8000
uv run frontend # Vite dev server → http://localhost:5173

See backend/README.md for the full command list, env-var overrides, and the standalone crew CLI.

🚀 Features

Multi-Agent Orchestration: Uses Crew AI to coordinate agents for GitHub analysis, resume parsing, profiling, resume tailoring, and interview preparation.
Custom Tools: Includes LinkedIn job extractor, GitHub repo summarizer, and Serper web search, built using Crew AI's extensible tool system.
Weaviate + Voyage AI: Stores and semantically searches GitHub project data in a Weaviate vector store, using Voyage AI's code-specialised voyage-code-3 embeddings and a rerank-2.5-lite second-stage reranker.
Structured Resume Parsing: A dedicated Resume Analyzer agent extracts the resume into a validated JSON schema, deriving years of experience with a deterministic tool rather than LLM arithmetic.
Automated Resume Tailoring: Aligns your resume with job requirements and optimizes for ATS.
Polished DOCX + PDF Output: The Resume Strategist sends the tailored content to a live document-generation microservice — a separate FastAPI service running on AWS (ECS Fargate behind an ALB) whose source lives at arijitde92/resume_docx_generator — which renders a professional Word (.docx) resume. The backend then converts that .docx to PDF locally (LibreOffice headless) for in-browser viewing. Up to 3 attempts; on failure the pipeline gracefully falls back to the Markdown resume.
Interview Prep: Researches the employer on the web, then generates tailored interview questions and talking points.
Purpose-Matched LLMs: Each agent runs on the model that suits its job — Gemini for synthesis, GLM-5.2 for repo analysis, Claude Sonnet 5 for structured extraction.

🧑‍💻 Agents & Tools

Crew AI Multi-Agent System

Crew AI (Homepage): The backbone of the system, enabling modular, collaborative agent workflows.

Agents: (each runs on the LLM best suited to its task — see backend/app/services/crew/agents.py)

Agent	Role	LLM
GitHub Project Summarizer	Indexes your repos and picks the ones most relevant to the job.	GLM-5.2 (Z.ai)
Resume Analyzer	Extracts the resume into validated structured JSON.	Claude Sonnet 5
Profiler	Compiles a comprehensive personal/professional profile.	Gemini
Resume Strategist	Tailors your resume for each job.	Gemini
Interview Preparer	Researches the employer via web search, then prepares interview questions and talking points.	Gemini

The GitHub summary and resume analysis tasks run in parallel; profiling waits on both. When no GitHub profile is supplied, that agent and its task are skipped entirely and the rest run under explicit grounding rules so nothing is fabricated.

Crew AI Tools Used

FileReadTool: Reads and processes resume files.
ScrapeWebsiteTool: Scrapes web content for job and company info.
MDXSearchTool: Performs semantic search on resume content.
SerperDevTool: Web search for the Resume Strategist.

Custom Tools

LinkedIn Job Extractor: Scrapes and parses job details from LinkedIn job postings.
GitHub Repos Extractor: Recursively fetches and summarizes public GitHub repositories for a user.
Web Search (web_search): Serper-backed Google search used by the Interview Preparer to research the employer's interview process, role-specific questions, and recent company news. Returns a compact ranked digest (featured answer + title/URL/snippet) rather than Serper's raw JSON. It never raises: if the key is missing or the API fails it returns an ERROR: ... observation and the agent falls back to the resume and job details alone, so a search outage cannot sink a run whose resume tailoring has already completed.
Weaviate/Voyage AI Integration: Custom logic to chunk, embed, upsert, and search GitHub repo content — one shared Weaviate collection, with every search scoped to the applicant's own repositories.
Resume Tools: A calculate_yoe tool for deterministic years-of-experience arithmetic and a per-run save_parsed_resume tool that validates the JSON against the schema before persisting it.
DOCX Generator (generate_resume_docx): A per-run tool for the Resume Strategist that POSTs the tailored resume JSON to the live document-generation microservice (POST /api/v1/resume/generate on arijitde92/resume_docx_generator) and saves the returned Word (.docx) resume. The tool wraps the agent's content in the service's metadata envelope deterministically and enforces a 3-attempt retry cap, after which the run falls back to Markdown output.

🏗️ Architecture

Python 3.11+ with uv for dependency & environment management (see backend/pyproject.toml)
FastAPI backend (async SQLAlchemy + Cloud SQL MySQL, JWT/Google OAuth auth)
React + Vite frontend with live progress via Server-Sent Events (SSE)
Crew AI for agent orchestration, across Gemini, GLM-5.2 (Z.ai) and Claude Sonnet 5 depending on the agent
Weaviate Cloud as the vector store for GitHub repo embeddings
Voyage AI for code-specialised embeddings (voyage-code-3) and reranking (rerank-2.5-lite)
Google Cloud Storage for resume & generated-output storage
Resume DOCX microservice — a standalone FastAPI service on AWS ECS Fargate that renders the tailored resume JSON into a Word document (source); the backend converts the returned .docx to PDF with LibreOffice headless
Bright Data MCP for LinkedIn job scraping; LangChain for document loading and chunking

🧭 System Design

Component architecture

flowchart LR
User(["👤 User (browser)"])
subgraph Frontend["React + Vite frontend :5173"]
UI["Pages & components<br/>Home · Job list · Resume preview"]
SSE["SSE client<br/>live agent progress"]
end
subgraph Backend["FastAPI backend :8000"]
API["API routers<br/>/api/auth · /api/resumes<br/>/api/github · /api/jobs"]
Runner["crew_runner<br/>ThreadPoolExecutor (4 jobs)<br/>+ progress store"]
Crew["CrewAI pipeline<br/>agents · tasks · tools"]
PDF["docx_to_pdf<br/>LibreOffice headless"]
end
subgraph GCP["Google Cloud"]
SQL[("Cloud SQL MySQL<br/>users · resumes · jobs")]
GCS[("Cloud Storage<br/>resumes & outputs")]
end
subgraph External["External services"]
BD["Bright Data MCP<br/>LinkedIn scraping"]
GH["GitHub API"]
WV[("Weaviate Cloud<br/>GithubRepoData")]
VOY["Voyage AI<br/>voyage-code-3 · rerank-2.5-lite"]
SERP["Serper<br/>web search"]
LLM["LLMs<br/>Gemini · GLM-5.2 · Claude Sonnet 5"]
DOCX["Resume DOCX microservice<br/>FastAPI on AWS ECS Fargate + ALB<br/>POST /api/v1/resume/generate"]
end
User --> UI
UI -->|"REST + JWT / Google OAuth"| API
SSE <-->|"text/event-stream"| API
API --> Runner --> Crew
API <--> SQL
API <--> GCS
Runner <--> GCS
Crew --> BD
Crew --> GH
Crew <--> WV
WV -.->|"embed & rerank"| VOY
Crew --> SERP
Crew --> LLM
Crew -->|"tailored resume JSON"| DOCX
DOCX -->|".docx bytes"| Crew
Crew --> PDF
PDF -->|".docx + .pdf"| GCS
classDef ext fill:#fff4e6,stroke:#e8a33d,color:#000
classDef svc fill:#e6f0ff,stroke:#4a7fd4,color:#000
class BD,GH,WV,VOY,SERP,LLM,DOCX ext
class SQL,GCS svc

Agent pipeline (one job run)

flowchart TD
Start(["Job submitted<br/>LinkedIn URL + resume + GitHub URL"]) --> Extract["Extract job details<br/>Bright Data MCP → JobDetails JSON"]
Extract --> Parse["Parse resume file<br/>.pdf / .docx / .md → text"]
Parse --> A1["GitHub Project Summarizer<br/><i>GLM-5.2</i><br/>index repos → Weaviate,<br/>retrieve + rerank relevant projects"]
Parse --> A2["Resume Analyzer<br/><i>Claude Sonnet 5</i><br/>resume → validated JSON<br/>(calculate_yoe · save_parsed_resume)"]
A1 --> A3
A2 --> A3
A3["Profiler<br/><i>Gemini</i><br/>compile personal & professional profile"]
A3 --> A4["Resume Strategist<br/><i>Gemini</i><br/>tailor resume · optimise for ATS"]
A4 --> Tool{"generate_resume_docx<br/>≤ 3 attempts"}
Tool -->|"success"| Conv["LibreOffice headless<br/>.docx → .pdf"]
Tool -->|"all attempts failed<br/>or LibreOffice missing"| MD["Markdown resume fallback"]
Conv --> A5
MD --> A5
A5["Interview Preparer<br/><i>Gemini</i><br/>web_search company research →<br/>questions & talking points"]
A5 --> Store["Upload outputs to GCS<br/>update job row in Cloud SQL"]
Store --> Done(["Job completed → SSE 'done'"])
subgraph Parallel["run in parallel"]
A1
A2
end
style Parallel fill:#f4f4f8,stroke:#bbb

The GitHub agent and its task are skipped entirely when no GitHub profile is supplied; the remaining agents then run under explicit grounding rules.

Request lifecycle

sequenceDiagram
autonumber
actor U as User
participant FE as React frontend
participant API as FastAPI (jobs router)
participant DB as Cloud SQL
participant CR as crew_runner (thread pool)
participant CW as CrewAI agents
participant MS as DOCX microservice (AWS)
participant GCS as Cloud Storage
U->>FE: Submit job URL + select resume (+ GitHub profile)
FE->>API: POST /api/jobs/tailor
API->>DB: INSERT job (status=pending)
API-->>FE: 201 { job_id }
API->>CR: asyncio.create_task(run_crew_for_job)
CR->>GCS: Download stored resume bytes
FE->>API: GET /api/jobs/{job_id}/progress (SSE)
loop Each pipeline step
CR->>CW: Run next agent/task
CW-->>CR: Step result
CR->>CR: progress_store[job_id] = step
API-->>FE: event: progress { current_step }
end
CW->>MS: POST /api/v1/resume/generate (resume JSON)
MS-->>CW: .docx bytes
CW->>CR: docx path via ResumeDocxContext.state
CR->>CR: convert_docx_to_pdf (LibreOffice)
CR->>GCS: Upload .docx, .pdf, interview materials
CR->>DB: UPDATE job (status=completed, GCS paths)
API-->>FE: event: done
FE->>API: GET tailored resume / interview materials
API-->>U: PDF preview + .docx download

⚙️ Configuration

All backend configuration lives in backend/.env (copy it from backend/.env.example). Key groups:

# ── GCP ──────────────────────────────────────────────────────────GOOGLE_APPLICATION_CREDENTIALS=/abs/path/to/backend/credentials/service-account.jsonGCP_PROJECT_ID=your-project-idGCP_LOCATION=asia-south2# ── Vector store (Weaviate Cloud + Voyage AI — not GCP) ──────────WEAVIATE_URL=...# cluster REST endpoint; scheme optionalWEAVIATE_API_KEY=...WEAVIATE_COLLECTION_NAME=GithubRepoDataVOYAGE_API_KEY=...# embeddings + rerankingVOYAGE_EMBED_MODEL=voyage-code-3VOYAGE_EMBED_DIMENSION=1024VOYAGE_RERANK_MODEL=rerank-2.5-lite# ── API keys ─────────────────────────────────────────────────────GITHUB_PERSONAL_ACCESS_TOKEN=...BRIGHT_DATA_API_KEY=...# LinkedIn scraping (Bright Data MCP)GEMINI_API_KEY=...# Profiler / Strategist / Interview PreparerANTHROPIC_API_KEY=...# Resume Analyzer (Claude Sonnet 5)ZAI_API_KEY=...# GitHub Project Summarizer (GLM-5.2)ZAI_BASE_URL=# optional override; blank = https://api.z.ai/api/paas/v4SERPER_API_KEY=...# web search (Interview Preparer research)# ── Resume DOCX generation microservice ──────────────────────────# Live service on AWS (ECS Fargate + ALB). Source & self-hosting instructions:# https://github.com/arijitde92/resume_docx_generatorRESUME_DOCX_SERVICE_URL=http://resume-service-alb-1115872566.us-east-2.elb.amazonaws.com/api/v1/resume/generateRESUME_DOCX_TIMEOUT_SECONDS=120# ── Cloud SQL (MySQL) ────────────────────────────────────────────MYSQL_HOST=127.0.0.1# 127.0.0.1 when using the Cloud SQL Auth ProxyMYSQL_PORT=3306MYSQL_USER=...MYSQL_PASSWORD=...MYSQL_DATABASE=job_applier# ── GCS / Auth ───────────────────────────────────────────────────GCS_BUCKET_NAME=your-resumes-bucketJWT_SECRET_KEY=# generate: python -c "import secrets; print(secrets.token_urlsafe(32))"GOOGLE_OAUTH_CLIENT_ID=# optional, for "Sign in with Google"GOOGLE_OAUTH_CLIENT_SECRET=

The frontend has its own frontend/.env (copy from frontend/.env.example) for VITE_* variables.

Never commit .env or service-account keys. They are gitignored. The db-proxy connection name is derived from GCP_PROJECT_ID, GCP_LOCATION, and instance job-applier-mysql by default — override with CLOUD_SQL_INSTANCE if yours differs.

For step-by-step GCP provisioning (project, Cloud SQL instance, GCS bucket, service account), see backend/docs/GCP_Setup.md.

🖥️ Standalone Crew CLI (no web app)

You can run the agent pipeline directly, without the frontend/DB, from backend/:

cd backend
uv run crew \
--url https://www.linkedin.com/jobs/view/<id>/ \
--github https://github.com/<user> \
--resume sample_data/Arijit_De_Resume.md \
--name "Your Name"

This extracts the job, indexes the GitHub repos into Weaviate, parses and tailors the resume, and writes interview materials — same pipeline the web app drives.

🔑 API Keys & Cloud Setup

All keys go in backend/.env.

GitHub:Create a Personal Access Token → GITHUB_PERSONAL_ACCESS_TOKEN.
Google Cloud:
- Enable the Cloud SQL Admin and Cloud Storage APIs. BigQuery and Vertex AI are no longer used.
- Create a service account with the relevant roles (Cloud SQL Client, Storage Object Admin).
- Download the JSON key into backend/credentials/ and point GOOGLE_APPLICATION_CREDENTIALS at it.
Weaviate: Create a cluster at Weaviate Cloud → WEAVIATE_URL + WEAVIATE_API_KEY (GitHub repo vector store).
Voyage AI:Get an API key → VOYAGE_API_KEY (embeddings + reranking for the GitHub RAG pipeline).
Gemini:Get a Gemini API key → GEMINI_API_KEY (Profiler, Resume Strategist, Interview Preparer, and the RAGAS judge).
Anthropic:Get an API key → ANTHROPIC_API_KEY (the Resume Analyzer runs on Claude Sonnet 5).
Z.ai:Get an API key → ZAI_API_KEY (the GitHub Project Summarizer runs on GLM-5.2).
Bright Data:Get an API key for the MCP scraper → BRIGHT_DATA_API_KEY (LinkedIn job extraction).
Serper:Get a Serper API key → SERPER_API_KEY (the web_search tool). Strictly optional — without it the crew still runs end to end, but the Interview Preparer loses its company research and works from the resume and job details alone.

📚 How it Works

Job Extraction: The job posting is scraped once via the Bright Data MCP LinkedIn extractor and parsed into structured JobDetails, which are injected into every downstream task.
GitHub Analysis & Resume Parsing (in parallel): The GitHub Project Summarizer chunks and indexes your public repos into Weaviate with Voyage embeddings, then retrieves and reranks the projects most relevant to the job. Meanwhile the Resume Analyzer extracts your resume into validated structured JSON.
Profile Compilation: The Profiler agent creates a comprehensive profile using the parsed resume, GitHub summaries, and job requirements.
Resume Tailoring: The Resume Strategist aligns your resume with the job description, then renders it as a polished Word (.docx) resume via the live document-generation microservice (generate_resume_docx tool, max 3 attempts) — the .docx is converted to PDF with LibreOffice for the in-browser preview, and the Markdown version remains the fallback when the service or conversion is unavailable. The microservice is a separate FastAPI app deployed on AWS ECS Fargate; its source is at arijitde92/resume_docx_generator.
Interview Prep: The Interview Preparer researches the company's interview process and role-specific questions with the web_search tool, then generates custom interview questions and talking points grounded in your tailored resume — citing sources for anything it found on the web.

🛠️ Extending the System

Add new agents or tools by following the Crew AI documentation.
Integrate additional data sources or cloud services as needed.

📄 License

MIT License

🤝 Acknowledgements

Crew AI
resume_docx_generator — the companion .docx rendering microservice
Weaviate
Voyage AI
LangChain
BeautifulSoup

Job Application Agent

A powerful, multi-agent automation system for job applications, leveraging Crew AI to orchestrate specialized agents that extract, analyze, and summarize job postings, tailor resumes, and prepare interview materials. Semantic search over your GitHub projects runs on a Weaviate vector store with Voyage AI embeddings and reranking, and the system features custom tools for LinkedIn and GitHub data extraction plus Serper-backed web research.

This project also includes a Full-Stack Web App version with a FastAPI backend (using Google Cloud SQL MySQL and GCS storage) and a React + Vite frontend that displays live agent execution progress via Server-Sent Events (SSE).

The final Word (.docx) resume is rendered by a separate live microservice deployed on AWS (ECS Fargate behind an ALB) — its source is available at arijitde92/resume_docx_generator. This backend calls that service over HTTP and converts the returned .docx to PDF locally (LibreOffice headless) for the in-browser preview. See System Design for the full picture.

📦 Project Structure

Job_Application_Agent/
├── backend/ # FastAPI backend + CrewAI pipeline (uv-managed)
│ ├── app/
│ │ ├── api/ # Route handlers (api/v1) + shared deps
│ │ ├── core/ # config, security, database, logging
│ │ ├── services/ # GCS, crew runner, CrewAI pipeline, extractors,
│ │ │ # vector_store (Weaviate + Voyage)
│ │ ├── models/ # SQLAlchemy ORM models
│ │ ├── schemas/ # Pydantic schemas
│ │ └── main.py # App init + CORS + routers
│ ├── credentials/ # GCP keys (gitignored)
│ ├── pyproject.toml # Dependencies (uv)
│ ├── Dockerfile # (empty for now)
│ └── README.md # Backend setup & run instructions
├── frontend/ # React + Vite (JavaScript) frontend
│ ├── src/
│ │ ├── components/ pages/ context/ hooks/ services/ assets/
│ │ └── main.jsx App.jsx
│ ├── Dockerfile # (empty for now)
│ └── package.json
├── .github/workflows/ # CI/CD (deploy.yml — empty for now)
├── docker-compose.yml # (empty for now)
└── README.md # This file

✅ Prerequisites

Requirement	Version / Notes
Python	3.11+ (3.12 recommended). You don't need to install it manually — `uv` will fetch a matching interpreter.
uv	The Python package & project manager used here. Install: `curl -LsSf https://astral.sh/uv/install.sh \| sh` (then restart your shell so `~/.local/bin` is on `PATH`).
Node.js	18+ (with `npm`), for the React frontend. Download.
Google Cloud account	A GCP project with the Cloud SQL Admin and Cloud Storage APIs enabled, plus a service-account JSON key. BigQuery and Vertex AI are no longer required — the vector store moved off GCP. See backend/docs/GCP_Setup.md.
Cloud SQL (MySQL)	A Cloud SQL MySQL instance (the web app stores users/jobs there).
Cloud SQL Auth Proxy	`cloud-sql-proxy` binary on `PATH` (the `uv run db-proxy` / `uv run dev` commands invoke it). See Installation to install
Weaviate Cloud	A cluster for the GitHub repo vector store (the free sandbox tier is enough to start).
LibreOffice	Used headless to convert the generated .docx resume to PDF for the in-browser preview: `sudo apt-get install -y libreoffice-writer fonts-liberation`. Optional — without it the .docx is still generated, the preview just falls back to Markdown. See backend/README.md.
API keys	GitHub PAT, Bright Data, Gemini, Anthropic, Z.ai, Weaviate, Voyage AI, and Serper. See API Keys & Cloud Setup.

All uv run commands are run from the backend/ directory — the project and its custom commands are defined in backend/pyproject.toml. There is no root-level pyproject.toml, so uv run dev from the repo root will not work.

⚡ Quick Start

git clone https://github.com/arijitde92/Job_Application_Agent.git
cd Job_Application_Agent

1. Backend (uv)

cd backend
cp .env.example .env # then fill in secrets (see below)
uv sync # creates backend/.venv and installs everything

Place your GCP service-account JSON key under backend/credentials/ and point GOOGLE_APPLICATION_CREDENTIALS in backend/.env at it (an absolute path is safest).

2. Frontend

cd ../frontend
cp .env.example .env
npm install

3. Run everything (from `backend/`)

cd ../backend
uv run dev # Cloud SQL proxy + API + frontend, together

Then open http://localhost:5173. The API is at http://localhost:8000 (docs at http://localhost:8000/docs).

Prefer separate terminals? Run them individually:

uv run db-proxy # Cloud SQL Auth Proxy → 127.0.0.1:3306
uv run api # FastAPI dev server → http://localhost:8000
uv run frontend # Vite dev server → http://localhost:5173

See backend/README.md for the full command list, env-var overrides, and the standalone crew CLI.

🚀 Features

Multi-Agent Orchestration: Uses Crew AI to coordinate agents for GitHub analysis, resume parsing, profiling, resume tailoring, and interview preparation.
Custom Tools: Includes LinkedIn job extractor, GitHub repo summarizer, and Serper web search, built using Crew AI's extensible tool system.
Weaviate + Voyage AI: Stores and semantically searches GitHub project data in a Weaviate vector store, using Voyage AI's code-specialised voyage-code-3 embeddings and a rerank-2.5-lite second-stage reranker.
Structured Resume Parsing: A dedicated Resume Analyzer agent extracts the resume into a validated JSON schema, deriving years of experience with a deterministic tool rather than LLM arithmetic.
Automated Resume Tailoring: Aligns your resume with job requirements and optimizes for ATS.
Polished DOCX + PDF Output: The Resume Strategist sends the tailored content to a live document-generation microservice — a separate FastAPI service running on AWS (ECS Fargate behind an ALB) whose source lives at arijitde92/resume_docx_generator — which renders a professional Word (.docx) resume. The backend then converts that .docx to PDF locally (LibreOffice headless) for in-browser viewing. Up to 3 attempts; on failure the pipeline gracefully falls back to the Markdown resume.
Interview Prep: Researches the employer on the web, then generates tailored interview questions and talking points.
Purpose-Matched LLMs: Each agent runs on the model that suits its job — Gemini for synthesis, GLM-5.2 for repo analysis, Claude Sonnet 5 for structured extraction.

🧑‍💻 Agents & Tools

Crew AI Multi-Agent System

Crew AI (Homepage): The backbone of the system, enabling modular, collaborative agent workflows.

Agents: (each runs on the LLM best suited to its task — see backend/app/services/crew/agents.py)

Agent	Role	LLM
GitHub Project Summarizer	Indexes your repos and picks the ones most relevant to the job.	GLM-5.2 (Z.ai)
Resume Analyzer	Extracts the resume into validated structured JSON.	Claude Sonnet 5
Profiler	Compiles a comprehensive personal/professional profile.	Gemini
Resume Strategist	Tailors your resume for each job.	Gemini
Interview Preparer	Researches the employer via web search, then prepares interview questions and talking points.	Gemini

The GitHub summary and resume analysis tasks run in parallel; profiling waits on both. When no GitHub profile is supplied, that agent and its task are skipped entirely and the rest run under explicit grounding rules so nothing is fabricated.

Crew AI Tools Used

FileReadTool: Reads and processes resume files.
ScrapeWebsiteTool: Scrapes web content for job and company info.
MDXSearchTool: Performs semantic search on resume content.
SerperDevTool: Web search for the Resume Strategist.

Custom Tools

LinkedIn Job Extractor: Scrapes and parses job details from LinkedIn job postings.
GitHub Repos Extractor: Recursively fetches and summarizes public GitHub repositories for a user.
Web Search (web_search): Serper-backed Google search used by the Interview Preparer to research the employer's interview process, role-specific questions, and recent company news. Returns a compact ranked digest (featured answer + title/URL/snippet) rather than Serper's raw JSON. It never raises: if the key is missing or the API fails it returns an ERROR: ... observation and the agent falls back to the resume and job details alone, so a search outage cannot sink a run whose resume tailoring has already completed.
Weaviate/Voyage AI Integration: Custom logic to chunk, embed, upsert, and search GitHub repo content — one shared Weaviate collection, with every search scoped to the applicant's own repositories.
Resume Tools: A calculate_yoe tool for deterministic years-of-experience arithmetic and a per-run save_parsed_resume tool that validates the JSON against the schema before persisting it.
DOCX Generator (generate_resume_docx): A per-run tool for the Resume Strategist that POSTs the tailored resume JSON to the live document-generation microservice (POST /api/v1/resume/generate on arijitde92/resume_docx_generator) and saves the returned Word (.docx) resume. The tool wraps the agent's content in the service's metadata envelope deterministically and enforces a 3-attempt retry cap, after which the run falls back to Markdown output.

🏗️ Architecture

Python 3.11+ with uv for dependency & environment management (see backend/pyproject.toml)
FastAPI backend (async SQLAlchemy + Cloud SQL MySQL, JWT/Google OAuth auth)
React + Vite frontend with live progress via Server-Sent Events (SSE)
Crew AI for agent orchestration, across Gemini, GLM-5.2 (Z.ai) and Claude Sonnet 5 depending on the agent
Weaviate Cloud as the vector store for GitHub repo embeddings
Voyage AI for code-specialised embeddings (voyage-code-3) and reranking (rerank-2.5-lite)
Google Cloud Storage for resume & generated-output storage
Resume DOCX microservice — a standalone FastAPI service on AWS ECS Fargate that renders the tailored resume JSON into a Word document (source); the backend converts the returned .docx to PDF with LibreOffice headless
Bright Data MCP for LinkedIn job scraping; LangChain for document loading and chunking

🧭 System Design

Component architecture

flowchart LR
User(["👤 User (browser)"])
subgraph Frontend["React + Vite frontend :5173"]
UI["Pages & components<br/>Home · Job list · Resume preview"]
SSE["SSE client<br/>live agent progress"]
end
subgraph Backend["FastAPI backend :8000"]
API["API routers<br/>/api/auth · /api/resumes<br/>/api/github · /api/jobs"]
Runner["crew_runner<br/>ThreadPoolExecutor (4 jobs)<br/>+ progress store"]
Crew["CrewAI pipeline<br/>agents · tasks · tools"]
PDF["docx_to_pdf<br/>LibreOffice headless"]
end
subgraph GCP["Google Cloud"]
SQL[("Cloud SQL MySQL<br/>users · resumes · jobs")]
GCS[("Cloud Storage<br/>resumes & outputs")]
end
subgraph External["External services"]
BD["Bright Data MCP<br/>LinkedIn scraping"]
GH["GitHub API"]
WV[("Weaviate Cloud<br/>GithubRepoData")]
VOY["Voyage AI<br/>voyage-code-3 · rerank-2.5-lite"]
SERP["Serper<br/>web search"]
LLM["LLMs<br/>Gemini · GLM-5.2 · Claude Sonnet 5"]
DOCX["Resume DOCX microservice<br/>FastAPI on AWS ECS Fargate + ALB<br/>POST /api/v1/resume/generate"]
end
User --> UI
UI -->|"REST + JWT / Google OAuth"| API
SSE <-->|"text/event-stream"| API
API --> Runner --> Crew
API <--> SQL
API <--> GCS
Runner <--> GCS
Crew --> BD
Crew --> GH
Crew <--> WV
WV -.->|"embed & rerank"| VOY
Crew --> SERP
Crew --> LLM
Crew -->|"tailored resume JSON"| DOCX
DOCX -->|".docx bytes"| Crew
Crew --> PDF
PDF -->|".docx + .pdf"| GCS
classDef ext fill:#fff4e6,stroke:#e8a33d,color:#000
classDef svc fill:#e6f0ff,stroke:#4a7fd4,color:#000
class BD,GH,WV,VOY,SERP,LLM,DOCX ext
class SQL,GCS svc

Agent pipeline (one job run)

flowchart TD
Start(["Job submitted<br/>LinkedIn URL + resume + GitHub URL"]) --> Extract["Extract job details<br/>Bright Data MCP → JobDetails JSON"]
Extract --> Parse["Parse resume file<br/>.pdf / .docx / .md → text"]
Parse --> A1["GitHub Project Summarizer<br/><i>GLM-5.2</i><br/>index repos → Weaviate,<br/>retrieve + rerank relevant projects"]
Parse --> A2["Resume Analyzer<br/><i>Claude Sonnet 5</i><br/>resume → validated JSON<br/>(calculate_yoe · save_parsed_resume)"]
A1 --> A3
A2 --> A3
A3["Profiler<br/><i>Gemini</i><br/>compile personal & professional profile"]
A3 --> A4["Resume Strategist<br/><i>Gemini</i><br/>tailor resume · optimise for ATS"]
A4 --> Tool{"generate_resume_docx<br/>≤ 3 attempts"}
Tool -->|"success"| Conv["LibreOffice headless<br/>.docx → .pdf"]
Tool -->|"all attempts failed<br/>or LibreOffice missing"| MD["Markdown resume fallback"]
Conv --> A5
MD --> A5
A5["Interview Preparer<br/><i>Gemini</i><br/>web_search company research →<br/>questions & talking points"]
A5 --> Store["Upload outputs to GCS<br/>update job row in Cloud SQL"]
Store --> Done(["Job completed → SSE 'done'"])
subgraph Parallel["run in parallel"]
A1
A2
end
style Parallel fill:#f4f4f8,stroke:#bbb

The GitHub agent and its task are skipped entirely when no GitHub profile is supplied; the remaining agents then run under explicit grounding rules.

Request lifecycle

sequenceDiagram
autonumber
actor U as User
participant FE as React frontend
participant API as FastAPI (jobs router)
participant DB as Cloud SQL
participant CR as crew_runner (thread pool)
participant CW as CrewAI agents
participant MS as DOCX microservice (AWS)
participant GCS as Cloud Storage
U->>FE: Submit job URL + select resume (+ GitHub profile)
FE->>API: POST /api/jobs/tailor
API->>DB: INSERT job (status=pending)
API-->>FE: 201 { job_id }
API->>CR: asyncio.create_task(run_crew_for_job)
CR->>GCS: Download stored resume bytes
FE->>API: GET /api/jobs/{job_id}/progress (SSE)
loop Each pipeline step
CR->>CW: Run next agent/task
CW-->>CR: Step result
CR->>CR: progress_store[job_id] = step
API-->>FE: event: progress { current_step }
end
CW->>MS: POST /api/v1/resume/generate (resume JSON)
MS-->>CW: .docx bytes
CW->>CR: docx path via ResumeDocxContext.state
CR->>CR: convert_docx_to_pdf (LibreOffice)
CR->>GCS: Upload .docx, .pdf, interview materials
CR->>DB: UPDATE job (status=completed, GCS paths)
API-->>FE: event: done
FE->>API: GET tailored resume / interview materials
API-->>U: PDF preview + .docx download

⚙️ Configuration

All backend configuration lives in backend/.env (copy it from backend/.env.example). Key groups:

# ── GCP ──────────────────────────────────────────────────────────GOOGLE_APPLICATION_CREDENTIALS=/abs/path/to/backend/credentials/service-account.jsonGCP_PROJECT_ID=your-project-idGCP_LOCATION=asia-south2# ── Vector store (Weaviate Cloud + Voyage AI — not GCP) ──────────WEAVIATE_URL=...# cluster REST endpoint; scheme optionalWEAVIATE_API_KEY=...WEAVIATE_COLLECTION_NAME=GithubRepoDataVOYAGE_API_KEY=...# embeddings + rerankingVOYAGE_EMBED_MODEL=voyage-code-3VOYAGE_EMBED_DIMENSION=1024VOYAGE_RERANK_MODEL=rerank-2.5-lite# ── API keys ─────────────────────────────────────────────────────GITHUB_PERSONAL_ACCESS_TOKEN=...BRIGHT_DATA_API_KEY=...# LinkedIn scraping (Bright Data MCP)GEMINI_API_KEY=...# Profiler / Strategist / Interview PreparerANTHROPIC_API_KEY=...# Resume Analyzer (Claude Sonnet 5)ZAI_API_KEY=...# GitHub Project Summarizer (GLM-5.2)ZAI_BASE_URL=# optional override; blank = https://api.z.ai/api/paas/v4SERPER_API_KEY=...# web search (Interview Preparer research)# ── Resume DOCX generation microservice ──────────────────────────# Live service on AWS (ECS Fargate + ALB). Source & self-hosting instructions:# https://github.com/arijitde92/resume_docx_generatorRESUME_DOCX_SERVICE_URL=http://resume-service-alb-1115872566.us-east-2.elb.amazonaws.com/api/v1/resume/generateRESUME_DOCX_TIMEOUT_SECONDS=120# ── Cloud SQL (MySQL) ────────────────────────────────────────────MYSQL_HOST=127.0.0.1# 127.0.0.1 when using the Cloud SQL Auth ProxyMYSQL_PORT=3306MYSQL_USER=...MYSQL_PASSWORD=...MYSQL_DATABASE=job_applier# ── GCS / Auth ───────────────────────────────────────────────────GCS_BUCKET_NAME=your-resumes-bucketJWT_SECRET_KEY=# generate: python -c "import secrets; print(secrets.token_urlsafe(32))"GOOGLE_OAUTH_CLIENT_ID=# optional, for "Sign in with Google"GOOGLE_OAUTH_CLIENT_SECRET=

The frontend has its own frontend/.env (copy from frontend/.env.example) for VITE_* variables.

Never commit .env or service-account keys. They are gitignored. The db-proxy connection name is derived from GCP_PROJECT_ID, GCP_LOCATION, and instance job-applier-mysql by default — override with CLOUD_SQL_INSTANCE if yours differs.

For step-by-step GCP provisioning (project, Cloud SQL instance, GCS bucket, service account), see backend/docs/GCP_Setup.md.

🖥️ Standalone Crew CLI (no web app)

You can run the agent pipeline directly, without the frontend/DB, from backend/:

cd backend
uv run crew \
--url https://www.linkedin.com/jobs/view/<id>/ \
--github https://github.com/<user> \
--resume sample_data/Arijit_De_Resume.md \
--name "Your Name"

This extracts the job, indexes the GitHub repos into Weaviate, parses and tailors the resume, and writes interview materials — same pipeline the web app drives.

🔑 API Keys & Cloud Setup

All keys go in backend/.env.

GitHub:Create a Personal Access Token → GITHUB_PERSONAL_ACCESS_TOKEN.
Google Cloud:
- Enable the Cloud SQL Admin and Cloud Storage APIs. BigQuery and Vertex AI are no longer used.
- Create a service account with the relevant roles (Cloud SQL Client, Storage Object Admin).
- Download the JSON key into backend/credentials/ and point GOOGLE_APPLICATION_CREDENTIALS at it.
Weaviate: Create a cluster at Weaviate Cloud → WEAVIATE_URL + WEAVIATE_API_KEY (GitHub repo vector store).
Voyage AI:Get an API key → VOYAGE_API_KEY (embeddings + reranking for the GitHub RAG pipeline).
Gemini:Get a Gemini API key → GEMINI_API_KEY (Profiler, Resume Strategist, Interview Preparer, and the RAGAS judge).
Anthropic:Get an API key → ANTHROPIC_API_KEY (the Resume Analyzer runs on Claude Sonnet 5).
Z.ai:Get an API key → ZAI_API_KEY (the GitHub Project Summarizer runs on GLM-5.2).
Bright Data:Get an API key for the MCP scraper → BRIGHT_DATA_API_KEY (LinkedIn job extraction).
Serper:Get a Serper API key → SERPER_API_KEY (the web_search tool). Strictly optional — without it the crew still runs end to end, but the Interview Preparer loses its company research and works from the resume and job details alone.

📚 How it Works

Job Extraction: The job posting is scraped once via the Bright Data MCP LinkedIn extractor and parsed into structured JobDetails, which are injected into every downstream task.
GitHub Analysis & Resume Parsing (in parallel): The GitHub Project Summarizer chunks and indexes your public repos into Weaviate with Voyage embeddings, then retrieves and reranks the projects most relevant to the job. Meanwhile the Resume Analyzer extracts your resume into validated structured JSON.
Profile Compilation: The Profiler agent creates a comprehensive profile using the parsed resume, GitHub summaries, and job requirements.
Resume Tailoring: The Resume Strategist aligns your resume with the job description, then renders it as a polished Word (.docx) resume via the live document-generation microservice (generate_resume_docx tool, max 3 attempts) — the .docx is converted to PDF with LibreOffice for the in-browser preview, and the Markdown version remains the fallback when the service or conversion is unavailable. The microservice is a separate FastAPI app deployed on AWS ECS Fargate; its source is at arijitde92/resume_docx_generator.
Interview Prep: The Interview Preparer researches the company's interview process and role-specific questions with the web_search tool, then generates custom interview questions and talking points grounded in your tailored resume — citing sources for anything it found on the web.

🛠️ Extending the System

Add new agents or tools by following the Crew AI documentation.
Integrate additional data sources or cloud services as needed.

📄 License

MIT License

🤝 Acknowledgements

Crew AI
resume_docx_generator — the companion .docx rendering microservice
Weaviate
Voyage AI
LangChain
BeautifulSoup

Job Application Agent

A powerful, multi-agent automation system for job applications, leveraging Crew AI to orchestrate specialized agents that extract, analyze, and summarize job postings, tailor resumes, and prepare interview materials. Semantic search over your GitHub projects runs on a Weaviate vector store with Voyage AI embeddings and reranking, and the system features custom tools for LinkedIn and GitHub data extraction plus Serper-backed web research.

This project also includes a Full-Stack Web App version with a FastAPI backend (using Google Cloud SQL MySQL and GCS storage) and a React + Vite frontend that displays live agent execution progress via Server-Sent Events (SSE).

The final Word (.docx) resume is rendered by a separate live microservice deployed on AWS (ECS Fargate behind an ALB) — its source is available at arijitde92/resume_docx_generator. This backend calls that service over HTTP and converts the returned .docx to PDF locally (LibreOffice headless) for the in-browser preview. See System Design for the full picture.

📦 Project Structure

Job_Application_Agent/
├── backend/ # FastAPI backend + CrewAI pipeline (uv-managed)
│ ├── app/
│ │ ├── api/ # Route handlers (api/v1) + shared deps
│ │ ├── core/ # config, security, database, logging
│ │ ├── services/ # GCS, crew runner, CrewAI pipeline, extractors,
│ │ │ # vector_store (Weaviate + Voyage)
│ │ ├── models/ # SQLAlchemy ORM models
│ │ ├── schemas/ # Pydantic schemas
│ │ └── main.py # App init + CORS + routers
│ ├── credentials/ # GCP keys (gitignored)
│ ├── pyproject.toml # Dependencies (uv)
│ ├── Dockerfile # (empty for now)
│ └── README.md # Backend setup & run instructions
├── frontend/ # React + Vite (JavaScript) frontend
│ ├── src/
│ │ ├── components/ pages/ context/ hooks/ services/ assets/
│ │ └── main.jsx App.jsx
│ ├── Dockerfile # (empty for now)
│ └── package.json
├── .github/workflows/ # CI/CD (deploy.yml — empty for now)
├── docker-compose.yml # (empty for now)
└── README.md # This file

✅ Prerequisites

Requirement	Version / Notes
Python	3.11+ (3.12 recommended). You don't need to install it manually — `uv` will fetch a matching interpreter.
uv	The Python package & project manager used here. Install: `curl -LsSf https://astral.sh/uv/install.sh \| sh` (then restart your shell so `~/.local/bin` is on `PATH`).
Node.js	18+ (with `npm`), for the React frontend. Download.
Google Cloud account	A GCP project with the Cloud SQL Admin and Cloud Storage APIs enabled, plus a service-account JSON key. BigQuery and Vertex AI are no longer required — the vector store moved off GCP. See backend/docs/GCP_Setup.md.
Cloud SQL (MySQL)	A Cloud SQL MySQL instance (the web app stores users/jobs there).
Cloud SQL Auth Proxy	`cloud-sql-proxy` binary on `PATH` (the `uv run db-proxy` / `uv run dev` commands invoke it). See Installation to install
Weaviate Cloud	A cluster for the GitHub repo vector store (the free sandbox tier is enough to start).
LibreOffice	Used headless to convert the generated .docx resume to PDF for the in-browser preview: `sudo apt-get install -y libreoffice-writer fonts-liberation`. Optional — without it the .docx is still generated, the preview just falls back to Markdown. See backend/README.md.
API keys	GitHub PAT, Bright Data, Gemini, Anthropic, Z.ai, Weaviate, Voyage AI, and Serper. See API Keys & Cloud Setup.

All uv run commands are run from the backend/ directory — the project and its custom commands are defined in backend/pyproject.toml. There is no root-level pyproject.toml, so uv run dev from the repo root will not work.

⚡ Quick Start

git clone https://github.com/arijitde92/Job_Application_Agent.git
cd Job_Application_Agent

1. Backend (uv)

cd backend
cp .env.example .env # then fill in secrets (see below)
uv sync # creates backend/.venv and installs everything

Place your GCP service-account JSON key under backend/credentials/ and point GOOGLE_APPLICATION_CREDENTIALS in backend/.env at it (an absolute path is safest).

2. Frontend

cd ../frontend
cp .env.example .env
npm install

3. Run everything (from `backend/`)

cd ../backend
uv run dev # Cloud SQL proxy + API + frontend, together

Then open http://localhost:5173. The API is at http://localhost:8000 (docs at http://localhost:8000/docs).

Prefer separate terminals? Run them individually:

uv run db-proxy # Cloud SQL Auth Proxy → 127.0.0.1:3306
uv run api # FastAPI dev server → http://localhost:8000
uv run frontend # Vite dev server → http://localhost:5173

See backend/README.md for the full command list, env-var overrides, and the standalone crew CLI.

🚀 Features

Multi-Agent Orchestration: Uses Crew AI to coordinate agents for GitHub analysis, resume parsing, profiling, resume tailoring, and interview preparation.
Custom Tools: Includes LinkedIn job extractor, GitHub repo summarizer, and Serper web search, built using Crew AI's extensible tool system.
Weaviate + Voyage AI: Stores and semantically searches GitHub project data in a Weaviate vector store, using Voyage AI's code-specialised voyage-code-3 embeddings and a rerank-2.5-lite second-stage reranker.
Structured Resume Parsing: A dedicated Resume Analyzer agent extracts the resume into a validated JSON schema, deriving years of experience with a deterministic tool rather than LLM arithmetic.
Automated Resume Tailoring: Aligns your resume with job requirements and optimizes for ATS.
Polished DOCX + PDF Output: The Resume Strategist sends the tailored content to a live document-generation microservice — a separate FastAPI service running on AWS (ECS Fargate behind an ALB) whose source lives at arijitde92/resume_docx_generator — which renders a professional Word (.docx) resume. The backend then converts that .docx to PDF locally (LibreOffice headless) for in-browser viewing. Up to 3 attempts; on failure the pipeline gracefully falls back to the Markdown resume.
Interview Prep: Researches the employer on the web, then generates tailored interview questions and talking points.
Purpose-Matched LLMs: Each agent runs on the model that suits its job — Gemini for synthesis, GLM-5.2 for repo analysis, Claude Sonnet 5 for structured extraction.

🧑‍💻 Agents & Tools

Crew AI Multi-Agent System

Crew AI (Homepage): The backbone of the system, enabling modular, collaborative agent workflows.

Agents: (each runs on the LLM best suited to its task — see backend/app/services/crew/agents.py)

Agent	Role	LLM
GitHub Project Summarizer	Indexes your repos and picks the ones most relevant to the job.	GLM-5.2 (Z.ai)
Resume Analyzer	Extracts the resume into validated structured JSON.	Claude Sonnet 5
Profiler	Compiles a comprehensive personal/professional profile.	Gemini
Resume Strategist	Tailors your resume for each job.	Gemini
Interview Preparer	Researches the employer via web search, then prepares interview questions and talking points.	Gemini

The GitHub summary and resume analysis tasks run in parallel; profiling waits on both. When no GitHub profile is supplied, that agent and its task are skipped entirely and the rest run under explicit grounding rules so nothing is fabricated.

Crew AI Tools Used

FileReadTool: Reads and processes resume files.
ScrapeWebsiteTool: Scrapes web content for job and company info.
MDXSearchTool: Performs semantic search on resume content.
SerperDevTool: Web search for the Resume Strategist.

Custom Tools

LinkedIn Job Extractor: Scrapes and parses job details from LinkedIn job postings.
GitHub Repos Extractor: Recursively fetches and summarizes public GitHub repositories for a user.
Web Search (web_search): Serper-backed Google search used by the Interview Preparer to research the employer's interview process, role-specific questions, and recent company news. Returns a compact ranked digest (featured answer + title/URL/snippet) rather than Serper's raw JSON. It never raises: if the key is missing or the API fails it returns an ERROR: ... observation and the agent falls back to the resume and job details alone, so a search outage cannot sink a run whose resume tailoring has already completed.
Weaviate/Voyage AI Integration: Custom logic to chunk, embed, upsert, and search GitHub repo content — one shared Weaviate collection, with every search scoped to the applicant's own repositories.
Resume Tools: A calculate_yoe tool for deterministic years-of-experience arithmetic and a per-run save_parsed_resume tool that validates the JSON against the schema before persisting it.
DOCX Generator (generate_resume_docx): A per-run tool for the Resume Strategist that POSTs the tailored resume JSON to the live document-generation microservice (POST /api/v1/resume/generate on arijitde92/resume_docx_generator) and saves the returned Word (.docx) resume. The tool wraps the agent's content in the service's metadata envelope deterministically and enforces a 3-attempt retry cap, after which the run falls back to Markdown output.

🏗️ Architecture

Python 3.11+ with uv for dependency & environment management (see backend/pyproject.toml)
FastAPI backend (async SQLAlchemy + Cloud SQL MySQL, JWT/Google OAuth auth)
React + Vite frontend with live progress via Server-Sent Events (SSE)
Crew AI for agent orchestration, across Gemini, GLM-5.2 (Z.ai) and Claude Sonnet 5 depending on the agent
Weaviate Cloud as the vector store for GitHub repo embeddings
Voyage AI for code-specialised embeddings (voyage-code-3) and reranking (rerank-2.5-lite)
Google Cloud Storage for resume & generated-output storage
Resume DOCX microservice — a standalone FastAPI service on AWS ECS Fargate that renders the tailored resume JSON into a Word document (source); the backend converts the returned .docx to PDF with LibreOffice headless
Bright Data MCP for LinkedIn job scraping; LangChain for document loading and chunking

🧭 System Design

Component architecture

flowchart LR
User(["👤 User (browser)"])
subgraph Frontend["React + Vite frontend :5173"]
UI["Pages & components<br/>Home · Job list · Resume preview"]
SSE["SSE client<br/>live agent progress"]
end
subgraph Backend["FastAPI backend :8000"]
API["API routers<br/>/api/auth · /api/resumes<br/>/api/github · /api/jobs"]
Runner["crew_runner<br/>ThreadPoolExecutor (4 jobs)<br/>+ progress store"]
Crew["CrewAI pipeline<br/>agents · tasks · tools"]
PDF["docx_to_pdf<br/>LibreOffice headless"]
end
subgraph GCP["Google Cloud"]
SQL[("Cloud SQL MySQL<br/>users · resumes · jobs")]
GCS[("Cloud Storage<br/>resumes & outputs")]
end
subgraph External["External services"]
BD["Bright Data MCP<br/>LinkedIn scraping"]
GH["GitHub API"]
WV[("Weaviate Cloud<br/>GithubRepoData")]
VOY["Voyage AI<br/>voyage-code-3 · rerank-2.5-lite"]
SERP["Serper<br/>web search"]
LLM["LLMs<br/>Gemini · GLM-5.2 · Claude Sonnet 5"]
DOCX["Resume DOCX microservice<br/>FastAPI on AWS ECS Fargate + ALB<br/>POST /api/v1/resume/generate"]
end
User --> UI
UI -->|"REST + JWT / Google OAuth"| API
SSE <-->|"text/event-stream"| API
API --> Runner --> Crew
API <--> SQL
API <--> GCS
Runner <--> GCS
Crew --> BD
Crew --> GH
Crew <--> WV
WV -.->|"embed & rerank"| VOY
Crew --> SERP
Crew --> LLM
Crew -->|"tailored resume JSON"| DOCX
DOCX -->|".docx bytes"| Crew
Crew --> PDF
PDF -->|".docx + .pdf"| GCS
classDef ext fill:#fff4e6,stroke:#e8a33d,color:#000
classDef svc fill:#e6f0ff,stroke:#4a7fd4,color:#000
class BD,GH,WV,VOY,SERP,LLM,DOCX ext
class SQL,GCS svc

Agent pipeline (one job run)

flowchart TD
Start(["Job submitted<br/>LinkedIn URL + resume + GitHub URL"]) --> Extract["Extract job details<br/>Bright Data MCP → JobDetails JSON"]
Extract --> Parse["Parse resume file<br/>.pdf / .docx / .md → text"]
Parse --> A1["GitHub Project Summarizer<br/><i>GLM-5.2</i><br/>index repos → Weaviate,<br/>retrieve + rerank relevant projects"]
Parse --> A2["Resume Analyzer<br/><i>Claude Sonnet 5</i><br/>resume → validated JSON<br/>(calculate_yoe · save_parsed_resume)"]
A1 --> A3
A2 --> A3
A3["Profiler<br/><i>Gemini</i><br/>compile personal & professional profile"]
A3 --> A4["Resume Strategist<br/><i>Gemini</i><br/>tailor resume · optimise for ATS"]
A4 --> Tool{"generate_resume_docx<br/>≤ 3 attempts"}
Tool -->|"success"| Conv["LibreOffice headless<br/>.docx → .pdf"]
Tool -->|"all attempts failed<br/>or LibreOffice missing"| MD["Markdown resume fallback"]
Conv --> A5
MD --> A5
A5["Interview Preparer<br/><i>Gemini</i><br/>web_search company research →<br/>questions & talking points"]
A5 --> Store["Upload outputs to GCS<br/>update job row in Cloud SQL"]
Store --> Done(["Job completed → SSE 'done'"])
subgraph Parallel["run in parallel"]
A1
A2
end
style Parallel fill:#f4f4f8,stroke:#bbb

The GitHub agent and its task are skipped entirely when no GitHub profile is supplied; the remaining agents then run under explicit grounding rules.

Request lifecycle

sequenceDiagram
autonumber
actor U as User
participant FE as React frontend
participant API as FastAPI (jobs router)
participant DB as Cloud SQL
participant CR as crew_runner (thread pool)
participant CW as CrewAI agents
participant MS as DOCX microservice (AWS)
participant GCS as Cloud Storage
U->>FE: Submit job URL + select resume (+ GitHub profile)
FE->>API: POST /api/jobs/tailor
API->>DB: INSERT job (status=pending)
API-->>FE: 201 { job_id }
API->>CR: asyncio.create_task(run_crew_for_job)
CR->>GCS: Download stored resume bytes
FE->>API: GET /api/jobs/{job_id}/progress (SSE)
loop Each pipeline step
CR->>CW: Run next agent/task
CW-->>CR: Step result
CR->>CR: progress_store[job_id] = step
API-->>FE: event: progress { current_step }
end
CW->>MS: POST /api/v1/resume/generate (resume JSON)
MS-->>CW: .docx bytes
CW->>CR: docx path via ResumeDocxContext.state
CR->>CR: convert_docx_to_pdf (LibreOffice)
CR->>GCS: Upload .docx, .pdf, interview materials
CR->>DB: UPDATE job (status=completed, GCS paths)
API-->>FE: event: done
FE->>API: GET tailored resume / interview materials
API-->>U: PDF preview + .docx download

⚙️ Configuration

All backend configuration lives in backend/.env (copy it from backend/.env.example). Key groups:

# ── GCP ──────────────────────────────────────────────────────────GOOGLE_APPLICATION_CREDENTIALS=/abs/path/to/backend/credentials/service-account.jsonGCP_PROJECT_ID=your-project-idGCP_LOCATION=asia-south2# ── Vector store (Weaviate Cloud + Voyage AI — not GCP) ──────────WEAVIATE_URL=...# cluster REST endpoint; scheme optionalWEAVIATE_API_KEY=...WEAVIATE_COLLECTION_NAME=GithubRepoDataVOYAGE_API_KEY=...# embeddings + rerankingVOYAGE_EMBED_MODEL=voyage-code-3VOYAGE_EMBED_DIMENSION=1024VOYAGE_RERANK_MODEL=rerank-2.5-lite# ── API keys ─────────────────────────────────────────────────────GITHUB_PERSONAL_ACCESS_TOKEN=...BRIGHT_DATA_API_KEY=...# LinkedIn scraping (Bright Data MCP)GEMINI_API_KEY=...# Profiler / Strategist / Interview PreparerANTHROPIC_API_KEY=...# Resume Analyzer (Claude Sonnet 5)ZAI_API_KEY=...# GitHub Project Summarizer (GLM-5.2)ZAI_BASE_URL=# optional override; blank = https://api.z.ai/api/paas/v4SERPER_API_KEY=...# web search (Interview Preparer research)# ── Resume DOCX generation microservice ──────────────────────────# Live service on AWS (ECS Fargate + ALB). Source & self-hosting instructions:# https://github.com/arijitde92/resume_docx_generatorRESUME_DOCX_SERVICE_URL=http://resume-service-alb-1115872566.us-east-2.elb.amazonaws.com/api/v1/resume/generateRESUME_DOCX_TIMEOUT_SECONDS=120# ── Cloud SQL (MySQL) ────────────────────────────────────────────MYSQL_HOST=127.0.0.1# 127.0.0.1 when using the Cloud SQL Auth ProxyMYSQL_PORT=3306MYSQL_USER=...MYSQL_PASSWORD=...MYSQL_DATABASE=job_applier# ── GCS / Auth ───────────────────────────────────────────────────GCS_BUCKET_NAME=your-resumes-bucketJWT_SECRET_KEY=# generate: python -c "import secrets; print(secrets.token_urlsafe(32))"GOOGLE_OAUTH_CLIENT_ID=# optional, for "Sign in with Google"GOOGLE_OAUTH_CLIENT_SECRET=

The frontend has its own frontend/.env (copy from frontend/.env.example) for VITE_* variables.

Never commit .env or service-account keys. They are gitignored. The db-proxy connection name is derived from GCP_PROJECT_ID, GCP_LOCATION, and instance job-applier-mysql by default — override with CLOUD_SQL_INSTANCE if yours differs.

For step-by-step GCP provisioning (project, Cloud SQL instance, GCS bucket, service account), see backend/docs/GCP_Setup.md.

🖥️ Standalone Crew CLI (no web app)

You can run the agent pipeline directly, without the frontend/DB, from backend/:

cd backend
uv run crew \
--url https://www.linkedin.com/jobs/view/<id>/ \
--github https://github.com/<user> \
--resume sample_data/Arijit_De_Resume.md \
--name "Your Name"

This extracts the job, indexes the GitHub repos into Weaviate, parses and tailors the resume, and writes interview materials — same pipeline the web app drives.

🔑 API Keys & Cloud Setup

All keys go in backend/.env.

GitHub:Create a Personal Access Token → GITHUB_PERSONAL_ACCESS_TOKEN.
Google Cloud:
- Enable the Cloud SQL Admin and Cloud Storage APIs. BigQuery and Vertex AI are no longer used.
- Create a service account with the relevant roles (Cloud SQL Client, Storage Object Admin).
- Download the JSON key into backend/credentials/ and point GOOGLE_APPLICATION_CREDENTIALS at it.
Weaviate: Create a cluster at Weaviate Cloud → WEAVIATE_URL + WEAVIATE_API_KEY (GitHub repo vector store).
Voyage AI:Get an API key → VOYAGE_API_KEY (embeddings + reranking for the GitHub RAG pipeline).
Gemini:Get a Gemini API key → GEMINI_API_KEY (Profiler, Resume Strategist, Interview Preparer, and the RAGAS judge).
Anthropic:Get an API key → ANTHROPIC_API_KEY (the Resume Analyzer runs on Claude Sonnet 5).
Z.ai:Get an API key → ZAI_API_KEY (the GitHub Project Summarizer runs on GLM-5.2).
Bright Data:Get an API key for the MCP scraper → BRIGHT_DATA_API_KEY (LinkedIn job extraction).
Serper:Get a Serper API key → SERPER_API_KEY (the web_search tool). Strictly optional — without it the crew still runs end to end, but the Interview Preparer loses its company research and works from the resume and job details alone.

📚 How it Works

Job Extraction: The job posting is scraped once via the Bright Data MCP LinkedIn extractor and parsed into structured JobDetails, which are injected into every downstream task.
GitHub Analysis & Resume Parsing (in parallel): The GitHub Project Summarizer chunks and indexes your public repos into Weaviate with Voyage embeddings, then retrieves and reranks the projects most relevant to the job. Meanwhile the Resume Analyzer extracts your resume into validated structured JSON.
Profile Compilation: The Profiler agent creates a comprehensive profile using the parsed resume, GitHub summaries, and job requirements.
Resume Tailoring: The Resume Strategist aligns your resume with the job description, then renders it as a polished Word (.docx) resume via the live document-generation microservice (generate_resume_docx tool, max 3 attempts) — the .docx is converted to PDF with LibreOffice for the in-browser preview, and the Markdown version remains the fallback when the service or conversion is unavailable. The microservice is a separate FastAPI app deployed on AWS ECS Fargate; its source is at arijitde92/resume_docx_generator.
Interview Prep: The Interview Preparer researches the company's interview process and role-specific questions with the web_search tool, then generates custom interview questions and talking points grounded in your tailored resume — citing sources for anything it found on the web.

🛠️ Extending the System

Add new agents or tools by following the Crew AI documentation.
Integrate additional data sources or cloud services as needed.

📄 License

MIT License

🤝 Acknowledgements

Crew AI
resume_docx_generator — the companion .docx rendering microservice
Weaviate
Voyage AI
LangChain
BeautifulSoup

Job Application Agent

A powerful, multi-agent automation system for job applications, leveraging Crew AI to orchestrate specialized agents that extract, analyze, and summarize job postings, tailor resumes, and prepare interview materials. Semantic search over your GitHub projects runs on a Weaviate vector store with Voyage AI embeddings and reranking, and the system features custom tools for LinkedIn and GitHub data extraction plus Serper-backed web research.

This project also includes a Full-Stack Web App version with a FastAPI backend (using Google Cloud SQL MySQL and GCS storage) and a React + Vite frontend that displays live agent execution progress via Server-Sent Events (SSE).

The final Word (.docx) resume is rendered by a separate live microservice deployed on AWS (ECS Fargate behind an ALB) — its source is available at arijitde92/resume_docx_generator. This backend calls that service over HTTP and converts the returned .docx to PDF locally (LibreOffice headless) for the in-browser preview. See System Design for the full picture.

📦 Project Structure

Job_Application_Agent/
├── backend/ # FastAPI backend + CrewAI pipeline (uv-managed)
│ ├── app/
│ │ ├── api/ # Route handlers (api/v1) + shared deps
│ │ ├── core/ # config, security, database, logging
│ │ ├── services/ # GCS, crew runner, CrewAI pipeline, extractors,
│ │ │ # vector_store (Weaviate + Voyage)
│ │ ├── models/ # SQLAlchemy ORM models
│ │ ├── schemas/ # Pydantic schemas
│ │ └── main.py # App init + CORS + routers
│ ├── credentials/ # GCP keys (gitignored)
│ ├── pyproject.toml # Dependencies (uv)
│ ├── Dockerfile # (empty for now)
│ └── README.md # Backend setup & run instructions
├── frontend/ # React + Vite (JavaScript) frontend
│ ├── src/
│ │ ├── components/ pages/ context/ hooks/ services/ assets/
│ │ └── main.jsx App.jsx
│ ├── Dockerfile # (empty for now)
│ └── package.json
├── .github/workflows/ # CI/CD (deploy.yml — empty for now)
├── docker-compose.yml # (empty for now)
└── README.md # This file

✅ Prerequisites

Requirement	Version / Notes
Python	3.11+ (3.12 recommended). You don't need to install it manually — `uv` will fetch a matching interpreter.
uv	The Python package & project manager used here. Install: `curl -LsSf https://astral.sh/uv/install.sh \| sh` (then restart your shell so `~/.local/bin` is on `PATH`).
Node.js	18+ (with `npm`), for the React frontend. Download.
Google Cloud account	A GCP project with the Cloud SQL Admin and Cloud Storage APIs enabled, plus a service-account JSON key. BigQuery and Vertex AI are no longer required — the vector store moved off GCP. See backend/docs/GCP_Setup.md.
Cloud SQL (MySQL)	A Cloud SQL MySQL instance (the web app stores users/jobs there).
Cloud SQL Auth Proxy	`cloud-sql-proxy` binary on `PATH` (the `uv run db-proxy` / `uv run dev` commands invoke it). See Installation to install
Weaviate Cloud	A cluster for the GitHub repo vector store (the free sandbox tier is enough to start).
LibreOffice	Used headless to convert the generated .docx resume to PDF for the in-browser preview: `sudo apt-get install -y libreoffice-writer fonts-liberation`. Optional — without it the .docx is still generated, the preview just falls back to Markdown. See backend/README.md.
API keys	GitHub PAT, Bright Data, Gemini, Anthropic, Z.ai, Weaviate, Voyage AI, and Serper. See API Keys & Cloud Setup.

All uv run commands are run from the backend/ directory — the project and its custom commands are defined in backend/pyproject.toml. There is no root-level pyproject.toml, so uv run dev from the repo root will not work.

⚡ Quick Start

git clone https://github.com/arijitde92/Job_Application_Agent.git
cd Job_Application_Agent

1. Backend (uv)

cd backend
cp .env.example .env # then fill in secrets (see below)
uv sync # creates backend/.venv and installs everything

Place your GCP service-account JSON key under backend/credentials/ and point GOOGLE_APPLICATION_CREDENTIALS in backend/.env at it (an absolute path is safest).

2. Frontend

cd ../frontend
cp .env.example .env
npm install

3. Run everything (from `backend/`)

cd ../backend
uv run dev # Cloud SQL proxy + API + frontend, together

Then open http://localhost:5173. The API is at http://localhost:8000 (docs at http://localhost:8000/docs).

Prefer separate terminals? Run them individually:

uv run db-proxy # Cloud SQL Auth Proxy → 127.0.0.1:3306
uv run api # FastAPI dev server → http://localhost:8000
uv run frontend # Vite dev server → http://localhost:5173

See backend/README.md for the full command list, env-var overrides, and the standalone crew CLI.

🚀 Features

Multi-Agent Orchestration: Uses Crew AI to coordinate agents for GitHub analysis, resume parsing, profiling, resume tailoring, and interview preparation.
Custom Tools: Includes LinkedIn job extractor, GitHub repo summarizer, and Serper web search, built using Crew AI's extensible tool system.
Weaviate + Voyage AI: Stores and semantically searches GitHub project data in a Weaviate vector store, using Voyage AI's code-specialised voyage-code-3 embeddings and a rerank-2.5-lite second-stage reranker.
Structured Resume Parsing: A dedicated Resume Analyzer agent extracts the resume into a validated JSON schema, deriving years of experience with a deterministic tool rather than LLM arithmetic.
Automated Resume Tailoring: Aligns your resume with job requirements and optimizes for ATS.
Polished DOCX + PDF Output: The Resume Strategist sends the tailored content to a live document-generation microservice — a separate FastAPI service running on AWS (ECS Fargate behind an ALB) whose source lives at arijitde92/resume_docx_generator — which renders a professional Word (.docx) resume. The backend then converts that .docx to PDF locally (LibreOffice headless) for in-browser viewing. Up to 3 attempts; on failure the pipeline gracefully falls back to the Markdown resume.
Interview Prep: Researches the employer on the web, then generates tailored interview questions and talking points.
Purpose-Matched LLMs: Each agent runs on the model that suits its job — Gemini for synthesis, GLM-5.2 for repo analysis, Claude Sonnet 5 for structured extraction.

🧑‍💻 Agents & Tools

Crew AI Multi-Agent System

Crew AI (Homepage): The backbone of the system, enabling modular, collaborative agent workflows.

Agents: (each runs on the LLM best suited to its task — see backend/app/services/crew/agents.py)

Agent	Role	LLM
GitHub Project Summarizer	Indexes your repos and picks the ones most relevant to the job.	GLM-5.2 (Z.ai)
Resume Analyzer	Extracts the resume into validated structured JSON.	Claude Sonnet 5
Profiler	Compiles a comprehensive personal/professional profile.	Gemini
Resume Strategist	Tailors your resume for each job.	Gemini
Interview Preparer	Researches the employer via web search, then prepares interview questions and talking points.	Gemini

The GitHub summary and resume analysis tasks run in parallel; profiling waits on both. When no GitHub profile is supplied, that agent and its task are skipped entirely and the rest run under explicit grounding rules so nothing is fabricated.

Crew AI Tools Used

FileReadTool: Reads and processes resume files.
ScrapeWebsiteTool: Scrapes web content for job and company info.
MDXSearchTool: Performs semantic search on resume content.
SerperDevTool: Web search for the Resume Strategist.

Custom Tools

LinkedIn Job Extractor: Scrapes and parses job details from LinkedIn job postings.
GitHub Repos Extractor: Recursively fetches and summarizes public GitHub repositories for a user.
Web Search (web_search): Serper-backed Google search used by the Interview Preparer to research the employer's interview process, role-specific questions, and recent company news. Returns a compact ranked digest (featured answer + title/URL/snippet) rather than Serper's raw JSON. It never raises: if the key is missing or the API fails it returns an ERROR: ... observation and the agent falls back to the resume and job details alone, so a search outage cannot sink a run whose resume tailoring has already completed.
Weaviate/Voyage AI Integration: Custom logic to chunk, embed, upsert, and search GitHub repo content — one shared Weaviate collection, with every search scoped to the applicant's own repositories.
Resume Tools: A calculate_yoe tool for deterministic years-of-experience arithmetic and a per-run save_parsed_resume tool that validates the JSON against the schema before persisting it.
DOCX Generator (generate_resume_docx): A per-run tool for the Resume Strategist that POSTs the tailored resume JSON to the live document-generation microservice (POST /api/v1/resume/generate on arijitde92/resume_docx_generator) and saves the returned Word (.docx) resume. The tool wraps the agent's content in the service's metadata envelope deterministically and enforces a 3-attempt retry cap, after which the run falls back to Markdown output.

🏗️ Architecture

Python 3.11+ with uv for dependency & environment management (see backend/pyproject.toml)
FastAPI backend (async SQLAlchemy + Cloud SQL MySQL, JWT/Google OAuth auth)
React + Vite frontend with live progress via Server-Sent Events (SSE)
Crew AI for agent orchestration, across Gemini, GLM-5.2 (Z.ai) and Claude Sonnet 5 depending on the agent
Weaviate Cloud as the vector store for GitHub repo embeddings
Voyage AI for code-specialised embeddings (voyage-code-3) and reranking (rerank-2.5-lite)
Google Cloud Storage for resume & generated-output storage
Resume DOCX microservice — a standalone FastAPI service on AWS ECS Fargate that renders the tailored resume JSON into a Word document (source); the backend converts the returned .docx to PDF with LibreOffice headless
Bright Data MCP for LinkedIn job scraping; LangChain for document loading and chunking

🧭 System Design

Component architecture

flowchart LR
User(["👤 User (browser)"])
subgraph Frontend["React + Vite frontend :5173"]
UI["Pages & components<br/>Home · Job list · Resume preview"]
SSE["SSE client<br/>live agent progress"]
end
subgraph Backend["FastAPI backend :8000"]
API["API routers<br/>/api/auth · /api/resumes<br/>/api/github · /api/jobs"]
Runner["crew_runner<br/>ThreadPoolExecutor (4 jobs)<br/>+ progress store"]
Crew["CrewAI pipeline<br/>agents · tasks · tools"]
PDF["docx_to_pdf<br/>LibreOffice headless"]
end
subgraph GCP["Google Cloud"]
SQL[("Cloud SQL MySQL<br/>users · resumes · jobs")]
GCS[("Cloud Storage<br/>resumes & outputs")]
end
subgraph External["External services"]
BD["Bright Data MCP<br/>LinkedIn scraping"]
GH["GitHub API"]
WV[("Weaviate Cloud<br/>GithubRepoData")]
VOY["Voyage AI<br/>voyage-code-3 · rerank-2.5-lite"]
SERP["Serper<br/>web search"]
LLM["LLMs<br/>Gemini · GLM-5.2 · Claude Sonnet 5"]
DOCX["Resume DOCX microservice<br/>FastAPI on AWS ECS Fargate + ALB<br/>POST /api/v1/resume/generate"]
end
User --> UI
UI -->|"REST + JWT / Google OAuth"| API
SSE <-->|"text/event-stream"| API
API --> Runner --> Crew
API <--> SQL
API <--> GCS
Runner <--> GCS
Crew --> BD
Crew --> GH
Crew <--> WV
WV -.->|"embed & rerank"| VOY
Crew --> SERP
Crew --> LLM
Crew -->|"tailored resume JSON"| DOCX
DOCX -->|".docx bytes"| Crew
Crew --> PDF
PDF -->|".docx + .pdf"| GCS
classDef ext fill:#fff4e6,stroke:#e8a33d,color:#000
classDef svc fill:#e6f0ff,stroke:#4a7fd4,color:#000
class BD,GH,WV,VOY,SERP,LLM,DOCX ext
class SQL,GCS svc

Agent pipeline (one job run)

flowchart TD
Start(["Job submitted<br/>LinkedIn URL + resume + GitHub URL"]) --> Extract["Extract job details<br/>Bright Data MCP → JobDetails JSON"]
Extract --> Parse["Parse resume file<br/>.pdf / .docx / .md → text"]
Parse --> A1["GitHub Project Summarizer<br/><i>GLM-5.2</i><br/>index repos → Weaviate,<br/>retrieve + rerank relevant projects"]
Parse --> A2["Resume Analyzer<br/><i>Claude Sonnet 5</i><br/>resume → validated JSON<br/>(calculate_yoe · save_parsed_resume)"]
A1 --> A3
A2 --> A3
A3["Profiler<br/><i>Gemini</i><br/>compile personal & professional profile"]
A3 --> A4["Resume Strategist<br/><i>Gemini</i><br/>tailor resume · optimise for ATS"]
A4 --> Tool{"generate_resume_docx<br/>≤ 3 attempts"}
Tool -->|"success"| Conv["LibreOffice headless<br/>.docx → .pdf"]
Tool -->|"all attempts failed<br/>or LibreOffice missing"| MD["Markdown resume fallback"]
Conv --> A5
MD --> A5
A5["Interview Preparer<br/><i>Gemini</i><br/>web_search company research →<br/>questions & talking points"]
A5 --> Store["Upload outputs to GCS<br/>update job row in Cloud SQL"]
Store --> Done(["Job completed → SSE 'done'"])
subgraph Parallel["run in parallel"]
A1
A2
end
style Parallel fill:#f4f4f8,stroke:#bbb

The GitHub agent and its task are skipped entirely when no GitHub profile is supplied; the remaining agents then run under explicit grounding rules.

Request lifecycle

sequenceDiagram
autonumber
actor U as User
participant FE as React frontend
participant API as FastAPI (jobs router)
participant DB as Cloud SQL
participant CR as crew_runner (thread pool)
participant CW as CrewAI agents
participant MS as DOCX microservice (AWS)
participant GCS as Cloud Storage
U->>FE: Submit job URL + select resume (+ GitHub profile)
FE->>API: POST /api/jobs/tailor
API->>DB: INSERT job (status=pending)
API-->>FE: 201 { job_id }
API->>CR: asyncio.create_task(run_crew_for_job)
CR->>GCS: Download stored resume bytes
FE->>API: GET /api/jobs/{job_id}/progress (SSE)
loop Each pipeline step
CR->>CW: Run next agent/task
CW-->>CR: Step result
CR->>CR: progress_store[job_id] = step
API-->>FE: event: progress { current_step }
end
CW->>MS: POST /api/v1/resume/generate (resume JSON)
MS-->>CW: .docx bytes
CW->>CR: docx path via ResumeDocxContext.state
CR->>CR: convert_docx_to_pdf (LibreOffice)
CR->>GCS: Upload .docx, .pdf, interview materials
CR->>DB: UPDATE job (status=completed, GCS paths)
API-->>FE: event: done
FE->>API: GET tailored resume / interview materials
API-->>U: PDF preview + .docx download

⚙️ Configuration

All backend configuration lives in backend/.env (copy it from backend/.env.example). Key groups:

# ── GCP ──────────────────────────────────────────────────────────GOOGLE_APPLICATION_CREDENTIALS=/abs/path/to/backend/credentials/service-account.jsonGCP_PROJECT_ID=your-project-idGCP_LOCATION=asia-south2# ── Vector store (Weaviate Cloud + Voyage AI — not GCP) ──────────WEAVIATE_URL=...# cluster REST endpoint; scheme optionalWEAVIATE_API_KEY=...WEAVIATE_COLLECTION_NAME=GithubRepoDataVOYAGE_API_KEY=...# embeddings + rerankingVOYAGE_EMBED_MODEL=voyage-code-3VOYAGE_EMBED_DIMENSION=1024VOYAGE_RERANK_MODEL=rerank-2.5-lite# ── API keys ─────────────────────────────────────────────────────GITHUB_PERSONAL_ACCESS_TOKEN=...BRIGHT_DATA_API_KEY=...# LinkedIn scraping (Bright Data MCP)GEMINI_API_KEY=...# Profiler / Strategist / Interview PreparerANTHROPIC_API_KEY=...# Resume Analyzer (Claude Sonnet 5)ZAI_API_KEY=...# GitHub Project Summarizer (GLM-5.2)ZAI_BASE_URL=# optional override; blank = https://api.z.ai/api/paas/v4SERPER_API_KEY=...# web search (Interview Preparer research)# ── Resume DOCX generation microservice ──────────────────────────# Live service on AWS (ECS Fargate + ALB). Source & self-hosting instructions:# https://github.com/arijitde92/resume_docx_generatorRESUME_DOCX_SERVICE_URL=http://resume-service-alb-1115872566.us-east-2.elb.amazonaws.com/api/v1/resume/generateRESUME_DOCX_TIMEOUT_SECONDS=120# ── Cloud SQL (MySQL) ────────────────────────────────────────────MYSQL_HOST=127.0.0.1# 127.0.0.1 when using the Cloud SQL Auth ProxyMYSQL_PORT=3306MYSQL_USER=...MYSQL_PASSWORD=...MYSQL_DATABASE=job_applier# ── GCS / Auth ───────────────────────────────────────────────────GCS_BUCKET_NAME=your-resumes-bucketJWT_SECRET_KEY=# generate: python -c "import secrets; print(secrets.token_urlsafe(32))"GOOGLE_OAUTH_CLIENT_ID=# optional, for "Sign in with Google"GOOGLE_OAUTH_CLIENT_SECRET=

The frontend has its own frontend/.env (copy from frontend/.env.example) for VITE_* variables.

Never commit .env or service-account keys. They are gitignored. The db-proxy connection name is derived from GCP_PROJECT_ID, GCP_LOCATION, and instance job-applier-mysql by default — override with CLOUD_SQL_INSTANCE if yours differs.

For step-by-step GCP provisioning (project, Cloud SQL instance, GCS bucket, service account), see backend/docs/GCP_Setup.md.

🖥️ Standalone Crew CLI (no web app)

You can run the agent pipeline directly, without the frontend/DB, from backend/:

cd backend
uv run crew \
--url https://www.linkedin.com/jobs/view/<id>/ \
--github https://github.com/<user> \
--resume sample_data/Arijit_De_Resume.md \
--name "Your Name"

This extracts the job, indexes the GitHub repos into Weaviate, parses and tailors the resume, and writes interview materials — same pipeline the web app drives.

🔑 API Keys & Cloud Setup

All keys go in backend/.env.

GitHub:Create a Personal Access Token → GITHUB_PERSONAL_ACCESS_TOKEN.
Google Cloud:
- Enable the Cloud SQL Admin and Cloud Storage APIs. BigQuery and Vertex AI are no longer used.
- Create a service account with the relevant roles (Cloud SQL Client, Storage Object Admin).
- Download the JSON key into backend/credentials/ and point GOOGLE_APPLICATION_CREDENTIALS at it.
Weaviate: Create a cluster at Weaviate Cloud → WEAVIATE_URL + WEAVIATE_API_KEY (GitHub repo vector store).
Voyage AI:Get an API key → VOYAGE_API_KEY (embeddings + reranking for the GitHub RAG pipeline).
Gemini:Get a Gemini API key → GEMINI_API_KEY (Profiler, Resume Strategist, Interview Preparer, and the RAGAS judge).
Anthropic:Get an API key → ANTHROPIC_API_KEY (the Resume Analyzer runs on Claude Sonnet 5).
Z.ai:Get an API key → ZAI_API_KEY (the GitHub Project Summarizer runs on GLM-5.2).
Bright Data:Get an API key for the MCP scraper → BRIGHT_DATA_API_KEY (LinkedIn job extraction).
Serper:Get a Serper API key → SERPER_API_KEY (the web_search tool). Strictly optional — without it the crew still runs end to end, but the Interview Preparer loses its company research and works from the resume and job details alone.

📚 How it Works

Job Extraction: The job posting is scraped once via the Bright Data MCP LinkedIn extractor and parsed into structured JobDetails, which are injected into every downstream task.
GitHub Analysis & Resume Parsing (in parallel): The GitHub Project Summarizer chunks and indexes your public repos into Weaviate with Voyage embeddings, then retrieves and reranks the projects most relevant to the job. Meanwhile the Resume Analyzer extracts your resume into validated structured JSON.
Profile Compilation: The Profiler agent creates a comprehensive profile using the parsed resume, GitHub summaries, and job requirements.
Resume Tailoring: The Resume Strategist aligns your resume with the job description, then renders it as a polished Word (.docx) resume via the live document-generation microservice (generate_resume_docx tool, max 3 attempts) — the .docx is converted to PDF with LibreOffice for the in-browser preview, and the Markdown version remains the fallback when the service or conversion is unavailable. The microservice is a separate FastAPI app deployed on AWS ECS Fargate; its source is at arijitde92/resume_docx_generator.
Interview Prep: The Interview Preparer researches the company's interview process and role-specific questions with the web_search tool, then generates custom interview questions and talking points grounded in your tailored resume — citing sources for anything it found on the web.

🛠️ Extending the System

Add new agents or tools by following the Crew AI documentation.
Integrate additional data sources or cloud services as needed.

📄 License

MIT License

🤝 Acknowledgements

Crew AI
resume_docx_generator — the companion .docx rendering microservice
Weaviate
Voyage AI
LangChain
BeautifulSoup

Job Application Agent

A powerful, multi-agent automation system for job applications, leveraging Crew AI to orchestrate specialized agents that extract, analyze, and summarize job postings, tailor resumes, and prepare interview materials. Semantic search over your GitHub projects runs on a Weaviate vector store with Voyage AI embeddings and reranking, and the system features custom tools for LinkedIn and GitHub data extraction plus Serper-backed web research.

This project also includes a Full-Stack Web App version with a FastAPI backend (using Google Cloud SQL MySQL and GCS storage) and a React + Vite frontend that displays live agent execution progress via Server-Sent Events (SSE).

The final Word (.docx) resume is rendered by a separate live microservice deployed on AWS (ECS Fargate behind an ALB) — its source is available at arijitde92/resume_docx_generator. This backend calls that service over HTTP and converts the returned .docx to PDF locally (LibreOffice headless) for the in-browser preview. See System Design for the full picture.

📦 Project Structure

Job_Application_Agent/
├── backend/ # FastAPI backend + CrewAI pipeline (uv-managed)
│ ├── app/
│ │ ├── api/ # Route handlers (api/v1) + shared deps
│ │ ├── core/ # config, security, database, logging
│ │ ├── services/ # GCS, crew runner, CrewAI pipeline, extractors,
│ │ │ # vector_store (Weaviate + Voyage)
│ │ ├── models/ # SQLAlchemy ORM models
│ │ ├── schemas/ # Pydantic schemas
│ │ └── main.py # App init + CORS + routers
│ ├── credentials/ # GCP keys (gitignored)
│ ├── pyproject.toml # Dependencies (uv)
│ ├── Dockerfile # (empty for now)
│ └── README.md # Backend setup & run instructions
├── frontend/ # React + Vite (JavaScript) frontend
│ ├── src/
│ │ ├── components/ pages/ context/ hooks/ services/ assets/
│ │ └── main.jsx App.jsx
│ ├── Dockerfile # (empty for now)
│ └── package.json
├── .github/workflows/ # CI/CD (deploy.yml — empty for now)
├── docker-compose.yml # (empty for now)
└── README.md # This file

✅ Prerequisites

Requirement	Version / Notes
Python	3.11+ (3.12 recommended). You don't need to install it manually — `uv` will fetch a matching interpreter.
uv	The Python package & project manager used here. Install: `curl -LsSf https://astral.sh/uv/install.sh \| sh` (then restart your shell so `~/.local/bin` is on `PATH`).
Node.js	18+ (with `npm`), for the React frontend. Download.
Google Cloud account	A GCP project with the Cloud SQL Admin and Cloud Storage APIs enabled, plus a service-account JSON key. BigQuery and Vertex AI are no longer required — the vector store moved off GCP. See backend/docs/GCP_Setup.md.
Cloud SQL (MySQL)	A Cloud SQL MySQL instance (the web app stores users/jobs there).
Cloud SQL Auth Proxy	`cloud-sql-proxy` binary on `PATH` (the `uv run db-proxy` / `uv run dev` commands invoke it). See Installation to install
Weaviate Cloud	A cluster for the GitHub repo vector store (the free sandbox tier is enough to start).
LibreOffice	Used headless to convert the generated .docx resume to PDF for the in-browser preview: `sudo apt-get install -y libreoffice-writer fonts-liberation`. Optional — without it the .docx is still generated, the preview just falls back to Markdown. See backend/README.md.
API keys	GitHub PAT, Bright Data, Gemini, Anthropic, Z.ai, Weaviate, Voyage AI, and Serper. See API Keys & Cloud Setup.

All uv run commands are run from the backend/ directory — the project and its custom commands are defined in backend/pyproject.toml. There is no root-level pyproject.toml, so uv run dev from the repo root will not work.

⚡ Quick Start

git clone https://github.com/arijitde92/Job_Application_Agent.git
cd Job_Application_Agent

1. Backend (uv)

cd backend
cp .env.example .env # then fill in secrets (see below)
uv sync # creates backend/.venv and installs everything

Place your GCP service-account JSON key under backend/credentials/ and point GOOGLE_APPLICATION_CREDENTIALS in backend/.env at it (an absolute path is safest).

2. Frontend

cd ../frontend
cp .env.example .env
npm install

3. Run everything (from `backend/`)

cd ../backend
uv run dev # Cloud SQL proxy + API + frontend, together

Then open http://localhost:5173. The API is at http://localhost:8000 (docs at http://localhost:8000/docs).

Prefer separate terminals? Run them individually:

uv run db-proxy # Cloud SQL Auth Proxy → 127.0.0.1:3306
uv run api # FastAPI dev server → http://localhost:8000
uv run frontend # Vite dev server → http://localhost:5173

See backend/README.md for the full command list, env-var overrides, and the standalone crew CLI.

🚀 Features

Multi-Agent Orchestration: Uses Crew AI to coordinate agents for GitHub analysis, resume parsing, profiling, resume tailoring, and interview preparation.
Custom Tools: Includes LinkedIn job extractor, GitHub repo summarizer, and Serper web search, built using Crew AI's extensible tool system.
Weaviate + Voyage AI: Stores and semantically searches GitHub project data in a Weaviate vector store, using Voyage AI's code-specialised voyage-code-3 embeddings and a rerank-2.5-lite second-stage reranker.
Structured Resume Parsing: A dedicated Resume Analyzer agent extracts the resume into a validated JSON schema, deriving years of experience with a deterministic tool rather than LLM arithmetic.
Automated Resume Tailoring: Aligns your resume with job requirements and optimizes for ATS.
Polished DOCX + PDF Output: The Resume Strategist sends the tailored content to a live document-generation microservice — a separate FastAPI service running on AWS (ECS Fargate behind an ALB) whose source lives at arijitde92/resume_docx_generator — which renders a professional Word (.docx) resume. The backend then converts that .docx to PDF locally (LibreOffice headless) for in-browser viewing. Up to 3 attempts; on failure the pipeline gracefully falls back to the Markdown resume.
Interview Prep: Researches the employer on the web, then generates tailored interview questions and talking points.
Purpose-Matched LLMs: Each agent runs on the model that suits its job — Gemini for synthesis, GLM-5.2 for repo analysis, Claude Sonnet 5 for structured extraction.

🧑‍💻 Agents & Tools

Crew AI Multi-Agent System

Crew AI (Homepage): The backbone of the system, enabling modular, collaborative agent workflows.

Agents: (each runs on the LLM best suited to its task — see backend/app/services/crew/agents.py)

Agent	Role	LLM
GitHub Project Summarizer	Indexes your repos and picks the ones most relevant to the job.	GLM-5.2 (Z.ai)
Resume Analyzer	Extracts the resume into validated structured JSON.	Claude Sonnet 5
Profiler	Compiles a comprehensive personal/professional profile.	Gemini
Resume Strategist	Tailors your resume for each job.	Gemini
Interview Preparer	Researches the employer via web search, then prepares interview questions and talking points.	Gemini

The GitHub summary and resume analysis tasks run in parallel; profiling waits on both. When no GitHub profile is supplied, that agent and its task are skipped entirely and the rest run under explicit grounding rules so nothing is fabricated.

Crew AI Tools Used

FileReadTool: Reads and processes resume files.
ScrapeWebsiteTool: Scrapes web content for job and company info.
MDXSearchTool: Performs semantic search on resume content.
SerperDevTool: Web search for the Resume Strategist.

Custom Tools

LinkedIn Job Extractor: Scrapes and parses job details from LinkedIn job postings.
GitHub Repos Extractor: Recursively fetches and summarizes public GitHub repositories for a user.
Web Search (web_search): Serper-backed Google search used by the Interview Preparer to research the employer's interview process, role-specific questions, and recent company news. Returns a compact ranked digest (featured answer + title/URL/snippet) rather than Serper's raw JSON. It never raises: if the key is missing or the API fails it returns an ERROR: ... observation and the agent falls back to the resume and job details alone, so a search outage cannot sink a run whose resume tailoring has already completed.
Weaviate/Voyage AI Integration: Custom logic to chunk, embed, upsert, and search GitHub repo content — one shared Weaviate collection, with every search scoped to the applicant's own repositories.
Resume Tools: A calculate_yoe tool for deterministic years-of-experience arithmetic and a per-run save_parsed_resume tool that validates the JSON against the schema before persisting it.
DOCX Generator (generate_resume_docx): A per-run tool for the Resume Strategist that POSTs the tailored resume JSON to the live document-generation microservice (POST /api/v1/resume/generate on arijitde92/resume_docx_generator) and saves the returned Word (.docx) resume. The tool wraps the agent's content in the service's metadata envelope deterministically and enforces a 3-attempt retry cap, after which the run falls back to Markdown output.

🏗️ Architecture

Python 3.11+ with uv for dependency & environment management (see backend/pyproject.toml)
FastAPI backend (async SQLAlchemy + Cloud SQL MySQL, JWT/Google OAuth auth)
React + Vite frontend with live progress via Server-Sent Events (SSE)
Crew AI for agent orchestration, across Gemini, GLM-5.2 (Z.ai) and Claude Sonnet 5 depending on the agent
Weaviate Cloud as the vector store for GitHub repo embeddings
Voyage AI for code-specialised embeddings (voyage-code-3) and reranking (rerank-2.5-lite)
Google Cloud Storage for resume & generated-output storage
Resume DOCX microservice — a standalone FastAPI service on AWS ECS Fargate that renders the tailored resume JSON into a Word document (source); the backend converts the returned .docx to PDF with LibreOffice headless
Bright Data MCP for LinkedIn job scraping; LangChain for document loading and chunking

🧭 System Design

Component architecture

flowchart LR
User(["👤 User (browser)"])
subgraph Frontend["React + Vite frontend :5173"]
UI["Pages & components<br/>Home · Job list · Resume preview"]
SSE["SSE client<br/>live agent progress"]
end
subgraph Backend["FastAPI backend :8000"]
API["API routers<br/>/api/auth · /api/resumes<br/>/api/github · /api/jobs"]
Runner["crew_runner<br/>ThreadPoolExecutor (4 jobs)<br/>+ progress store"]
Crew["CrewAI pipeline<br/>agents · tasks · tools"]
PDF["docx_to_pdf<br/>LibreOffice headless"]
end
subgraph GCP["Google Cloud"]
SQL[("Cloud SQL MySQL<br/>users · resumes · jobs")]
GCS[("Cloud Storage<br/>resumes & outputs")]
end
subgraph External["External services"]
BD["Bright Data MCP<br/>LinkedIn scraping"]
GH["GitHub API"]
WV[("Weaviate Cloud<br/>GithubRepoData")]
VOY["Voyage AI<br/>voyage-code-3 · rerank-2.5-lite"]
SERP["Serper<br/>web search"]
LLM["LLMs<br/>Gemini · GLM-5.2 · Claude Sonnet 5"]
DOCX["Resume DOCX microservice<br/>FastAPI on AWS ECS Fargate + ALB<br/>POST /api/v1/resume/generate"]
end
User --> UI
UI -->|"REST + JWT / Google OAuth"| API
SSE <-->|"text/event-stream"| API
API --> Runner --> Crew
API <--> SQL
API <--> GCS
Runner <--> GCS
Crew --> BD
Crew --> GH
Crew <--> WV
WV -.->|"embed & rerank"| VOY
Crew --> SERP
Crew --> LLM
Crew -->|"tailored resume JSON"| DOCX
DOCX -->|".docx bytes"| Crew
Crew --> PDF
PDF -->|".docx + .pdf"| GCS
classDef ext fill:#fff4e6,stroke:#e8a33d,color:#000
classDef svc fill:#e6f0ff,stroke:#4a7fd4,color:#000
class BD,GH,WV,VOY,SERP,LLM,DOCX ext
class SQL,GCS svc

Agent pipeline (one job run)

flowchart TD
Start(["Job submitted<br/>LinkedIn URL + resume + GitHub URL"]) --> Extract["Extract job details<br/>Bright Data MCP → JobDetails JSON"]
Extract --> Parse["Parse resume file<br/>.pdf / .docx / .md → text"]
Parse --> A1["GitHub Project Summarizer<br/><i>GLM-5.2</i><br/>index repos → Weaviate,<br/>retrieve + rerank relevant projects"]
Parse --> A2["Resume Analyzer<br/><i>Claude Sonnet 5</i><br/>resume → validated JSON<br/>(calculate_yoe · save_parsed_resume)"]
A1 --> A3
A2 --> A3
A3["Profiler<br/><i>Gemini</i><br/>compile personal & professional profile"]
A3 --> A4["Resume Strategist<br/><i>Gemini</i><br/>tailor resume · optimise for ATS"]
A4 --> Tool{"generate_resume_docx<br/>≤ 3 attempts"}
Tool -->|"success"| Conv["LibreOffice headless<br/>.docx → .pdf"]
Tool -->|"all attempts failed<br/>or LibreOffice missing"| MD["Markdown resume fallback"]
Conv --> A5
MD --> A5
A5["Interview Preparer<br/><i>Gemini</i><br/>web_search company research →<br/>questions & talking points"]
A5 --> Store["Upload outputs to GCS<br/>update job row in Cloud SQL"]
Store --> Done(["Job completed → SSE 'done'"])
subgraph Parallel["run in parallel"]
A1
A2
end
style Parallel fill:#f4f4f8,stroke:#bbb

The GitHub agent and its task are skipped entirely when no GitHub profile is supplied; the remaining agents then run under explicit grounding rules.

Request lifecycle

sequenceDiagram
autonumber
actor U as User
participant FE as React frontend
participant API as FastAPI (jobs router)
participant DB as Cloud SQL
participant CR as crew_runner (thread pool)
participant CW as CrewAI agents
participant MS as DOCX microservice (AWS)
participant GCS as Cloud Storage
U->>FE: Submit job URL + select resume (+ GitHub profile)
FE->>API: POST /api/jobs/tailor
API->>DB: INSERT job (status=pending)
API-->>FE: 201 { job_id }
API->>CR: asyncio.create_task(run_crew_for_job)
CR->>GCS: Download stored resume bytes
FE->>API: GET /api/jobs/{job_id}/progress (SSE)
loop Each pipeline step
CR->>CW: Run next agent/task
CW-->>CR: Step result
CR->>CR: progress_store[job_id] = step
API-->>FE: event: progress { current_step }
end
CW->>MS: POST /api/v1/resume/generate (resume JSON)
MS-->>CW: .docx bytes
CW->>CR: docx path via ResumeDocxContext.state
CR->>CR: convert_docx_to_pdf (LibreOffice)
CR->>GCS: Upload .docx, .pdf, interview materials
CR->>DB: UPDATE job (status=completed, GCS paths)
API-->>FE: event: done
FE->>API: GET tailored resume / interview materials
API-->>U: PDF preview + .docx download

⚙️ Configuration

All backend configuration lives in backend/.env (copy it from backend/.env.example). Key groups:

# ── GCP ──────────────────────────────────────────────────────────GOOGLE_APPLICATION_CREDENTIALS=/abs/path/to/backend/credentials/service-account.jsonGCP_PROJECT_ID=your-project-idGCP_LOCATION=asia-south2# ── Vector store (Weaviate Cloud + Voyage AI — not GCP) ──────────WEAVIATE_URL=...# cluster REST endpoint; scheme optionalWEAVIATE_API_KEY=...WEAVIATE_COLLECTION_NAME=GithubRepoDataVOYAGE_API_KEY=...# embeddings + rerankingVOYAGE_EMBED_MODEL=voyage-code-3VOYAGE_EMBED_DIMENSION=1024VOYAGE_RERANK_MODEL=rerank-2.5-lite# ── API keys ─────────────────────────────────────────────────────GITHUB_PERSONAL_ACCESS_TOKEN=...BRIGHT_DATA_API_KEY=...# LinkedIn scraping (Bright Data MCP)GEMINI_API_KEY=...# Profiler / Strategist / Interview PreparerANTHROPIC_API_KEY=...# Resume Analyzer (Claude Sonnet 5)ZAI_API_KEY=...# GitHub Project Summarizer (GLM-5.2)ZAI_BASE_URL=# optional override; blank = https://api.z.ai/api/paas/v4SERPER_API_KEY=...# web search (Interview Preparer research)# ── Resume DOCX generation microservice ──────────────────────────# Live service on AWS (ECS Fargate + ALB). Source & self-hosting instructions:# https://github.com/arijitde92/resume_docx_generatorRESUME_DOCX_SERVICE_URL=http://resume-service-alb-1115872566.us-east-2.elb.amazonaws.com/api/v1/resume/generateRESUME_DOCX_TIMEOUT_SECONDS=120# ── Cloud SQL (MySQL) ────────────────────────────────────────────MYSQL_HOST=127.0.0.1# 127.0.0.1 when using the Cloud SQL Auth ProxyMYSQL_PORT=3306MYSQL_USER=...MYSQL_PASSWORD=...MYSQL_DATABASE=job_applier# ── GCS / Auth ───────────────────────────────────────────────────GCS_BUCKET_NAME=your-resumes-bucketJWT_SECRET_KEY=# generate: python -c "import secrets; print(secrets.token_urlsafe(32))"GOOGLE_OAUTH_CLIENT_ID=# optional, for "Sign in with Google"GOOGLE_OAUTH_CLIENT_SECRET=

The frontend has its own frontend/.env (copy from frontend/.env.example) for VITE_* variables.

Never commit .env or service-account keys. They are gitignored. The db-proxy connection name is derived from GCP_PROJECT_ID, GCP_LOCATION, and instance job-applier-mysql by default — override with CLOUD_SQL_INSTANCE if yours differs.

For step-by-step GCP provisioning (project, Cloud SQL instance, GCS bucket, service account), see backend/docs/GCP_Setup.md.

🖥️ Standalone Crew CLI (no web app)

You can run the agent pipeline directly, without the frontend/DB, from backend/:

cd backend
uv run crew \
--url https://www.linkedin.com/jobs/view/<id>/ \
--github https://github.com/<user> \
--resume sample_data/Arijit_De_Resume.md \
--name "Your Name"

This extracts the job, indexes the GitHub repos into Weaviate, parses and tailors the resume, and writes interview materials — same pipeline the web app drives.

🔑 API Keys & Cloud Setup

All keys go in backend/.env.

GitHub:Create a Personal Access Token → GITHUB_PERSONAL_ACCESS_TOKEN.
Google Cloud:
- Enable the Cloud SQL Admin and Cloud Storage APIs. BigQuery and Vertex AI are no longer used.
- Create a service account with the relevant roles (Cloud SQL Client, Storage Object Admin).
- Download the JSON key into backend/credentials/ and point GOOGLE_APPLICATION_CREDENTIALS at it.
Weaviate: Create a cluster at Weaviate Cloud → WEAVIATE_URL + WEAVIATE_API_KEY (GitHub repo vector store).
Voyage AI:Get an API key → VOYAGE_API_KEY (embeddings + reranking for the GitHub RAG pipeline).
Gemini:Get a Gemini API key → GEMINI_API_KEY (Profiler, Resume Strategist, Interview Preparer, and the RAGAS judge).
Anthropic:Get an API key → ANTHROPIC_API_KEY (the Resume Analyzer runs on Claude Sonnet 5).
Z.ai:Get an API key → ZAI_API_KEY (the GitHub Project Summarizer runs on GLM-5.2).
Bright Data:Get an API key for the MCP scraper → BRIGHT_DATA_API_KEY (LinkedIn job extraction).
Serper:Get a Serper API key → SERPER_API_KEY (the web_search tool). Strictly optional — without it the crew still runs end to end, but the Interview Preparer loses its company research and works from the resume and job details alone.

📚 How it Works

Job Extraction: The job posting is scraped once via the Bright Data MCP LinkedIn extractor and parsed into structured JobDetails, which are injected into every downstream task.
GitHub Analysis & Resume Parsing (in parallel): The GitHub Project Summarizer chunks and indexes your public repos into Weaviate with Voyage embeddings, then retrieves and reranks the projects most relevant to the job. Meanwhile the Resume Analyzer extracts your resume into validated structured JSON.
Profile Compilation: The Profiler agent creates a comprehensive profile using the parsed resume, GitHub summaries, and job requirements.
Resume Tailoring: The Resume Strategist aligns your resume with the job description, then renders it as a polished Word (.docx) resume via the live document-generation microservice (generate_resume_docx tool, max 3 attempts) — the .docx is converted to PDF with LibreOffice for the in-browser preview, and the Markdown version remains the fallback when the service or conversion is unavailable. The microservice is a separate FastAPI app deployed on AWS ECS Fargate; its source is at arijitde92/resume_docx_generator.
Interview Prep: The Interview Preparer researches the company's interview process and role-specific questions with the web_search tool, then generates custom interview questions and talking points grounded in your tailored resume — citing sources for anything it found on the web.

🛠️ Extending the System

Add new agents or tools by following the Crew AI documentation.
Integrate additional data sources or cloud services as needed.

📄 License

MIT License

🤝 Acknowledgements

Crew AI
resume_docx_generator — the companion .docx rendering microservice
Weaviate
Voyage AI
LangChain
BeautifulSoup

Name		Name	Last commit message	Last commit date
.agent/skills		.agent/skills
.github/workflows		.github/workflows
backend		backend
frontend		frontend
.gitignore		.gitignore
README.md		README.md
docker-compose.yml		docker-compose.yml

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Job Application Agent

📦 Project Structure

✅ Prerequisites

⚡ Quick Start

1. Backend (uv)

2. Frontend

3. Run everything (from backend/)

🚀 Features

🧑‍💻 Agents & Tools

Crew AI Multi-Agent System

Crew AI Tools Used

Custom Tools

🏗️ Architecture

🧭 System Design

Component architecture

Agent pipeline (one job run)

Request lifecycle

⚙️ Configuration

🖥️ Standalone Crew CLI (no web app)

🔑 API Keys & Cloud Setup

📚 How it Works

🛠️ Extending the System

📄 License

🤝 Acknowledgements

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Job Application Agent

📦 Project Structure

✅ Prerequisites

⚡ Quick Start

1. Backend (uv)

2. Frontend

3. Run everything (from backend/)

🚀 Features

🧑‍💻 Agents & Tools

Crew AI Multi-Agent System

Crew AI Tools Used

Custom Tools

🏗️ Architecture

🧭 System Design

Component architecture

Agent pipeline (one job run)

Request lifecycle

⚙️ Configuration

🖥️ Standalone Crew CLI (no web app)

🔑 API Keys & Cloud Setup

📚 How it Works

🛠️ Extending the System

📄 License

🤝 Acknowledgements

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3. Run everything (from `backend/`)

3. Run everything (from `backend/`)

3. Run everything (from `backend/`)

3. Run everything (from `backend/`)