fu7ur3pr00f 0.2.0

Career intelligence system - gather professional data, generate CVs, analyze career alignment

fu7ur3pr00f

Career intelligence agent that gathers professional data, searches job boards, analyzes career trajectories, and generates ATS-optimized CVs through conversational chat.

Quick Start

# Install
pipx install fu7ur3pr00f

# Run
fu7ur3pr00f

In the chat:

• /setup — Configure your LLM provider
• /gather — Import LinkedIn, GitHub, portfolio, CliftonStrengths
• /analyze — Get skill gap analysis
• /search — Query 7 job boards + Hacker News
• /generate — Create ATS-optimized CV (Markdown + PDF)

Installation

Debian/Ubuntu (amd64)

curl -fsSL https://juanmanueldaza.github.io/fu7ur3pr00f/fu7ur3pr00f-archive-keyring.gpg | \
  sudo tee /usr/share/keyrings/fu7ur3pr00f-archive-keyring.gpg >/dev/null

echo "deb [arch=amd64 signed-by=/usr/share/keyrings/fu7ur3pr00f-archive-keyring.gpg] \
https://juanmanueldaza.github.io/fu7ur3pr00f stable main" | \
  sudo tee /etc/apt/sources.list.d/fu7ur3pr00f.list

sudo apt update && sudo apt install fu7ur3pr00f

Development

git clone https://github.com/juanmanueldaza/fu7ur3pr00f.git
cd fu7ur3pr00f
pip install -e .
pip install -r requirements-dev.txt

# Run locally
fu7ur3pr00f

# Run with debug logs
fu7ur3pr00f --debug

Configuration

Run /setup in the chat, or manually edit ~/.fu7ur3pr00f/.env:

# Pick ONE provider (auto-detected if empty)
FUTUREPROOF_PROXY_KEY=fp-...   # Default, zero config
OPENAI_API_KEY=sk-...
ANTHROPIC_API_KEY=sk-ant-...
GOOGLE_API_KEY=...
OLLAMA_BASE_URL=http://localhost:11434  # Local, offline

See .env.example for all options.

Chat Commands

Command	Description
/help or /h	Show help message
/setup	Configure LLM providers and API keys
/gather	Gather career data (LinkedIn, CliftonStrengths, CV, portfolio)
/profile	View your career profile
/goals	View your career goals
/thread [name]	Show or switch conversation thread
/threads	List saved user conversation threads
/memory	Show memory and profile stats
/debug	Toggle debug mode (verbose logging)
/verbose	Show system information
/agents	List available specialist agents
/clear	Clear current thread history
/reset	Factory reset (delete all generated data)
/quit, /q, or /exit	Exit chat

Architecture

Multi-Agent with Blackboard Pattern

graph TB
    User --> Chat[Chat Client]
    Chat --> Engine[Engine<br/>invoke_turn]
    Engine --> Outer[Outer Graph<br/>SessionState persistent]
    Outer --> Classify[classify_turn<br/>factual / follow_up / new_query]
    Classify --> Route[route_turn<br/>LLM routing + keyword fallback]
    Route --> Inner[Inner Blackboard Graph<br/>per-turn execution]
    Inner --> S1[Coach]
    Inner --> S2[Jobs]
    Inner --> S3[Learning]
    Inner --> S4[Code / Founder]
    S1 & S2 & S3 & S4 --> KB[(ChromaDB<br/>career knowledge)]
    Inner --> Synth[Synthesis<br/>LLM]
    Outer --> Accum[accumulate findings<br/>cross-turn context]
    Accum --> Suggest[suggest_next<br/>proactive follow-ups]

Routing Architecture:

• LLM-based semantic routing: Understands query intent, selects 1-4 specialists
• Keyword fallback: Deterministic fallback if LLM unavailable (rate limits, network errors)
• Fast paths: Factual queries → coach only; follow-ups → reuse previous specialists
• Structured output: RoutingDecision model guarantees valid specialist names
• Specialist guidance: All instructions load from prompts/md/specialist_guidance.md (no hardcoded fallbacks)
• Direct model selection: Purpose-specific models are selected from configured provider settings; invocation errors surface directly instead of retrying across models

Design decisions:

Decision	Why
Multi-agent blackboard	Single agent with 41 tools; specialists provide focused reasoning via blackboard pattern
LLM routing	Keyword matching too brittle — "leverage strengths to win money" should route to 3 specialists, not 1
Keyword fallback	Network-resilient: keeps routing obvious queries to the right specialist even if the LLM is unavailable
Direct model selection	Keeps model behavior explicit: choose the configured model and surface real errors instead of hiding them behind runtime fallback
Blackboard pattern	Multi-specialist analysis with shared context and iteration
Database-first	Gatherers index directly to ChromaDB — no intermediate files
Two-pass synthesis	AnalysisSynthesisMiddleware replaces generic LLM output with focused reasoning
HITL confirmation	Destructive/expensive operations require user approval via interrupt()
Prompt-driven	All specialist behavior from prompts folder, zero hardcoded fallbacks

Tools

41 tools organized by domain:

Category	Tools
Profile (7)	get_user_profile, update_user_name, update_current_role, update_salary_info, update_user_skills, set_target_roles, update_user_goal
Gathering (5)	gather_portfolio_data, gather_linkedin_data, gather_assessment_data, gather_cv_data, gather_all_career_data
GitHub (3)	search_github_repos, get_github_repo, get_github_profile
GitLab (3)	search_gitlab_projects, get_gitlab_project, get_gitlab_file
Knowledge (4)	search_career_knowledge, get_knowledge_stats, index_career_knowledge, clear_career_knowledge
Analysis (3)	analyze_skill_gaps, analyze_career_alignment, get_career_advice
Market (6)	search_jobs, get_tech_trends, get_salary_insights, analyze_market_fit, analyze_market_skills, gather_market_data
Financial (2)	convert_currency, compare_salary_ppp
Generation (2)	generate_cv, generate_cv_draft
Memory (4)	remember_decision, remember_job_application, recall_memories, get_memory_stats
Settings (2)	get_current_config, update_setting

MCP Clients

12 MCP clients for real-time data access:

Client	Purpose
github	Repository search, file access, profile
financial	Currency conversion, PPP comparison
tavily	Web search, salary research
hn	Hacker News jobs, trending discussions
jobspy	Multi-board job aggregation
remoteok	Remote job listings
himalayas	Remote job listings
remotive	Remote job listings
jobicy	Remote job listings
weworkremotely	Remote job listings
devto	Developer articles, trends
stackoverflow	Tag trends, popular questions

Development

# Install dev tools
pip install pyright pytest ruff

# Test
pytest tests/ -q
pyright src/fu7ur3pr00f
ruff check .
ruff check . --fix

Scripts

Script	Purpose
scripts/setup.sh	One-time Azure/config setup
scripts/fresh_install_check.sh	Validate pipx install
scripts/clean_dev_artifacts.sh	Clean build artifacts
scripts/build_deb.sh	Build .deb package
scripts/build_apt_repo.sh	Build apt repository
scripts/validate_apt_artifact.sh	Test .deb in Docker
scripts/vagrant.sh	Vagrant VM management

Testing

# Unit tests
pytest tests/ -q

# Benchmarks
pytest tests/benchmarks/ -v

# Fresh install check
scripts/fresh_install_check.sh --source local --config-from .env

# Vagrant apt repo testing
scripts/vagrant.sh test-apt

# Multi-agent system testing
scripts/vagrant.sh multi

Offline behavior:

• Specialist routing falls back to deterministic keyword scoring in CI or other offline environments.
• CV parsing falls back to local heading extraction for Markdown and plain-text resumes when LLM section extraction is unavailable.
• Sensitive LinkedIn/social sections such as conversations and sponsored message threads are excluded before knowledge indexing, not just hidden at search time.

System Dependencies (Optional)

Feature	Package
GitLab CLI	sudo apt install glab
CliftonStrengths PDF parsing	sudo apt install poppler-utils
CV PDF export	sudo apt install libpango-1.0-0 libpangoft2-1.0-0 libcairo2 libfontconfig1 libgdk-pixbuf-2.0-0

Tech Stack

Python 3.13 · LangChain + LangGraph · ChromaDB · Typer + Rich · WeasyPrint · MCP

Documentation

Documentation is embedded in the codebase:

• Prompts: src/fu7ur3pr00f/prompts/md/ — All system and specialist prompts
• Tools: src/fu7ur3pr00f/agents/tools/ — Tool implementations with docstrings
• MCP Clients: src/fu7ur3pr00f/mcp/ — MCP client implementations
• Memory: src/fu7ur3pr00f/memory/ — ChromaDB, RAG, episodic memory
• Gatherers: src/fu7ur3pr00f/gatherers/ — Data collection modules
• Specialists: src/fu7ur3pr00f/agents/specialists/ — Multi-agent specialists and orchestrator
• Blackboard: src/fu7ur3pr00f/agents/blackboard/ — Blackboard pattern implementation

Key documentation files:

• QWEN.md — Project context for AI assistants
• GEMINI.md — Additional project documentation
• .env.example — Configuration reference

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Licensed under GPL-2.0.