LifeGraph: AI Social Resource Navigator
Neo4j Aura Agent Hackathon 2026 - Final Submission
PROBLEM STATEMENT
Millions of people qualify for life-changing assistance but never receive it.
Consider a foster youth aging out of care at 18. She qualifies for 20+ programs that could transform her life:
- Extended Foster Care (housing + support)
- Job Corps (training + $500/month stipend)
- CalFresh (food assistance)
- Medi-Cal (healthcare)
- Housing Vouchers (independent housing)
- And 15+ more programs
But she discovers maybe two.
Why? It's a Graph Problem, Not a Search Problem
Social services are fundamentally interconnected—but presented as isolated silos:
-
Fragmentation: 50+ agencies manage these programs
-
Opacity: Eligibility rules are buried in government documents
-
Hidden Dependencies: She doesn't know that Program A unlocks Program B, which unlocks Program C
-
Missing Context: Traditional search returns: "You might qualify for CalFresh, Job Corps, and housing" — no sequence, no why, no what comes next
The Result: Missed opportunities, prolonged poverty, and human potential left unrealized.
LifeGraph solves this by treating social services as what they really are: an interconnected graph of programs, eligibility, and outcomes.
VISION
Every person facing a life transition deserves a clear, intelligent path to stability.
Instead of searching blindly through disconnected programs, users see:
-
What they qualify for (based on their situation)
-
The best sequence to apply (programs unlock each other)
-
Where to go (real organizations with contact info)
-
What it leads to (concrete outcomes: employment, housing, stability)
Graph reasoning makes this possible.
SOLUTION: LifeGraph
A Neo4j knowledge graph of social programs + three specialized AI agents that reason through relationships to guide users to stability.
How It Works
Traditional Search:
"What programs can I get?"
→ Search engine returns list
→ User is overwhelmed
→ User gives up
LifeGraph (Graph-Powered):
"I'm 19, aging out of foster care in Alameda. What's my path?"
↓ GRAPH REASONS:
Extended Foster Care (YOU TRIGGER)
↓ UNLOCKS
Transportation Assistance
↓ MAKES ACCESSIBLE
Job Corps Oakland (location, contact, benefits)
↓ LEADS TO
Stable employment → Housing secured
Result: Clear 6-month pathway from aging out to financial stability
The difference: Graph reasoning reveals dependencies, sequences, and outcomes that search engines cannot discover.
WHY GRAPHS ARE NECESSARY FOR THIS PROBLEM
Graph Structure Solves What Relational Databases Cannot
Traditional Database Approach (Relational):
SELECT programs WHERE user_age < 21 AND location = 'Alameda'
→ Returns isolated list: [Extended Foster Care, CalFresh, Job Corps]
→ No understanding of how they connect
→ User still doesn't know the sequence or dependencies
Graph Approach:
MATCH (user:LifeEvent)-[:TRIGGERS]->(p:Program)-[:UNLOCKS]->(p2:Program)-[:LEADS_TO]->(outcome:Outcome)
WHERE user.event = 'AGED_OUT_OF_FOSTER_CARE' AND p.location = 'Alameda'
→ Returns complete journeys
→ Shows implicit dependencies
→ Reveals hidden programs user didn't know existed
→ Explains the "why" and "what comes next"
The Key Insight: Dependencies ARE the Intelligence
In relational databases, relationships are metadata ("this program ID links to this rule ID").
In a knowledge graph, relationships are the intelligence itself:
Extended_Foster_Care -[:UNLOCKS]-> Transportation_Assistance (hidden program revealed)AGED_OUT_OF_FOSTER_CARE -[:TRIGGERS]-> Extended_Foster_Care (entry point identified)Job_Corps -[:LEADS_TO]-> EMPLOYED (outcome clarity)
Only a graph can efficiently traverse and reason through these chains.
KNOWLEDGE GRAPH STRUCTURE
Graph Statistics
| Type |
Count |
Examples |
|
| Benefit (Programs) |
45+ |
CalFresh, Extended Foster Care, Job Corps, Housing Vouchers, Medi-Cal |
|
| Organization |
35+ |
First Place for Youth, Job Corps Oakland, Alameda Workforce Development, YouthBuilt Collective |
|
| LifeEvent |
12 |
AGED_OUT_OF_FOSTER_CARE, LOST_JOB, HAD_CHILD, HOMELESSNESS_RISK, DOMESTIC_VIOLENCE |
|
| EligibilityRule |
20+ |
Age limits, income caps (<$1,500/month), residency (Bay Area), work requirements |
|
| Document |
15+ |
Birth Certificate, ID Card, Proof of Income, Foster Care Documentation |
|
| Outcome |
10 |
HOUSING_SECURED, EMPLOYED, FINANCIAL_STABILITY, HEALTHCARE_ACCESS, EDUCATION_COMPLETED |
|
Relationship Types (7 Core Types)
LifeEvent -[:TRIGGERS]-> Benefit
↳ When this life event occurs, this program becomes available
↳ Example: AGED_OUT_OF_FOSTER_CARE -[:TRIGGERS]-> Extended_Foster_Care
↳ Agent use: "Here are programs you can access right now"
Benefit -[:UNLOCKS]-> Benefit
↳ Qualifying for one program automatically makes you eligible for another
↳ Example: Extended_Foster_Care -[:UNLOCKS]-> Transportation_Assistance
↳ Agent use: "This program unlocks three others"
Benefit -[:REQUIRES]-> Document
↳ You need this document to apply
↳ Example: Foster_Youth_Housing -[:REQUIRES]-> Foster_Care_Documentation
↳ Agent use: "Here's what paperwork to gather"
Benefit -[:HAS_RULE]-> EligibilityRule
↳ You must meet this condition to qualify
↳ Example: CalFresh -[:HAS_RULE]-> (income < $1,500/month)
↳ Agent use: "You qualify because your income is under $1,500"
Benefit -[:LEADS_TO]-> Outcome
↳ Successfully completing this program leads to this outcome
↳ Example: Job_Corps -[:LEADS_TO]-> EMPLOYED
↳ Agent use: "This leads to stable employment"
Organization -[:SERVES]-> LifeEvent
↳ This organization specializes in helping people in this situation
↳ Example: First_Place_for_Youth -[:SERVES]-> AGED_OUT_OF_FOSTER_CARE
↳ Agent use: "Organization X helps foster youth"
Organization -[:PROVIDES]-> Benefit
↳ This organization delivers this program
↳ Example: Job_Corps_Oakland -[:PROVIDES]-> Job_Corps
↳ Agent use: "Here's where to go and how to contact them"
Complete Graph Schema
Graph Schema - Complete Node and Relationship Network
Graph Design Principles:
-
Benefit is the Hub
-
TRIGGERS + UNLOCKS Create Pathways
LifeEvent -[:TRIGGERS]-> Program1 -[:UNLOCKS]-> Program2 -[:LEADS_TO]-> Outcome
= Complete journey from situation to stability
-
Organization Layer Bridges Graph to Reality
THREE SPECIALIZED AURA AGENTS
Agent 1: Eligibility Navigator 
Problem it solves: "What programs can I actually qualify for?"
User Example:
"I'm 19, aging out of foster care in Alameda. What programs can I access?"
Agent Response:
- Lists all programs triggered by user's life event
- Applies eligibility rules (age, income, residency)
- Shows required documents
- Explains why user qualifies
- Returns actionable next steps
How it works:
- Matches
(user_event:LifeEvent) -[:TRIGGERS]-> (program:Benefit)
- Filters by
(program) -[:HAS_RULE]-> (rule) where rule applies to user
- Collects
(program) -[:REQUIRES]-> (document)
- Returns: Programs + Rules + Documents + Explanation
Agent 2: Pathway Advisor 
Problem it solves: "What's my path to stability? What's the sequence?"
User Example:
"I'm 19 in Alameda, aging out of foster care. I need housing and a job.
Show me the best pathway to stability."
Agent Response:
- Builds step-by-step sequences showing program dependencies
- Explains why each step unlocks the next
- Shows timeline to outcomes (employment, housing, stability)
- Reasons through multi-hop relationships
How it works:
- Starts with
(user_event:LifeEvent) -[:TRIGGERS]-> (p1:Program)
- Follows
(p1) -[:UNLOCKS]-> (p2) -[:UNLOCKS]-> (p3)
- Traces
(p_final) -[:LEADS_TO]-> (outcome:Outcome)
- Orders programs logically
- Returns: Step-by-step plan with "why" explanations
Example Journey:
Step 1: Extended Foster Care
Why: You aged out of foster care + age < 21 ✓
Step 2: Transportation Assistance (UNLOCKED by Step 1)
Why: Extended Foster Care automatically includes this
Impact: Makes Job Corps accessible
Step 3: Job Corps Oakland
Where: 1801 Embarcadero, Oakland
Contact: (510) 622-4600
Benefits: Free training + $500/month stipend
Why Matters: Transportation enables you to attend
Step 4: Employment (LEADS TO from Job Corps)
Outcome: Stable monthly income
Timeline: 6-month training program
Step 5: Independent Housing
Why Possible: Income from employment enables housing
Outcome: Financial stability achieved
Agent 3: Resource Locator 
Problem it solves: "Where can I actually find help?"
User Example:
"Where can I find job training in Alameda County?"
Agent Response:
- Finds organizations that SERVE user's situation
- Lists organizations that PROVIDE relevant programs
- Returns real contact info, locations, eligibility
- Connects programs to real-world access points
How it works:
- Identifies
(user_event:LifeEvent)
- Finds
(org:Organization) -[:SERVES]-> (user_event)
- Also finds
(org) -[:PROVIDES]-> (program)
- Returns: Organization details + Services + Contact + Eligibility
Real Results:
Job Corps Oakland
├─ What: Federal job training & education
├─ Where: Oakland, Alameda County
├─ Contact: (510) 622-4600
├─ Eligibility: Ages 16-24, no income limits
└─ Serves: Individuals seeking employment
Alameda Workforce Development
├─ What: Career services & job training
├─ Where: Alameda, Alameda County
├─ Contact: (510) 832-3500
├─ Eligibility: Free for eligible residents
└─ Serves: Job seekers & displaced workers
YouthBuilt Collective
├─ What: Construction & green jobs training
├─ Where: Alameda County
├─ Contact: (510) 567-9000
├─ Eligibility: Ages 16-29
└─ Serves: Young adults seeking career training
DATA SOURCES
All data is publicly available from official government and nonprofit sources:
Layer 1: Government Programs & Benefits
-
CA Open Data - CalHHS (https://data.ca.gov/)
- CalFresh eligibility, WIC rules, Medi-Cal income limits, TANF/CalWORKs
-
CA Foster Care Indicators Dashboard (https://www.lab.data.ca.gov/)
- Extended Foster Care age limits, youth transition programs, education support
-
California Department of Social Services (CDSS) (https://www.cdss.ca.gov/)
- CalWORKs, TANF, welfare programs, emergency assistance
Layer 2: Community Resources & Organizations
-
211.org Database - Alameda County (https://www.211.org/)
- 50-80+ organizations, services, contact info, eligibility requirements
-
Alameda County Office of Education (https://www.acoe.org/)
- Scholarships, youth programs, career pathways
-
California Foster Care Resources (https://www.fostercare.ca.gov/)
- Foster youth-focused organizations, education benefits, transition resources
Data Characteristics
- No sensitive personal data — only public program information
- Official sources — government agencies and established nonprofits
- Current — 2024-2025 program documentation
- Regional focus — Bay Area/Alameda County with statewide programs
- Validated — cross-referenced across multiple official sources
HOW JUDGES CAN TEST
Option 1: Test in Aura Console (Live Agents)
Step 1: Access Agents
-
Open Neo4j Aura Console
-
Navigate to "Data Services → Agents"
-
Select the three LifeGraph agents
Step 2: Test Eligibility Navigator
Ask: "I'm 19, aging out of foster care in Alameda. What can I get?"
See:
- List of eligible programs
- Why user qualifies for each
- Required documents
- Next steps
Aura Agent UI
Step 3: Test Pathway Advisor
Ask: "Show me the path to stability. I need housing and a job."
See:
- Step-by-step sequence
- How programs unlock each other
- Why each step matters
- Outcomes (employment, housing, stability)
Step 4: Test Resource Locator
Ask: "Where's job training in Alameda County?"
See:
- Real organizations (Job Corps, Alameda Workforce, YouthBuilt)
- Contact info and locations
- What services they provide
- Eligibility requirements
Step 5: View Reasoning Traces
- Open agent reasoning panel
- See agents explicitly acknowledge graph structure
- Watch them traverse relationship chains
- See multi-hop pathways being constructed
Option 2: Test Web Application
go to git repo : GitHub - kirthistaank/LifeGraph · GitHub
Setup & Run (See README.md for detailed instructions):
-
Navigate to the LifeGraph project directory
-
Install dependencies: pip install -r requirements.txt
-
Configure environment variables (see STREAMLIT_SETUP_GUIDE.md):
-
Run the app: streamlit run streamlit_app.py
-
Open browser to http://localhost:8501
Interact with the Application:
- Select an agent (Eligibility Navigator, Pathway Advisor, or Resource Locator) in the sidebar
- Fill your profile (age, county, situation/goals) using the guided inputs
- Review the auto-generated question pre-populated in the main prompt area
- Ask your question - Edit the initial question or ask as-is
- See real-time response with graph reasoning
- Ask follow-up questions - The agent remembers your conversation context
- View reasoning traces - Expand "View graph reasoning" to see how the agent traversed the knowledge graph
WHY NEO4J AURA MADE THIS SMOOTH
Challenge: Multi-Hop Reasoning at Scale
Without Neo4j Aura:
- SQL queries would require complex JOINs (5-7 tables minimum)
- Impossible to efficiently explore unknown paths ("find all programs that lead to employment")
- Poor performance with relationship traversal
With Neo4j Aura:
MATCH (event:LifeEvent)-[:TRIGGERS]->(p1:Program)
-[:UNLOCKS]->(p2:Program)-[:LEADS_TO]->(outcome:Outcome)
WHERE event.name = 'AGED_OUT_OF_FOSTER_CARE'
AND p1.location = 'Alameda'
RETURN p1, p2, outcome
Result: Complex multi-hop reasoning in milliseconds.
Why Aura Specifically
-
Managed Infrastructure
-
Agents Integration
-
Neo4j Aura Agent framework is purpose-built for knowledge graphs
-
Agents can reason through relationships natively
-
Text2Cypher automatically converts questions to graph queries
-
Relationship Reasoning
-
Aura excels at what other databases struggle with: traversing relationships
-
Each relationship is indexed and optimized
-
Multi-hop queries scale linearly, not exponentially
-
Cost-Effective
-
Free tier sufficient for hackathon demo
-
~$0.35/hour for external agent access (minimal)
-
No database redesign needed for scaling
WEB APP INTEGRATION: Easy + Elegant
Architecture: Graph + Agents + Streamlit
┌─────────────────────────────────────────┐
│ Streamlit Web App │
│ ┌─────────────────────────────────────┤
│ │ Chat Interface │
│ │ - Select Agent │
│ │ - Fill Profile │
│ │ - Ask Question │
│ └─────────────────────────────────────┤
└────────────────┬────────────────────────┘
│
↓ (REST API)
┌─────────────────────────────────────────┐
│ Neo4j Aura Agent (External) │
│ ┌─────────────────────────────────────┤
│ │ Text2Cypher │
│ │ ↓ converts question to query │
│ │ Cypher Template │
│ │ ↓ executes on graph │
│ └─────────────────────────────────────┤
└────────────────┬────────────────────────┘
│
↓
┌─────────────────────────────────────────┐
│ Neo4j Aura Database │
│ ┌─────────────────────────────────────┤
│ │ Knowledge Graph │
│ │ - 127 nodes (programs, orgs, etc) │
│ │ - 246 relationships │
│ │ - Multi-hop paths optimized │
│ └─────────────────────────────────────┤
└─────────────────────────────────────────┘
Why Integration Was Seamless
-
REST API First
-
Authentication Simple
-
Neo4j API credentials (not database credentials)
-
Standard OAuth token exchange
-
Works with any web framework
-
Stateless Design
-
Each question is independent
-
No session management needed
-
Agents handle all reasoning
-
Rapid Development
-
No custom agent logic needed
-
Aura agents work out-of-the-box
-
Focus on UX, not backend plumbing
Code Example: Calling Agent from Streamlit
def call_agent(agent_name: str, question: str) -> dict:
"""Call a Neo4j Aura Agent via REST API"""
# Get bearer token (simple OAuth exchange)
token = get_bearer_token(api_key, api_secret)
# Call agent endpoint
response = requests.post(
agent_endpoint,
headers={
"Authorization": f"Bearer {token}",
"Content-Type": "application/json"
},
json={"input": question},
timeout=60
)
# Return parsed response
return response.json()
That's it. No database connection, no query building, no response parsing.
WHAT DIFFERENTIATES LIFEGRAPH
vs. Traditional Benefits Directory
Traditional: Search → List of programs → User confused → User gives up
LifeGraph: Ask question → Get sequenced plan → User empowered → User acts
vs. Search Engine
Search: "What programs can I get?" → Irrelevant results
LifeGraph: Same question → Relationship reasoning → Relevant, sequenced, complete
vs. Linear Rule Engine
Rule Engine: "If age < 21 THEN extend foster care" (one condition)
LifeGraph: "Extend foster care TRIGGERS transportation assistance UNLOCKS job corps LEADS_TO employment"
(complete chain, revealing hidden dependencies)
Unique Value Propositions
-
Dependency Navigation
-
Multi-Hop Reasoning
-
Connects 3-4 program hops in seconds
-
Reveals outcomes (housing, employment, stability)
-
Real Data, Real Impact
-
Based on actual CA social services
-
Real organizations, real phone numbers, real eligibility rules
-
Graph-Powered Intelligence
-
Transparency
IMPACT
For Foster Youth
-
Clear path from aging out → employment → independence
-
Access to interconnected support system
-
Transparent reasoning (understanding why programs matter)
For Government
-
Reduced duplicated effort (agencies know who's applying where)
-
Better outcomes tracking (see which programs lead to employment)
-
Smarter resource allocation
LifeGraph: Because foster youth deserve a clear path to stability.
Built with Neo4j Aura. Powered by graph reasoning. Made for people.
Showcase
Live Demo
Technology Stack
Key Decisions
Links
What it does
Screenshot or short demo of your agent in action
I just finished the Neo4j course and I'm so excited for the free credits! I wish I could have built a full-on agent, but I only started taking the courses last week. While I've built a RAG pipeline before as part of an art project to spread awareness about domestic violence, I'm still getting familiar with this specific workflow. With that said, I am SO excited 
to see what others have built and I hope Neo4j comes out with more competitions like this in the future! 
Agent Submission Details
Agent Name
Dataset and why a graph fits
