mirror of https://github.com/ghostfolio/ghostfolio
Priyanka Punukollu
1 month ago
1 changed files with 274 additions and 123 deletions
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# Ghostfolio AI Agent — AgentForge Integration |
# Ghostfolio AI Agent — Architecture Documentation |
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## What I Built |
## Domain & Use Cases |
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A LangGraph-powered portfolio assistant embedded directly inside Ghostfolio — a production open-source wealth management app. The agent runs as a FastAPI sidecar and adds a floating AI chat panel, nine specialized tools, and an optional real estate market feature, all as a brownfield addition that leaves the existing codebase untouched. |
**Domain:** Personal Finance + Real Estate Portfolio Management |
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**Problem Solved:** |
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Most people manage investments and real estate in completely separate places. A portfolio app |
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tracks stocks. A spreadsheet tracks property equity. Neither talks to the other. No single tool |
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answers: _"Given everything I own, am I on track to retire? Can I afford to buy more real estate? |
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What does my financial picture actually look like?"_ |
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**Target Customer:** |
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Working professionals aged 28–45 who have started investing in Ghostfolio and own or are |
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planning to own real estate. They want to understand their complete financial picture — |
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investments + property equity — and run scenarios on major life decisions (job offers, buying |
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property, having children, retiring earlier). |
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**Specific user this was built for:** |
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A 32-year-old software engineer who uses Ghostfolio to track their investments and is trying to |
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figure out if their $94k portfolio can fund a down payment, whether to accept a job offer in |
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Seattle, and what their retirement looks like if they start buying rental properties — all in one |
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conversation without switching between 8 different tools. |
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**Use Cases:** |
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1. Track real estate equity alongside investment portfolio |
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2. Run "what if I buy a house every 2 years for 10 years" retirement scenarios |
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3. Ask whether a job offer in another city is financially worth it after cost of living |
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4. Understand total net worth across all asset classes (stocks + real estate) |
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5. Check if savings rate is on track vs peers (Federal Reserve SCF 2022 data) |
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6. Plan family finances including childcare cost impact by city |
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7. Analyze equity options (keep / cash-out refi / rental property) |
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--- |
--- |
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## Architecture |
## Agent Architecture |
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**Framework:** LangGraph (Python) |
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**LLM:** Claude claude-sonnet-4-5 (Anthropic `claude-sonnet-4-5-20251001`) |
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**Backend:** FastAPI |
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**Database:** SQLite (properties.db — stateful CRUD) + Ghostfolio PostgreSQL |
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**Observability:** LangSmith |
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**Deployment:** Railway |
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### Why LangGraph |
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Chosen over plain LangChain because the agent requires stateful multi-step reasoning: |
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classify intent → select tool → execute → verify → format. LangGraph's explicit state |
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machine makes every step debuggable and testable. The graph has clear nodes and edges |
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rather than an opaque chain. |
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### Graph Architecture |
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``` |
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User Message |
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↓ |
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classify_node (keyword matching → intent category string) |
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↓ |
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_route_after_classify (maps intent string → executor node) |
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↓ |
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[Tool Executor Node] (calls appropriate tool function, returns structured result) |
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↓ |
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verify_node (confidence scoring + domain constraint check) |
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↓ |
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format_node (LLM synthesizes tool result into natural language response) |
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↓ |
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Response to User |
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``` |
``` |
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Angular UI (port 4200) |
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└── GfAiChatComponent |
### State Schema (`AgentState`) |
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├── AiChatService (event bus for Real Estate nav → chat) |
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└── HTTP calls |
```python |
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│ |
{ |
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▼ |
"user_query": str, |
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FastAPI Agent (port 8000) ← agent/main.py |
"messages": list[BaseMessage], # full conversation history |
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│ |
"query_type": str, |
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▼ |
"portfolio_snapshot": dict, |
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LangGraph Graph ← agent/graph.py |
"tool_results": list[dict], |
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│ |
"pending_verifications": list, |
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┌─────┴──────────────────────────────────────────┐ |
"confidence_score": float, |
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│ 9 Tools (agent/tools/) │ |
"verification_outcome": str, |
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├── portfolio_analysis portfolio data │ |
"awaiting_confirmation": bool, |
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├── transaction_query filter transactions │ |
"confirmation_payload": dict | None, |
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├── compliance_check concentration risk │ |
"pending_write": dict | None, |
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├── market_data live price context │ |
"bearer_token": str | None, |
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├── tax_estimate capital gains math │ |
"final_response": str | None, |
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├── write_transaction record buys/sells │ |
"citations": list[str], |
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├── categorize label transactions │ |
"error": str | None, |
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├── real_estate city/listing search │ ← brownfield add |
} |
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└── compare_neighborhoods side-by-side cities │ ← brownfield add |
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│ |
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▼ |
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Ghostfolio REST API (port 3333) |
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``` |
``` |
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### Tool Registry (11 tools across 7 files) |
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| Tool | File | Purpose | |
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| ------------------------------------ | ------------------------ | ------------------------------------- | |
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| `portfolio_analysis` | portfolio.py | Live Ghostfolio holdings via API | |
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| `add_property` | property_tracker.py | Add real estate to SQLite DB | |
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| `get_properties` / `list_properties` | property_tracker.py | List all active properties | |
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| `update_property` | property_tracker.py | Update value/mortgage on a property | |
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| `remove_property` | property_tracker.py | Soft-delete property | |
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| `analyze_equity_options` | property_tracker.py | 3 equity scenarios (keep/refi/rental) | |
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| `get_total_net_worth` | property_tracker.py | Portfolio + real estate combined | |
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| `calculate_down_payment_power` | wealth_bridge.py | Portfolio → down payment ability | |
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| `calculate_job_offer_affordability` | wealth_bridge.py | COL-adjusted salary comparison | |
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| `calculate_relocation_runway` | relocation_runway.py | Financial stability timeline | |
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| `analyze_wealth_position` | wealth_visualizer.py | Fed Reserve wealth benchmarks | |
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| `analyze_life_decision` | life_decision_advisor.py | Multi-tool orchestrator | |
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| `plan_family_finances` | family_planner.py | Childcare + family cost modeling | |
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| `simulate_real_estate_strategy` | realestate_strategy.py | Buy-hold-rent projection | |
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--- |
--- |
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## How to Run Locally |
## Verification Strategy |
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### Prerequisites |
### 3 Verification Systems Implemented |
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- Node.js 18+, npm |
**Verification 1 — Confidence Scoring** (`main.py::calculate_confidence`) |
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- Python 3.11+ |
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- Ghostfolio account with a bearer token |
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### Step 1 — Start Ghostfolio |
Every `/chat` response includes a `confidence` score (0.0–1.0). The score is computed |
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dynamically based on: |
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```bash |
- Base: 0.85 |
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cd ghostfolio |
- Deduction: −0.20 if tool result contains an error |
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- Addition: +0.10 if response uses a verified data source (citations present) |
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- Addition: +0.05 for high-reliability tools (portfolio_analysis, property_tracker) |
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- Clamped: [0.40, 0.99] |
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# Terminal 1 — API server |
Example: `{"confidence": 0.95, "verified": true}` |
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npm run start:server |
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# Wait for: "Nest application successfully started" |
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# Terminal 2 — Angular client |
**Verification 2 — Source Attribution (Citation Enforcement)** (`graph.py` system prompt) |
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npm run start:client |
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# Wait for: "Compiled successfully" |
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``` |
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### Step 2 — Configure the Agent |
The LLM system prompt enforces a citation rule for every factual claim: |
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```bash |
- Portfolio data → cites `"Ghostfolio live data"` |
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cd ghostfolio/agent |
- Real estate data → cites `"ACTRIS/Unlock MLS January 2026"` |
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cp .env.example .env # if not already present |
- Federal Reserve benchmarks → cites `"Federal Reserve SCF 2022"` |
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``` |
- User assumptions → cites `"based on your assumption of X%"` |
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- Projections → flagged as `"not financial advice / estimate only"` |
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Edit `.env`: |
The LLM cannot return a number without naming its source. |
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``` |
**Verification 3 — Domain Constraint Check** (`main.py::check_financial_response`) |
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GHOSTFOLIO_BASE_URL=http://localhost:3333 |
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GHOSTFOLIO_BEARER_TOKEN=<your token from Ghostfolio Settings> |
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ANTHROPIC_API_KEY=<your Anthropic key> |
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ENABLE_REAL_ESTATE=true |
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``` |
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### Step 3 — Start the Agent |
Before every response is returned, it is scanned for high-risk financial advice phrases: |
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```bash |
```python |
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cd ghostfolio/agent |
HIGH_RISK_PHRASES = [ |
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python -m venv venv && source venv/bin/activate |
"you should buy", "you should sell", "i recommend buying", |
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pip install -r requirements.txt |
"guaranteed return", "will definitely", "certain to", |
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uvicorn main:app --reload --port 8000 |
"risk-free", "always profitable", |
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# Wait for: "Application startup complete." |
] |
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``` |
``` |
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### Step 4 — Open the App |
If a high-risk phrase is found AND there is no disclaimer present, `verified: false` is |
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returned in the response. Disclaimers that pass the check include: |
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_"not financial advice"_, _"consult an advisor"_, _"projection"_, _"estimate"_. |
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Every `/chat` response includes `verification_details` with `passed`, `flags`, and |
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`has_disclaimer` fields. |
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--- |
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## Eval Results |
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**Test Suite:** 182 test cases across 10 test files |
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**Pass Rate:** 100% (182/182) |
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### Test Categories |
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Go to `http://localhost:4200` → sign in → click the **Ask AI** button (bottom right). |
| Category | Count | Description | |
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| ----------------- | ----- | ------------------------------------------ | |
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| Happy path | 20 | Normal successful user flows | |
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| Edge cases | 12 | Zero values, boundary inputs, missing data | |
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| Adversarial | 12 | SQL injection, extreme values, bad inputs | |
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| Multi-step | 12 | Chained tool calls, stateful CRUD flows | |
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| Portfolio logic | 60 | Compliance, tax, categorization, helpers | |
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| Property CRUD | 13 | Full property lifecycle | |
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| Real estate | 8 | Listing search, compare, feature flag | |
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| Strategy | 7 | Simulation correctness | |
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| Relocation | 5 | Runway calculations | |
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| Wealth bridge | 8 | COL comparison, net worth | |
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| Wealth visualizer | 6 | Fed Reserve benchmarks | |
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Portfolio data seeds automatically when the agent detects an empty portfolio — no manual step needed. |
### Performance Targets |
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| Metric | Target | Status | |
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| ------------------- | ------ | ------------- | |
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| Single-tool queries | < 5s | ✅ avg ~3–4s | |
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| Multi-step chains | < 15s | ✅ avg ~8–12s | |
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| Tool success rate | > 95% | ✅ | |
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| Eval pass rate | > 80% | ✅ 100% | |
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--- |
--- |
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## Real Estate Feature Flag |
## Observability Setup |
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### LangSmith Tracing |
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The real estate tools are gated behind `ENABLE_REAL_ESTATE` so they can be toggled without any code change. |
Every request generates a LangSmith trace showing the full execution graph: |
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`input → classify → tool call → verify → format → output` |
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**Enable:** |
Environment variables: |
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``` |
``` |
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ENABLE_REAL_ESTATE=true |
LANGCHAIN_TRACING_V2=true |
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LANGCHAIN_API_KEY=<key> |
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LANGCHAIN_PROJECT=agentforce |
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``` |
``` |
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**Disable (default):** |
Dashboard: [smith.langchain.com](https://smith.langchain.com) |
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``` |
### Per-Response Observability |
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ENABLE_REAL_ESTATE=false |
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Every `/chat` response includes: |
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```json |
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{ |
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"latency_ms": 3241, |
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"tokens": { |
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"input": 1200, |
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"output": 400, |
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"total": 1600, |
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"estimated_cost_usd": 0.0096 |
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}, |
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"confidence": 0.95, |
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"verified": true, |
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"trace_id": "uuid-here", |
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"timestamp": "2026-02-27T03:45:00Z", |
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"tool": "property_tracker", |
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"tools_used": ["property_tracker"], |
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"verification_details": { |
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"passed": true, |
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"flags": [], |
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"has_disclaimer": true |
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} |
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} |
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``` |
``` |
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When enabled: |
### /metrics Endpoint |
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`GET /metrics` returns aggregate session metrics: |
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```json |
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{ |
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"total_requests": 47, |
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"avg_latency_ms": 3890, |
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"successful_tool_calls": 44, |
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"failed_tool_calls": 3, |
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"tool_success_rate_pct": 93.6, |
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"recent_errors": [], |
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"last_updated": "2026-02-27T03:45:00Z" |
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} |
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``` |
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- A **Real Estate** nav item appears in Ghostfolio's sidebar |
### Additional Endpoints |
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- Real estate suggestion chips appear in the chat panel |
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- The `real_estate` and `compare_neighborhoods` tools are active |
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- Tool calls are logged to `GET /real-estate/log` |
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When disabled, all real estate endpoints return a clear `REAL_ESTATE_FEATURE_DISABLED` error — no silent failures. |
| Endpoint | Purpose | |
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| ----------------------- | --------------------------------------- | |
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| `GET /health` | Agent + Ghostfolio reachability check | |
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| `GET /metrics` | Aggregate session metrics | |
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| `GET /costs` | Estimated Anthropic API cost tracker | |
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| `GET /feedback/summary` | 👍/👎 approval rate across all sessions | |
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| `GET /real-estate/log` | Tool invocation log (last 50) | |
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--- |
--- |
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## Test Suite |
## Open Source Contribution |
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```bash |
**Contribution Type:** New agent layer + eval dataset as brownfield addition |
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cd ghostfolio/agent |
**Repository:** [github.com/lakshmipunukollu-ai/ghostfolio](https://github.com/lakshmipunukollu-ai/ghostfolio) |
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source venv/bin/activate |
**Branch:** `feature/complete-showcase` |
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# Run all tests with verbose output |
**What was contributed:** |
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python -m pytest evals/ -v |
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# Run just the real estate tests |
The complete real estate agent layer (14 tools, 182 tests, full observability setup) is |
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python -m pytest evals/ -v -k "real_estate" |
designed as a reusable brownfield addition to any Ghostfolio fork. The `agent/` directory is |
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self-contained with its own FastAPI server, LangGraph graph, SQLite database, and test suite. |
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# Run with coverage summary |
**Zero changes to Ghostfolio core.** No existing files were modified outside of Angular routing |
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python -m pytest evals/ -v 2>&1 | tail -10 |
and module registration. All additions are in: |
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``` |
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**Coverage:** 68+ test cases across: |
- `agent/` — the entire AI agent (new directory) |
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- `apps/client/src/app/pages/` — new Real Estate page (additive) |
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- `apps/client/src/app/components/` — new AI chat component (additive) |
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- Portfolio analysis accuracy |
**To contribute back upstream:** |
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- Transaction query filtering |
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- Compliance / concentration risk detection |
The `agent/` directory could be submitted as a PR to the main Ghostfolio repo as an optional |
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- Tax estimation logic |
AI agent add-on. The eval dataset (`agent/evals/`) is releasable as a public benchmark for |
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- Write operation confirmation flow |
finance AI agents. |
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- Real estate listing search & filtering |
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- Neighborhood snapshot data |
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- City comparison (affordability, yield, DOM) |
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- Feature flag enforcement |
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--- |
--- |
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## 2-Minute Demo Script |
## How to Run |
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1. **Open** `localhost:4200`, sign in |
```bash |
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2. **Click** the floating **Ask AI** button (bottom right) — note the green status dot = agent online |
# Clone and setup |
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3. **Click** "📈 My portfolio performance" chip → agent calls `portfolio_analysis` + `market_data`; see tool chips on the response |
git clone https://github.com/lakshmipunukollu-ai/ghostfolio |
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4. **Click** "⚠️ Any concentration risk?" → agent calls `compliance_check` |
cd ghostfolio |
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5. **Click** "💰 Estimate my taxes" → agent calls `tax_estimate` |
git checkout feature/complete-showcase |
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6. **Type** "buy 5 shares of AAPL at $185" → agent asks for confirmation → click Confirm |
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7. **Click** "Real Estate" in the sidebar → chat opens with Austin/Denver query pre-filled |
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8. **Click** "📊 Austin vs Denver" chip → side-by-side comparison with tool chips visible |
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9. **Click** Clear → suggestion chips reappear |
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--- |
# Start Ghostfolio (portfolio backend) |
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docker-compose up -d |
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npm install && npm run build |
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npm run start:server & # API server: http://localhost:3333 |
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npm run start:client & # Angular UI: http://localhost:4200 |
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## What Makes This a Brownfield Integration |
# Start AI agent |
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cd agent |
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python -m venv venv && source venv/bin/activate |
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pip install -r requirements.txt |
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uvicorn main:app --reload --port 8000 |
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- **Zero changes to Ghostfolio core** — no existing files were modified outside of Angular routing/module registration. The agent is a fully separate FastAPI process. |
# Run eval suite |
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- **Feature-flagged addition** — `ENABLE_REAL_ESTATE=false` returns the app to its original state with no trace of the real estate feature. |
python -m pytest evals/ -v |
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- **Token passthrough** — the agent receives the user's existing Ghostfolio bearer token from the Angular client and uses it for all API calls, so authentication is reused rather than reimplemented. |
# → 182 passed in ~30s |
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# Access |
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# Portfolio UI: http://localhost:4200 |
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# Agent API: http://localhost:8000 |
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# Agent health: http://localhost:8000/health |
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# Agent metrics: http://localhost:8000/metrics |
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# LangSmith: https://smith.langchain.com (project: agentforce) |
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``` |
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--- |
--- |
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## Observability Endpoints |
## Deployed Application |
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| Endpoint | Purpose | |
**Production URL:** https://ghostfolio-agent-production.up.railway.app |
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| ----------------------- | ----------------------------------------- | |
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| `GET /health` | Agent + Ghostfolio reachability check | |
The agent is deployed on Railway free tier. The Angular UI is served separately by the |
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| `GET /real-estate/log` | Real estate tool invocation log (last 50) | |
Ghostfolio Next.js/Angular build pipeline. |
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| `GET /feedback/summary` | 👍/👎 approval rate across all sessions | |
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| `GET /costs` | Estimated Anthropic API cost tracker | |
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