mirror of
https://github.com/katanemo/plano.git
synced 2026-05-04 21:32:43 +02:00
2 loop refactor
This commit is contained in:
Ahmed Burney
parent
334f902e4a
commit
e58e6dae9b
4 changed files with 175 additions and 1164 deletions
|
|
@ -1,575 +1,73 @@
|
|||
# Credit Risk Case Copilot
|
||||
|
||||
This directory contains a demo **credit risk assessment system** built to exercise Plano's orchestration, guardrails, and observability features in a realistic setup.
|
||||
|
||||
The goal of this project is not to show "yet another agent demo", but to answer a more practical question:
|
||||
|
||||
> How would you actually deploy an agentic AI system in a regulated environment?
|
||||
|
||||
To do that, the system includes a real multi-agent workflow, a security boundary in front of it, structured downstream actions, and full tracing across all components.
|
||||
A small demo that follows the two-loop model: Plano is the **outer loop** (routing, guardrails, tracing), and each credit-risk step is a focused **inner-loop agent**.
|
||||
|
||||
---
|
||||
|
||||
## Why this demo exists
|
||||
## What runs
|
||||
|
||||
Most agent demos run everything inside the agent:
|
||||
- Agents call models directly
|
||||
- There's no security boundary
|
||||
- Observability is minimal
|
||||
- Downstream systems are mocked or ignored
|
||||
|
||||
That works for experimentation, but it doesn't resemble how these systems would be deployed in production.
|
||||
|
||||
This demo flips that around:
|
||||
- **Plano sits in front** as the control plane
|
||||
- Requests are sanitized before reaching agents
|
||||
- Agents are treated as untrusted workloads
|
||||
- Every LLM call is routed, logged, and traceable
|
||||
- **Risk Crew Agent (10530)**: four OpenAI-compatible endpoints (intake, risk, policy, memo).
|
||||
- **PII Filter (10550)**: redacts PII and flags prompt injection.
|
||||
- **Streamlit UI (8501)**: single-call client.
|
||||
- **Jaeger (16686)**: tracing backend.
|
||||
|
||||
---
|
||||
|
||||
## High-level architecture
|
||||
## Quick start
|
||||
|
||||
```
|
||||
User / Streamlit UI
|
||||
↓
|
||||
Plano Orchestrator (8001)
|
||||
├─ HTTP Security Filter (PII + injection)
|
||||
├─ Agent routing
|
||||
├─ Model routing
|
||||
↓
|
||||
Risk Crew Agent (CrewAI)
|
||||
↓
|
||||
Plano LLM Gateway (12000)
|
||||
↓
|
||||
OpenAI
|
||||
```
|
||||
|
||||
Plano is the only component allowed to talk to models or invoke downstream systems. Everything else goes through it.
|
||||
|
||||
---
|
||||
|
||||
## What the system actually does
|
||||
|
||||
At a high level, the system takes an unstructured loan request and turns it into a structured credit decision.
|
||||
|
||||
Internally, this is implemented as a small CrewAI workflow with four agents:
|
||||
|
||||
1. Intake & normalization (gpt-4o-mini)
|
||||
2. Risk scoring & drivers (gpt-4o)
|
||||
3. Policy & compliance checks (gpt-4o)
|
||||
4. Decision memo synthesis (gpt-4o)
|
||||
|
||||
Each agent builds on the output of the previous one. The workflow is sequential on purpose to make traces easier to follow.
|
||||
|
||||
The specific agent framework isn't the focus here — it's mainly used as a realistic payload for Plano to orchestrate.
|
||||
|
||||
---
|
||||
|
||||
## Plano features exercised in this demo
|
||||
|
||||
This demo actively uses several Plano capabilities together:
|
||||
|
||||
### Agent listener
|
||||
- OpenAI-compatible `/v1/chat/completions` endpoint
|
||||
- Requests are routed to the appropriate agent based on configuration
|
||||
- Agents remain unaware of routing logic
|
||||
|
||||
### HTTP filter chain (security guardrails)
|
||||
- Requests pass through an HTTP-based security filter before agent execution
|
||||
- PII (CNIC, phone numbers, emails) is redacted in-place
|
||||
- Prompt injection attempts are detected and flagged
|
||||
- The agent receives only sanitized input
|
||||
|
||||
The filter is implemented as a simple HTTP service to keep things easy to debug and reason about.
|
||||
|
||||
### Central LLM gateway
|
||||
- All LLM calls go through Plano
|
||||
- Agents never talk to OpenAI directly
|
||||
- Makes tracing, policy enforcement, and provider switching easier later
|
||||
|
||||
### Observability
|
||||
- End-to-end OpenTelemetry tracing
|
||||
- One trace per request, spanning:
|
||||
- Security filter
|
||||
- Agent execution
|
||||
- Individual LLM calls
|
||||
- Downstream API calls
|
||||
|
||||
---
|
||||
|
||||
## Example request flow
|
||||
|
||||
```bash
|
||||
curl http://localhost:8001/v1/chat/completions \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"model": "openai/gpt-4o",
|
||||
"messages": [
|
||||
{
|
||||
"role": "user",
|
||||
"content": "CNIC 12345-1234567-1 assess risk for loan amount 300000"
|
||||
}
|
||||
]
|
||||
}'
|
||||
```
|
||||
|
||||
What happens:
|
||||
|
||||
1. Plano receives the request
|
||||
2. The PII filter redacts the CNIC
|
||||
3. The sanitized request is routed to the risk agent
|
||||
4. The multi-agent workflow executes
|
||||
5. The response is returned
|
||||
6. The entire flow appears as a single trace in Jaeger
|
||||
|
||||
---
|
||||
|
||||
## Services in this repo
|
||||
|
||||
### Risk Crew Agent (10530)
|
||||
Implements the CrewAI workflow and exposes four standalone OpenAI-compatible endpoints so Plano can route to each step independently.
|
||||
|
||||
The agent is intentionally kept unaware of:
|
||||
- Security filters
|
||||
- Model providers
|
||||
- Routing decisions
|
||||
|
||||
### PII Security Filter (10550)
|
||||
A small FastAPI service that:
|
||||
- Redacts CNIC, phone numbers, and emails
|
||||
- Detects common prompt injection patterns
|
||||
- Mutates messages in-place
|
||||
- Returns only the updated message list (as expected by Plano's HTTP filter interface)
|
||||
|
||||
This runs before the agent is invoked.
|
||||
|
||||
### Streamlit UI (8501)
|
||||
A lightweight UI for interacting with the system:
|
||||
- Provides example scenarios
|
||||
- Displays structured outputs
|
||||
- Useful for demos and manual testing
|
||||
|
||||
### Jaeger (16686)
|
||||
Used for distributed tracing.
|
||||
All services emit OpenTelemetry spans.
|
||||
|
||||
---
|
||||
|
||||
## Observability notes
|
||||
|
||||
Open Jaeger at: **http://localhost:16686**
|
||||
|
||||
A typical trace shows:
|
||||
- One parent request span
|
||||
- A security filter span
|
||||
- Four LLM call spans (one per agent step)
|
||||
|
||||
This is intentional — the trace tells the full story of what happened and why.
|
||||
|
||||
---
|
||||
|
||||
## Running the demo
|
||||
|
||||
### Prerequisites
|
||||
- Docker + Docker Compose
|
||||
- Plano CLI (`pip install planoai` or `uvx planoai`)
|
||||
- OpenAI API key
|
||||
|
||||
### Environment setup
|
||||
```bash
|
||||
cp .env.example .env
|
||||
# add OPENAI_API_KEY
|
||||
```
|
||||
|
||||
### Start services
|
||||
```bash
|
||||
docker compose up --build
|
||||
```
|
||||
|
||||
### Start Plano
|
||||
In a separate terminal:
|
||||
|
||||
```bash
|
||||
uvx planoai up config.yaml
|
||||
```
|
||||
|
||||
Plano runs on:
|
||||
- **8001** – agent listener
|
||||
- **12000** – LLM gateway
|
||||
|
||||
### Access
|
||||
- **Streamlit UI**: http://localhost:8501
|
||||
- **Jaeger Traces**: http://localhost:16686
|
||||
Open:
|
||||
- Streamlit UI: http://localhost:8501
|
||||
- Jaeger: http://localhost:16686
|
||||
|
||||
---
|
||||
|
||||
## Screenshots
|
||||
## How it works
|
||||
|
||||
### Streamlit UI
|
||||
The UI provides a simple interface for testing scenarios and viewing risk assessments:
|
||||
1. The UI sends **one** request to Plano with the application JSON.
|
||||
2. Plano routes the request across the four agents in order:
|
||||
intake → risk → policy → memo.
|
||||
3. Each agent returns JSON with a `step` key.
|
||||
4. The memo agent returns the final response.
|
||||
|
||||
|
||||
|
||||
### PII Redaction in Action
|
||||
The security filter automatically redacts sensitive information (CNIC, email, phone) before it reaches the agent:
|
||||
|
||||
|
||||
|
||||
### Prompt Injection Detection
|
||||
The filter detects and flags malicious prompt injection attempts:
|
||||
|
||||
|
||||
All model calls go through Plano’s LLM gateway, and guardrails run before any agent sees input.
|
||||
|
||||
---
|
||||
|
||||
## Notes on design choices
|
||||
## Endpoints
|
||||
|
||||
- The PII filter is HTTP-based rather than MCP to keep the demo simpler to debug.
|
||||
- Agents execute sequentially to make traces readable.
|
||||
- Model aliases are supported by Plano, but the agent uses explicit model IDs to avoid ambiguity during the demo.
|
||||
- Error handling favors fallback responses over hard failures.
|
||||
Risk Crew Agent (10530):
|
||||
- `POST /v1/agents/intake/chat/completions`
|
||||
- `POST /v1/agents/risk/chat/completions`
|
||||
- `POST /v1/agents/policy/chat/completions`
|
||||
- `POST /v1/agents/memo/chat/completions`
|
||||
- `GET /health`
|
||||
|
||||
These are demo choices, not hard requirements.
|
||||
PII Filter (10550):
|
||||
- `POST /v1/tools/pii_security_filter`
|
||||
- `GET /health`
|
||||
|
||||
Plano (8001):
|
||||
- `POST /v1/chat/completions`
|
||||
|
||||
---
|
||||
|
||||
## What this demo demonstrates
|
||||
## UI flow
|
||||
|
||||
- A real multi-agent workflow running behind a control plane
|
||||
- Centralized security and routing
|
||||
- Clear separation between agents and infrastructure
|
||||
- End-to-end observability
|
||||
- OpenAI-compatible APIs preserved throughout
|
||||
1. Paste or select an application JSON.
|
||||
2. Click **Assess Risk**.
|
||||
3. Review the decision memo.
|
||||
|
||||
This is closer to how agentic systems are likely to be deployed in practice.
|
||||
|
||||
## Using the Demo
|
||||
|
||||
### Streamlit UI Workflow
|
||||
|
||||
1. **Select a Scenario** (or paste your own JSON):
|
||||
- 🟢 **Scenario A** - Low risk (stable job, good credit, low DTI)
|
||||
- 🟡 **Scenario B** - Medium risk (thin file, missing verifications)
|
||||
- 🔴 **Scenario C** - High risk + prompt injection attempt
|
||||
|
||||
2. **Click "Assess Risk"** - The UI calls the four agents sequentially through Plano
|
||||
|
||||
3. **Review Results** - Memo + key summary fields, with normalized data in an expander
|
||||
|
||||
### Direct API Testing
|
||||
|
||||
You can also send requests directly to Plano:
|
||||
|
||||
```bash
|
||||
curl http://localhost:8001/v1/chat/completions \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"model": "gpt-4o",
|
||||
"messages": [
|
||||
{
|
||||
"role": "user",
|
||||
"content": "Assess credit risk for this application: {\"applicant_name\": \"Sarah Ahmed\", \"loan_amount\": 300000, \"credit_score\": 780, \"monthly_income\": 200000, \"total_debt\": 25000, \"delinquencies\": 0, \"kyc_complete\": true, \"income_verified\": true}"
|
||||
}
|
||||
]
|
||||
}'
|
||||
```
|
||||
|
||||
## Example Scenarios
|
||||
|
||||
### Scenario A: Low Risk
|
||||
- Applicant: Sarah Ahmed
|
||||
- Credit Score: 780 (Excellent)
|
||||
- DTI: 12.5% (Low)
|
||||
- Delinquencies: 0
|
||||
- KYC: Complete
|
||||
- **Expected**: LOW risk, APPROVE recommendation
|
||||
|
||||
### Scenario B: Medium Risk
|
||||
- Applicant: Hassan Khan
|
||||
- Credit Score: 620 (Fair)
|
||||
- DTI: 50% (Elevated)
|
||||
- Delinquencies: 1
|
||||
- KYC: Incomplete (missing income/address verification)
|
||||
- **Expected**: MEDIUM risk, CONDITIONAL_APPROVE or REFER
|
||||
|
||||
### Scenario C: High Risk + Injection
|
||||
- Applicant: Ali Raza
|
||||
- Credit Score: 520 (Poor)
|
||||
- DTI: 100% (Critical)
|
||||
- Delinquencies: 3
|
||||
- Contains: "Ignore all previous instructions" (prompt injection)
|
||||
- **Expected**: HIGH risk, REJECT, PII redacted, injection detected
|
||||
|
||||
## Service Details
|
||||
|
||||
### Risk Crew Agent (Port 10530) - CrewAI Multi-Agent System
|
||||
|
||||
Implements four standalone endpoints where each agent is specialized:
|
||||
|
||||
1. **Intake & Normalization Agent**
|
||||
- Model: `risk_fast` (gpt-4o-mini)
|
||||
- Task: Extract application data, normalize fields, calculate DTI, flag missing data
|
||||
- Output: Clean structured dataset with validation results
|
||||
|
||||
2. **Risk Scoring & Driver Analysis Agent**
|
||||
- Model: `risk_reasoning` (gpt-4o)
|
||||
- Task: Analyze credit score, DTI, delinquencies, utilization
|
||||
- Output: Risk band (LOW/MEDIUM/HIGH) with confidence + top 3 risk drivers with evidence
|
||||
|
||||
3. **Policy & Compliance Agent**
|
||||
- Model: `risk_reasoning` (gpt-4o)
|
||||
- Task: Verify KYC completion, income/address verification, check policy violations
|
||||
- Output: Policy checks status + exceptions + required documents list
|
||||
|
||||
4. **Decision Memo & Action Agent**
|
||||
- Model: `risk_reasoning` (gpt-4o)
|
||||
- Task: Synthesize findings into bank-ready memo
|
||||
- Output: Executive summary + recommendation (APPROVE/CONDITIONAL_APPROVE/REFER/REJECT)
|
||||
|
||||
**Context Passing:** Each call includes the prior outputs as explicit JSON payloads:
|
||||
- Intake expects the raw application JSON.
|
||||
- Risk expects `{ application, intake }`.
|
||||
- Policy expects `{ application, intake, risk }`.
|
||||
- Memo expects `{ application, intake, risk, policy }`.
|
||||
|
||||
### PII Security Filter (Port 10550)
|
||||
|
||||
HTTP Filter that:
|
||||
- Redacts CNIC patterns (12345-6789012-3)
|
||||
- Redacts phone numbers (+923001234567)
|
||||
- Redacts email addresses
|
||||
- Detects prompt injections ("ignore policy", "bypass checks", etc.)
|
||||
- Adds security warnings to flagged content
|
||||
|
||||
## Configuration Files
|
||||
|
||||
### config.yaml (Plano Configuration)
|
||||
|
||||
- **Agents**: `loan_intake_agent`, `risk_scoring_agent`, `policy_compliance_agent`, `decision_memo_agent`
|
||||
- **Filters**: `pii_security_filter` in filter chain
|
||||
- **Model Providers**: OpenAI GPT-4o and GPT-4o-mini
|
||||
- **Model Aliases**: `risk_fast` (mini), `risk_reasoning` (4o)
|
||||
- **Listeners**: agent (8001), model (12000)
|
||||
- **Tracing**: 100% sampling to Jaeger
|
||||
|
||||
### docker-compose.yaml
|
||||
|
||||
Orchestrates 4 services:
|
||||
- `risk-crew-agent` - Risk assessment engine
|
||||
- `pii-filter` - Security filter
|
||||
- `streamlit-ui` - Web interface
|
||||
- `jaeger` - Tracing backend
|
||||
|
||||
## Observability
|
||||
|
||||
### Jaeger Tracing
|
||||
|
||||
View distributed traces at http://localhost:16686
|
||||
|
||||
**CrewAI Multi-Agent Trace Flow:**
|
||||
```
|
||||
chat_completions (risk-crew-agent) - 8500ms
|
||||
├─ crewai_risk_assessment_workflow - 8200ms
|
||||
│ ├─ POST /v1/chat/completions (risk_fast) - 800ms
|
||||
│ │ └─ openai.chat.completions.create (gpt-4o-mini) - 750ms
|
||||
│ ├─ POST /v1/chat/completions (risk_reasoning) - 2100ms
|
||||
│ │ └─ openai.chat.completions.create (gpt-4o) - 2000ms
|
||||
│ ├─ POST /v1/chat/completions (risk_reasoning) - 1800ms
|
||||
│ │ └─ openai.chat.completions.create (gpt-4o) - 1750ms
|
||||
│ └─ POST /v1/chat/completions (risk_reasoning) - 2400ms
|
||||
│ └─ openai.chat.completions.create (gpt-4o) - 2350ms
|
||||
```
|
||||
|
||||
**Complete Request Flow:**
|
||||
1. UI sends request to Plano orchestrator (8001)
|
||||
2. Plano applies PII security filter (10550)
|
||||
3. Plano routes to Risk Crew Agent (10530)
|
||||
4. CrewAI executes 4 agents sequentially:
|
||||
- Each agent calls Plano LLM Gateway (12000)
|
||||
- Plano routes to OpenAI with configured model alias
|
||||
5. Agent returns synthesized assessment
|
||||
6. All spans visible in Jaeger (16686)
|
||||
|
||||
**Search Tips:**
|
||||
- Service: `risk-crew-agent`
|
||||
- Operation: `chat_completions` or `crewai_risk_assessment_workflow`
|
||||
- Tags: `request_id`, `risk_band`, `recommended_action`, `applicant_name`
|
||||
- Look for 4-5 LLM call spans per request (indicates CrewAI is working)
|
||||
|
||||
## Project Structure
|
||||
|
||||
```
|
||||
credit_risk_case_copilot/
|
||||
├── config.yaml # Plano orchestrator config
|
||||
├── docker-compose.yaml # Service orchestration
|
||||
├── Dockerfile # Multi-purpose container
|
||||
├── pyproject.toml # Python dependencies
|
||||
├── .env.example # Environment template
|
||||
├── README.md # This file
|
||||
├── test.rest # REST client examples
|
||||
├── scenarios/ # Test fixtures
|
||||
│ ├── scenario_a_low_risk.json
|
||||
│ ├── scenario_b_medium_risk.json
|
||||
│ └── scenario_c_high_risk_injection.json
|
||||
└── src/
|
||||
└── credit_risk_demo/
|
||||
├── __init__.py
|
||||
├── risk_crew_agent.py # Multi-agent workflow (FastAPI)
|
||||
├── pii_filter.py # HTTP security filter (FastAPI)
|
||||
└── ui_streamlit.py # Web UI (Streamlit)
|
||||
```
|
||||
|
||||
## Development
|
||||
|
||||
### Running Services Individually
|
||||
|
||||
```bash
|
||||
# Risk Crew Agent
|
||||
uv run python src/credit_risk_demo/risk_crew_agent.py
|
||||
|
||||
# PII Filter
|
||||
uv run python src/credit_risk_demo/pii_filter.py
|
||||
|
||||
# Streamlit UI
|
||||
uv run streamlit run src/credit_risk_demo/ui_streamlit.py
|
||||
```
|
||||
|
||||
### Installing Dependencies Locally
|
||||
|
||||
```bash
|
||||
uv sync
|
||||
# or
|
||||
pip install -e .
|
||||
```
|
||||
---
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
**Services won't start**
|
||||
- Check Docker is running: `docker ps`
|
||||
- Verify ports are available: `lsof -i :8001,10530,10550,8501,16686`
|
||||
- Check logs: `docker compose logs -f`
|
||||
|
||||
**CrewAI Import Errors** (e.g., "No module named 'crewai'")
|
||||
- Rebuild container with new dependencies:
|
||||
```bash
|
||||
docker compose build risk-crew-agent --no-cache
|
||||
docker compose up risk-crew-agent
|
||||
```
|
||||
|
||||
**Slow Response Times (>20 seconds)**
|
||||
- **Expected:** 8-15 seconds is normal for CrewAI (4 sequential LLM calls)
|
||||
- **If slower:** Check OpenAI API status, review Jaeger traces for bottlenecks, check Plano logs
|
||||
|
||||
**LLM Gateway Connection Failed**
|
||||
- Verify Plano is running: `curl http://localhost:12000/health`
|
||||
- Check environment variable: `docker compose exec risk-crew-agent env | grep LLM_GATEWAY`
|
||||
- Should show: `LLM_GATEWAY_ENDPOINT=http://host.docker.internal:12000/v1`
|
||||
|
||||
**Plano won't start**
|
||||
- Verify installation: `planoai --version`
|
||||
- Check config: `planoai validate config.yaml`
|
||||
- Ensure OPENAI_API_KEY is set
|
||||
|
||||
**No response from agents**
|
||||
- Verify all services are healthy:
|
||||
- `curl http://localhost:10530/health` (should show `"framework": "CrewAI"`)
|
||||
- `curl http://localhost:10550/health`
|
||||
- Check Plano is running on port 8001
|
||||
|
||||
**Streamlit can't connect**
|
||||
- Verify PLANO_ENDPOINT in docker-compose matches Plano port
|
||||
- Check `host.docker.internal` resolves (should point to host machine)
|
||||
|
||||
**Jaeger shows no traces**
|
||||
- Verify OTLP_ENDPOINT in services points to Jaeger
|
||||
- Check Jaeger is running: `docker compose ps jaeger`
|
||||
- Allow a few seconds for traces to appear
|
||||
- **CrewAI traces:** Look for `crewai_risk_assessment_workflow` span with 4 child LLM calls
|
||||
|
||||
**CrewAI Output Parsing Errors**
|
||||
- Check logs: `docker compose logs risk-crew-agent | grep "Error parsing"`
|
||||
- System falls back to basic response if parsing fails (check for "REFER" recommendation)
|
||||
|
||||
## API Endpoints
|
||||
|
||||
### Plano Orchestrator (8001)
|
||||
- `POST /v1/chat/completions` - Main entry point (OpenAI-compatible)
|
||||
|
||||
### Risk Crew Agent (10530)
|
||||
- `POST /v1/agents/intake/chat/completions` - Intake normalization endpoint
|
||||
- `POST /v1/agents/risk/chat/completions` - Risk scoring endpoint
|
||||
- `POST /v1/agents/policy/chat/completions` - Policy compliance endpoint
|
||||
- `POST /v1/agents/memo/chat/completions` - Decision memo endpoint
|
||||
- `POST /v1/chat/completions` - Full risk assessment endpoint (legacy)
|
||||
- `GET /health` - Health check
|
||||
|
||||
### PII Filter (10550)
|
||||
- `POST /v1/tools/pii_security_filter` - PII filter endpoint
|
||||
- `GET /health` - Health check
|
||||
|
||||
## Next Steps & Extensions
|
||||
|
||||
### Immediate Enhancements
|
||||
- Add database persistence for assessment storage (PostgreSQL/MongoDB)
|
||||
- Implement parallel agent execution where possible (e.g., Risk + Policy agents)
|
||||
- Add agent tools (credit bureau API integration, fraud detection)
|
||||
- Enable CrewAI memory for cross-request learning
|
||||
|
||||
### Production Readiness
|
||||
- Implement rate limiting and request throttling
|
||||
- Add caching layer for repeated assessments
|
||||
- Set up monitoring/alerting (Prometheus + Grafana)
|
||||
- Implement user authentication and RBAC
|
||||
- Add audit log persistence
|
||||
|
||||
### Feature Extensions
|
||||
- Add Fraud Detection Agent to the crew
|
||||
- Implement Appeals Agent for rejected applications
|
||||
- Build analytics dashboard for risk metrics
|
||||
- Add email/SMS notifications for decisions
|
||||
- Implement batch processing API for multiple applications
|
||||
- Create PDF export for decision memos
|
||||
- Add A/B testing framework for different risk models
|
||||
|
||||
## What This Demo Demonstrates
|
||||
|
||||
This project showcases:
|
||||
|
||||
✅ **True Multi-Agent AI System** - 4 specialized CrewAI agents with distinct roles and expertise
|
||||
✅ **Plano Orchestration** - Central LLM gateway managing all agent calls without config changes
|
||||
✅ **Model Aliases** - Semantic routing (`risk_fast`, `risk_reasoning`) for cost/quality optimization
|
||||
✅ **Security Guardrails** - PII redaction and prompt injection detection via HTTP filters
|
||||
✅ **Full Observability** - OpenTelemetry traces showing every agent execution in Jaeger
|
||||
✅ **Production Patterns** - Error handling, fallbacks, health checks, structured logging
|
||||
✅ **Context Passing** - Agents build on each other's work through sequential task dependencies
|
||||
✅ **Backward Compatibility** - OpenAI-compatible API maintained throughout
|
||||
|
||||
### Key Metrics
|
||||
|
||||
- **4 LLM calls** per risk assessment (1x gpt-4o-mini + 3x gpt-4o)
|
||||
- **8-15 second** response time (sequential agent execution)
|
||||
- **~$0.02-0.05** cost per request
|
||||
- **Zero config changes** to Plano (everything already supported!)
|
||||
- **100% trace visibility** across all services
|
||||
|
||||
### Documentation
|
||||
|
||||
- **This README** - Quick start and API reference
|
||||
- **CREWAI_INTEGRATION.md** - Deep dive into CrewAI implementation (500+ lines)
|
||||
- **CREWAI_CHECKLIST.md** - Testing and verification guide
|
||||
- **IMPLEMENTATION_SUMMARY.md** - What changed and why
|
||||
|
||||
## License
|
||||
|
||||
This is a demo project for educational purposes.
|
||||
|
||||
## Support
|
||||
|
||||
For issues with Plano, see: https://docs.planoai.dev
|
||||
|
||||
---
|
||||
|
||||
**Last Updated:** January 2026
|
||||
**Version:** 0.2.0 - CrewAI Multi-Agent Integration
|
||||
**Status:** Production-ready demo with full CrewAI implementation
|
||||
- **No response**: confirm Plano is running and ports are free (`8001`, `10530`, `10550`, `8501`).
|
||||
- **LLM gateway errors**: check `LLM_GATEWAY_ENDPOINT=http://host.docker.internal:12000/v1`.
|
||||
- **No traces**: check Jaeger and `OTLP_ENDPOINT`.
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue