scrapeRL / docs /architecture.md
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# System Architecture
## Overview
WebScraper-OpenEnv is designed as a modular, dashboard-first RL environment with extensible APIs, MCP tools, and multi-model routing.
## High-Level Topology
```text
Frontend Dashboard (React/Vite)
|
v
FastAPI Control Plane
- episode lifecycle
- action dispatch
- reward engine
- tool registry API
- settings + policy
|
+--> Agent Runtime
| - planner/navigator/extractor/verifier
| - memory manager
| - model router
|
+--> MCP Gateway
| - tool discovery
| - lazy install/load
| - schema + timeout + retries
|
+--> Search Layer
| - provider routing
| - query optimization
| - credibility scoring
|
+--> Memory Layer
| - short/working/long/shared
| - vector index + persistent storage
|
+--> Observability
- traces/logs/metrics/cost dashboard
```
## Core Subsystems
### 1. Control Plane
Responsibilities:
- reset/step/state APIs
- request validation
- action authorization and policy checks
- deterministic episode management
### 2. Agent Runtime
Responsibilities:
- policy inference
- strategy execution
- fallback handling
- action explainability
### 3. Tooling Plane (MCP)
Responsibilities:
- dynamic tool registry
- server health checks
- lazy installation
- composition workflows
### 4. Data Plane
Responsibilities:
- HTML ingestion and chunking
- extraction and normalization
- verification and reconciliation
- output persistence
### 5. Analytics Plane
Responsibilities:
- reward component logging
- model/token/cost accounting
- tool usage telemetry
- memory quality analytics
## Processing Pipeline
1. `reset(task_id, seed)`
2. observation emitted
3. policy selects action
4. action executes (native/MCP/search/memory)
5. reward computed and logged
6. done check
7. repeat until terminal
## Batch and Parallel Design
### Batch
- large HTML split into semantic chunks
- chunk extraction batched with bounded size
- merge + dedupe + confidence rank
### Parallel
- independent chunk tasks run concurrently
- search and verification can run in parallel branches
- configurable worker limits and queue priorities
## Queue and Scheduler
Task queue supports:
- priority classes (`high`, `normal`, `low`)
- cancellation tokens
- retry policy with backoff
- dead-letter queue for repeated failures
## Storage Architecture
- Episode state: in-memory + optional persistence
- Long-term memory: vector DB + metadata store
- Logs/metrics: append-only time-series-friendly sink
- Exports: JSON/CSV trace packs
## Reliability
- per-tool timeout and retry
- per-step safety budget
- circuit breaker for failing providers
- deterministic fallback chains
## Security
- API key vaulting via env/config secrets
- MCP allowlist
- output sanitization
- redaction of sensitive tokens in logs
## Deployment
Single-container baseline:
- frontend static build served by API backend
- optional sidecars for DB/vector/MCP infra
Scale-out profile:
- separate API and worker pools
- managed vector DB
- queue-backed distributed execution
- central observability backend
## Compatibility Goals
- local dev mode with minimal dependencies
- cloud mode with managed infra
- optional self-hosted LLM endpoints
## Future Architecture Extensions
- distributed multi-agent graph execution
- adaptive autoscaling by queue pressure
- global memory federation across projects