Michael Chihlas 3f0a132058 refactor(ai): rename _call_anthropic_cached → chat_call_cached; extract cache plumbing (Phase 0.4)
Renames the chat caller to a name that signals its actual purpose, and
factors the reusable cached-system-block + cached-history + cache-usage-log
primitives out to app.core.ai_provider so they can be shared with the
provider-generic path without pulling MCP/beta/images into the abstract
interface.

Helpers added to ai_provider.py:
- `build_anthropic_chat_messages(history, new_message, images, format_reminder)`
  — owns: copy history, apply cache_control to last history message,
  append format reminder to new message, render images as multimodal blocks.
  Anthropic-shaped by design; do not call from Gemini paths.

chat_call_cached keeps exactly the concerns that are unique to the one
MCP/beta/multimodal chat caller:
- Anthropic beta endpoint invocation
- Microsoft Learn MCP server wiring (ENABLE_MCP_MICROSOFT_LEARN)
- Retry-without-MCP fallback
- Format-reminder content string (declared as module constant)
- Phase 0.5 telemetry (mcp.turn, mcp.fallback)

Documents in the module docstring AND at the function site that this is
the ONE MCP/beta chat caller and should not become the general provider
path. MCP/beta/images are features of exactly one optional Anthropic beta
endpoint; routing them through AnthropicProvider would leak a provider-
specific concern into the abstract interface that also serves Gemini.

Behavior change: chat_call_cached now reuses the singleton AnthropicProvider
HTTP client via `_get_anthropic_client(...)` instead of instantiating a new
`anthropic.AsyncAnthropic(...)` per call. Matches the provider's own pattern
and avoids burning connections per-turn. No user-visible difference.

No runtime verification from code-server. TODO(phase0-verify) in
ai_provider.py tracks the cache-hit verification owed on the new dev env.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-17 17:03:09 +00:00

ResolutionFlow

Stop writing ticket notes. Start generating them.

ResolutionFlow is an AI-powered troubleshooting platform for MSP professionals. Engineers follow guided flows while an AI copilot assists — and documentation writes itself as a byproduct of the work.

Production: resolutionflow.com


Quick Start

# Prerequisites: Docker, Python 3.11+, Node.js 20+

# Start PostgreSQL
docker start patherly_postgres

# Backend
cd backend
source venv/bin/activate
pip install -r requirements.txt
alembic upgrade head
uvicorn app.main:app --reload

# Frontend (separate terminal)
cd frontend
npm install
npm run dev

See DEV-ENV.md for full environment setup (devserver, Docker, CORS).


Features

FlowPilot AI Copilot

Like having a senior engineer on every call. FlowPilot guides troubleshooting decisions, suggests next steps with context-aware intelligence, and automatically captures documentation as a byproduct of the session.

  • Confidence-tiered model routing (fast responses for simple steps, deeper reasoning for complex decisions)
  • AI-generated ticket summaries and session documentation
  • Standalone assistant chat with RAG for open-ended troubleshooting
  • Knowledge Flywheel: AI analyzes completed sessions and proposes new flows automatically

Guided Flows

  • Troubleshooting Flows — Decision trees with branching paths for diagnosing issues
  • Procedural Flows (Projects) — Step-by-step checklists for onboarding, migrations, deployments
  • Maintenance Flows — Scheduled recurring tasks with batch execution across multiple targets
  • Visual Flow Editor with drag-and-drop canvas, undo/redo, markdown support
  • AI Flow Builder — describe what you need, get a complete flow generated

Auto-Documentation

Every session generates timestamped, detailed notes formatted for your PSA. Engineers never write another ticket note.

  • Export to Markdown, plain text, or HTML
  • Sensitive data redaction
  • One-click push to ConnectWise PSA tickets

ConnectWise PSA Integration

  • Post session documentation directly to ConnectWise tickets as internal notes
  • Pull ticket details and client context into FlowPilot sessions
  • Member mapping between ResolutionFlow and ConnectWise users
  • Credentials encrypted at rest (Fernet), stored per-team

Team & Knowledge Management

  • Role-based access (super_admin, team_admin, engineer, viewer)
  • Shared flow library with categories, tags, folders, full-text search
  • Step Library — reusable troubleshooting steps with ratings and reviews
  • Session sharing via link (authenticated and public views)
  • Escalation workflow with AI-enhanced briefing packages
  • Flow proposals from AI analysis (review queue for team leads)

Tech Stack

Layer Technology
Frontend React 19, TypeScript, Vite, Tailwind CSS v4
State Zustand (immer + zundo for undo/redo)
Routing React Router v7
Canvas @xyflow/react (React Flow) + dagre
Backend Python FastAPI, async SQLAlchemy 2.0 + asyncpg
Database PostgreSQL 16
Migrations Alembic (75+ migrations)
Auth JWT (python-jose) + bcrypt, refresh token rotation
AI Anthropic Claude API (tiered model routing)
Embeddings Voyage AI (semantic search)
Scheduling APScheduler 3.x (async)
Analytics PostHog
Hosting Railway (auto-deploy on push to main)

Project Structure

patherly/
├── backend/
│   ├── app/
│   │   ├── main.py                 # FastAPI entry point
│   │   ├── api/endpoints/          # Route handlers (35+ endpoints)
│   │   ├── core/                   # Config, database, permissions, security
│   │   ├── models/                 # SQLAlchemy models
│   │   ├── schemas/                # Pydantic schemas
│   │   └── services/psa/           # PSA provider abstraction layer
│   ├── alembic/                    # Database migrations
│   └── tests/                      # Integration tests (100+)
├── frontend/
│   ├── src/
│   │   ├── components/             # UI components by domain
│   │   ├── pages/                  # Page components
│   │   ├── store/                  # Zustand stores
│   │   └── types/                  # TypeScript interfaces
├── docs/                           # Design docs, plans, ConnectWise reference
├── brand-assets/                   # SVGs, brand guide
├── CLAUDE.md                       # AI assistant project context
├── CURRENT-STATE.md                # Detailed feature status
└── CHANGELOG.md                    # Release history

Running Tests

# Backend integration tests
cd backend
pytest --override-ini="addopts="

# Frontend build (stricter than tsc --noEmit)
cd frontend
npm run build

Documentation

Document Purpose
CLAUDE.md Full project context for AI-assisted development
CURRENT-STATE.md Detailed feature status
03-DEVELOPMENT-ROADMAP.md Development roadmap
UI-DESIGN-SYSTEM.md Design system (Slate & Ice)
DEV-ENV.md Development environment setup
CHANGELOG.md Release history

License

Proprietary. All rights reserved.

Description
Troubleshooting decision tree application for MSP engineers - automatically generates professional documentation from guided diagnostic workflows
Readme 16 MiB
Languages
Python 54.7%
TypeScript 43.5%
HTML 1.1%
CSS 0.6%