devanshudhoble/FDE-_Ticket_-Escalator
User repository on GitHub
- Forks
- 0
- Last pushed
- Updated Oct 2026
Repo stats from the GitHub API, cached Oct 2026.
Jev directory
CommunityJev Agents & MCP
FDE Ticket Escalator
A LangGraph agent for the Forward Deployed Engineer role that classifies incoming support tickets with a Jev decision node, auto-closes basic issues to Tier 1 and sends technical ones to an LLM summarizer. The README notes Jev is mocked by a standard LLM in this demo.
- Stars
- 1
- Language
- Python
- Reading time
- 2 min
- Published by
- Community
- Author
- devanshudhoble
- Last verified
- Oct 2026
- Languages
- Python
- Capabilities
- Requires API key
Highlights
- LangGraph state machine with a Jev decision node that classifies tickets as FDE Required or Basic Issue.
- Basic issues are auto-closed to Tier 1; technical ones go to a System Two LLM that extracts endpoint and error code.
- The README states Jev is simulated by a standard LLM in this demo, so no real Jev latency is measured.
- Run: pip install -r requirements.txt, set GOOGLE_API_KEY, python app.py.
- 1 star, no license declared, created 2026-10-07 as a demonstration agent.
Quickstart
bash
pip install -r requirements.txt
python app.pyWatch out
README and GitHub metadata reviewed 2026-10-17; the digest truncates the architecture diagram, and the README states plainly that Jev's high-speed decision capabilities are mocked with a standard LLM call in this demo, so no real Jev performance is measured.
Questions about this entry
Short answers drawn from the linked project or page.
A LangGraph agent built for the Forward Deployed Engineer role. It reads incoming support tickets, uses a Jev decision node to judge whether a real technical problem exists, and routes basic issues away from senior engineers.
It classifies each ticket as FDE Required or Basic Issue, and LangGraph uses that output to route: basic issues are instantly closed and sent to Tier 1, and technical ones go to a summarizer.
No. The README states plainly that Jev's high-speed decision capabilities are mocked using a standard LLM call acting as the classifier, so the repository demonstrates the workflow rather than measuring real Jev performance.
A heavier System Two LLM extracts the API endpoint and error code and summarizes the issue in three bullet points for the senior engineer, which the README frames as letting them fix it faster.
Install dependencies with pip install -r requirements.txt, set GOOGLE_API_KEY or the API key of your chosen LLM in the environment, then run python app.py.
It is a single-author demonstration created on 2026-10-07, with 1 star, no license declared and no captured tests or benchmarks. It reads as a workflow pattern rather than a production system.
The README's premise is speed and cost: a fast typed decision filters basic issues before the expensive System Two summarizer runs, so the forward deployed engineer only sees tickets that are already summarized.
More Jev resources like this
Long-term memory for AI agents whose reads and writes are driven by Jev's typed, probabilistic decisions instead of a generative LLM in the loop, inspired by the Jev-Mem paper and shipped as an MCP server, Claude Code hooks, a CLI and a Python library.
Jev Agents & MCP#community#github#memory
Community
illescasdaniel/jev-memPython
illescasdaniel/jev-memPythonwangkuangkuang's Python MCP bridge to Jev: three official question types plus compare, verify, and batch classify, with a one-command installer that writes client config for Claude Code, Pi, Cursor, OpenCode, or Codex.
Jev Agents & MCP#community#mcp#python
Community
A free Claude Code skill that puts Jev in front of the expensive model: thirteen commands sort, check, score, gate and verify with Jev, so Claude reads only the unsure slice and writes the output. Needs python3 and an OpenRouter key.
Jev Agents & MCP#community#python#claude-code
Community
charlesdove977/claude-x-jevPython
charlesdove977/claude-x-jevPythonA Chinese-language e-commerce customer-service demo on FastAPI, LangGraph and React: adaptive retrieval routing with hybrid BM25-plus-vector search, and intent classification switchable between TypeSafe Jev's structured choice interface and DeepSeek.
Jev Demos & Experiments#community#github#python
Community
gbh1234/Adaptive-ragPython
gbh1234/Adaptive-ragPythonAn event-driven multi-agent network where JEV observes live state and assigns pending work to available agents. Its AgentRouter reference implementation routes on every completion event instead of waiting for a round to end, while general-purpose agents with OpenAI Responses API tools execute work concurrently.
Jev Agents & MCP#community#github#agents
Community
Kelly-Leeee/AgentNetwork-jevPython
Kelly-Leeee/AgentNetwork-jevPythonA dependency-free MCP server that exposes TypeSafe's Jev to Claude Code, Codex, Cursor, Hermes, and other agents through the ask_jev and list_jev_models tools, so your code gets typed decisions with probabilities instead of prose to parse.
Jev Agents & MCP#community#catalog-import#logicrw
Community
codeia-academy/jev-mcpPython
codeia-academy/jev-mcpPythonFrom the Jev guides
Original write-ups on what this entry is for.
From the Jev AI model community
Posts from builders shipping with Jev right now.