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JEV is fast enough to sit inside the multi-agent network—not just before it.
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CommunityJev Agents & MCP
JEV AgentNetwork
An 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.
- Stars
- 4
- Language
- Python
- License
- APACHE-2.0
- Reading time
- 2 min
- Published by
- Community
- Author
- Kelly-Leeee
- Last verified
- Oct 2026
Used inJev for Software & DevTools
Highlights
- JEV makes the routing decisions and general-purpose agents execute work concurrently.
- Event-driven routing: each completion can immediately release and reassign capacity, with no synchronization point.
- State-aware scheduler sees pending work, recent results, and which agents are idle or busy.
- Agents are interchangeable by default; roles are optional prompt hints, not fixed capability boundaries.
- Requirements: Python 3.10+, an OpenAI-compatible API key and a JEV key; ./start.sh opens the canvas at 127.0.0.1:8765.
Quickstart
bash
git clone https://github.com/Kelly-Leeee/AgentNetwork-jev.git
cd AgentNetwork-jev
export OPENAI_API_KEY="your-openai-key"
export JEV_API_KEY="your-jev-key"
./start.shWatch out
README reviewed 2026-10-17; the README calls JEV AgentNetwork an early research prototype that ships with two editable demo tasks and runs with up to five agents, and says it does not aim to replace a general-purpose agent SDK.
Questions about this entry
Short answers drawn from the linked project or page.
It is an event-driven network of general-purpose AI agents coordinated by JEV. JEV observes live network state and assigns pending work to available agents; AgentRouter is the reference implementation, where JEV makes routing decisions while agents execute work concurrently.
The router sends JEV the pending work, recent results and which agents are idle or busy, and JEV decides which agent gets which task. Agents are interchangeable by default, and optional roles serve only as prompt hints rather than hard job boundaries.
Whenever an agent finishes, its completion event triggers a new routing decision immediately; the router does not wait for the rest of the team to reach a synchronization point, so finished capacity can be released and reassigned right away.
Clone the repo, export OPENAI_API_KEY and JEV_API_KEY, then run ./start.sh. The script installs the Python environment, starts AgentRouter and opens http://127.0.0.1:8765; on a remote or headless machine use ./start.sh --no-open --host 0.0.0.0.
An OpenAI-compatible API key for the agents (the README shows OPENAI_BASE_URL, OPENAI_MODEL defaulting to gpt-5-mini) and a JEV key with JEV_MODEL defaulting to jev-latest. The README says to keep keys in environment variables and never commit them.
It is an early research prototype: two editable demo tasks, support for user-created tasks, human-in-the-loop workflows and up to five agents. Task and workstream state is stored as one chronological OpenAI-style messages stream per task file and restored when the canvas restarts.
The GitHub user Kelly-Leeee publishes it under the Apache-2.0 license; the repository was created 2026-10-09 and pushed 2026-10-13, with an English README and a Chinese translation.
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