Gemma 4 E2B Tested as Multi-Agent Coordinator in TypeScript Framework

Coordinator Capabilities Tested

The test evaluated whether Gemma 4 E2B could handle the coordinator role in a multi-agent system, specifically: taking a natural language goal, breaking it into a task graph, assigning agents, calling tools, and stitching results together.

Technical Implementation

The framework used was open-multi-agent (TypeScript, open-source) with Ollama via an OpenAI-compatible API. The coordinator receives a goal and agent roster, then outputs a JSON task array with title, description, assignee, and dependencies. Agents execute with tool-calling capabilities including bash and file read/write operations.

Model Details

Gemma 4 E2B ("Effective 2B") has 2.3B effective parameters and 5.1B total parameters. The extra ~2.8B parameters are for the embedding layer supporting 140+ languages and multimodal capabilities.

Test Scenario

The goal provided was: "Check this machine's Node.js version, npm version, and OS info, then write a short Markdown summary report to /tmp/report.md"

E2B correctly:

• Broke it into 2 tasks with a dependency (researcher → summarizer)
• Assigned each to the right agent
• Used bash to run system commands
• Used file_write to save the report
• Synthesized the final output

Both runTasks() (explicit pipeline) and runTeam() (model plans everything autonomously) worked.

Performance and Observations

On an M1 with 16GB RAM:

• Full runTeam() takes ~2 minutes
• 6–9 sequential LLM calls under the hood (coordinator planning → researcher multi-turn tool use → summarizer → coordinator synthesis)
• ~10–15 seconds per call on M1
• E2B uses ~3–4 GB RAM with no memory pressure

What worked well:

• JSON output: The coordinator produced the correct schema for task decomposition. The framework has tolerant parsing that tries fenced blocks first, then falls back to bare array extraction.
• Tool-calling: Works through the OpenAI-compatible endpoint, correctly deciding when to call, parsing arguments, and handling multi-turn results.

Limitations noted:

• Output quality: The prose in final synthesis is noticeably weaker than larger models. Functional but not polished.

Reproduction Steps

ollama pull gemma4:e2b
git clone https://github.com/JackChen-me/open-multi-agent
cd open-multi-agent && npm install
no_proxy=localhost npx tsx examples/08-gemma4-local.ts

The test file is ~190 lines at examples/08-gemma4-local.ts. The no_proxy=localhost setting is only needed if you have an HTTP proxy configured.