Tried for a whole day to get it working on Win11+Docker+Gemini API with no luck.

[richrz]

What exact combination of Gemini models, config settings, and initialize.py functions are required to run the UI successfully in the current Docker build? Has anyone actually done it from a clean install?

Here is what I've tried (AI summarized)

User Context:

Experienced dev with Docker and AI tools
Attempted to run Agent Zero on Windows 11 via Docker with only Gemini APIs as I don't have Perplexity, Claude...

Goal: Run a local agent that uses Gemini via embedded key.

✅ What Worked:
Docker container built and ran
Embedded Gemini API key setup successfully (agent_zero_config.py)
System logs indicated several services launching (Whisper, MCP middleware, etc.)
Tunnel server and some subservices started
Logs confirmed MCP/Gemini middleware registration

❌ What Didn’t Work (Major Issues):
Container Reboots Due to Missing Attributes
run_ui.py references non-existent functions like:
initialize.initialize_chats()
initialize.initialize_mcp()
initialize.initialize_job_loop()

These cause hard crashes and a boot loop (exit status 1) in Supervisor.

Broken Module Calls
Repeated failures on models.get_huggingface_embedding suggest either:
The models.py structure has changed, or
Preload script expects outdated huggingface API logic
Poor Graceful Failure Handling

Any missing attribute in Python causes container-wide failure. There’s no fallback or recovery mechanism.
Attempts to access web GUI shows JSON parse errors or network backend unavailable.

Developer-Unfriendly Debugging
Docker container doesn’t include even basic tools (vi, nano, or paste support in terminal)
Manual editing becomes nightmarish without escape hatches
Patch cycle was slow due to restarting entire containers to remove broken lines

🛠 Suggestions for Developer:
Robust Function Checks
Use hasattr() or try/except in initialize.py to check for optional functions (e.g. initialize_job_loop) before calling them
This would prevent crashes from minor config mismatches

Graceful UI Startup
If run_ui fails, don’t crash the entire stack — log the error and continue showing basic UI

Default Model Safety
If get_huggingface_embedding fails, use a fallback or clearly document setup instructions for that dependency

Better Developer Console Support
Add paste-capable terminal, or mount /a0 to allow editing from host

Include nano or vi in container image by default

Configuration Validation Tool
Before boot, run a CLI command like python validate_config.py to check that all references (e.g., API keys, model functions) are defined

Documentation Improvements

Include:

Troubleshooting flowchart

List of optional vs required services

Example .env or agent_zero_config.py setup
Expected port/URL mappings

Final Thoughts:
Agent Zero shows a ton of promise, with just a little more polish, this could be a STELLAR local LLM agent platform.

[retro33]

This should be everyone"s goal in this space . agent0 is here ! a agent to be built upon, way ahead of most . Teamwork is key for most this agent gives HOPE that we can have a place in this AI race without getting outpaced by the rich, always keep in mind there coming for our money 1 subscription at a time . Transparency Figurative ( communication) is key to growth allowing others , to reflect sharing experiences , allowing past and present achievements to be explored by others. PLEASE SHARE with INTEGRITY ALLOWING a complete sense of achievement. WE ARE THE STANDARD designed executions , tool acceptance within ecosystem system. skills, agent.md, prompts, data and a host of resources at our availability . Acceptance understanding this is where we"re AT.

[Phoscur]

After a few days I now have a working setup with Win11/Docker and Gemini (free tier) through LiteLLM, routing utility and memory through a local ollama (for which it helped me to fix some access issues).

// settings.json excerpt
    "util_model_provider": "openai",
    "util_model_name": "llama3.2:3b",
    "util_model_api_base": "http://ollama:11434/v1",
    "util_model_ctx_length": 100000,
    "util_model_ctx_input": 0.7,
    "util_model_kwargs": {
        "temperature": "0.1"
    },
    "util_model_rl_requests": 0,
    "util_model_rl_input": 0,
    "util_model_rl_output": 0,
    "embed_model_provider": "ollama",
    "embed_model_name": "nomic-embed-text",
    "embed_model_api_base": "http://ollama:11434",

I haven't unlocked the free tier for OpenRouter as it requires some credit on the account (prevents bot abuse), so I was stuck for a while configuring local models to avoid the 402 paywall. The default embedding huggingface model did not work for me either.
Managed to hit rate limits on the Gemini Free Tier pretty quickly now ;)

Note: Gemini API calls go through OpenAI config via LiteLLM, so I haven't added my GEMINI_API_KEY to the a0 container but rather to a sibling litellm container!

    "chat_model_provider": "openai",
    "chat_model_name": "gemini-2.5-flash-lite",
    "chat_model_api_base": "http://litellm:4000/v1",