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AI Troubleshooting

Solutions for common Atmos AI issues, organized by symptom.

Experimental

Quick Checks​

Before diving into specific errors, verify these basics:

ai:
  enabled: true
  default_provider: "anthropic"
Verify your API key is set
[ -n "$ANTHROPIC_API_KEY" ] && echo "Set" || echo "NOT set"
Test connectivity
atmos ai ask "test"

Common Errors​

"AI features are not enabled"​

Add ai.enabled: true to your atmos.yaml.

"API key not found"​

Export the key for your provider:

export ANTHROPIC_API_KEY="sk-ant-..."  # From console.anthropic.com

"Failed to create AI client"​

Usually means an invalid or revoked API key, or insufficient credits. Test the key directly:

Test Anthropic API key
curl https://api.anthropic.com/v1/messages \
-H "x-api-key: $ANTHROPIC_API_KEY" \
-H "anthropic-version: 2023-06-01" \
-H "content-type: application/json" \
-d '{"model": "claude-sonnet-4-6", "max_tokens": 1024, "messages": [{"role": "user", "content": "Hello"}]}'

"Unsupported AI provider"​

Valid provider names:

  • API providers: anthropic, openai, gemini, github, grok, openrouter, deepseek, zai, ollama, bedrock, azureopenai
  • CLI providers: claude-code, codex-cli, copilot-cli, opencode, gemini-cli

Rate Limiting (429 Errors)​

Long conversations send full history with each request. Reduce it:

ai:
  max_history_messages: 20

Provider Issues​

Ollama​

"Connection refused" — Start the server and verify your config:

shell
ollama serve
curl http://localhost:11434/api/version
providers:
  ollama:
    base_url: "http://localhost:11434/v1"

"Model not found" — Pull the model and use the exact name from ollama list:

shell
ollama list
ollama pull llama4

Slow or out of memory — Use a smaller model (e.g. llama3.1:8b needs ~8GB RAM) or reduce max_tokens.

AWS Bedrock​

"Authentication failed" — Verify AWS credentials:

shell
aws sts get-caller-identity

"Access denied" — Your IAM role needs bedrock:InvokeModel and bedrock:InvokeModelWithResponseStream permissions. Also enable model access in AWS Console under Bedrock > Model access.

"Model not found" — Use the full Bedrock model ID and check your region:

providers:
  bedrock:
    model: "anthropic.claude-sonnet-4-6"
    base_url: "us-east-1"

Azure OpenAI​

"Authentication failed" — Get the key from Azure Portal > OpenAI resource > Keys and Endpoint.

"Resource not found" — Copy the exact endpoint URL from Azure Portal:

providers:
  azureopenai:
    base_url: "https://your-resource-name.openai.azure.com"

"Deployment not found" — Use your deployment name (not the model name):

providers:
  azureopenai:
    model: "gpt-4o"           # Your deployment name
    api_version: "2025-04-01-preview"

List deployments with az cognitiveservices account deployment list --name your-resource --resource-group your-rg.

Other Providers​

Anthropic — "Authentication error"
Key must start with sk-ant-.
OpenAI — "Insufficient quota"
Add billing info at platform.openai.com.
Grok — "Connection failed"
Set base_url: "https://api.x.ai/v1".

See AI Providers for full provider documentation.

CLI Providers​

"CLI provider binary not found"​

The CLI tool isn't installed or not on PATH:

brew install --cask claude-code
claude auth login

Or set an explicit path:

providers:
  claude-code:
    binary: /usr/local/bin/claude

MCP Servers Not Working with Codex CLI​

Codex CLI MCP servers don't inherit the parent process environment. If atmos auth exec fails with "identity not found", ensure ATMOS_PROFILE is exported:

export ATMOS_PROFILE=managers  # Or your profile name
atmos ai ask "What did we spend on EC2?"

Atmos automatically injects ATMOS_* env vars into each MCP server's config, but the env var must be set in the shell.

Gemini CLI MCP Blocked​

Gemini CLI blocks MCP for all personal Google accounts (oauth-personal auth) regardless of subscription tier. This is a server-side Google restriction.

Workaround: Use claude-code, codex-cli, copilot-cli, or opencode for MCP workflows. Gemini CLI works for prompt-only queries.

Sessions​

Sessions Not Persisting​

Enable sessions and use named sessions:

ai:
  sessions:
    enabled: true
    path: ".atmos/sessions"
shell
atmos ai chat --session my-project

Database Errors​

If you get "Failed to initialize session storage", back up and recreate:

shell
cp .atmos/sessions/sessions.db .atmos/sessions/sessions.db.backup
rm .atmos/sessions/sessions.db
atmos ai chat  # Creates a new database

Conversation Memory Not Working​

  • Use --session name — anonymous sessions don't persist
  • Add sessions.enabled: true to atmos.yaml
  • Check directory permissions: chmod 755 .atmos/sessions

All providers support conversation memory.

Auto-Compact Not Working​

Verify the required settings are all present:

ai:
  max_history_messages: 50     # Required — threshold is calculated from this
  sessions:
    enabled: true
    auto_compact:
      enabled: true
      trigger_threshold: 0.75  # Triggers at 38 messages (50 x 0.75)

If use_ai_summary: true and no summaries appear, check that the AI provider is configured and the API key is valid. Test with use_ai_summary: false first.

Triggers too often? Increase trigger_threshold or max_history_messages.

Too expensive? Set use_ai_summary: false to use simple concatenation instead of AI summaries.

See Auto-Compact for details.

MCP Server​

"MCP server is not enabled"​

The MCP server is disabled by default and must be explicitly enabled:

mcp:
  enabled: true
ai:
  enabled: true
  tools:
    enabled: true

Not Starting​

Test the MCP server directly:

shell
atmos mcp start  # Should output: Starting Atmos MCP server on stdio...

For Claude Desktop, use the full path to atmos:

{
  "mcpServers": {
    "atmos": {
      "command": "/usr/local/bin/atmos",
      "args": ["mcp", "start"]
    }
  }
}

Tools Not Available​

Restart the MCP client and check for errors with debug logging:

shell
ATMOS_LOGS_LEVEL=Debug atmos mcp start

See MCP Server for the complete setup guide.

LSP Integration​

Server Not Found​

Install and verify:

shell
npm install -g yaml-language-server && yaml-language-server --version
brew install terraform-ls && terraform-ls version

If installed but not found, use the full path:

lsp:
  servers:
    yaml-ls:
      command: "/usr/local/bin/yaml-language-server"

Not Validating Files​

Verify LSP is enabled and the server config matches your file types:

lsp:
  enabled: true
  servers:
    yaml-ls:
      filetypes: ["yaml", "yml"]

See LSP Client for detailed configuration.

Skills​

Skill Not Appearing​

Press Ctrl+A in chat to open the skill selector. Skills need all three required fields:

ai:
  skills:
    my-skill:
      display_name: "My Skill"    # Required
      description: "..."          # Required
      system_prompt: "..."        # Required

Ctrl+A only works in the main chat view — press Esc first if you're in another panel.

Skill Gives Generic Responses​

The system_prompt needs more detail. Include a role definition, focus areas, and tool usage instructions:

system_prompt: |
  You are a specialized Atmos stacks analyst.
  FOCUS: Stack configuration, dependency analysis.
  APPROACH: Use atmos_describe_component first, then recommend.
  Always use tools to gather data before answering.

Tool Access Denied​

The tool isn't in the skill's allowed_tools. Add it, or switch to the General skill (Ctrl+A) which has access to all tools.

Tool names use the atmos_ prefix: atmos_describe_component, atmos_list_stacks, atmos_validate_stacks, etc. See AI Tools for the full list.

Claude Code Subagents​

Subagent Not Invoked​

Check the file exists and has proper frontmatter:

shell
ls -la .claude/agents/atmos-expert.md
---
name: atmos-expert
description: Expert in Atmos infrastructure...
tools:
  - mcp__atmos__describe_stacks
model: inherit
---

Invoke with @atmos-expert your question.

Can't Access MCP Tools​

Tool names need the mcp__atmos__ prefix. The MCP server must be running and the server name must match:

tools:
  - mcp__atmos__describe_stacks      # Correct
  - describe_stacks                   # Wrong

Restart Claude Desktop after changing subagent or MCP configuration.

See Claude Code Integration for the complete guide.

Project Instructions​

AI Not Using Instructions​

Verify instructions are enabled and the file exists:

ai:
  instructions:
    enabled: true
    file: "ATMOS.md"
shell
ls -la ATMOS.md

If the file doesn't exist, instructions are silently skipped. Create it manually.

Large File Performance​

Keep ATMOS.md under 10KB. Remove outdated content to reduce tokens per request.

Debugging​

When nothing else works:

1. Enable debug logging
atmos ai ask "test" --logs-level=Debug
2. Inspect configuration
atmos describe config | grep -A 10 "ai"

3. Test network connectivity:

curl -sI https://api.anthropic.com | head -1

4. Try a minimal config to rule out config problems:

ai:
  enabled: true
  default_provider: "anthropic"

Performance​

Slow responses — Try a faster model (claude-haiku-4-5-20251001, gpt-5-mini, gemini-2.5-flash), go local with Ollama, reduce max_tokens, or limit history with max_history_messages: 20.

Timeout errors — Break complex questions into smaller parts.

Getting Help​

  1. Review AI Configuration
  2. Search GitHub Issues
  3. Open a new issue with: Atmos version, provider/model, error message, and reproduction steps