chore: remove llm provider folders from architecture

.github/ISSUE_TEMPLATE/bug_report.md

@@ -10,7 +10,7 @@ assignees: ''
 A clear and concise description of what the bug is.
 
 **Which guide?**
-The guide this affects, e.g. `python/ag2/bug-fixer-agent/openai` or
+The guide this affects, e.g. `python/ag2/bug-fixer-agent` or
 `typescript/openai/codex-sdk`.
 
 **To Reproduce**

.sisyphus/run-continuation/ses_04cd4d215ffea9HvVL8tDqnhbz.json

@@ -0,0 +1,10 @@
+{
+  "sessionID": "ses_04cd4d215ffea9HvVL8tDqnhbz",
+  "updatedAt": "2026-07-30T13:41:32.743Z",
+  "sources": {
+    "background-task": {
+      "state": "idle",
+      "updatedAt": "2026-07-30T13:41:32.743Z"
+    }
+  }
+}

README.md

@@ -25,14 +25,13 @@ Then follow the guide's own README to install dependencies and run it.
 
 | Guide | Description |
 | --- | --- |
-| [AG2 Bug Fixer](python/ag2/bug-fixer-agent/openai) | Two AG2 agents that iteratively fix broken code, running each attempt in a sandbox. |
-| [Data Analyst (LiteLLM)](python/ai-data-analyst/litellm) | Natural-language data analysis with LiteLLM, executing Python in a sandbox. |
-| [Data Analyst (OpenAI)](python/ai-data-analyst/openai) | Natural-language data analysis with the OpenAI API, executing Python in a sandbox. |
+| [AG2 Bug Fixer](python/ag2/bug-fixer-agent) | Two AG2 agents that iteratively fix broken code, running each attempt in a sandbox. |
+| [Data Analyst](python/ai-data-analyst) | Natural-language data analysis with LiteLLM (works with any LLM provider), executing Python in a sandbox. |
 | [Claude Managed Agents](python/claude/claude-managed-agents) | Self-host Claude Managed Agents inside Daytona sandboxes. |
 | [Windows Computer-Use Evals](python/computer-use/windows-evals) | Windows GUI computer-use eval suite driven by Claude Code / Codex via MCP. |
 | [DSPy RLMs](python/dspy-rlms) | Run DSPy's Recursive Language Model (REPL-in-a-loop) module on Daytona. |
-| [Google ADK Code Generator](python/google-adk/code-generator-agent/gemini) | Google ADK agent that generates and verifies code with the Daytona plugin. |
-| [LangChain Data Analysis](python/langchain/data-analysis/anthropic) | LangChain agent doing secure data analysis via the Daytona data-analysis tool. |
+| [Google ADK Code Generator](python/google-adk/code-generator-agent) | Google ADK agent that generates and verifies code with the Daytona plugin. |
+| [LangChain Data Analysis](python/langchain/data-analysis) | LangChain agent doing secure data analysis via the Daytona data-analysis tool. |
 | [LangGraph Plan-and-Execute](python/langgraph/plan-and-execute-data-agent) | Plan-and-execute ETL + analytical-SQL agent wired as a six-node state machine. |
 | [Model Serving (SGLang)](python/model-serving/sglang) | Serve gpt-oss-20b with SGLang on a GPU sandbox behind a preview URL. |
 | [Model Serving (vLLM)](python/model-serving/vllm) | Serve an open-weights model with vLLM on a GPU sandbox behind a preview URL. |
@@ -45,8 +44,8 @@ Then follow the guide's own README to install dependencies and run it.
 
 | Guide | Description |
 | --- | --- |
-| [Inngest AgentKit Coding Agent](typescript/agentkit-inngest/coding-agent/anthropic) | Autonomous coding agent built with Inngest AgentKit. |
-| [Data Analyst (OpenAI)](typescript/ai-data-analyst/openai) | Natural-language data analysis with the OpenAI API, executing code in a sandbox. |
+| [Inngest AgentKit Coding Agent](typescript/agentkit-inngest/coding-agent) | Autonomous coding agent built with Inngest AgentKit. |
+| [Data Analyst (OpenAI)](typescript/ai-data-analyst) | Natural-language data analysis with the OpenAI API, executing code in a sandbox. |
 | [Amp Code Coding Agent](typescript/amp/amp-sdk) | Coding agent powered by the Amp Code CLI. |
 | [Claude Two-Agent System](typescript/anthropic/multi-agent-claude-sdk) | Two-agent Claude system coordinating work across sandboxes. |
 | [Claude Coding Agent](typescript/anthropic/single-claude-agent-sdk) | Single Claude Code agent you drive from the CLI. |
@@ -57,7 +56,7 @@ Then follow the guide's own README to install dependencies and run it.
 | [Gemini CLI Coding Agent](typescript/gemini/gemini-cli) | Headless coding agent powered by the Gemini CLI. |
 | [Kiro CLI Coding Agent](typescript/kiro/kiro-cli) | Coding agent powered by AWS Kiro's CLI. |
 | [Letta Code Agent](typescript/letta-code) | Letta Code agent driven from the CLI. |
-| [Mastra Code Execution Agent](typescript/mastra/coding-agent/openai) | Mastra agent that plans, writes, executes, and iterates on code. |
+| [Mastra Code Execution Agent](typescript/mastra/coding-agent) | Mastra agent that plans, writes, executes, and iterates on code. |
 | [OpenAI Codex Agent](typescript/openai/codex-sdk) | OpenAI Codex agent driven from the CLI. |
 | [OpenClaw Assistant](typescript/openclaw) | OpenClaw general-purpose AI assistant with a web Control UI. |
 | [OpenCode Server](typescript/opencode/opencode-sdk) | OpenCode coding-agent server driven from the CLI. |

python/ai-data-analyst/litellm/README.md → python/ai-data-analyst/README.md

@@ -1,4 +1,4 @@
-# LiteLLM Data Analysis Example (LiteLLM + Daytona)
+# AI Data Analyst (LiteLLM + Daytona)
 
 ## Overview
 
@@ -77,8 +77,8 @@ Create a `.env` file in the project directory with the appropriate variables for
 
 The main prompt is configured in the `user_prompt` variable in `ai_data_analyst.py`:
 
-```typescript
-const userPrompt = `Give the three highest revenue products for the month of January and show them as a bar chart.`;
+```python
+user_prompt = "Give the three highest revenue products for the month of January and show them as a bar chart."
 ```
 
 You can modify this to analyze different aspects of the data or try different visualization types.

python/ai-data-analyst/litellm/ai_data_analyst.py → python/ai-data-analyst/ai_data_analyst.py

@@ -6,7 +6,7 @@
 from pathlib import Path
 
 # pylint: disable=import-error
-from litellm import completion  # pylint: disable=import-error
+from litellm import completion
 
 from daytona import CreateSandboxFromSnapshotParams, Daytona
 
@@ -68,7 +68,6 @@ def main() -> None:
         )
 
         first_message = llm_output.choices[0].message
-        print("LLM output:", first_message)
         messages.append({"role": first_message.role, "content": first_message.content})
 
         # Extract and execute Python code from the LLM's response