BuzzLLM Gateway

Use buzzllm CLI to perform web searches, execute Python code in Docker, search/analyze local codebases, or generate code change blocks. Trigger when user asks for web search, Python execution, code analysis, or code modifications via LLM.

16 stars

Best use case

BuzzLLM Gateway is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Use buzzllm CLI to perform web searches, execute Python code in Docker, search/analyze local codebases, or generate code change blocks. Trigger when user asks for web search, Python execution, code analysis, or code modifications via LLM.

Teams using BuzzLLM Gateway should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/buzzllm-gateway/SKILL.md --create-dirs "https://raw.githubusercontent.com/diegosouzapw/awesome-omni-skill/main/skills/development/buzzllm-gateway/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/buzzllm-gateway/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How BuzzLLM Gateway Compares

Feature / AgentBuzzLLM GatewayStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Use buzzllm CLI to perform web searches, execute Python code in Docker, search/analyze local codebases, or generate code change blocks. Trigger when user asks for web search, Python execution, code analysis, or code modifications via LLM.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

## What is BuzzLLM

A CLI gateway that invokes LLMs with specialized tools:
- **websearch**: Search the web and scrape pages
- **pythonexec**: Execute Python code in a sandboxed Docker container
- **codesearch**: Search and read files in local codebases
- **hackhub**: Generate Search-Replace blocks for code modifications

## Command Structure

```bash
buzzllm "MODEL" "API_URL" "PROMPT" \
    --provider PROVIDER \
    --api-key-name ENV_VAR \
    --system-prompt TEMPLATE \
    [--think]  # for extended thinking
    [--brief]  # hide tool calls/results, show only final output
```

## Providers

| Provider | API URL | Notes |
|----------|---------|-------|
| `openai-chat` | `https://api.openai.com/v1/chat/completions` | Standard chat completions |
| `openai-responses` | `https://api.openai.com/v1/responses` | Reasoning models (o1, o3) |
| `anthropic` | `https://api.anthropic.com/v1/messages` | Claude models |
| `vertexai-anthropic` | `https://REGION-aiplatform.googleapis.com/v1/projects/PROJECT/locations/REGION/publishers/anthropic/models/MODEL:streamRawPredict` | Claude via GCP |

## System Prompt Templates

### With Tools
- `websearch` - Web search + page scraping (`search_web`, `scrape_webpage`)
- `codesearch` - Codebase analysis (`bash_find`, `bash_ripgrep`, `bash_read`)
- `pythonexec` - Python in Docker (`python_execute`)

### Without Tools
- `hackhub` - Search-Replace block generation
- `generate` - General generation
- `helpful` - Helpful assistant
- `replace` - Replacement template

## Usage Examples

### Web Search
```bash
buzzllm "gpt-4o-mini" \
    "https://api.openai.com/v1/chat/completions" \
    "What is the current price of Bitcoin?" \
    --provider openai-chat \
    --api-key-name OPENAI_API_KEY \
    --system-prompt websearch
```

### Python Execution
```bash
# Requires Docker container: cd python_runtime_docker && bash build_docker.sh build-python-exec
buzzllm "claude-sonnet-4-20250514" \
    "https://api.anthropic.com/v1/messages" \
    "Calculate the first 20 Fibonacci numbers using Python" \
    --provider anthropic \
    --api-key-name ANTHROPIC_API_KEY \
    --system-prompt pythonexec
```

### Code Search
```bash
buzzllm "gpt-4o-mini" \
    "https://api.openai.com/v1/chat/completions" \
    "How does error handling work in this codebase?" \
    --provider openai-chat \
    --api-key-name OPENAI_API_KEY \
    --system-prompt codesearch
```

### Code Modifications (HackHub)
```bash
buzzllm "claude-sonnet-4-20250514" \
    "https://api.anthropic.com/v1/messages" \
    "$(cat src/main.py)\nAdd logging to all functions" \
    --provider anthropic \
    --api-key-name ANTHROPIC_API_KEY \
    --system-prompt hackhub
```

### Extended Thinking (Claude/OpenAI reasoning)
```bash
buzzllm "claude-sonnet-4-20250514" \
    "https://api.anthropic.com/v1/messages" \
    "Solve this complex problem step by step" \
    --provider anthropic \
    --api-key-name ANTHROPIC_API_KEY \
    --think
```

## When to Use

1. **User asks for web search**: Use `--system-prompt websearch`
2. **User wants Python execution**: Use `--system-prompt pythonexec` (ensure Docker is running)
3. **User asks about codebase**: Use `--system-prompt codesearch`
4. **User wants code changes**: Use `--system-prompt hackhub`
5. **Complex reasoning needed**: Add `--think` flag

## Environment Variables Required

- `OPENAI_API_KEY` - For OpenAI models
- `ANTHROPIC_API_KEY` - For Anthropic models
- `BRAVE_SEARCH_AI_API_KEY` - Fallback for websearch if DuckDuckGo fails

## Output

Default: streams text to stdout
With `-S` flag: SSE format for programmatic consumption

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