Api Router
VerifiedGorilla: Training and Evaluating LLMs for Function Calls (Tool Calls) api-router, python, api, api-documentation, chatgpt, claude-api.
$ Add to .claude/skills/ About This Skill
# Gorilla
Gorilla: Training and Evaluating LLMs for Function Calls (Tool Calls) ## Commands
- `help` - Help
- `run` - Run
- `info` - Info
- `status` - Status
Features
- Core functionality from ShishirPatil/api-router
Usage
Run any command: `api-router <command> [args]` --- 💬 Feedback & Feature Requests: https://bytesagain.com/feedback Powered by BytesAgain | bytesagain.com
Examples
```bash # Show help api-router help
# Run api-router run ```
- Run `api-router help` for all commands
- Run `api-router help` for all commands
When to Use
- as part of a larger automation pipeline
- when you need quick api router from the command line
Output
Returns formatted output to stdout. Redirect to a file with `api-router run > output.txt`.
Configuration
Set `API_ROUTER_DIR` environment variable to change the data directory. Default: `~/.local/share/api-router/`
Use Cases
- Route natural language requests to the correct API function call automatically
- Evaluate LLM accuracy in selecting and calling the right tools from a function library
- Train and benchmark models on function calling tasks using the Gorilla framework
- Build applications that translate user intents into structured API calls
- Test different LLMs' ability to generate correct function signatures and parameters
Pros & Cons
Pros
- +Based on Gorilla, a research-backed framework for LLM function calling evaluation
- +Helps solve the critical problem of accurate tool/API selection in agent systems
- +Supports evaluation across multiple LLM providers for comparison
Cons
- -Thin skill wrapper — essentially just references the Gorilla project
- -Research-oriented tool that may require significant setup for practical use
- -No pre-built API catalog — users must define their own function libraries
FAQ
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