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How to send unmatched commands to a local model with Master Agent

Point sparx at a llama-server you run yourself so unmatched phrases reach a local model.

Credentials. Keep the model API key in an environment variable and pass only its name to --api-key-env, never as a command-line argument.

Who it is forA developer who already runs a local GGUF model and wants natural-language commands validated before any tool runs.

The problemModel output can name tools that do not exist or pass bad arguments, so the model's answer needs strict checks before execution.

The outcomePhrases that the skills do not match go to your model, and only valid tool calls run, with the real tool result returned.

What you need first

  • CMake 3.18+ and a C++17 compiler on Linux or macOS
  • Python 3 for tests and packaging
  • libcurl development files only if you want the HTTP model adapter
  • SQLite development files only if you want durable storage

Steps

  1. Start a compatible llama-server yourself with your chosen GGUF model, since the CLI does not download or start one.
  2. Build the CLI with libcurl available, following the Quick start steps.
  3. Run ./build/cli/sparx run --endpoint 127.0.0.1:8080 --model local --input 'Set the AC to 24 degrees'. (inferred, check the README)
  4. Set --timeout-ms 3000 if the model is slow, and confirm malformed model output returns an error rather than a guessed call.
  5. For an authenticated endpoint, export your key and pass only its variable name with --api-key-env MODEL_API_KEY.

Prompt for your agent

Using the repo OpenSparX/MasterAgent, start a local llama-server with <your GGUF model>, then run `sparx run --endpoint 127.0.0.1:8080 --model local --input '<phrase>'`. Expected output is either a validated tool result or an error. Verify that a phrase asking for an unknown tool returns an error, not a tool call.

Caveat. The README says model compliance depends on the model you choose, and it does not verify real-model latency or Qualcomm NPU performance for this release.

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