What this error means

bufio.Scanner: token too long (status code: -1) — Error from the python API: llama-server response is a Ollama failure pattern reported for developers trying to developers integrating ollama via python api get token-too-long errors when llama-server outputs responses exceeding bufio.scanner buffer limits, breaking llm pipeline processing. Based on the imported evidence, treat this as a tool-specific troubleshooting page rather than a generic API error.

Why this happens

Source: GitHub Issue #16243 on ollama/ollama (opened May 21, 2026, yesterday). Specific runtime error where llama-server response exceeds internal scanner buffer, returning status code -1. Concrete error signature with actionable fix path (configuring scanner buffer size or streaming response handling). Duplicate check vs covered-errors.md: no matching entry. Category: Ollama → Ollama local LLM serving.

Common causes

  • Source: GitHub Issue #16243 on ollama/ollama (opened May 21, 2026, yesterday). Specific runtime error where llama-server response exceeds internal scanner buffer, returning status code -1. Concrete error signature with actionable fix path (configuring scanner buffer size or streaming response handling). Duplicate check vs covered-errors.md: no matching entry. Category: Ollama → Ollama local LLM serving.

Quick fixes

  1. Confirm the exact error signature matches bufio.Scanner: token too long (status code: -1) — Error from the python API: llama-server response.
  2. Check the Ollama account, local tool state, and provider configuration involved in the failing workflow.
  3. Compare the failing environment with a known working setup, then change one configuration value at a time.

Platform/tool-specific checks

  • Verify the command, editor, extension, or API client that produced the error.
  • Compare local settings with CI, deployment, or editor-level settings when the error appears in only one environment.
  • Avoid deleting credentials, local model data, or project settings until the failing scope is clear.

Step-by-step troubleshooting

  1. Capture the exact error message and the command, editor action, or request that triggered it.
  2. Check whether the failure is account/auth, quota/rate, model/provider, local runtime, or deployment configuration.
  3. Review the source evidence below and compare it with your environment.
  4. Apply one change at a time and rerun the smallest failing action.
  5. Keep the working fix documented for the team or deployment environment.

How to prevent it

  • Keep provider/tool configuration documented.
  • Record non-secret diagnostics such as tool version, provider name, model name, and command path.
  • Add a lightweight check before CI or production workflows depend on the tool.