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agilab-local-llm

thalesgroup/agilab-local-llm

AI & ML

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thalesgroup/agilab-local-llm

AI & ML

Guidance for using local LLM backends (Ollama/GPT-OSS) inside AGILAB with correctness-first prompts.

Use this skill when working on local/offline engines or prompts.

Prefer deterministic settings for code edits (lower temperature, explicit constraints).
In WORKFLOW, prefer validated generated-action JSON contracts over raw Python snippets for normal dataframe transformations. This keeps AGILAB lightweight and power-efficient without requiring containers or VMs for the default path.
Require the model to return:
- file list to edit
- exact patch intent
- tests/commands to validate

Treat raw model Python as an advanced/manual mode, not the default UX.
For dataframe transformations, ask the model for a versioned action contract, validate it against the loaded dataframe schema, then let AGILAB convert the approved contract into deterministic pandas code.
If the request cannot be represented by the safe action registry, fail closed with an actionable message instead of executing or repairing arbitrary code.
Keep container/VM/process sandbox guidance for explicitly untrusted apps or advanced raw-Python execution. Do not make it the primary recommendation for AGILAB's normal local, energy-efficient workflow.

Detect missing local endpoints/models up-front and surface an actionable error.
Do not auto-switch APIs or rewrite parameters silently.
Probe the requested backend before claiming validation:
- GPT-OSS Responses-compatible endpoint, usually http://127.0.0.1:8000/v1/responses
- Ollama tags endpoint, usually http://127.0.0.1:11434/api/tags
If the endpoint is unavailable or the selected model is absent, report that condition directly. Do not silently replace it with a fixture, cloud API, or different local model.

For local contract tests, a temporary Responses-compatible HTTP server is acceptable when the goal is to prove AGILAB's request/response path, schema validation, and artifact generation without downloading a model.
Label that result as a local contract engine, not as real model validation. Real model validation requires the actual requested engine to be running and selected.
Validate the generated payload against the app schema and downstream artifacts, not only the raw model response. For educational diagnostics, assert fields such as student_score are persisted in CSV/JSON summaries when the UI or README claims them.
Keep generated examples deterministic where possible by constraining prompts, seeds, and output schemas. Reject invalid JSON with an actionable error rather than repairing it silently.

Guidance for using local LLM backends (Ollama/GPT-OSS) inside AGILAB with correctness-first prompts.

Use this skill when working on local/offline engines or prompts.

Prefer deterministic settings for code edits (lower temperature, explicit constraints).
In WORKFLOW, prefer validated generated-action JSON contracts over raw Python snippets for normal dataframe transformations. This keeps AGILAB lightweight and power-efficient without requiring containers or VMs for the default path.
Require the model to return:
- file list to edit
- exact patch intent
- tests/commands to validate

Treat raw model Python as an advanced/manual mode, not the default UX.
For dataframe transformations, ask the model for a versioned action contract, validate it against the loaded dataframe schema, then let AGILAB convert the approved contract into deterministic pandas code.
If the request cannot be represented by the safe action registry, fail closed with an actionable message instead of executing or repairing arbitrary code.
Keep container/VM/process sandbox guidance for explicitly untrusted apps or advanced raw-Python execution. Do not make it the primary recommendation for AGILAB's normal local, energy-efficient workflow.

Detect missing local endpoints/models up-front and surface an actionable error.
Do not auto-switch APIs or rewrite parameters silently.
Probe the requested backend before claiming validation:
- GPT-OSS Responses-compatible endpoint, usually http://127.0.0.1:8000/v1/responses
- Ollama tags endpoint, usually http://127.0.0.1:11434/api/tags
If the endpoint is unavailable or the selected model is absent, report that condition directly. Do not silently replace it with a fixture, cloud API, or different local model.

For local contract tests, a temporary Responses-compatible HTTP server is acceptable when the goal is to prove AGILAB's request/response path, schema validation, and artifact generation without downloading a model.
Label that result as a local contract engine, not as real model validation. Real model validation requires the actual requested engine to be running and selected.
Validate the generated payload against the app schema and downstream artifacts, not only the raw model response. For educational diagnostics, assert fields such as student_score are persisted in CSV/JSON summaries when the UI or README claims them.
Keep generated examples deterministic where possible by constraining prompts, seeds, and output schemas. Reject invalid JSON with an actionable error rather than repairing it silently.