model-migration

Open-source LLM migration and regression testing for AI agents. Compare models, detect tool-call regressions, and gate model changes in CI.

Source code for the paper "Accelerating Hierarchical Federated Learning under Mobility via Model Migration in Cloud–Edge–End Collaborative Networks".

Agent skill that upgrades LLM code to Claude Opus 5.5, GPT-6, Gemini 3.8 without breaking prod. Fixes breaking changes, then proves it with tests and evals.

Detect behavioural drift between LLM versions before you upgrade. Compare model responses, classify regressions, and generate migration reports with validated prompt patches.

Local, cross-provider preflight checks for LLM integration changes

Early-stop canary testing for LLM model migrations

A portable memory skill and CLI for AI agents: curated context, user preferences, topic interruptions, handoff packets, and model/runtime migration without transcript dumps.

Snapshot tests for AI model upgrades: see which tool-calling behaviors consistently change before you switch LLMs. No expected answers to write.

Open-source capture SDK for AI agents — records model and tool calls to build golden suites for LLM migration and regression testing.

Why a passing benchmark isn't safe to ship: a free 2-stage (benchmark + replay) validation recipe for LLM model swaps & prompt changes, run on flat-rate coding-agent subagents — no eval API bill.

Plan, research, and evaluate LLM application migrations between models, providers, and platforms (Claude, GPT, Bedrock) before changing code

Dependabot for AI models — replay your app's scenarios across a model migration and catch real behavioral regressions before they ship. Low false-positive by design. CLI + GitHub Action. Open-core, Apache-2.0.

Is a cheaper model as good as the one you run now? Paired-bootstrap non-inferiority check for swapping models.

Prove a model swap is safe before shipping it: golden set, field-level parity, per-class floors and a cost model. Python, 82 tests.

git diff for LLM behaviour. Capture what your model actually did, replay it against a candidate model, and catch what silently broke. No test authoring, no API keys required.

Ollama model store porter: re-hash every blob, export/import models offline as verified tarballs

Check whether your prompts, tool calls and JSON outputs still work on a replacement LLM before you switch. Go / no-go verdict, BYOK, no telemetry.

Switch AI models without breaking your product: find every model your code calls and when it retires, turn real usage logs into tests, compare and pick replacement models. CLI, GitHub Action and MCP server.

A minimal eval harness for LLM model migrations: golden set with trap cases, LLM-as-judge, A/B on quality, latency and cost.

Find the LLM model IDs in your code that OpenAI, Anthropic or Google is retiring: shutdown date, recommended replacement, its price ratio and the API changes to make, from the providers' own pages. No account, no keys.