Saving a program and reloading it

Saving the optimized state, or the entire program

.save(path) has two modes. Pointing it at a .json file stores just the program’s learned state, the optimized instructions and few-shot demos, not the program’s structure. Pointing it at a directory with save_program=True writes the entire module as a pickled artifact, structure and state together.

The state-only mode is what you usually want. The JSON is small, human-readable, safe to share, and easy to diff in version control. To reload it, you instantiate your program in code as usual and call .load() to apply the saved state on top.

Reach for save_program=True when whoever is loading the program won’t have your Python class definitions handy. Shipping an optimized program to another team is one example; serving it from a different repo than the one you developed it in is another. The cost is that the saved directory contains executable Python, so only load programs from sources you trust.

# state-only — small file, requires re-instantiating the program before loading
optimized_haiku_bot.save("haiku_bot.json")

# whole program — directory, rehydrates without you re-defining the class
optimized_haiku_bot.save("haiku_bot/", save_program=True)

Reloading a saved program

For the directory form, dspy.load(path) rehydrates the full module in one call. For the state-only form, you build a fresh copy of the program in code and call .load(path) on it to apply the saved state.

# whole program
loaded = dspy.load("haiku_bot/")

# state-only
fresh = dspy.ReAct(HaikuBot, tools=[wikipedia_search, get_wikipedia_page])
fresh.load("haiku_bot.json")

The save file contains the optimized instructions, demos, and signature metadata. It does not contain the LM client configuration: your API keys, your provider choice, your temperature. That separation is intentional: configure your LM as usual after loading and the same program targets whichever model you point it at today.

See Saving and loading for versioning saved programs, swapping models against the same checkpoint, and managing demo lifecycles.

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