dspy-reasoning-modules

DSPy Reasoning Modules

Goal

Choose the appropriate DSPy reasoning module for long-context exploration, code-assisted reasoning, or parallel execution.

Module Selection

Module	Use it for	Important constraint
dspy.RLM	Exploring very large contexts with iterative REPL code and recursive sub-LM calls	Experimental; requires Deno by default
dspy.ProgramOfThought	Solving tasks by generating and executing Python	Requires Deno by default
dspy.CodeAct	Combining generated Python with predefined tool functions	Functions only; requires Deno
dspy.Parallel	Running (module, example) pairs concurrently	Tune threads and error handling

RLM for Large Contexts

RLM treats long inputs as external data in a sandbox rather than placing the full context in each LM prompt.

import dspy

dspy.configure(lm=dspy.LM("openai/gpt-4o"))

rlm = dspy.RLM(
    "document, question -> answer",
    max_iterations=12,
    max_llm_calls=30,
    sub_lm=dspy.LM("openai/gpt-4o-mini"),
)

result = rlm(
    document=very_long_document,
    question="What were the main revenue drivers?",
)
print(result.answer)

Use max_iterations, max_llm_calls, and max_output_chars as explicit cost and output bounds.

Sandboxed Execution

The default dspy.PythonInterpreter uses Deno and Pyodide. It denies host filesystem, environment, and network access unless explicitly enabled.

from pathlib import Path
import dspy

with dspy.PythonInterpreter(
    enable_read_paths=[Path("./inputs")],
    enable_network_access=["api.example.com"],
) as interpreter:
    print(interpreter.execute("print('ready')"))

Grant only the minimum paths, environment variables, and network hosts needed by the task.

ProgramOfThought and CodeAct

import dspy

dspy.configure(lm=dspy.LM("openai/gpt-4o-mini"))

math = dspy.ProgramOfThought("question -> answer")
print(math(question="What is the sum of the first 100 integers?").answer)

Use CodeAct when generated code also needs curated host-side tools:

def lookup_rate(currency: str) -> float:
    """Return a trusted exchange rate from the application service."""
    return rates[currency]

agent = dspy.CodeAct("amount, currency -> converted", tools=[lookup_rate])

Parallel Execution

parallel = dspy.Parallel(num_threads=8, return_failed_examples=True)
results, failed_examples, exceptions = parallel(
    [(program, {"question": question}) for question in questions]
)

Best Practices

1. Prefer Predict or ChainOfThought until code execution or long-context exploration is justified.
2. Treat RLM as experimental and load-test before production deployment.
3. Bound loops and sub-LM calls.
4. Keep sandbox permissions narrow.
5. Create separate interpreters for concurrent custom-interpreter use.

Official Documentation

• RLM API: https://dspy.ai/api/modules/RLM/
• ProgramOfThought API: https://dspy.ai/api/modules/ProgramOfThought/
• CodeAct API: https://dspy.ai/api/modules/CodeAct/
• Parallel API: https://dspy.ai/api/modules/Parallel/