| name | dspy-reasoning-modules |
| version | 1.0.0 |
| dspy-compatibility | 3.2.1 |
| tags | ["reasoning"] |
| requires-extras | [] |
| description | Use for DSPy reasoning modules including RLM, ProgramOfThought, CodeAct, Parallel, sandboxed execution, and long-context workflows. |
| allowed-tools | ["Read","Write","Glob","Grep"] |
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
- Prefer
Predict or ChainOfThought until code execution or long-context exploration is justified.
- Treat
RLM as experimental and load-test before production deployment.
- Bound loops and sub-LM calls.
- Keep sandbox permissions narrow.
- Create separate interpreters for concurrent custom-interpreter use.
Official Documentation
