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dspy-rlm-module

Use dspy.RLM (Recursive Language Model) for reasoning over contexts too large to fit in an LLM's working window — entire codebases, long logs, massive documents, or multi-step data exploration that needs a sandboxed Python REPL. Use when the input is >100k tokens, needs recursive

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Price unconfirmed★ 277 GitHub starsRegistry updated · Sep 6, 2026agent-skill

Overview

Use dspy.RLM (Recursive Language Model) for reasoning over contexts too large to fit in an LLM's working window — entire codebases, long logs, massive documents, or multi-step data exploration that needs a sandboxed Python REPL. Use when the input is >100k tokens, needs recursive chunking, or benefits from the LLM writing and running code to probe data.

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dspy.RLM — Recursive Language Model

dspy.RLM runs the LLM in a sandboxed Python REPL (Pyodide/WASM via Deno) with access to the full context as variables. The LLM writes code to slice, grep, summarize, and recursively sub-query the data, iterating until it can answer. Use it when the context is too large to cram into a single prompt.

Prerequisites

  • Deno installed (for the default PythonInterpreter): brew install deno or see https://deno.land. The interpreter is a Pyodide-in-WASM sandbox spawned by Deno.
  • A sub-LM for inner calls — usually a cheaper model than the outer LM. Defaults to dspy.settings.lm.

Canonical usage

import dspy

dspy.configure(lm=dspy.LM("openai/gpt-4o"))
sub_lm = dspy.LM("openai/gpt-4o-mini")    # cheap inner model

rlm = dspy.RLM(
    "context, query -> answer",
    max_iterations=20,
    max_llm_calls=50,
    max_output_chars=10_000,
    sub_lm=sub_lm,
    tools=[],
    verbose=False,
)

result = rlm(
    context=open("huge_log.txt").read(),   # can be 500k+ tokens
    query="Summarize every unique error class and how many times each appeared.",
)
print(result.answer)

Full constructor

dspy.RLM(
    signature: type[Signature] | str,
    max_iterations: int = 20,       # REPL loop cap
    max_llm_calls: int = 50,        # sub-LM call cap (stops runaway recursion)
    max_output_chars: int = 10_000, # truncate REPL stdout per step
    verbose: bool = False,          # print the REPL trace
    tools: list[Callable] | None = None,
    sub_lm: dspy.LM | None = None,
    interpreter: CodeInterpreter | None = None,  # custom sandbox
)

When to reach for RLM vs. alternatives

SituationUse
Context <100k, answer fits one LM calldspy.Predict / dspy.ChainOfThought
Need external tools (web, db)dspy.ReAct(tools=[...])
Math/code that must rundspy.ProgramOfThought
Huge context, recursive chunking, or data-exploration loopdspy.RLM
Entire-codebase reasoning where the LM should grep/read filesdspy.RLM with file-reading tools=[...]

Composition — RLM as a module inside a larger program

Wrap the RLM in your own dspy.Module and optimize the enclosing program with GEPA. GEPA can tune both the RLM's outer signature instruction and the surrounding predictors.

class RepoAuditor(dspy.Module):
    def __init__(self):
        super().__init__()
        self.explore = dspy.RLM("repo_tree, question -> findings",
                                max_iterations=30, sub_lm=dspy.LM("openai/gpt-4o-mini"))
        self.synth = dspy.ChainOfThought("findings, question -> report")

    def forward(self, repo_tree, question):
        f = self.explore(repo_tree=repo_tree, question=question).findings
        return self.synth(findings=f, question=question)

Then: dspy.GEPA(metric=..., ...).compile(student=RepoAuditor(), trainset=..., valset=...).

Practical tips

  • Budget carefully. A single RLM call can issue dozens of sub-LM calls. Keep max_llm_calls tight (20–50) in production; raise for research.
  • The default stdout cap is smaller in DSPy 3.2.x. max_output_chars now defaults to 10_000; raise it deliberately if your REPL tools print large tables or document slices.
  • Use a cheap sub_lm. The outer LM orchestrates; inner calls (summarize, filter, score) don't need the flagship model.
  • Pass data as kwargs, not in the instruction. rlm(context=huge_string, query="...") lets the REPL treat context as a Python variable. Avoid concatenating it into the prompt.
  • verbose=True while debugging. Prints every REPL step — invaluable when the RLM appears to hang or loop.
  • Custom tools are regular Python callables passed via tools=[...]; they are exposed inside the sandbox. Useful for read_file, grep, vector_search, etc. In DSPy 3.2.x they are invoked by keyword, so give them named, typed parameters rather than positional-only signatures.
  • Deno install is required. Missing Deno is the #1 RLM error. Check which deno before reporting bugs.

Security note

The default interpreter is a Deno-sandboxed Pyodide WASM runtime — no filesystem, network, or subprocess access by default. If you pass custom tools that do I/O, your tools' security posture is yours. Never hand raw subprocess.run to the RLM.

Anti-patterns

  • Using RLM when a 32k-token prompt would fit — overhead is not worth it.
  • Missing Deno → hard-to-diagnose failures. Install it.
  • max_llm_calls left at default in a production path — runaway cost.
  • Passing secrets in the context string — they get echoed into REPL state.

Next

  • Wrap-and-optimize with GEPA → dspy-gepa-optimizer.
  • Full reference → reference.md.
View source ↗
File metadata
name: dspy-rlm-module
description: Use dspy.RLM (Recursive Language Model) for reasoning over contexts too large to fit in an LLM's working window — entire codebases, long logs, massive documents, or multi-step data exploration that needs a sandboxed Python REPL. Use when the input is >100k tokens, needs recursive chunking, or benefits from the LLM writing and running code to probe data.
when_to_use: User has a very long document/codebase/log, asks about "long context", mentions RLM or recursive reasoning, or is trying to stuff a huge context into a single predictor and hitting token limits.
View original text
---
name: dspy-rlm-module
description: Use dspy.RLM (Recursive Language Model) for reasoning over contexts too large to fit in an LLM's working window — entire codebases, long logs, massive documents, or multi-step data exploration that needs a sandboxed Python REPL. Use when the input is >100k tokens, needs recursive chunking, or benefits from the LLM writing and running code to probe data.
when_to_use: User has a very long document/codebase/log, asks about "long context", mentions RLM or recursive reasoning, or is trying to stuff a huge context into a single predictor and hitting token limits.
---

# `dspy.RLM` — Recursive Language Model

`dspy.RLM` runs the LLM in a sandboxed Python REPL (Pyodide/WASM via Deno) with access to the full context as variables. The LLM writes code to slice, grep, summarize, and recursively sub-query the data, iterating until it can answer. Use it when the context is too large to cram into a single prompt.

## Prerequisites

- **Deno installed** (for the default `PythonInterpreter`): `brew install deno` or see https://deno.land. The interpreter is a Pyodide-in-WASM sandbox spawned by Deno.
- A sub-LM for inner calls — usually a cheaper model than the outer LM. Defaults to `dspy.settings.lm`.

## Canonical usage

```python
import dspy

dspy.configure(lm=dspy.LM("openai/gpt-4o"))
sub_lm = dspy.LM("openai/gpt-4o-mini")    # cheap inner model

rlm = dspy.RLM(
    "context, query -> answer",
    max_iterations=20,
    max_llm_calls=50,
    max_output_chars=10_000,
    sub_lm=sub_lm,
    tools=[],
    verbose=False,
)

result = rlm(
    context=open("huge_log.txt").read(),   # can be 500k+ tokens
    query="Summarize every unique error class and how many times each appeared.",
)
print(result.answer)
```

## Full constructor

```python
dspy.RLM(
    signature: type[Signature] | str,
    max_iterations: int = 20,       # REPL loop cap
    max_llm_calls: int = 50,        # sub-LM call cap (stops runaway recursion)
    max_output_chars: int = 10_000, # truncate REPL stdout per step
    verbose: bool = False,          # print the REPL trace
    tools: list[Callable] | None = None,
    sub_lm: dspy.LM | None = None,
    interpreter: CodeInterpreter | None = None,  # custom sandbox
)
```

## When to reach for RLM vs. alternatives

| Situation | Use |
|---|---|
| Context <100k, answer fits one LM call | `dspy.Predict` / `dspy.ChainOfThought` |
| Need external tools (web, db) | `dspy.ReAct(tools=[...])` |
| Math/code that must run | `dspy.ProgramOfThought` |
| **Huge context, recursive chunking, or data-exploration loop** | **`dspy.RLM`** |
| Entire-codebase reasoning where the LM should grep/read files | `dspy.RLM` with file-reading `tools=[...]` |

## Composition — RLM as a module inside a larger program

Wrap the RLM in your own `dspy.Module` and optimize the enclosing program with GEPA. GEPA can tune both the RLM's outer signature instruction and the surrounding predictors.

```python
class RepoAuditor(dspy.Module):
    def __init__(self):
        super().__init__()
        self.explore = dspy.RLM("repo_tree, question -> findings",
                                max_iterations=30, sub_lm=dspy.LM("openai/gpt-4o-mini"))
        self.synth = dspy.ChainOfThought("findings, question -> report")

    def forward(self, repo_tree, question):
        f = self.explore(repo_tree=repo_tree, question=question).findings
        return self.synth(findings=f, question=question)
```

Then: `dspy.GEPA(metric=..., ...).compile(student=RepoAuditor(), trainset=..., valset=...)`.

## Practical tips

- **Budget carefully.** A single RLM call can issue dozens of sub-LM calls. Keep `max_llm_calls` tight (20–50) in production; raise for research.
- **The default stdout cap is smaller in DSPy 3.2.x.** `max_output_chars` now defaults to `10_000`; raise it deliberately if your REPL tools print large tables or document slices.
- **Use a cheap `sub_lm`.** The outer LM orchestrates; inner calls (summarize, filter, score) don't need the flagship model.
- **Pass data as kwargs, not in the instruction.** `rlm(context=huge_string, query="...")` lets the REPL treat `context` as a Python variable. Avoid concatenating it into the prompt.
- **`verbose=True` while debugging.** Prints every REPL step — invaluable when the RLM appears to hang or loop.
- **Custom tools** are regular Python callables passed via `tools=[...]`; they are exposed inside the sandbox. Useful for `read_file`, `grep`, `vector_search`, etc. In DSPy 3.2.x they are invoked by keyword, so give them named, typed parameters rather than positional-only signatures.
- **Deno install is required.** Missing Deno is the #1 RLM error. Check `which deno` before reporting bugs.

## Security note

The default interpreter is a Deno-sandboxed Pyodide WASM runtime — no filesystem, network, or subprocess access by default. If you pass custom `tools` that do I/O, your tools' security posture is yours. Never hand raw `subprocess.run` to the RLM.

## Anti-patterns

- Using RLM when a 32k-token prompt would fit — overhead is not worth it.
- Missing Deno → hard-to-diagnose failures. Install it.
- `max_llm_calls` left at default in a production path — runaway cost.
- Passing secrets in the `context` string — they get echoed into REPL state.

## Next

- Wrap-and-optimize with GEPA → `dspy-gepa-optimizer`.
- Full reference → [reference.md](reference.md).

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Source & usage notes

Indexed

Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.

Source repository
intertwine/dspy-agent-skills
License
MIT
Version
1.0.0
Last GitHub push
Sep 5, 2026
Registry updated
Sep 6, 2026
Instruction path
skills/dspy-rlm-module/SKILL.md @ ccd5498ade05

Version reported in registry metadata; check source releases before relying on it.

Quality

68/100

Promising

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Do not auto-install

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Open full auditView eval report
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  • The SKILL.md excerpt appears to be truncated at the 'Anti-patterns' section; ensure the full document is present in the repository.
  • The example_rlm.py file also appears truncated in the provided excerpt; verify the complete script is included.
  • Quality score needs review
  • Permission surface needs review: secrets or environment access, shell or command execution
  • Stars/forks activity: 277 stars, 24 forks; issue activity unavailable in current metadata
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  • Permission surface: secrets or environment access, shell or command execution
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    "risk_level": "needs_review",
    "risk_label": "Needs review",
    "warnings": [
      "Dependency or permission surface needs review",
      "Permission surface may require sandboxing",
      "The SKILL.md excerpt appears to be truncated at the 'Anti-patterns' section; ensure the full document is present in the repository.",
      "The example_rlm.py file also appears truncated in the provided excerpt; verify the complete script is included.",
      "Quality score needs review",
      "Permission surface needs review: secrets or environment access, shell or command execution",
      "Stars/forks activity: 277 stars, 24 forks; issue activity unavailable in current metadata",
      "Dependency/runtime risk: command execution surface, credential or environment access"
    ]
  },
  "safety_gate": {
    "tier": "blocked",
    "label": "Blocked for auto-install",
    "auto_install_policy": "block",
    "auto_install_allowed": false,
    "human_review_required": true,
    "blocked": true,
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
  },
  "quality": {
    "score": 68,
    "label": "Promising"
  },
  "supply": {
    "track": "Research and knowledge work",
    "scenario": "Research agents",
    "maintenance": "2mo since push",
    "risk": "Needs review"
  },
  "alternative_skills": [
    {
      "slug": "orchestra-research-peft-fine-tuning",
      "name": "peft-fine-tuning",
      "url": "https://www.openagentskill.com/skills/orchestra-research-peft-fine-tuning",
      "stars": 13443,
      "install_command": "npx skills add Orchestra-Research/AI-Research-SKILLs --skill peft-fine-tuning",
      "trust_score": 80,
      "audit_score": 85
    },
    {
      "slug": "anthropics-kb-article",
      "name": "kb-article",
      "url": "https://www.openagentskill.com/skills/anthropics-kb-article",
      "stars": 29822,
      "install_command": "npx skills add anthropics/knowledge-work-plugins --skill kb-article",
      "trust_score": 74,
      "audit_score": 85
    }
  ],
  "do_not_use_when": [
    "teams that need a vendor-supported SLA",
    "production agents without a repository review",
    "The SKILL.md excerpt appears to be truncated at the 'Anti-patterns' section; ensure the full document is present in the repository.",
    "High-risk permission hints: Shell or command execution, Secrets or environment access",
    "Dependency or permission surface needs review",
    "Permission surface may require sandboxing",
    "The example_rlm.py file also appears truncated in the provided excerpt; verify the complete script is included.",
    "Quality score needs review"
  ],
  "agent_contract": {
    "task_input": "Use dspy-rlm-module in an agent workflow",
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
    "install_policy": "block",
    "minimum_review_before_use": [
      "Trust: 65/100 Manual review",
      "Audit: 73/100 Needs review",
      "Safety: 29/100 Avoid automatic install",
      "Review repository, license, install command, and permission surface before production use."
    ],
    "expected_agent_output": {
      "selected_skill": "intertwine-dspy-rlm-module (dspy-rlm-module)",
      "install_command": "npx skills add intertwine/dspy-agent-skills --skill dspy-rlm-module",
      "risk_summary": "Needs review; Blocked for auto-install; Review before production",
      "verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
    }
  },
  "outcome_feedback": {
    "endpoint": "https://www.openagentskill.com/api/agent/outcome",
    "method": "POST",
    "requires_resolve_event_id": true,
    "event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
    "expected_outcomes": [
      "success",
      "failed",
      "not_relevant",
      "blocked_by_risk",
      "setup_required"
    ],
    "payload_template": {
      "event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
      "skill_slug": "intertwine-dspy-rlm-module",
      "task": "Use dspy-rlm-module in an agent workflow",
      "agent": "codex",
      "outcome": "success",
      "install_used": true,
      "risk_blocked": false,
      "setup_required": false,
      "task_success": true,
      "output_quality": 4,
      "error_type": null,
      "human_review_required": false,
      "workspace": "sandbox",
      "time_to_useful_ms": 120000,
      "notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
    }
  },
  "endpoints": {
    "web": "https://www.openagentskill.com/skills/intertwine-dspy-rlm-module",
    "api": "https://www.openagentskill.com/api/agent/skills/intertwine-dspy-rlm-module",
    "audit": "https://www.openagentskill.com/skills/intertwine-dspy-rlm-module/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=intertwine-dspy-rlm-module&task=Use%20dspy-rlm-module%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20dspy-rlm-module%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20dspy-rlm-module%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/intertwine-dspy-rlm-module/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/intertwine-dspy-rlm-module"
  }
}

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