haiku.rag/docs/skills/rlm.md

RLM Skill

The RLM (Reflexion Language Model) skill provides computational analysis via code execution. It writes and runs Python code in an isolated Docker sandbox to answer questions that require computation, aggregation, or data traversal.

!!! warning "Requires Docker" The analyze tool executes code in a Docker sandbox. Docker must be running on the host machine. This skill is not suitable for Docker-deployed applications — use the rag skill alone in those environments.

create_skill(db_path?, config?)

from haiku.rag.skills.rlm import create_skill

skill = create_skill(db_path=db_path, config=config)

Parameter	Default	Description
db_path	None	Path to LanceDB database. Falls back to HAIKU_RAG_DB env var, then config default.
config	None	AppConfig instance. If None, uses get_config().

Tools

Tool	Purpose
analyze(question, document?, filter?)	Answer analytical questions using code execution

Parameters:

• question — The analytical question to answer.
• document — Optional document ID or title to pre-load for analysis.
• filter — Optional SQL WHERE clause to filter documents.

State

The skill manages an RLMState under the "rlm" namespace:

class RLMState(BaseModel):
    analyses: list[AnalysisEntry] = []

class AnalysisEntry(BaseModel):
    question: str
    answer: str
    program: str | None = None

Each analyze call appends an AnalysisEntry with the question, answer, and executed program.

Usage with RAG Skill

Combine both skills to give the agent full RAG + analysis capabilities:

from haiku.rag.skills.rag import create_skill as create_rag_skill
from haiku.rag.skills.rlm import create_skill as create_rlm_skill
from haiku.skills.agent import SkillToolset
from pydantic_ai import Agent

rag = create_rag_skill(db_path=db_path)
rlm = create_rlm_skill(db_path=db_path)
toolset = SkillToolset(skills=[rag, rlm])

agent = Agent(
    "openai:gpt-4o",
    instructions=toolset.system_prompt,
    toolsets=[toolset],
)

See the RLM Agent documentation for details on how the underlying agent works.