everalgo-rank

Memory ranking for EverAlgo — four business-facing ranker classes (EpisodicRanker / CaseRanker / SkillRanker / profile rank) composed over a shared toolkit (fusion / weight / rerank). Pure in-memory rank, no storage I/O.

See the umbrella project: EverAlgo monorepo and the architecture document at docs/concepts/architecture.md.

Install

pip install everalgo-rank

What this distribution provides

Quick start

import asyncio
import json

from everalgo.llm.types import ChatResponse
from everalgo.rank import EpisodicRanker, DEFAULT_RANK_CONFIG
from everalgo.testing.fake_llm import FakeLLMClient
from everalgo.types import Candidate, RankInput

_RERANK_JSON = json.dumps({"ranked": [{"id": "ep_a", "score": 0.95}]})

async def main() -> None:
    fake = FakeLLMClient(responses=[ChatResponse(content=_RERANK_JSON, model="fake")])
    ranker = EpisodicRanker(llm=fake)

    rank_input = RankInput(
        query="Python async retry patterns",
        memory_type="episodic",
        dense_candidates=[
            Candidate(id="ep_a", score=0.9, metadata={"episode": "Alice asked about async retries."}),
            Candidate(id="ep_b", score=0.5, metadata={"episode": "Team discussed lunch."}),
        ],
        top_k=1,
    )

    output = await ranker.arank(rank_input, config=DEFAULT_RANK_CONFIG, enable_rerank=True)
    for item in output.items:
        print(f"{item.id}  score={item.score:.2f}")

asyncio.run(main())

LLM rerank step

arerank is the standalone LLM reranking primitive used internally by the ranker facades and exposed for direct use:

from everalgo.rank.rerank import arerank
from everalgo.rank.prompts.en.episodic import EPISODIC_RERANK_PROMPT_EN
from everalgo.types import Candidate

reranked = await arerank(
    candidates,
    prompt=EPISODIC_RERANK_PROMPT_EN,
    top_k=5,
    llm=client,
)

API surface

Cross-links

• everalgo-core — RankInput, RankOutput, Candidate, ScoredItem, LLMClient
• everalgo-user-memory — produces Episode / AtomicFact / Profile that the caller's recall layer packages into Candidate for ranking
• everalgo-agent-memory — produces AgentCase / AgentSkill candidates