EverAlgo

EverAlgo is the algorithm library for memory extraction and ranking used by EverOS, EverMind's open-source AI memory framework.

It is stateless and storage-free: every operator takes in-memory data structures and returns in-memory data structures. No database connections, no filesystem reads, no business-state ownership — those responsibilities belong to EverOS, the orchestration layer that calls EverAlgo.

---

What EverAlgo does

EverAlgo provides two symmetric algorithm axes:

Extract (write path) — Turn raw conversation messages or structured inputs into typed memories.

from everalgo.user_memory import BoundaryDetector, EpisodeExtractor
from everalgo.testing.fake_llm import FakeLLMClient

fake = FakeLLMClient(responses=["..."])
result = await BoundaryDetector(llm=fake).adetect(messages, is_final=True)
episode = await EpisodeExtractor(llm=fake).aextract(result.cells[0], sender_id="u_alice")

Rank (read path) — Re-rank multi-route recall candidates for a retrieval response.

from everalgo import rank

# Synchronous pure-compute fusion (no LLM needed)
merged = rank.fusion.rrf(vec_hits, keyword_hits)

---

What EverAlgo does not do

• Connect to any database or filesystem
• Own retry logic, multi-key rotation, or provider fallback
• Route requests to different LLM models by business scene (that is EverOS's SceneRouter)
• Compute embeddings (the caller computes and passes vectors in)

---

Distributions

EverAlgo is a monorepo of 8 independently versioned PyPI distributions sharing the everalgo.* namespace via PEP 420. Install only what your use case needs:

Distribution	Purpose
everalgo-core	Types, LLM client + providers, prompt validators, testing helpers
everalgo-boundary	MemCell boundary detection (chat / workspace / agent)
everalgo-clustering	Cluster value object + cluster_by_geometry / cluster_by_llm operators
everalgo-rank	4 retrieval strategies (hybrid / agentic / cluster / maxsim) + 4 business rankers (episodic / profile / case / skill) + fusion / weight / rerank tools
everalgo-parser	Multimodal raw-file → ParsedContent
everalgo-user-memory	Episode / Foresight / AtomicFact / Profile extractors
everalgo-agent-memory	AgentCase / AgentSkill extractors
everalgo-knowledge	KnowledgeMemory extractor

---

Where to start

Goal	Document
Install and run a first example in 5 minutes	Getting started
Understand the full architecture	Architecture
Install options and prerequisites	Installation
Understand why operators are pure functions	Stateless design
Understand the a-prefix async convention	Async–sync bridge
Browse per-package API docs	API index
Versioning and support policy	Version policy
Contribute to EverAlgo	Contributing

---

Quick install

# Full user-memory pipeline (boundary + 4 extractors)
pip install everalgo-user-memory

# Agent memory
pip install everalgo-agent-memory

# Retrieval re-ranking only
pip install everalgo-rank

# Multimodal knowledge extraction
pip install everalgo-knowledge

Development install (all 8 packages, editable):

git clone git@github.com:EverMind-AI/EverAlgo.git
cd everalgo
uv sync --all-packages

---

License

Apache License 2.0. See LICENSE.