everalgo-boundary 0.2.0

EverAlgo boundary: MemCell extractors (chat / workspace / agent).

everalgo-boundary

Chat boundary detection for EverAlgo — segments a flat list of ChatMessage objects into coherent MemCell slices using an LLM-based batch algorithm.

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

Install

pip install everalgo-boundary

For the user-scenario class facade, install everalgo-user-memory instead — it re-exports BoundaryDetector which wraps this package.

What this distribution provides

Symbol	Role
detect_boundaries	Low-level async function: (list[ChatMessage], *, llm, is_final, ...) → DetectionResult
DetectionResult	NamedTuple(cells: list[MemCell], tail: list[ChatMessage])

The class-style facades (BoundaryDetector for user-scenario chat, AgentBoundaryDetector for agent trajectories with tool calls) live in everalgo-user-memory and everalgo-agent-memory respectively.

Quick start

import asyncio
import json

from everalgo.boundary import detect_boundaries
from everalgo.llm.types import ChatMessage as LLMChatMessage, ChatResponse
from everalgo.testing.fake_llm import FakeLLMClient
from everalgo.types import ChatMessage

_BOUNDARY_JSON = json.dumps(
    {"reasoning": "single topic", "boundaries": [], "should_wait": False}
)

async def main() -> None:
    fake = FakeLLMClient(responses=[ChatResponse(content=_BOUNDARY_JSON, model="fake")])
    messages = [
        ChatMessage(id="m1", role="user",   content="Let's talk about deployment.",     timestamp=1_700_000_000_000, sender_id="u_alice"),
        ChatMessage(id="m2", role="assistant", content="Sure — what's the target env?",  timestamp=1_700_000_001_000, sender_id="assistant"),
        ChatMessage(id="m3", role="user",   content="K8s. Switching topic: lunch?",     timestamp=1_700_000_002_000, sender_id="u_alice"),
    ]

    # Streaming: hold `tail` between calls; pass prior tail + new messages each time.
    result = await detect_boundaries(messages, llm=fake)
    cells, tail = result  # NamedTuple unpacking

    # End-of-session: tail is forced into the last cell.
    result = await detect_boundaries(messages, llm=fake, is_final=True)
    assert result.tail == []
    for mc in result.cells:
        print(mc.timestamp, len(mc.items))


asyncio.run(main())

The streaming state machine

detect_boundaries deliberately holds back trailing messages as tail — the LLM cannot know whether a conversation continues beyond the last seen message. The caller maintains state:

tail: list[ChatMessage] = []

for batch in incoming_batches:
    result = await detect_boundaries(tail + batch, llm=client)
    await persist(result.cells)
    tail = result.tail

# Session ends — flush everything.
final = await detect_boundaries(tail, llm=client, is_final=True)
await persist(final.cells)

Tokenizer utilities

everalgo._tokenize (in everalgo-core) exposes two module-private utilities used by boundary algorithms; not part of the public surface:

• count_tokens(text: str) → int — token count under OpenAI o200k_base encoding via tiktoken.
• force_split(text: str, *, max_tokens: int) → list[str] — last-resort token-bounded chunking; no semantic awareness.

Stubs

WorkspaceMemCellExtractor (Jira / Email / Confluence) is a placeholder in v0.x — all methods raise NotImplementedError. It is deliberately excluded from everalgo.boundary.__all__; import it from everalgo.boundary.workspace directly if you need the reserved name. Implementation lands in a future minor bump when the RawData contract is finalised.

Related distributions

• everalgo-user-memory — BoundaryDetector class facade for chat scenarios
• everalgo-agent-memory — AgentBoundaryDetector class facade for agent trajectories