Building a Streaming Local AI Agent with Wikipedia and Ollama
Build a local AI agent that watches Wikipedia's live edit feed and reasons about vandalism using a two-stage filtering pipeline and Ollama.
“Streaming” gets used in two different ways when people talk about AI agents, and most tutorials only build one of them. Sometimes it means the agent consumes a live stream of events instead of waiting for someone to type a message. Sometimes it means the agent’s own output streams out token by token instead of appearing all at once after a long pause. This build does both, on purpose, because they solve two different problems, and a genuinely useful always-on agent needs both solved.
The framing worth borrowing here comes from what’s usually called an ambient agent, one LangChain describes as triggered by events rather than by a human message, and Google’s Agent Development Kit describes from the infrastructure side the same way: agents woken by something arriving on a stream, not sitting behind a request-response call. The scenario for this build is concrete and genuinely real: a local agent that watches Wikipedia’s live, public edit feed, no API key required, and reasons about which edits look like vandalism, running entirely on your own machine through Ollama. Every line of code below was written, then actually tested, before it went into this article.
These are your prerequisites:
- Python 3.11 or newer
- Ollama installed locally, with a model pulled (
ollama pull llama3.1:8b, or any model that supports structured JSON output) pip install fastapi uvicorn httpx pydantic ollama sse-starlette- No API keys, no cloud account, and no cost beyond your own electricity. The only outbound network connection this service makes is to Wikipedia’s public EventStreams endpoint, which requires no authentication
The One Design Decision That Matters
Wikipedia’s edit stream isn’t a trickle. On an active day, it pushes several edits per second across every language edition combined. Hand every single one of those to a language model and two things happen at once: you burn through your machine’s compute on edits that were never interesting in the first place, and the agent falls behind the live stream it’s supposed to be watching, which defeats the entire point of building something “always on.”
The fix is a two-stage funnel, and it’s the single most important idea in this build:
- Stage one is cheap, plain Python math that runs on every event with no model involved at all: how many bytes did this edit remove, how many edits has this user made in the last couple of minutes? The overwhelming majority of edits are boring, and boring is free to detect.
- Stage two, the actual local LLM, only wakes up for the small fraction of events that trip a threshold in stage one. This is the same principle behind any good monitoring system: cheap filters up front, expensive reasoning reserved for the candidates that survive.
Folder Structure
streaming-local-agent/
├── src/
│ ├── __init__.py
│ ├── config.py
│ ├── schemas.py
│ ├── stream_source.py
│ ├── filters.py
│ ├── agent.py
│ ├── broadcaster.py
│ └── main.py
├── tests/
│ └── test_filters.py
├── requirements.txt
└── .env.exampleEach file maps to exactly one stage of the pipeline described above, which makes the whole thing easy to reason about and easy to test in isolation, which is exactly how it was actually built for this article.
Build Section 1: The Event Stream Consumer
Wikipedia’s EventStreams service pushes edits as Server-Sent Events over plain HTTP. No key, no handshake beyond an ordinary GET request that stays open.
# src/stream_source.py
import asyncio
import json
import re
import time
from typing import AsyncIterator, Optional
import httpx
from .schemas import RecentChangeEvent
from . import config
# Wikipedia doesn't send an explicit "is this user anonymous" flag on this
# stream; anonymous edits are attributed to the editor's IP address instead
# of a username, so an IP-shaped username is how you detect one in practice.
_IPV4_RE = re.compile(r"^\d{1,3}(\.\d{1,3}){3}$")
_IPV6_RE = re.compile(r"^[0-9A-Fa-f:]+:[0-9A-Fa-f:]+$")
def is_anonymous_user(username: str) -> bool:
return bool(_IPV4_RE.match(username) or _IPV6_RE.match(username))
def parse_sse_line(line: str) -> Optional[dict]:
"""SSE frames data as lines prefixed with 'data: '. Comment lines
(starting with ':') and blank keep-alive lines are common on this
feed and should be silently ignored, not treated as errors."""
if not line or line.startswith(":"):
return None
if line.startswith("data:"):
raw = line[len("data:"):].strip()
if not raw:
return None
try:
return json.loads(raw)
except json.JSONDecodeError:
return None
return None
def to_event(raw: dict) -> Optional[RecentChangeEvent]:
"""Converts a raw Wikimedia payload into our normalized schema.
Returns None for event types we don't care about rather than
raising, since a stream this high-volume constantly includes shapes
we're not watching for."""
if raw.get("type") != "edit":
return None
length = raw.get("length") or {}
if "old" not in length or "new" not in length:
return None
return RecentChangeEvent(
wiki=raw.get("wiki", "unknown"),
user=raw.get("user", "unknown"),
title=raw.get("title", "unknown"),
is_anonymous=is_anonymous_user(raw.get("user", "")),
is_bot=raw.get("bot", False),
old_length=length["old"],
new_length=length["new"],
timestamp=raw.get("timestamp", time.time()),
comment=raw.get("comment", "") or "",
)
async def wikipedia_event_stream() -> AsyncIterator[RecentChangeEvent]:
"""The live async generator used by main.py. Reconnects automatically
on a dropped connection rather than letting the whole service die
because of one network hiccup, which matters a lot for something
meant to run unattended."""
while True:
try:
async with httpx.AsyncClient(timeout=None) as client:
async with client.stream("GET", config.WIKIPEDIA_STREAM_URL) as response:
async for line in response.aiter_lines():
raw = parse_sse_line(line)
if raw is None:
continue
if raw.get("wiki") not in config.WATCHED_WIKIS:
continue
event = to_event(raw)
if event is not None:
yield event
except httpx.HTTPError:
await asyncio.sleep(5)What this does: anonymity detection here is worth calling out specifically, because the naive approach — checking for an explicit “is anonymous” field — doesn’t actually exist on this feed.
Wikipedia attributes anonymous edits to the editor’s IP address as their username, so is_anonymous_user checks whether the username is shaped like an IPv4 or IPv6 address instead, which is how this detection genuinely works in production. parse_sse_line and to_event are both deliberately pure functions with no network dependency, which is what lets you test the parsing logic directly against realistic sample payloads before ever touching a live connection, catching real bugs in earlier drafts of the anonymity check in the process.
wikipedia_event_stream wraps the actual connection in a while True with a reconnect-and-sleep on any HTTP error, since an always-on service that dies on the first dropped connection isn’t actually always-on.
Build Section 2: The Cheap Filter, Stage One
# src/filters.py
import time
from collections import defaultdict, deque
from typing import Optional
from .schemas import RecentChangeEvent, FilterSignal
from . import config
class EditVelocityTracker:
"""Tracks recent edit timestamps per user in a sliding window, so the
filter can catch rapid-fire editing bursts, not just single large
deletions. Bounded memory: old users get evicted, not kept forever."""
def __init__(self, window_seconds: int = config.EDIT_VELOCITY_WINDOW_SECONDS,
max_tracked: int = config.MAX_TRACKED_WINDOWS):
self.window_seconds = window_seconds
self.max_tracked = max_tracked
self._history: dict[str, deque[float]] = defaultdict(deque)
def record_and_count(self, user: str, timestamp: float) -> int:
"""Records this edit and returns how many edits this user has
made within the trailing window, including this one."""
history = self._history[user]
history.append(timestamp)
cutoff = timestamp - self.window_seconds
while history and history[0] < cutoff:
history.popleft()
if len(self._history) > self.max_tracked:
self._evict_oldest()
return len(history)
def _evict_oldest(self) -> None:
oldest_user = min(self._history, key=lambda u: self._history[u][-1] if self._history[u] else 0)
del self._history[oldest_user]
class Stage1Filter:
"""Wraps the velocity tracker and the byte-removal check into one
pass/fail decision per event."""
def __init__(self, tracker: Optional[EditVelocityTracker] = None):
self.tracker = tracker or EditVelocityTracker()
def evaluate(self, event: RecentChangeEvent) -> Optional[FilterSignal]:
"""Returns a FilterSignal if this event is worth the LLM's time,
otherwise None, and None is the common case by a wide margin."""
if event.is_bot:
return None # bot edits have their own, separate review path
recent_count = self.tracker.record_and_count(event.user, event.timestamp)
bytes_removed = event.bytes_removed
reasons = []
if bytes_removed >= config.BYTES_REMOVED_THRESHOLD:
reasons.append(f"removed {bytes_removed} bytes in one edit")
if recent_count >= config.EDIT_VELOCITY_THRESHOLD:
reasons.append(f"{recent_count} edits in {self.tracker.window_seconds}s")
if not reasons:
return None
return FilterSignal(
event=event, bytes_removed=bytes_removed,
recent_edit_count=recent_count, reason="; ".join(reasons),
)What this does: EditVelocityTracker keeps a per-user deque of recent edit timestamps within a configurable sliding window, so the filter can detect rapid-fire editing bursts and not just single large deletions. Memory is explicitly bounded — old users are evicted rather than accumulated indefinitely — which matters for a service expected to run continuously. Stage1Filter.evaluate returns a FilterSignal only when at least one threshold is crossed, meaning the LLM in stage two is invoked only for the small fraction of events that clear the bar, keeping compute costs low and the agent in step with the live stream.