Building a Streaming Local AI Agent

A new tutorial by an unnamed author demonstrates building a streaming local AI agent that monitors Wikipedia's live edit feed for vandalism using Ollama, with a two-stage funnel to filter events before invoking a local LLM. The agent consumes Wikipedia's public EventStreams endpoint without API keys and streams output token by token, addressing both meanings of streaming in AI agents. The build uses Python 3.11+, FastAPI, and Ollama with a model like llama3.1:8b, and was fully tested before publication.

Building a Streaming Local AI Agent Streaming gets used in two different ways when people talk about AI agents. Straighten out your understanding here. " 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 https://www.langchain.com/blog/introducing-ambient-agents , 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 https://ollama.com/ 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.example Each 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. python 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 me test the parsing logic directly against realistic sample payloads before ever touching a live connection, catching a real bug in an earlier draft 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 python 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 and trims anything outside the trailing window on every single call, which is what makes " 5 edits in 2 minutes " a real, continuously accurate number rather than an approximation. The max tracked eviction guard exists because this dictionary would otherwise grow forever on a stream that never stops, a detail that's easy to skip in a demo and expensive to discover in production. Stage1Filter.evaluate is the actual gate: it returns None, meaning " not interesting, " for the overwhelming majority of events, and only builds a FilterSignal object when a real threshold is crossed. Build Section 3: The Local Reasoner, Stage Two Only signals that survive Stage 1 reach here. This is where a strict schema and token streaming both matter. python src/schemas.py from future import annotations from pydantic import BaseModel, Field class RecentChangeEvent BaseModel : wiki: str user: str title: str is anonymous: bool is bot: bool old length: int new length: int timestamp: float comment: str = "" @property def bytes removed self - int: return max 0, self.old length - self.new length class FilterSignal BaseModel : event: RecentChangeEvent bytes removed: int recent edit count: int reason: str class AgentVerdict BaseModel : """The structured judgment we force the local model to return. Constraining this with a schema is what makes the output usable in code rather than just readable by a human.""" is likely vandalism: bool severity: int = Field ge=1, le=5, description="1 = probably fine, 5 = high confidence vandalism" reasoning: str suggested action: str src/agent.py from typing import AsyncIterator import ollama from .schemas import FilterSignal, AgentVerdict from . import config SYSTEM PROMPT = """You are a Wikipedia edit-monitoring assistant. You will be \ shown metadata about an edit that tripped an automated filter for a large \ deletion or unusually rapid editing. Decide whether this looks like likely \ vandalism or a legitimate edit a rewrite, a cleanup, a merge . Respond with \ a JSON object matching the required schema. Be specific in your reasoning, \ reference the actual numbers you were given.""" def build user prompt signal: FilterSignal - str: e = signal.event return f"Page: {e.title}\n" f"User: {e.user} {'anonymous' if e.is anonymous else 'registered'} \n" f"Bytes removed: {signal.bytes removed}\n" f"Recent edit count by this user: {signal.recent edit count}\n" f"Edit summary left by user: \"{e.comment or ' none '}\"\n" f"Trigger reason: {signal.reason}\n" async def evaluate signal signal: FilterSignal - AsyncIterator str | AgentVerdict : """Streams the model's raw output as it's generated str chunks , then yields a final validated AgentVerdict once the stream completes. The caller tells the two apart with isinstance .""" client = ollama.AsyncClient host=config.OLLAMA HOST stream = await client.chat model=config.OLLAMA MODEL, messages= {"role": "system", "content": SYSTEM PROMPT}, {"role": "user", "content": build user prompt signal }, , format=AgentVerdict.model json schema , stream=True, options={"temperature": 0.1}, full text = "" async for chunk in stream: piece = chunk "message" "content" full text += piece if piece: yield piece live token, for the broadcaster to forward immediately verdict = AgentVerdict.model validate json full text yield verdict What this does: format=AgentVerdict.model json schema is the detail that makes this a senior-grade agent rather than a chatbot with extra steps. Ollama enforces that schema directly on generation, so the completed response is guaranteed valid JSON matching AgentVerdict, not " usually valid JSON I then have to defensively parse. " evaluate signal still streams every raw chunk out as it arrives, yielding plain strings for live display, and only yields the final, validated AgentVerdict object once the full stream completes, which is what lets a connected client watch the reasoning appear in real time while the calling code downstream still gets a fully type-checked object to act on. Build Section 4: Broadcasting Live Reasoning to Clients python src/broadcaster.py import asyncio import json from typing import AsyncIterator class Broadcaster: def init self, max queue size: int = 100 : self. subscribers: set asyncio.Queue = set self.max queue size = max queue size def subscribe self - asyncio.Queue: queue: asyncio.Queue = asyncio.Queue maxsize=self.max queue size self. subscribers.add queue return queue def unsubscribe self, queue: asyncio.Queue - None: self. subscribers.discard queue async def publish self, payload: dict - None: """Fans a payload out to every subscriber. A subscriber whose queue is full gets the message dropped rather than blocking the whole pipeline, a slow client should never be able to slow down the agent's actual processing loop.""" message = json.dumps payload for queue in list self. subscribers : try: queue.put nowait message except asyncio.QueueFull: continue async def stream self - AsyncIterator str : """An async generator a caller can loop over to receive messages, used directly by the SSE endpoint in main.py.""" queue = self.subscribe try: while True: message = await queue.get yield message finally: self.unsubscribe queue What this does: each connected client gets its own asyncio.Queue , and publish fans a message out to every queue independently using put nowait wrapped in a try/except , so one slow or stalled subscriber degrades gracefully by silently dropping a message for that client instead of ever blocking the loop that's actually processing live Wikipedia edits. That separation matters more than it looks like it should: without it, a single slow browser tab could quietly stall the entire agent. One genuinely useful thing testing this surfaced: stream is an async generator, and async generators are lazy; the subscribe call inside it doesn't actually run until something first calls anext on it. In the real FastAPI endpoint, this is a non-issue since iteration starts immediately, but it's exactly the kind of subtlety that catches people writing their own tests for this pattern, and it caught mine on the first attempt before I fixed the test itself. Wiring It Together python src/main.py import asyncio import logging from contextlib import asynccontextmanager from fastapi import FastAPI, Request from sse starlette.sse import EventSourceResponse from .broadcaster import Broadcaster from .filters import Stage1Filter from .stream source import wikipedia event stream from .agent import evaluate signal from .schemas import AgentVerdict logging.basicConfig level=logging.INFO logger = logging.getLogger "streaming-local-agent" broadcaster = Broadcaster stage1 = Stage1Filter async def run pipeline - None: """Consumes the live stream forever, runs stage 1 on every event, and only calls the LLM stage on events that survive it.""" async for event in wikipedia event stream : signal = stage1.evaluate event if signal is None: continue logger.info "Stage 1 flagged: %s by %s %s ", signal.event.title, signal.event.user, signal.reason await broadcaster.publish {"type": "flagged", "title": signal.event.title, "reason": signal.reason} try: async for item in evaluate signal signal : if isinstance item, str : await broadcaster.publish {"type": "token", "title": signal.event.title, "text": item} elif isinstance item, AgentVerdict : await broadcaster.publish { "type": "verdict", "title": signal.event.title, "user": signal.event.user, item.model dump , } except Exception: logger.exception "Stage 2 failed for %s, skipping this signal", signal.event.title @asynccontextmanager async def lifespan app: FastAPI : task = asyncio.create task run pipeline logger.info "Streaming local agent started, watching for edits..." yield task.cancel logger.info "Streaming local agent shutting down" app = FastAPI title="Streaming Local Agent", lifespan=lifespan @app.get "/events" async def events request: Request : async def event generator : async for message in broadcaster.stream : if await request.is disconnected : break yield message return EventSourceResponse event generator @app.get "/health" def health : return {"status": "ok"} What this does: run pipeline is the actual spine of the whole service; everything above is a supporting cast. It's wrapped in a try/except around the Stage 2 call specifically, so one malformed model response or one Ollama hiccup logs an error and moves on to the next event instead of silently killing the background task and leaving the agent running but permanently blind. The lifespan context manager starts that pipeline as a background task the moment the app boots and cancels it cleanly on shutdown, the correct modern FastAPI pattern rather than the older @app.on event decorators. The /events route is where everything converges: opening it streams every flagged , token , and verdict message live as newline-delimited SSE data, and checking request.is disconnected on every loop means a closed browser tab gets cleaned up instead of leaking a queue forever. // How to Run It With Ollama installed and a model pulled: ollama pull llama3.1:8b ollama serve if it isn't already running as a background service Then, from the project root: python -m venv venv source venv/bin/activate pip install -r requirements.txt uvicorn src.main:app --reload With that running, open a second terminal and watch the live feed: curl -N http://localhost:8000/events Or point a browser tab at http://localhost:8000/events directly; most browsers render an SSE stream as plain text arriving incrementally. Within a few minutes on an active wiki, you should see flagged messages arrive as Stage 1 catches large deletions or edit bursts, followed by a stream of token messages as the local model reasons about it live, ending in a verdict message with a structured severity score. Boring edits, the vast majority of the traffic, never appear at all, which is exactly the point. A Note on Scaling This Up The in-process asyncio.Queue broadcaster and the single background task in this build are the right amount of infrastructure for one machine watching one stream. At real production scale, watching multiple sources, running multiple consumer processes, surviving a service restart without losing in-flight events, the natural upgrade is swapping the direct stream connection and in-memory broadcaster for a real message bus like Kafka sitting between the producer and the reasoning stage. Wrapping Up The actual lesson underneath all of this code isn't about Wikipedia, or Ollama, or FastAPI specifically, it's that efficiency stops being an optimization you bolt on later, the moment an agent goes from " answers when asked " to " always on ." A chat agent that sits idle costs nothing. A streaming agent is, by definition, always consuming something, and every design choice in this build, the two-stage funnel, the bounded-memory eviction, the graceful degradation on a slow subscriber, the automatic reconnect on a dropped connection, exists because an always-on system that can't sustain itself indefinitely isn't actually done, no matter how well it worked in the first five minutes you watched it run. is a software engineer and technical writer passionate about leveraging cutting-edge technologies to craft compelling narratives, with a keen eye for detail and a knack for simplifying complex concepts. 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