cyrrus

Context routing for LLM calls.

Instead of sending a full static system prompt every time, cyrrus decides what context is relevant right now and sends only that.

from cyrrus import Projector
from cyrrus.providers import ollama

bot = Projector.minimal(llm_call=ollama("llama3.2"))
reply = bot.ask("hello")

That's it. No config required to start.

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What it does

Every time a user sends a message, cyrrus:

1. Routes — matches the message against your slides to find what's relevant
2. Remembers — pulls stored facts about this user that relate to this message
3. Packs — fits the relevant context into your token budget, highest priority first
4. Sends — builds a proper messages array and calls your LLM
5. Learns — extracts new facts from the conversation and stores them for later

The LLM receives clean, relevant context. Not a wall of everything you've ever defined.

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Numbers

• 51.6% token reduction on average across mixed conversations (real tiktoken counts)
• 0.018 MB core install — zero required dependencies
• 121.9 MB / 241.7 MB RAM for the optional embeddings block
• 500 concurrent users with zero errors
• Surviving every adversarial test thrown at it

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Install

pip install cyrrus                  # core, zero dependencies
pip install cyrrus[embeddings]      # + semantic routing and memory

The import is always:

from cyrrus import Projector

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Three ways to use it

Zero config

from cyrrus import Projector
from cyrrus.providers import ollama

bot = Projector.minimal(llm_call=ollama("llama3.2"))
reply = bot.ask("write me a python function")

With context routing

config = {
    "core_lamp": {
        "content": "You are a helpful assistant.",
    },
    "code_lens": {
        "content": "Output only clean code blocks, no filler.",
        "triggers": ["code", "script", "python", "function", "bug"],
    },
    "casual_lens": {
        "content": "Keep it short and natural.",
        "triggers": ["hey", "hi", "hello"],
    },
}

bot = Projector(config, llm_call=ollama("llama3.2"))
reply = await bot.process("write a sorting function", session_id=str(user_id))

tokens and priority are optional — cyrrus fills them in automatically.

Individual components

from cyrrus import MemoryVault, IntentRouter, SlideTray

# Use any component standalone
memory = MemoryVault()
facts = await memory.retrieve("user_123", "what is my name", limit=3)

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Providers

Ready-made wrappers for common APIs. Pass them directly as llm_call:

from cyrrus.providers import ollama, openai, anthropic, groq

# Local Ollama — free, no API key
bot = Projector(config, llm_call=ollama("llama3.2"))

# OpenAI
bot = Projector(config, llm_call=openai("gpt-4o-mini", api_key="sk-..."))

# Anthropic
bot = Projector(config, llm_call=anthropic("claude-haiku-4-5", api_key="sk-..."))

# Groq — fast, generous free tier
bot = Projector(config, llm_call=groq("llama-3.1-8b-instant", api_key="..."))

Or bring your own — any async function that takes a messages list and returns a string:

async def my_llm(messages: list) -> str:
    response = await client.chat.completions.create(
        model="my-model",
        messages=messages,
    )
    return response.choices[0].message.content

bot = Projector(config, llm_call=my_llm)

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Memory

cyrrus remembers things users tell it. No setup needed — the default extractor runs automatically:

User: "my name is Muratha"          → stored: user_name = Muratha
User: "I'm building cyrrus"         → stored: user_project = cyrrus
User: "I prefer Ollama"             → stored: user_preference = Ollama

On the next message, relevant facts are retrieved and injected into the system prompt automatically.

With cyrrus[embeddings]: retrieval uses semantic similarity, so "what am I working on" finds user_project: cyrrus even without shared keywords.

Without: keyword overlap matching. Fast, zero dependencies, catches most cases.

Memory is per session_id. Facts never leak between users.

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Conversation history

cyrrus maintains a sliding window of conversation history per session. The LLM always sees recent context, so follow-up questions work:

User: "what's the capital of France?"
Bot:  "Paris."
User: "what's the population there?"   ← LLM knows "there" means Paris
Bot:  "Around 2.1 million in the city proper."

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What cyrrus is not

• Not an agent framework. It controls input, never output.
• Not a vector database. Memory is SQLite with semantic retrieval optional.
• Not a replacement for LangChain. It's a layer, not a framework.
• Not magic. Keyword routing misses natural phrasing. Install cyrrus[embeddings] if you need paraphrase matching.

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session_id

This is important. Every user needs a unique session_id:

# Discord bot
reply = await bot.process(message.content, session_id=str(message.author.id))

# Web app
reply = await bot.process(user_input, session_id=request.session["user_id"])

# Single-user script — use ask() and don't worry about it
reply = bot.ask("hello")

Without a unique session_id, users share memory. That's a bug, not a feature.

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Tracing

Every call stores a trace:

await bot.process("write a script", session_id="u1")

trace = bot.last_trace
print(trace["routed_slide_ids"])        # which slides matched
print(trace["memory_slide_ids"])        # which facts were retrieved  
print(trace["dropped_slide_ids"])       # what didn't fit the budget
print(trace["messages"])                # exact messages sent to LLM
print(trace["stats"]["history_turns"])  # how many turns of history

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License

AGPLv3. If you run cyrrus as part of a network service, you must make your modifications available. See LICENSE.

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cyrrus — formerly known as slid3s