LiteLLM & Anthropic - xysq documentation

xysq plugs into the loop you already have. You keep your model, your messages, your control flow; xysq is two tools the model can call — pull context before it answers, push context after — bound to one vault. This works with any LiteLLM-compatible model (GPT, Claude, Gemini, Mistral, …) and with the Anthropic SDK directly.

pip install 'xysq[agent]'   # LiteLLM
pip install 'xysq[claude]'  # Anthropic SDK

LiteLLM tools

XysqLiteLLMTools gives you OpenAI-compatible tool definitions and an executor. You run the loop; the model decides when to remember and recall.

from xysq import Xysq
from xysq.integrations.litellm import XysqLiteLLMTools
import litellm

client = Xysq(api_key="xysq_agent_...")           # an agent key
vault = client.vaults.create("Support Bot")
tools = XysqLiteLLMTools(client, vault.vault_id)   # bind the vault

messages = [{"role": "user", "content": "what's our refund policy?"}]
while True:
    response = litellm.completion(
        model="gpt-4o",
        messages=messages,
        tools=tools.definitions,      # <- xysq's two tools
    )
    msg = response.choices[0].message
    messages.append(msg.model_dump(exclude_none=True))
    if not msg.tool_calls:
        print(msg.content)
        break
    messages.extend(tools.execute(msg.tool_calls))   # <- runs pull/push

The model sees two tools:

Tool	What it does
`xysq_pull_context`	Pull relevant context from the vault before answering
`xysq_push_context`	Push what happened back, verbatim, so it persists

The vault_id is bound when you construct XysqLiteLLMTools — the model decides when to remember and recall, never which vault.

Anthropic SDK

Same idea, in Anthropic tool_use format:

import anthropic
from xysq import Xysq
from xysq.integrations.anthropic import XysqAnthropicTools

client = Xysq(api_key="xysq_agent_...")
vault = client.vaults.create("Support Bot")
tools = XysqAnthropicTools(client, vault.vault_id)
ac = anthropic.Anthropic(api_key="sk-ant-...")

messages = [{"role": "user", "content": "what's our refund policy?"}]
while True:
    msg = ac.messages.create(
        model="claude-sonnet-4-20250514",
        max_tokens=1024,
        tools=tools.definitions,
        messages=messages,
    )
    messages.append({"role": "assistant", "content": msg.content})
    if msg.stop_reason != "tool_use":
        print(next(b.text for b in msg.content if hasattr(b, "text")))
        break
    messages.append({"role": "user", "content": tools.execute(msg.content)})

XysqAgent — the batteries-included wrapper

If you don’t want to run the loop yourself, XysqAgent wraps a LiteLLM model. The conversation itself lives in a server-side thread — not in the Python process — so history survives restarts and is shared across instances, and every turn is promoted into the vault’s long-term memory automatically. On each chat() it stores your message, pulls relevant long-term context, rebuilds the window from the server, calls the model, and stores the reply:

from xysq import Xysq, XysqAgent

client = Xysq(api_key="xysq_agent_...")
vault = client.vaults.create("Support Bot")

agent = XysqAgent(
    client=client,
    vault_id=vault.vault_id,
    model="claude-sonnet-4-20250514",
    api_key="sk-ant-...",
    system_prompt="You are a helpful support agent.",
    thread_id="ticket-4471",   # optional: reuse an id to RESUME a conversation
)

print(agent.chat("what's our refund policy?"))
# a crash after this line loses nothing: the question was stored before
# the model ran, and a fresh agent on the same thread_id picks up mid-talk

recall=False skips the long-term pull, recall_limit caps injected context, history_turns sizes the conversation window (the current message plus up to N−1 prior turns). clear_history() ends the conversation: it flushes to long-term memory first, then resets the working window.

Vaults

The vault API the tools are built on: create, push, pull, delete

Getting started

Install the SDK and push your first vault

​LiteLLM tools

​Anthropic SDK

​XysqAgent — the batteries-included wrapper

​Related

Vaults

Getting started

LiteLLM tools

Anthropic SDK

XysqAgent — the batteries-included wrapper

Related