Agent cost control

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
    base_url="https://api.prysm1.com/v1",
    api_key="prysm_sk_your_key",
    model="auto",
    default_headers={
        "X-Max-Cost-USD": "0.005",   # This call can never exceed $0.005
    },
)

response = llm.invoke("Summarize the quarterly report in 3 bullet points")
print(response.response_metadata)   # Includes prysm.routing, prysm.cost, prysm.proof

from langchain_openai import ChatOpenAI
from langchain.agents import create_react_agent, AgentExecutor
from langchain.tools import Tool

llm = ChatOpenAI(
    base_url="https://api.prysm1.com/v1",
    api_key="prysm_sk_your_key",
    model="auto",
    default_headers={"X-Max-Cost-USD": "0.01"},
)

tools = [Tool(name="search", func=lambda q: "...", description="Web search")]
agent = create_react_agent(llm, tools, prompt=...)
executor = AgentExecutor(agent=agent, tools=tools, max_iterations=10)
result = executor.invoke({"input": "Research the top 5 AI companies"})

import httpx
from langchain_openai import ChatOpenAI

class BudgetCheckTransport(httpx.HTTPTransport):
    def handle_request(self, request):
        response = super().handle_request(request)
        remaining = response.headers.get("X-Prysm-Budget-Remaining")
        if remaining and float(remaining) < 0.01:
            print(f"[cost-guard] budget almost exhausted: ${remaining} remaining")
        return response

llm = ChatOpenAI(
    base_url="https://api.prysm1.com/v1",
    api_key="prysm_sk_your_key",
    model="auto",
    http_client=httpx.Client(transport=BudgetCheckTransport()),
)

from crewai import Agent, Task, Crew
from langchain_openai import ChatOpenAI

def make_agent(role, goal, backstory, max_cost_per_call=0.005):
    llm = ChatOpenAI(
        base_url="https://api.prysm1.com/v1",
        api_key="prysm_sk_your_key",
        model="auto",
        default_headers={"X-Max-Cost-USD": str(max_cost_per_call)},
    )
    return Agent(role=role, goal=goal, backstory=backstory, llm=llm)

researcher = make_agent("Researcher", "Find top AI startups", "...", max_cost_per_call=0.003)
writer     = make_agent("Writer",     "Write the report",      "...", max_cost_per_call=0.015)

crew = Crew(
    agents=[researcher, writer],
    tasks=[
        Task(description="Research AI startups", agent=researcher, expected_output="List of 5"),
        Task(description="Write report",          agent=writer,     expected_output="2-page report"),
    ],
)
result = crew.kickoff()

import openai

client = openai.OpenAI(base_url="https://api.prysm1.com/v1", api_key="prysm_sk_your_key")

run = client.chat.completions.create(
    model="auto",
    messages=[{"role": "user", "content": "Research AI startups and write a report"}],
    extra_body={
        "agent": True,
        "max_cost_usd": 0.10,   # Hard ceiling for the entire crew run
        "max_steps": 20,
    },
)
prysm = run.model_extra.get("prysm", {})
print(f"Spent: ${prysm.get('cost', {}).get('total_usd', 0):.4f}")
print(f"Trajectory proof: {prysm.get('trajectory_proof')}")

import autogen

config = {
    "model": "auto",
    "api_key": "prysm_sk_your_key",
    "base_url": "https://api.prysm1.com/v1",
    "default_headers": {"X-Max-Cost-USD": "0.01"},
}

assistant = autogen.AssistantAgent("assistant", llm_config={"config_list": [config]})
user_proxy = autogen.UserProxyAgent(
    "user", human_input_mode="NEVER", max_consecutive_auto_reply=5, code_execution_config=False
)
user_proxy.initiate_chat(assistant, message="What are the main AI routing strategies?")

import autogen

def make_config(max_cost: float) -> dict:
    return {"config_list": [{
        "model": "auto",
        "api_key": "prysm_sk_your_key",
        "base_url": "https://api.prysm1.com/v1",
        "default_headers": {"X-Max-Cost-USD": str(max_cost)},
    }]}

planner  = autogen.AssistantAgent("planner",  llm_config=make_config(0.002))
coder    = autogen.AssistantAgent("coder",    llm_config=make_config(0.015))
reviewer = autogen.AssistantAgent("reviewer", llm_config=make_config(0.010))

from openai import OpenAI
from agents import Agent, Runner

client = OpenAI(
    base_url="https://api.prysm1.com/v1",
    api_key="prysm_sk_your_key",
    default_headers={"X-Max-Cost-USD": "0.02"},
)

agent = Agent(name="assistant", instructions="Be helpful and concise.", model="auto", client=client)
result = Runner.run_sync(agent, "Explain quantum computing in 3 sentences")
print(result.final_output)

max_cost_per_request: 0.005
rules:
  - when: "code"
    model: "deepseek-v3.2"
  - when: "analysis"
    model: "gpt-5-mini"
fallback: "deepseek-v3.2"