Baar-Core

Pre-flight runtime governance for LLM agents. Enforces hard execution limits locally — before requests reach the provider.

Stop LLM API calls before they happen. Not after.

pip install baar-core

I left an agent loop running overnight. Woke up to a $47 bill — 20,000 GPT-4o tokens answering "what time is it?" queries.

Baar-Core would have stopped it at $0.10. Before the first overage call. No network request made. $0 spent.

from baar import BAARRouter

router = BAARRouter(budget=0.10)          # hard cap: $0.10 total
router.chat("What time is it?")           # → cheap model, ~$0.0001
router.chat("Write a CUDA matmul kernel") # → capable model if budget allows
# budget exhausted → raises BudgetExhausted, zero API calls made

84–94% cost reduction in benchmarks — see benchmarks

Built for

• Teams deploying autonomous agents in production
• SaaS founders giving users LLM access with per-user quotas
• Anyone who has had (or fears) a runaway agent bill

The problem with every other solution

Most cost tools track spend after the fact. You get an alert when the bill is already large.

LiteLLM's budget manager, Portkey rate limits, provider spend alerts — they all tell you what happened. They don't stop it mid-flight.

Helicone observes the disaster. Portkey rate-limits after the fact. Baar prevents it.

Baar-Core is a local kill-switch. Before each call, it estimates the cost. If the remaining budget is too low, it raises an exception locally — no DNS lookup, no TCP connection, no token consumed. The call never leaves your machine.

How it works

User task
    │
    ▼
┌─────────────────────────────────┐
│  Pre-flight budget check        │ ← if estimated cost > remaining budget
│  (local, zero network)          │   raise BudgetExhausted immediately
└────────────┬────────────────────┘
             │ affordable
             ▼
┌─────────────────────────────────┐
│  Semantic complexity router     │ ← cheap LLM scores complexity 0.0–1.0
│  (gpt-4o-mini, ~$0.000015/call) │
└────────────┬────────────────────┘
             │
      ┌──────┴───────┐
      │              │
   simple         complex
      │              │
      ▼              ▼
 Cheap model    Budget check
 (fast, $)      ├─ affordable → Capable model ($$$)
                └─ too close  → Downgrade to cheap model ($)

1. Pre-flight check — Estimates cost locally before any network call. Kills the request if it would overshoot.
2. Semantic routing — A fast, cheap model scores task complexity. Not keyword matching — actual semantic understanding.
3. Budget-aware downgrade — Running low? Hard tasks automatically fall back to the cheaper model so the turn still completes.

Quick start

from baar import BAARRouter, BudgetExhausted

# Basic usage
router = BAARRouter(budget=0.10)
reply = router.chat("Explain recursion with a Python example")
print(reply)
print(f"Spent: ${router.spent:.5f} / Remaining: ${router.remaining:.5f}")

# Multi-step with a report
log = router.run([
    "What is 42 * 17?",
    "Translate 'good morning' to Japanese",
    "Design a distributed rate-limiter for 100k RPS — include trade-offs",
    "Convert 72°F to Celsius",
])
log.print_report()

# Async
import asyncio
async def main():
    router = BAARRouter(budget=0.05)
    reply = await router.achat("Summarize the CAP theorem")
    print(reply)

asyncio.run(main())

# Kill-switch in action
router = BAARRouter(budget=0.00001)
try:
    router.chat("Any prompt at all")
except BudgetExhausted as e:
    print(f"Blocked before API call. Remaining: ${e.remaining:.6f}")
    # Zero network calls made. $0 spent.

Works with any LiteLLM-supported provider: OpenAI, Anthropic, Groq, Together, Ollama, OpenRouter, Azure, and more.

LangChain & LangGraph integration

pip install baar-core[langchain]

Pattern 1 — add a kill-switch to an existing chain (zero refactoring):

from baar import BAARRouter
from baar.integrations.langchain import BaarCallbackHandler
from langchain_openai import ChatOpenAI

router  = BAARRouter(budget=0.05)
handler = BaarCallbackHandler(router)

llm   = ChatOpenAI(model="gpt-4o", callbacks=[handler])
chain = prompt | llm | StrOutputParser()
# BudgetExhausted is raised before the API call when budget is too low

Pattern 2 — BaarChatModel as a drop-in ChatModel (full routing + downgrade):

from baar.integrations.langchain import BaarChatModel

router = BAARRouter(budget=0.10, small_model="gpt-4o-mini", big_model="gpt-4o")
llm    = BaarChatModel(router=router)

chain = prompt | llm | StrOutputParser()   # works anywhere ChatOpenAI would

Pattern 3 — LangGraph agent with budget protection:

from langgraph.prebuilt import create_react_agent

agent = create_react_agent(model=llm, tools=[...])
# every tool-call iteration is budget-checked before the LLM is invoked
# BudgetExhausted stops the loop locally — no dangling API calls

Full example: langchain_guardrail.py

LangGraph middleware

BaarMiddleware wraps any compiled LangGraph graph and enforces hard limits at the outer loop level — before a single node executes.

from baar import BAARRouter
from baar.integrations.langgraph import BaarMiddleware, StepLimitExceeded

router = BAARRouter(budget=0.50)
graph  = workflow.compile()          # your compiled LangGraph graph

protected = BaarMiddleware(
    graph=graph,
    router=router,
    max_steps=10,        # stop after 10 outer invocations
    max_budget=0.05,     # block if less than $0.05 remaining
    recursion_limit=25,  # injected into LangGraph config automatically
)

try:
    result = protected.invoke({"messages": [...]})
    print(f"Steps used: {protected.steps}/10")
except StepLimitExceeded as e:
    print(f"Stopped at {e.steps}/{e.max_steps} steps")
except BudgetExhausted as e:
    print(f"Budget gate: ${e.remaining:.6f} remaining")

Works with all four call modes: invoke, stream, ainvoke, astream.

Full example: langgraph_middleware.py

Tool execution guards

@baar_guard wraps any tool function — sync or async — with hard call limits and budget gates. Works with Claude, OpenAI/Codex, LangChain, LangGraph, or plain Python. The AI never knows the guard is there.

from baar.integrations.tools import baar_guard, ToolCallLimitExceeded

@baar_guard(router=router, max_calls=5, cost_per_call=0.001)
def web_search(query: str) -> str:
    return search_api(query)

@baar_guard(router=router, max_calls=3, min_budget=0.02)
async def generate_image(prompt: str) -> str:
    return await image_api(prompt)

• max_calls — hard call count limit per session. None = unlimited.
• min_budget — block if router.remaining drops below this threshold.
• cost_per_call — deduct a fixed amount from budget on each successful call. No charge on failure.

Catch limit exceptions in your agent dispatcher so the AI gets a readable error instead of a crash:

try:
    result = web_search(query)
except ToolCallLimitExceeded as e:
    result = f"Tool '{e.tool_name}' limit reached ({e.calls}/{e.max_calls})."
except BudgetExhausted as e:
    result = f"Budget too low. Remaining: ${e.remaining:.6f}."

Works with OpenAI Codex CLI — set openai_base_url = "http://localhost:8000/v1" in ~/.codex/config.toml to route all Codex calls through Baar.

Full examples: tool_guard.py · claude_tool_guard.py · openai_tool_guard.py

Loop detection

A budget stops a looping agent once the money is gone. LoopDetector stops it after a few wasted calls. It watches what each tool call returned, not just that it was called, so an agent repeating a call that keeps giving the same answer is stopped while the same call making progress (a job going queued → running → done, a search with a new cursor) is left alone.

from baar import BAARRouter, LoopDetector, LoopDetected
from baar.integrations.tools import baar_guard

router = BAARRouter(budget=0.50)
loops = LoopDetector(on_loop=lambda e: log.warning("possible loop: %s", e))  # one per run

@baar_guard(router=router, loop_detector=loops)
def web_search(query: str) -> str:
    return search_api(query)

@baar_guard(router=router, loop_detector=loops)
def read_file(path: str) -> str:
    return open(path).read()

try:
    result = web_search(query)
except LoopDetected as e:
    result = f"Stopped: '{e.tool_name}' {e.pattern} loop ({e.count}x). Try a different approach."

It refuses the call that would continue a loop before it runs, and warns on_loop one step earlier. Three patterns, with defaults from OpenHands' StuckDetector:

• exempt_tools=["job_status"] — never count a tool that legitimately returns the same thing many times (status polls).
• ignore_args=["request_id", "timestamp"] — drop volatile arguments, at any depth, before comparing.
• normalize_result=fn — map a result before hashing, e.g. to drop a timestamp it carries.
• loops.tighten() — lower every threshold by one after summarizing the agent's history (summarization roughly doubles looping, per DeLoop).
• Not a framework user? Call loops.before(tool, args) and loops.after(tool, args, result=...) (or error=exc) yourself.

What it can't catch: a loop that rephrases itself every step (new wording, same empty result) never repeats exactly. Keep a run budget for those: the two together stop every loop in the eval below without stopping a healthy run.

Loop eval

python benchmarks/loop_eval.py replays 180 labeled, deterministic agent traces (no API keys): 75 loops (exact repeats, error retries, ping-pong, repeats with a changing request id, rephrased) and 105 healthy runs built to look like loops (polling, pagination, rate-limit retries, a flaky API failing on and off, re-reading a changed file, long research). Loops run until something stops them; the run budget is sized above the longest healthy run.

max_calls wastes less money only by also stopping 41% of healthy runs (pagination, long research, the flaky API). Counting calls without their results stops every flaky-API run; the detector stops none, because errors only count in a row. detector+budget is how you'd ship it: exempt_tools=["job_status"] plus a $0.40 run budget. Without the exemption, the 6-identical-result polls are stopped (the 14.3% false stops above). Overhead is about 10 µs per tool call. The headline holds for every generator seed from 1 to 50 (100% caught, 0% false stops, median 3 wasted calls). Per-category results: python benchmarks/loop_eval.py.

MCP server (Claude Code / Cursor)

examples/mcp_server.py exposes three budget-guarded tools to any MCP client — no API key required on the client side.

# pip install baar-core mcp firecrawl-py

from baar import BAARRouter
from baar.integrations.tools import baar_guard

router = BAARRouter(budget=0.10, use_llm_router=False)

@baar_guard(router=router, max_calls=20, cost_per_call=0.001)
async def web_search(query: str) -> str: ...

@baar_guard(router=router, max_calls=10, min_budget=0.005)
async def run_python(code: str) -> str: ...

Register with Claude Code — add to ~/.claude/claude.json:

{
  "mcpServers": {
    "baar": {
      "command": "python3",
      "args": ["/path/to/examples/mcp_server.py"],
      "env": { "BAAR_BUDGET": "0.10" }
    }
  }
}

Claude Code will show get_budget_status, web_search, and run_python as tools automatically. Every call is budget-gated — the session stops spending the moment the cap is hit.

OpenAI-compatible HTTP server (Vercel AI SDK, LlamaIndex, curl)

pip install baar-core[vercel]

Wraps BAARRouter as a /v1/chat/completions endpoint — any OpenAI-compatible client works without code changes.

from baar import BAARRouter
from baar.integrations.vercel import create_app
import uvicorn

router = BAARRouter(budget=0.10)
app    = create_app(router, api_key="your-secret")  # api_key optional

uvicorn.run(app, host="0.0.0.0", port=8000)

Vercel AI SDK (TypeScript):

import { createOpenAI } from '@ai-sdk/openai';
import { streamText } from 'ai';

const baar = createOpenAI({ baseURL: 'http://localhost:8000/v1', apiKey: 'your-secret' });

const { textStream } = streamText({
    model: baar('baar'),
    messages: [{ role: 'user', content: 'Hello!' }],
});

LlamaIndex (Python):

# pip install llama-index-llms-openai
from llama_index.llms.openai import OpenAI as LlamaOpenAI
from llama_index.core import Settings

llm = LlamaOpenAI(
    model="baar",
    api_base="http://localhost:8000/v1",
    api_key="your-secret",
)

Settings.llm = llm  # drop-in for any LlamaIndex agent, RAG pipeline, or query engine
response = llm.complete("Explain the GIL in Python")

Budget errors surface as standard HTTP codes — 402 when the budget is exhausted, 422 when the value gate rejects the task. Streaming errors are delivered inside the SSE stream so the connection stays clean.

Real-world examples

Multi-tenant & per-user budgets

Give each user their own hard quota — enforced locally, persisted across restarts, no proxy or external service required.

from baar import BAARRouter, BudgetExhausted
from baar.core.stores import SQLiteBudgetStore

def router_for(user_id: str) -> BAARRouter:
    return BAARRouter(
        budget=0.10,
        store=SQLiteBudgetStore("budgets.db", namespace=user_id),
    )

# Alice and Bob share one database file — budgets are fully isolated
alice = router_for("alice")
bob   = router_for("bob")

alice.chat("Summarise this document")  # deducted from Alice's $0.10 only
bob.chat("Translate to French")        # Bob's quota untouched

Namespace isolation is enforced at the store level — one user exhausting their quota cannot affect another's. Concurrent writes across threads and across OS processes (gunicorn/uvicorn workers) are safe: SQLiteBudgetStore uses WAL mode and an exclusive transaction for the check-and-reserve step, eliminating the TOCTOU race that affects most multi-tenant budget implementations.

SaaS pattern — return HTTP 402 when a user's quota is exhausted:

@app.post("/chat")
async def chat(req: ChatRequest):
    try:
        reply = await router_for(req.user_id).achat(req.message)
    except BudgetExhausted as e:
        raise HTTPException(status_code=402, detail={
            "error": "quota_exhausted",
            "remaining_usd": e.remaining,
        })
    return {"reply": reply}

No billing service. No proxy. One SQLite file. Full example: fastapi_per_user_budget.py

Time-windowed quotas — scope budgets to a day or month by encoding the period in the namespace:

import datetime
period = datetime.date.today().strftime("%Y-%m")   # "2026-05"
store  = SQLiteBudgetStore("budgets.db", namespace=f"user:{user_id}:{period}")
# quota resets automatically next month — no cron job needed

Single-process scripts — JSON file store, no SQLite needed:

from baar.core.stores import FileBudgetStore

router = BAARRouter(budget=1.00, store=FileBudgetStore("my_budget.json"))

Benchmarks

Routing cost benchmark — mock mode

Mock mode runs the full routing pipeline with simulated completions to measure routing overhead and cost allocation without spending real money. Use this to tune thresholds before a live run.

Note: Accuracy figures in mock mode reflect simulated task responses, not real model capability. Use live mode for accuracy measurement. The cost figures and routing split percentages are the meaningful outputs here.

baar-bench --dataset all --limit 200 --budget 10 --mock \
  --complexity-threshold 0.80 --coding-threshold 0.75 --seed 42

HumanEval routes fewer tasks to the cheap tier because coding questions score high complexity — the router correctly identifies them as hard.

Live benchmark — real API calls (10 tasks per dataset)

baar-bench --dataset all --limit 10 --budget 2 \
  --complexity-threshold 0.80 --coding-threshold 0.75 --seed 42

Run it yourself: pip install baar-core datasets then baar-bench --limit 10 --mock (free) or add your API key for live results.

vs. alternatives

The key difference: every alternative routes and tracks. Baar-Core prevents — the exception is raised before a single byte leaves your machine.

Security

Baar-Core maps to OWASP LLM10:2025 — Unbounded Consumption. The pre-flight kill-switch is a direct mitigation for Denial-of-Wallet attacks: even if an adversary crafts a prompt designed to trigger expensive model calls, the local budget cap catches it before any provider request is made.

Details: RESEARCH.md

Configuration

router = BAARRouter(
    budget=0.10,                    # hard cap in USD
    small_model="gpt-4o-mini",      # cheap tier (any LiteLLM model)
    big_model="gpt-4o",             # capable tier
    complexity_threshold=0.80,      # 0.0–1.0: higher = more traffic to cheap model
    min_cost_threshold=0.0001,      # kill-switch floor — reject if any call costs more
    routing_task_char_limit=500,    # chars sent to routing LLM (head+mid+tail sample)
    use_llm_router=True,            # False = rule-based heuristic only (no routing cost)
    small_fallback_models=["gpt-4o-mini-2024-07-18"],  # failover chain
    big_fallback_models=["gpt-4o-2024-08-06"],
    telemetry_jsonl_path="telemetry.jsonl",  # optional audit log
)

Budget pressure — as spend approaches the cap, the effective complexity threshold rises automatically. The big model becomes harder to justify as you run low, so more traffic shifts to cheap naturally.

Telemetry — inspect spend, routing splits, and reject rates:

baar-telemetry telemetry.jsonl

Resilience testing — adversarial scenarios (complexity games, tight budgets, padding attacks):

baar-stress

License

MIT — LICENSE