Your AI agent has no cost guard. Here's the fix.

Why this exists

The $400 wake-up call

An agent hit a PDF API returning 429s. No circuit breaker. No cost guard. No DLQ. Here's what the billing alert looked like.

02:14 · BILLING ALERT: $47 charged this hour
02:15 · BILLING ALERT: $91 charged this hour
02:31 · BILLING ALERT: $203 charged this hour
02:47 · BILLING ALERT: $400 charged since midnight

The framework was fine. The reliability layer didn't exist. 3,000 tasks left in unknown state. The fix was 20 lines of code — a circuit breaker, a cost guard, and a DLQ. We just didn't have them pre-built.

The Audit

We ran lore audit on 10 open-source agent frameworks

Four reliability checks. Ten projects. Here's the scorecard.

Framework	Circuit Breaker	Dead Letter Queue	Cost Guard	Observability	Criticals
OpenAI Swarm	MISSING	MISSING	MISSING	MISSING	1
BabyAGI	MISSING	MISSING	MISSING	partial	2
GPT-Researcher	MISSING	MISSING	MISSING	present	0
CrewAI Examples	MISSING	MISSING	MISSING	MISSING	2
Phidata / Agno	present	partial	partial	present	1
AutoGen (Microsoft)	MISSING	MISSING	MISSING	MISSING	1
LangGraph	partial	MISSING	MISSING	present	0
LlamaIndex	MISSING	MISSING	MISSING	present	0
Pydantic AI	MISSING	MISSING	MISSING	present	0
AutoGPT	MISSING	MISSING	MISSING	MISSING	2

10/10

Cost guard missing

9/10

DLQ missing

8/10

Circuit breaker missing

9

Critical findings total

60-Second Demo

Four commands. Four controls. Any framework.

Each scaffold generates production-ready Python. Drop it into your existing project — LangGraph, CrewAI, AutoGen, or plain Python.

⏱️

Cost Guard

Hard stop before budget burns

$ lore scaffold cost_guard

class CostGuard:
  # hard stop at budget_tokens
  # warns at 80% threshold
  # raises CostGuardExceeded
  # zero external dependencies

guard = CostGuard(budget_tokens=100_000)
guard.consume("llm_call", tokens=2400)

🔴

Circuit Breaker

Stop cascades before they drain budget

$ lore scaffold circuit_breaker

CLOSED → (5 failures) → OPEN
OPEN → (30s wait) → HALF_OPEN
HALF_OPEN → (1 success) → CLOSED

breaker = CircuitBreaker(
name="openai_api",
failure_threshold=5
)
result = await breaker.call(fn)

📦

Dead Letter Queue

Nothing lost silently

$ lore scaffold dead_letter_queue

TRANSIENT → replay queue
  # 429s, timeouts → auto-retry
PERMANENT → human review
  # schema errors → inspect
AMBIGUOUS → one more try
  # modified prompt + replay

# nothing discarded silently

👁️

Observability

Know what happened and why

$ lore scaffold sentinel_observability

{
  "model": "gpt-4.1",
  "latency_s": 1.24,
  "tokens_used": 2847,
  "cost_usd": 0.0028,
  "error_rate_1h": 0.02
}

Getting Started

From zero to production-hardened in 5 steps.

Wrap any agent. Audit any codebase. No code changes needed in your existing agent scripts.

1

Install

Pure Python. No mandatory dependencies. Works on Python 3.10+.

pip install lore-agents

2

Init — generate your lore.yaml

Declares your reliability contracts: budget, circuit breaker, DLQ, observability.

lore init
# creates lore.yaml with all options documented
# budget_tokens: 500000
# circuit_breaker: enabled
# dlq: on_permanent: log

3

Run your agent — any framework

lore run wraps your existing script. Cost guard, circuit breaker, observability enforced automatically.

lore run my_crewai_agent.py --budget 100k
lore run my_langgraph_flow.py
lore run my_autogen_team.py --dry-run
# no changes to your agent code

4

Audit any codebase

Scan for missing patterns. Get a real scorecard — JSON + shareable HTML.

lore audit /path/to/crewai --html
# CRITICAL  Webhook URL lost on HITL resume
# HIGH      No tool-level circuit breaker
# → lore scaffold circuit_breaker  (auto-suggested fix)

5

Monitor live + evolve nightly

Watch cost burn, circuit states, and DLQ depth in real time. Run evolve to find new gaps automatically.

lore monitor
# Cost Burn: 42,331 / 500,000 (8.5%)
# Circuits:  tools CLOSED ✓  search CLOSED ✓
# DLQ depth: 3 (1 permanent, 2 transient)

lore evolve
# Audits analyzed: 18 · Top gap: dead_letter_queue 12x
# Evolution report: .lore/evolution/2026-04-07.md

All Patterns

19 patterns. Every major framework.

Each pattern is a character in the AI Agent Codex. Scaffold any of them with one command.

#	Character	Pattern (scaffold name)	What it solves	Frameworks
⏱️	The Timekeeper	`cost_guard`	Hard-stop token budget before overspend	python
🔴	The Breaker	`circuit_breaker`	Stops cascade failures before they drain budget	pythonlanggraph
📦	The Archivist	`dead_letter_queue`	Captures every failed task, nothing lost silently	python
👁️	The Sentinel	`sentinel_observability`	4 golden signals: error rate, latency, cost, drift	python
⚖️	The Council	`reviewer_loop`	Generate → review → revise quality gate	pythonlanggraphcrewai
👑	The Commander	`supervisor_worker`	Fan-out parallel workers, merge results	pythonlanggraphcrewaiopenai_agents
🧠	The Stack	`three_layer_memory`	Working, episodic, procedural memory across sessions	python
🗺️	The Router	`model_routing`	DeepSeek for triage, GPT-5 for judgment — cost-optimal	pythonopenai_agents
🕸️	The Weaver	`handoff_pattern`	Agent-to-agent context passing without state loss	pythoncrewaiopenai_agents
🛡️	The Warden	`tool_health_monitor`	Proactive tool failure detection before the call	python
📖	The Librarian	`librarian_retrieval`	Hybrid BM25+semantic RAG that actually works	python
🔭	The Scout	`scout_discovery`	Autonomous research loops, finds knowledge gaps	python

+ cartographer_knowledge_graph, timekeeper_scheduling, architect_system_design, alchemist_prompt_routing, react_loop, reflexion_loop, plan_execute

How it fits

Not a framework — the reliability layer beside your framework.

Every tool below is excellent at what it does. LORE fills the gap they all leave to the application developer.

Tool	Great at	Where LORE fills the gap
LangGraph	Durable orchestration, stateful graphs	Drop-in circuit breaker, cost guard, DLQ without graph rewrites
CrewAI	Easy multi-agent demos	Production scaffolds for the reliability controls CrewAI examples don't ship
AutoGen	Rich multi-agent collaboration	Opinionated guardrails that AutoGen's flexibility leaves to you
Langfuse	Observability and tracing	Complementary: Langfuse traces + LORE enforces (cost guard, circuit breaker)
Temporal	Durable execution	Lightweight on-ramp for teams before or beside full Temporal adoption

What score does your codebase get?

Run the same 4-dimension audit we ran on the 10 frameworks. Get a JSON report with findings, severity, and the exact scaffold commands to fix each gap.

pip install lore-agents
lore audit .

Tell us your score — that's the feedback loop that makes this better.

View on GitHub → Browse the Codex

LangChain gave you the pipes.Nobody gave you the circuit breakers.That's Lore.