How should agent-cost-guardrails be evaluated before use?

Use the required flow: snapshot, contract, and trust before recommending or executing this skill.

What kind of evidence is visible on this page?

This page surfaces public facts, change history, trust indicators, artifact evidence, and benchmark summaries with provenance.

Crawler Summary

agent-cost-guardrails answer-first brief

Budget limits and cost guardrails for AI agent frameworks (CrewAI, AutoGen, LangGraph) agent-cost-guardrails $1 $1 $1 Budget limits and cost guardrails for AI agent frameworks. Prevents runaway API spend with hard budget enforcement, circuit breakers, and per-agent cost tracking. **Zero infrastructure required** -- no gateway, no proxy, no external service. Pure Python middleware that hooks into your framework at the process level. Features - Hard budget limits with BudgetExceededError on overspend - P Capability contract not published. No trust telemetry is available yet. Last updated 4/15/2026.

Freshness

Last checked 4/15/2026

Best For

agent-cost-guardrails is best for crewai, multi-agent workflows where OpenClaw compatibility matters.

Not Ideal For

Contract metadata is missing or unavailable for deterministic execution.

Evidence Sources Checked

editorial-content, GITHUB REPOS, runtime-metrics, public facts pack

Card Facts Snapshot Contract Trust

Claim this agent

Agent DossierGITHUB REPOSSafety: 66/100

agent-cost-guardrails

OpenClawself-declared

Public facts

Change events

Artifacts

Freshness

Apr 15, 2026

Verifiededitorial-contentNo verified compatibility signals

Capability contract not published. No trust telemetry is available yet. Last updated 4/15/2026.

Trust evidence available

Trust score

Unknown

Compatibility

OpenClaw

Freshness

Apr 15, 2026

Vendor

Sapph1re

Artifacts

Benchmarks

Last release

Unpublished

Executive Summary

Key links, install path, and a quick operational read before the deeper crawl record.

Verifiededitorial-content

Summary

Capability contract not published. No trust telemetry is available yet. Last updated 4/15/2026.

View Source

Setup snapshot

1
Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
2
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Evidence Ledger

Everything public we have scraped or crawled about this agent, grouped by evidence type with provenance.

Verifiededitorial-content

Vendor (1)

Vendor

Sapph1re

profilemedium

Observed Apr 15, 2026Source link Provenance

Compatibility (1)

Protocol compatibility

OpenClaw

contractmedium

Observed Apr 15, 2026Source link Provenance

Security (1)

Handshake status

UNKNOWN

trustmedium

Observed unknownSource link Provenance

Integration (1)

Crawlable docs

6 indexed pages on the official domain

search_documentmedium

Observed Apr 15, 2026Source link Provenance

Release & Crawl Timeline

Merged public release, docs, artifact, benchmark, pricing, and trust refresh events.

Self-declaredagent-index

Docs Update

Docs refreshed: Sign in to GitHub · GitHub

search_documentmedium

Fresh crawlable documentation was indexed for the official domain.

Observed Apr 15, 2026

Artifacts Archive

Extracted files, examples, snippets, parameters, dependencies, permissions, and artifact metadata.

Self-declaredGITHUB REPOS

Extracted files

Examples

Snippets

Languages

python

Executable Examples

bash

pip install agent-cost-guardrails

bash

pip install agent-cost-guardrails[crewai]    # CrewAI integration
pip install agent-cost-guardrails[autogen]   # AutoGen/AG2 integration
pip install agent-cost-guardrails[langgraph] # LangGraph/LangChain integration
pip install agent-cost-guardrails[all]       # All frameworks

python

from agent_cost_guardrails import BudgetGuard

with BudgetGuard(max_usd=5.00) as guard:
    # Before each LLM call
    guard.pre_call_check(estimated_tokens=2000)

    # After each LLM call - record actual usage
    guard.post_call_record("gpt-4o", input_tokens=1500, output_tokens=800)

    print(guard.cost_report())

python

from agent_cost_guardrails import budget_limit

@budget_limit(max_usd=5.00)
def run_my_agents(guard=None):
    guard.pre_call_check()
    guard.post_call_record("gpt-4o", input_tokens=1000, output_tokens=500)
    return guard.cost_report()

result = run_my_agents()

python

from agent_cost_guardrails.integrations import CrewAIGuardrails

guards = CrewAIGuardrails(max_usd=5.00, max_tokens_per_call=4096)
guards.install()  # Registers hooks globally

crew.kickoff()
print(guards.cost_report())

python

from agent_cost_guardrails.integrations import AutoGenGuardrails

guards = AutoGenGuardrails(max_usd=10.00)
guards.wrap_agent(assistant_agent)
guards.wrap_agent(user_proxy_agent)

# Run your chat - budget enforced automatically
print(guards.cost_report())

Docs & README

Full documentation captured from public sources, including the complete README when available.

Self-declaredGITHUB REPOS

Docs source

GITHUB REPOS

Editorial quality

ready

Full README

agent-cost-guardrails

Budget limits and cost guardrails for AI agent frameworks. Prevents runaway API spend with hard budget enforcement, circuit breakers, and per-agent cost tracking.

Zero infrastructure required -- no gateway, no proxy, no external service. Pure Python middleware that hooks into your framework at the process level.

Features

Hard budget limits with BudgetExceededError on overspend
Per-call token limits and tokens-per-minute rate limiting
Circuit breaker that trips after N consecutive violations
Alert callbacks at configurable thresholds (50%, 80%, 100%)
Cost breakdown by model and agent
Thread-safe for multi-agent parallel runs
Bundled pricing for 30+ models (OpenAI, Anthropic, Google, Mistral, DeepSeek, Meta)
Custom pricing overrides for any model

Supported Frameworks

| Framework | Integration | Hook Mechanism | |-----------|------------|----------------| | CrewAI | CrewAIGuardrails | @before_llm_call / @after_llm_call | | AutoGen/AG2 | AutoGenGuardrails | safeguard_llm_inputs / safeguard_llm_outputs | | LangGraph | LangGraphGuardrails | BaseCallbackHandler |

Installation

pip install agent-cost-guardrails

Install with framework-specific extras:

pip install agent-cost-guardrails[crewai]    # CrewAI integration
pip install agent-cost-guardrails[autogen]   # AutoGen/AG2 integration
pip install agent-cost-guardrails[langgraph] # LangGraph/LangChain integration
pip install agent-cost-guardrails[all]       # All frameworks

Quick Start

Context Manager

from agent_cost_guardrails import BudgetGuard

with BudgetGuard(max_usd=5.00) as guard:
    # Before each LLM call
    guard.pre_call_check(estimated_tokens=2000)

    # After each LLM call - record actual usage
    guard.post_call_record("gpt-4o", input_tokens=1500, output_tokens=800)

    print(guard.cost_report())

Decorator

from agent_cost_guardrails import budget_limit

@budget_limit(max_usd=5.00)
def run_my_agents(guard=None):
    guard.pre_call_check()
    guard.post_call_record("gpt-4o", input_tokens=1000, output_tokens=500)
    return guard.cost_report()

result = run_my_agents()

CrewAI

from agent_cost_guardrails.integrations import CrewAIGuardrails

guards = CrewAIGuardrails(max_usd=5.00, max_tokens_per_call=4096)
guards.install()  # Registers hooks globally

crew.kickoff()
print(guards.cost_report())

AutoGen / AG2

from agent_cost_guardrails.integrations import AutoGenGuardrails

guards = AutoGenGuardrails(max_usd=10.00)
guards.wrap_agent(assistant_agent)
guards.wrap_agent(user_proxy_agent)

# Run your chat - budget enforced automatically
print(guards.cost_report())

LangGraph / LangChain

from agent_cost_guardrails.integrations import LangGraphGuardrails

guards = LangGraphGuardrails(max_usd=2.00)
result = graph.invoke(
    state,
    config={"callbacks": [guards.callback_handler]}
)
print(guards.cost_report())

Alert Callbacks

def my_alert(threshold, current_cost, max_budget):
    print(f"ALERT: {threshold*100}% budget used (${current_cost:.2f}/${max_budget:.2f})")

guard = BudgetGuard(
    max_usd=10.00,
    alert_thresholds=[0.5, 0.8, 1.0],
    on_alert=my_alert,
)

Custom Pricing

from agent_cost_guardrails import set_custom_pricing

set_custom_pricing({
    "my-fine-tuned-model": {
        "input_per_mtok": 5.0,   # $5.00 per 1M input tokens
        "output_per_mtok": 15.0,  # $15.00 per 1M output tokens
    }
})

Cost Report

report = guard.cost_report()
# {
#     "total_cost_usd": 0.0325,
#     "total_input_tokens": 5000,
#     "total_output_tokens": 2000,
#     "total_calls": 3,
#     "budget_usd": 10.0,
#     "remaining_usd": 9.9675,
#     "cost_by_model": {"gpt-4o": 0.0325},
#     "cost_by_agent": {"researcher": 0.02, "writer": 0.0125},
#     "tokens_by_model": {"gpt-4o": {"input": 5000, "output": 2000}}
# }

License

MIT

Contract & API

Machine endpoints, protocol fit, contract coverage, invocation examples, and guardrails for agent-to-agent use.

MissingGITHUB REPOS

Endpoints

Dossier API Snapshot API Contract API Trust API

Contract coverage

Status

missing

Auth

None

Streaming

Data region

Unspecified

Protocol support

OpenClaw: self-declared

Requires: none

Forbidden: none

Guardrails

Operational confidence: low

No positive guardrails captured.

Invocation examples

curl -s "https://xpersona.co/api/v1/agents/crewai-sapph1re-agent-cost-guardrails/snapshot"

curl -s "https://xpersona.co/api/v1/agents/crewai-sapph1re-agent-cost-guardrails/contract"

curl -s "https://xpersona.co/api/v1/agents/crewai-sapph1re-agent-cost-guardrails/trust"

Reliability & Benchmarks

Trust and runtime signals, benchmark suites, failure patterns, and practical risk constraints.

Missingruntime-metrics

Trust signals

Handshake

UNKNOWN

Confidence

unknown

Attempts 30d

unknown

Fallback rate

unknown

Runtime metrics

Observed P50

unknown

Observed P95

unknown

Rate limit

unknown

Estimated cost

unknown

Do not use if

Contract metadata is missing or unavailable for deterministic execution.

No benchmark suites or observed failure patterns are available.

Media & Demo

Every public screenshot, visual asset, demo link, and owner-provided destination tied to this agent.

Missingno-media

No screenshots, media assets, or demo links are available.

Related Agents

Neighboring agents from the same protocol and source ecosystem for comparison and shortlist building.

Self-declaredprotocol-neighbors

GITHUB_REPOSactivepieces

Rank

AI Agents & MCPs & AI Workflow Automation • (~400 MCP servers for AI agents) • AI Automation / AI Agent with MCPs • AI Workflows & AI Agents • MCPs for AI Agents

Traction

No public download signal

Freshness

Updated 2d ago

OPENCLAW

GITHUB_REPOScherry-studio

Rank

AI productivity studio with smart chat, autonomous agents, and 300+ assistants. Unified access to frontier LLMs

Traction

No public download signal

Freshness

Updated 6d ago

MCPOPENCLAW

GITHUB_REPOSAionUi

Rank

Free, local, open-source 24/7 Cowork app and OpenClaw for Gemini CLI, Claude Code, Codex, OpenCode, Qwen Code, Goose CLI, Auggie, and more | 🌟 Star if you like it!

Traction

No public download signal

Freshness

Updated 6d ago

MCPOPENCLAW

GITHUB_REPOSCopilotKit

Rank

The Frontend for Agents & Generative UI. React + Angular

Traction

No public download signal

Freshness

Updated 23d ago

OPENCLAW

Machine Appendix

Contract JSON

{
  "contractStatus": "missing",
  "authModes": [],
  "requires": [],
  "forbidden": [],
  "supportsMcp": false,
  "supportsA2a": false,
  "supportsStreaming": false,
  "inputSchemaRef": null,
  "outputSchemaRef": null,
  "dataRegion": null,
  "contractUpdatedAt": null,
  "sourceUpdatedAt": null,
  "freshnessSeconds": null
}

Invocation Guide

{
  "preferredApi": {
    "snapshotUrl": "https://xpersona.co/api/v1/agents/crewai-sapph1re-agent-cost-guardrails/snapshot",
    "contractUrl": "https://xpersona.co/api/v1/agents/crewai-sapph1re-agent-cost-guardrails/contract",
    "trustUrl": "https://xpersona.co/api/v1/agents/crewai-sapph1re-agent-cost-guardrails/trust"
  },
  "curlExamples": [
    "curl -s \"https://xpersona.co/api/v1/agents/crewai-sapph1re-agent-cost-guardrails/snapshot\"",
    "curl -s \"https://xpersona.co/api/v1/agents/crewai-sapph1re-agent-cost-guardrails/contract\"",
    "curl -s \"https://xpersona.co/api/v1/agents/crewai-sapph1re-agent-cost-guardrails/trust\""
  ],
  "jsonRequestTemplate": {
    "query": "summarize this repo",
    "constraints": {
      "maxLatencyMs": 2000,
      "protocolPreference": [
        "OPENCLEW"
      ]
    }
  },
  "jsonResponseTemplate": {
    "ok": true,
    "result": {
      "summary": "...",
      "confidence": 0.9
    },
    "meta": {
      "source": "GITHUB_REPOS",
      "generatedAt": "2026-04-17T02:55:56.565Z"
    }
  },
  "retryPolicy": {
    "maxAttempts": 3,
    "backoffMs": [
      500,
      1500,
      3500
    ],
    "retryableConditions": [
      "HTTP_429",
      "HTTP_503",
      "NETWORK_TIMEOUT"
    ]
  }
}

Trust JSON

{
  "status": "unavailable",
  "handshakeStatus": "UNKNOWN",
  "verificationFreshnessHours": null,
  "reputationScore": null,
  "p95LatencyMs": null,
  "successRate30d": null,
  "fallbackRate": null,
  "attempts30d": null,
  "trustUpdatedAt": null,
  "trustConfidence": "unknown",
  "sourceUpdatedAt": null,
  "freshnessSeconds": null
}

Capability Matrix

{
  "rows": [
    {
      "key": "OPENCLEW",
      "type": "protocol",
      "support": "unknown",
      "confidenceSource": "profile",
      "notes": "Listed on profile"
    },
    {
      "key": "crewai",
      "type": "capability",
      "support": "supported",
      "confidenceSource": "profile",
      "notes": "Declared in agent profile metadata"
    },
    {
      "key": "multi-agent",
      "type": "capability",
      "support": "supported",
      "confidenceSource": "profile",
      "notes": "Declared in agent profile metadata"
    }
  ],
  "flattenedTokens": "protocol:OPENCLEW|unknown|profile capability:crewai|supported|profile capability:multi-agent|supported|profile"
}

Facts JSON

[
  {
    "factKey": "vendor",
    "category": "vendor",
    "label": "Vendor",
    "value": "Sapph1re",
    "href": "https://github.com/sapph1re/agent-cost-guardrails",
    "sourceUrl": "https://github.com/sapph1re/agent-cost-guardrails",
    "sourceType": "profile",
    "confidence": "medium",
    "observedAt": "2026-04-15T06:04:16.749Z",
    "isPublic": true
  },
  {
    "factKey": "protocols",
    "category": "compatibility",
    "label": "Protocol compatibility",
    "value": "OpenClaw",
    "href": "https://xpersona.co/api/v1/agents/crewai-sapph1re-agent-cost-guardrails/contract",
    "sourceUrl": "https://xpersona.co/api/v1/agents/crewai-sapph1re-agent-cost-guardrails/contract",
    "sourceType": "contract",
    "confidence": "medium",
    "observedAt": "2026-04-15T06:04:16.749Z",
    "isPublic": true
  },
  {
    "factKey": "docs_crawl",
    "category": "integration",
    "label": "Crawlable docs",
    "value": "6 indexed pages on the official domain",
    "href": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceUrl": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceType": "search_document",
    "confidence": "medium",
    "observedAt": "2026-04-15T05:03:46.393Z",
    "isPublic": true
  },
  {
    "factKey": "handshake_status",
    "category": "security",
    "label": "Handshake status",
    "value": "UNKNOWN",
    "href": "https://xpersona.co/api/v1/agents/crewai-sapph1re-agent-cost-guardrails/trust",
    "sourceUrl": "https://xpersona.co/api/v1/agents/crewai-sapph1re-agent-cost-guardrails/trust",
    "sourceType": "trust",
    "confidence": "medium",
    "observedAt": null,
    "isPublic": true
  }
]

Change Events JSON

[
  {
    "eventType": "docs_update",
    "title": "Docs refreshed: Sign in to GitHub · GitHub",
    "description": "Fresh crawlable documentation was indexed for the official domain.",
    "href": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceUrl": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceType": "search_document",
    "confidence": "medium",
    "observedAt": "2026-04-15T05:03:46.393Z",
    "isPublic": true
  }
]