agentshield/public/comparisons/langfuse.html at fix/trace-detail-precision · ghzhost/agentshield · GitHub

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<title>AgentShield vs LangFuse: Enforcement vs LLM Tracing</title>
<meta name="description" content="LangFuse traces LLM calls. AgentShield blocks runaway spending before it happens. Tracing vs prevention — see why you need both for production AI agents.">
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<a href="https://agentshield.fly.dev">🛡️ AgentShield</a>
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<h1>AgentShield vs LangFuse: Enforcement vs LLM Tracing</h1>
<p style="font-size:1.1em;color:var(--muted);margin-bottom:32px">LangFuse traces LLM calls. AgentShield blocks runaway spending before it happens. Tracing vs prevention — see why you need both for production AI agents.</p>

<h2>What LangFuse Does</h2>
<p>LangFuse is an open-source LLM observability platform. It captures traces, tracks token usage, scores outputs, and provides cost analytics. It's widely used for debugging LLM pipelines and understanding cost patterns.</p>

<h2>What AgentShield Does</h2>
<p>AgentShield is not an observability tool. It's an enforcement engine that evaluates each transaction against spend rules BEFORE the API call executes. We prevent bad transactions; we don't just log them.</p>

<h2>Feature Comparison</h2>
<table>
<thead><tr><th>Capability</th><th>AgentShield</th><th>LangFuse</th></tr></thead>
<tbody>
<tr><td>Blocks transactions before execution</td><td><span class="feature-check">✅ <1ms eval</span></td><td><span class="feature-x">❌ Logs after execution</span></td></tr>
<tr><td>Transaction limit rules</td><td><span class="feature-check">✅ Per-call caps</span></td><td><span class="feature-x">❌ Cost tracking only</span></td></tr>
<tr><td>Daily spend caps</td><td><span class="feature-check">✅ Per-agent enforcement</span></td><td><span class="feature-x">❌ Budget alerts only</span></td></tr>
<tr><td>Velocity detection</td><td><span class="feature-check">✅ Rolling window</span></td><td><span class="feature-x">❌ Not available</span></td></tr>
<tr><td>LLM trace / span capture</td><td><span class="feature-check">❌ Not a tracing tool</span></td><td><span class="feature-x">✅ Core feature</span></td></tr>
<tr><td>Token-level cost analytics</td><td><span class="feature-check">✅ Decision audit trail</span></td><td><span class="feature-x">✅ Detailed breakdown</span></td></tr>
<tr><td>Open source</td><td><span class="feature-check">✅ MIT license</span></td><td><span class="feature-x">✅ MIT license</span></td></tr>
<tr><td>Zero dependencies</td><td><span class="feature-check">✅ Python stdlib only</span></td><td><span class="feature-x">❌ Requires SDK + infra</span></td></tr>
<tr><td>Self-hostable</td><td><span class="feature-check">✅ 60-second Docker deploy</span></td><td><span class="feature-x">✅ Docker Compose</span></td></tr>

</tbody>
</table>

<h2>When to Use LangFuse</h2>
<ul style="color:#ccc;margin-bottom:20px;padding-left:20px">
<li>You need detailed LLM call tracing and debugging</li><li>You want per-token cost analytics and dashboards</li><li>Quality scoring and evaluation of LLM outputs matters to you</li>
</ul>

<h2>When to Use AgentShield</h2>
<ul style="color:#ccc;margin-bottom:20px;padding-left:20px">
<li>You need hard financial limits, not cost analytics</li><li>You run autonomous agents that could trigger retry storms</li><li>Prevention is more important to you than post-hoc analysis</li>
</ul>

<h2>Use Both Together</h2>
<p>Trace calls with LangFuse. Enforce limits with AgentShield. One tells you what happened; the other prevents it from happening.</p>
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<h2 style="margin-top:0">The $2,800 Test</h2>
<p>If an AI agent makes 21 API calls at $133 each in 60 seconds at 3 AM:</p>
<p><strong style="color:var(--danger)">LangFuse</strong> logs it. You see the damage in the morning dashboard.</p>
<p><strong style="color:var(--accent)">AgentShield</strong> blocks it in under 1ms. The agent never spends the money.</p>
<p style="margin-top:12px"><strong>Prevention costs $19/month. A single runaway agent costs $2,800 in 60 seconds.</strong></p>
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