7 LLM Observability Alternatives When You Need Proof, Not Just Logs

You're here because your current LLM observability tool showed you a trace, and you realized a trace isn't proof.

Maybe a customer disputed an action. Maybe your auditor asked for evidence. Maybe you just realized that every log your monitoring tool produces lives in someone else's database, in someone else's format, and requires trusting that someone else not to alter it.

Whatever the trigger, you're looking for an alternative that can do one thing your current stack can't: prove what your agent did.

Here are 7 options, ranked not by feature count but by what they actually prove.

1. Aegisys Record — Signed Receipts

What it does: Wraps every agent action in a cryptographically signed receipt. Ed25519 signature, hash chain, Merkle anchoring. Open verification — anyone can verify with the free CLI.

What it proves: The action happened, at this time, with these parameters, and the record hasn't been altered by anyone — including you.

The difference: Every other tool on this list produces logs. Aegisys produces evidence.

import { createWitness } from "@aegisys/record";

const witness = createWitness({ tenantId: "my-agent" });

const receipt = await witness.wrap(
  { action: "stripe.charge", customer: "cus_123", amount: 2000 },
  async () => stripe.charges.create({ amount: 2000, currency: "usd" })
);

# Anyone, anywhere, without an Aegisys account:
npx @aegisys/record verify receipt.json

Open source: Yes — Apache 2.0

Pricing: Free (Record SDK) → $49/mo (Watch monitoring)

Best for: Teams that need to prove agent actions to third parties — customers, auditors, regulators, boards.

2. Langfuse — Open-Source LLM Tracing

What it does: Full tracing of LLM calls and agent chains. Self-hostable. LangChain-native. Active community. Prompt management, token tracking, user feedback scoring.

What it proves: The chain of calls that produced the output. You can see every step in the tree.

The gap: Traces are mutable. They live in Langfuse's database (or yours, if self-hosted). They're good logs. They're not cryptographic proof. An auditor would need to trust your self-hosted instance hasn't been modified.

Open source: Yes

Pricing: Free self-hosted → cloud plans

Best for: LangChain/CrewAI teams who need deep chain visibility and prompt debugging.

3. Helicone — Proxy-Level Observability

What it does: Sits between your app and OpenAI/Anthropic. Captures every request and response automatically — no code changes needed.

What it proves: The API call was made and returned this response. Good for cost tracking and prompt debugging.

The gap: It's a proxy. It sees the API boundary, not the agent's reasoning. Helicone doesn't know why the agent made the call — only that it did. And the logs live on Helicone's servers.

Open source: Partially

Pricing: Free tier → usage-based

Best for: Zero-instrumentation setup and quick cost visibility across LLM providers.

4. Arize AI — ML Observability

What it does: Model performance monitoring, drift detection, evaluation pipelines. Enterprise-grade dashboards for tracking model behavior over time.

What it proves: Your model's output distribution is drifting. Confidence scores are declining. Evaluation metrics are changing.

The gap: Arize watches the model, not the agent. It tells you the model is behaving differently. It doesn't capture what the agent did about it — the Stripe charge, the API call, the refund processed.

Open source: No

Pricing: Enterprise (custom)

Best for: ML teams with enterprise budgets who need drift detection and model comparison across versions.

5. LangSmith — LangChain-Native Tracing

What it does: Visual trace trees for LangChain and CrewAI agents. Deep integration with the LangChain ecosystem. Debugging, evaluation, monitoring.

What it proves: The execution path through your LangChain agent. Which tools were called, in what order, with what inputs.

The gap: Tied to the LangChain ecosystem. Traces are stored in LangSmith's format in LangSmith's cloud. Independent verification requires a LangSmith account.

Open source: No

Pricing: Free tier → per-seat pricing

Best for: Teams already all-in on LangChain/CrewAI who want tight integration.

6. Braintrust — Evaluation-First

What it does: Scores model outputs against your evaluations. Tracks quality over time. Human feedback integration.

What it proves: The model scored well on your test cases and production samples. Quality is (or isn't) improving.

The gap: Evaluation ≠ verification. A high eval score tells you the output looks good. It doesn't prove what the agent did with that output. The refund was processed. The email was sent. The database was written to. Evals don't capture that.

Open source: No

Pricing: Free tier → per-seat

Best for: Teams optimizing output quality with systematic evaluation pipelines.

7. Datadog LLM Observability — Enterprise Monitoring

What it does: LLM metrics inside your existing Datadog infrastructure. Token usage, latency, error rates. Integrates with your existing dashboards and alerting.

What it proves: System-level health. Your LLM endpoints are up. Latency is acceptable. Error rate is stable.

The gap: Generic monitoring applied to LLMs. Datadog knows how to watch servers. It doesn't know what "prove this agent action" means. No agent-level accountability.

Open source: No

Pricing: Datadog infrastructure pricing

Best for: Teams already paying for Datadog who want LLM metrics in the same place.

The Feature Comparison

	Aegisys	Langfuse	Helicone	Arize	LangSmith	Braintrust	Datadog
Signed receipts	✅	❌	❌	❌	❌	❌	❌
Third-party verification	✅	❌	❌	❌	❌	❌	❌
Open source SDK	✅	✅	✅	❌	❌	❌	❌
LLM tracing	❌	✅	✅	❌	✅	❌	✅
Evaluation scoring	❌	✅	❌	✅	✅	✅	❌
Compliance audit trail	✅	❌	❌	❌	❌	❌	❌
Offline verification	✅	❌	❌	❌	❌	❌	❌
Tamper-evident logs	✅	❌	❌	❌	❌	❌	❌

When to Use Which

Need to debug a failing prompt? → Langfuse or Helicone

Need to track model drift? → Arize

Need deep LangChain integration? → LangSmith

Need to optimize output quality? → Braintrust

Need LLM metrics in existing Datadog? → Datadog

Need to prove what your agent did — to a customer, an auditor, a lawyer, or yourself? → Aegisys

The Honest Answer

Most teams need two layers. Keep your observability tool for debugging and development. Add Aegisys Record for the actions where someone will eventually ask: "Can you prove that?"

The first time you hand a signed receipt to an angry customer and say "verify it yourself — here's the open-source tool," you'll understand the difference.

npm install @aegisys/record

Get the SDK → | See Aegisys vs {competitor} →