Observyze

What is Observyze?

Observyze is an observability and runtime control layer for teams shipping LLM features and AI agents. It traces every model call, tool call, and token cost, then adds guardrails such as execution budgets, circuit breakers, and prompt injection checks. Connect it through a Node.js SDK or by pointing your client at its proxy.

Top Features:

• Distributed tracing: capture full agent runs, tool calls, latency, and per-step token costs.
• Circuit breakers: halt retry loops and runaway spending once set limits are hit.
• Hallucination evaluations: score outputs from zero to one and flag risky responses asynchronously.

Use Cases:

• Agent debugging: follow each step of a multi-step workflow to find failures.
• Cost control: attribute provider spend by model, project, and request to spot waste.
• Injection defense: block malicious inputs before they ever reach the model provider.

Who Can Use Observyze?

• Agent builders: debug LangGraph, CrewAI, and AutoGen workflows with clear step-level traces.
• AI product teams: ship LLM features with guardrails, alerts, and spending limits in place.
• Engineering leaders: track usage and costs as projects move from prototype to production.

Pricing

• Early access (free for 90 days): full Pro features, 100,000 traces monthly, and no credit card.
• Observatory Pro ($79 per month): planned price after early access, with 90-day retention and alerts.
• Galactic Enterprise ($499 per month): higher trace volumes, custom retention, SSO, and private VPC deployment.

Pros and Cons

Pros:

• Two setup paths: use the SDK or swap a base URL to use the proxy.
• Active guardrails: it stops bad runs instead of only reporting them afterwards.
• Provider support: works with OpenAI, Anthropic, Gemini, Mistral, Groq, Cohere, and more.

Cons:

• Limited SDKs: only Node.js and TypeScript have an official SDK so far.
• Young product: it is still in early access, so pricing may change.
• Small community: there are few tutorials or third-party guides compared with older tools.

FAQs:

1) What counts as a trace?

A trace is one end-to-end AI request, including its steps and tool calls.

2) Does it support Python?

No official Python SDK exists yet, but the HTTP API works from Python.

3) Will I be charged automatically?

No, no card is taken upfront, so you pick a paid or community tier.

4) Which frameworks are supported?

It integrates with LangChain, LangGraph, LlamaIndex, Vercel AI SDK, CrewAI, and AutoGen.

5) Does the proxy add latency?

It is designed to add under 100 milliseconds per request.