10 LLMOps tools for building production AI systems

Running a large language model in production requires more than choosing a model and sending prompts to an API. A dependable system also needs testing, tracing, access controls, failure handling, feedback collection, and a repeatable deployment process.

The tools below cover different parts of that operational stack. They are not direct substitutes for one another: the right combination depends on whether you are building a structured application, a multi-model gateway, a long-running agent, or an evaluation pipeline.

LLMOps tools at a glance

1. PydanticAI for structured AI applications

PydanticAI is a Python framework for building model-powered applications with typed dependencies and validated outputs. It is a practical option when model responses must be converted into predictable application data rather than passed around as unstructured text.

Validation does not eliminate model errors, but it makes malformed output easier to detect and handle. Teams already using Python and Pydantic may also find the framework familiar.

2. Bifrost for model routing

Bifrost provides a gateway layer between an application and multiple model providers. A gateway can centralize provider configuration, routing, retries, caching, and access policies instead of scattering that logic throughout the codebase.

This category of tool is most valuable when a team needs provider flexibility or a controlled fallback path. If an application uses only one provider and has simple requirements, a gateway may add unnecessary operational complexity.

3. Traceloop and OpenLLMetry for observability

OpenLLMetry uses OpenTelemetry conventions to capture traces from LLM applications. It can help teams connect model calls, tool calls, latency, token use, and errors to the wider request that triggered them.

Useful tracing should answer specific questions: Which prompt version ran? Which model handled the request? Where did an agent fail? Observability data can contain sensitive prompts or responses, so retention and redaction rules should be set before broad collection begins.

4. Promptfoo for repeatable evaluation

Promptfoo lets teams define test cases, compare prompts or models, and run security-oriented checks. It can be used locally and incorporated into a CI workflow so important regressions are found before a release.

The quality of an evaluation still depends on its test set. Include normal requests, known failure cases, boundary conditions, and adversarial inputs that reflect the actual application instead of relying on a single aggregate score.

5. Invariant Guardrails for runtime policies

Invariant Guardrails focuses on controlling agent behavior while the application is running. This matters when a model can call APIs, access data, or trigger actions where an incorrect decision has real consequences.

Guardrails should complement—not replace—ordinary authorization, input validation, audit logging, and least-privilege access. The application must remain secure even when a model produces an unexpected instruction.

6. Letta for persistent agent state

Letta is designed for stateful agents that retain information beyond a single prompt window. It is relevant to assistants and workflows that need to remember selected facts or continue work across sessions.

Long-term memory needs governance. Decide what may be stored, how memories are updated, how users can correct them, and when they expire. More retained context is not automatically better context.

7. OpenPipe for learning from production data

OpenPipe supports workflows that collect model interactions and turn selected examples into datasets for evaluation or model improvement. This can help teams focus on cases their application actually encounters.

Production logs should not be treated as clean training data. Remove sensitive information, filter low-quality examples, document consent and retention rules, and compare any adapted model against a stable evaluation set.

8. Argilla for human feedback

Argilla provides data curation and annotation workflows for teams reviewing model outputs. Human reviewers can label errors, rank alternatives, or add corrections that later support evaluation and improvement work.

A useful review project needs clear labeling guidelines and agreement checks. Without them, a larger dataset can simply preserve inconsistent judgments.

9. KitOps for versioned AI artifacts

KitOps addresses the deployment problem created when models, datasets, prompts, code, and configuration are versioned separately. Packaging related artifacts together can make releases easier to reproduce, share, and roll back.

Artifact packaging is especially useful when several environments or teams must run the same tested combination. It does not replace source control or a model registry; it connects pieces that otherwise drift independently.

10. Composio for application integrations

Composio helps developers connect agents to external services and tools. It can reduce the amount of custom integration and authentication code required for common business applications.

Every integration expands the agent's possible actions. Limit permissions to the specific operations required, confirm consequential actions in the user interface, and keep an audit trail of tool calls.

How to choose an LLMOps stack

Start with the failure you need to prevent, not with the longest tool list. A conventional LLM application may need structured outputs, evaluation, and tracing. A multi-provider service may add a gateway. An action-taking agent may also require persistent state, strict tool permissions, and runtime policies.

• Define success: build an evaluation set from real tasks and known failures.
• Trace the full request: connect model activity to application logs without collecting more sensitive data than necessary.
• Separate model behavior from authorization: the model may propose an action, but application code should enforce whether it is allowed.
• Version related components: record the model, prompt, tools, policies, and dataset used for each release.
• Add complexity only when justified: each platform creates its own maintenance, security, and cost burden.

A complete LLMOps stack is therefore not a fixed collection of products. It is a set of controls that makes an AI feature testable, observable, recoverable, and safe enough for its intended use.