LLMOps focuses on orchestration, observability, and evaluation.
* **PydanticAI:** type-safe outputs for LLMs, supporting multiple models and complex workflows for more reliable software-like behavior.
* **Bifrost:** gateway for multiple models/providers, offering a single API with features like failover, load balancing, and observability.
* **Traceloop / OpenLLMetry:** Integrates LLM with OpenTelemetry
* **Promptfoo:** CI/CD pipelines for automated checks.
* **Invariant Guardrails:** runtime rules between applications and LLMs/tools, enforcing constraints without code changes.
* **Letta:** version-controlled memory for agents, tracking interactions like a Git repository for debugging and rollback.
* **OpenPipe:** continuous model improvement through logging, data export, evaluation, and fine-tuning within a single platform.
* **Argilla:** human feedback and data curation for tasks like annotation and error analysis, improving model performance.
* **KitOps:** Packages models, datasets, prompts, and configurations into versioned artifacts for clean deployments and reproducibility.
* **Composio:** authentication, permissions, and execution for agents interacting with hundreds of external applications.
* **PydanticAI:** type-safe outputs for LLMs, supporting multiple models and complex workflows for more reliable software-like behavior.
* **Bifrost:** gateway for multiple models/providers, offering a single API with features like failover, load balancing, and observability.
* **Traceloop / OpenLLMetry:** Integrates LLM with OpenTelemetry
* **Promptfoo:** CI/CD pipelines for automated checks.
* **Invariant Guardrails:** runtime rules between applications and LLMs/tools, enforcing constraints without code changes.
* **Letta:** version-controlled memory for agents, tracking interactions like a Git repository for debugging and rollback.
* **OpenPipe:** continuous model improvement through logging, data export, evaluation, and fine-tuning within a single platform.
* **Argilla:** human feedback and data curation for tasks like annotation and error analysis, improving model performance.
* **KitOps:** Packages models, datasets, prompts, and configurations into versioned artifacts for clean deployments and reproducibility.
* **Composio:** authentication, permissions, and execution for agents interacting with hundreds of external applications.
sudo for AI agents - allow, deny, or ask before any tool runs. AI agents run tools autonomously, but some calls are too risky to run unchecked. agentpriv gives you a permission layer to control what goes through.
An article discussing the hidden costs and limitations of popular AI frameworks like LangChain, CrewAI, and PydanticAI, and introducing Atomic Agents as a potential solution.
An article detailing the capabilities and application of PydanticAI in building production-grade AI applications, particularly focusing on multi-agent systems.
An article on building an AI agent to interact with Apache Airflow using PydanticAI and Gemini 2.0, providing a structured and reliable method for managing DAGs through natural language queries.
- Agent interacts with Apache Airflow via the Airflow REST API.
- Agent can understand natural language queries about workflows, fetch real-time status updates, and return structured data.
- Sample DAGs are implemented for demonstration purposes.
- Agent interacts with Apache Airflow via the Airflow REST API.
- Agent can understand natural language queries about workflows, fetch real-time status updates, and return structured data.
- Sample DAGs are implemented for demonstration purposes.
