Lets AI agents query, manage, and operate their LLM observability data directly from the conversation. Provides 87 tools for cost analysis, alerting, anomaly detection, and runtime control gates.
Enables language models to access LangSmith observability platform features including fetching conversation history, managing prompts, retrieving traces and runs, working with datasets and examples, and analyzing experiments.
Enables AI assistants to query and analyze AI agent sessions from observability providers like Shepherd (AIOBS) and Langfuse, allowing users to debug agent runs, compare sessions, track performance, and analyze LLM usage patterns.
Provides AI agents with access to file metadata, vector search, and workflow metrics. It enables operations such as file metadata retrieval and semantic search over file embeddings using pgvector.