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An “integration” in Zespan is auto-instrumentation, not a data connector. There’s no one-way webhook or export job to configure — you call zespan.autopatch() or a specific patch_X() function in Python, or use the SDK’s wrap_X() / patch_X() functions in TypeScript, and Zespan traces the calls your existing code already makes to that library. No changes to call sites, no separate sync process, and nothing leaves your app until the library method you patched is actually invoked.
autopatch()
LLM Providers
Agent Frameworks
RAG Frameworks
Vector Databases
Custom / Other

Categories

  • LLM Providers — trace chat completions, streaming, tool calls, and token/cost usage for direct LLM API clients like OpenAI, Anthropic, and Bedrock.
  • Agent Frameworks — trace chains, agents, tools, and multi-step runs in orchestration frameworks like LangChain, CrewAI, and AutoGen.
  • RAG Frameworks — trace retrieval and generation steps in RAG frameworks like LlamaIndex and Haystack.
  • Vector Databases — trace query, add, and upsert calls against vector stores like Chroma, Pinecone, Qdrant, and Weaviate.
  • Custom / Other — instrument anything not covered above yourself with startSpan / start_span.

Which integration for your stack