Fiddler LangChain SDK

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1.1

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1.1.2

May 25, 2026

• Bug Fixes
  • Suppressed gen_ai.llm.context on routing-only LLM spans: When an LLM span’s output is purely tool_calls (a routing decision with no text content), the SDK now blanks gen_ai.llm.context on that span. Previously, RAG context set earlier in the same turn via set_llm_context() leaked onto these routing spans, triggering faithfulness evaluation on spans that produce no answer text.

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1.1.1

May 12, 2026

• Bug Fixes
  • Fixed wrapt 2.x compatibility: wrapt 2.0 renamed the first parameter of wrap_function_wrapper() from module to target. The SDK passed it as a keyword argument (module='...'), which raised TypeError: wrap_function_wrapper() got an unexpected keyword argument 'module' on wrapt >= 2.0. Fixed with a version-detecting helper that dispatches the correct keyword argument name based on the installed wrapt major version (module= for 1.x, target= for 2.x).
• Dependencies
  • Bumps fiddler-otel dependency to 1.2.0.

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1.1.0

April 14, 2026

• New Features
  • clear_llm_context() function: New convenience function to explicitly remove RAG context from an LLM instance, preventing faithfulness evaluation on subsequent non-RAG spans. In multi-step agent workflows, context set after a RAG retrieval step previously leaked into later non-RAG LLM calls (tool planning, routing, etc.), causing unintended faithfulness evaluation. set_llm_context(llm, None) also now clears context.
    from fiddler_langchain import set_llm_context, clear_llm_context
    
    # After RAG retrieval
    set_llm_context(model, retrieved_documents)
    response = model.invoke(rag_prompt)  # faithfulness evaluated
    
    # Before non-RAG steps
    clear_llm_context(model)
    plan = model.invoke(planning_prompt)  # no faithfulness evaluation
    
  • Python 3.14 support: Added Python 3.14 to CI test matrix and PyPI classifiers. All runtime dependencies have compatible wheels.

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1.0

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1.0.1

March 20, 2026

• Bug Fixes
  • Fixed fiddler-otel dependency constraint: Relaxed the fiddler-otel upper bound from <1.0.0 to <2.0.0, resolving a dependency conflict when installing alongside fiddler-otel 1.x.

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1.0.0

March 18, 2026 Initial release of the Fiddler LangChain SDK.

• Added
  • FiddlerLangChainInstrumentor: Auto-instrumentor for LangChain V1 agents. Monkey-patches langchain.agents.create_agent so every subsequent call automatically receives a FiddlerAgentMiddleware. Idempotent — calling instrument() multiple times is safe.
    • instrument(): Enable automatic instrumentation of all create_agent calls.
    • uninstrument(): Restore the original create_agent function. Does not affect already-created agents.
    • is_instrumented_by_opentelemetry: Property indicating whether instrumentation is currently active.
  • FiddlerAgentMiddleware: LangChain V1 AgentMiddleware that instruments agents with Fiddler tracing. Produces a clean, flat trace hierarchy: agent → LLM calls → tool calls, with no noisy Chain wrappers.
    • before_agent(): Opens a root Agent span (TYPE=agent) at the start of each invocation.
    • after_agent(): Closes the root Agent span.
    • wrap_model_call() / awrap_model_call(): Wraps synchronous and asynchronous model calls with LLM spans (TYPE=llm). Captures model name, provider, system prompt, user prompt, full message history, output messages, token usage, LLM context, and tool definitions.
    • wrap_tool_call() / awrap_tool_call(): Wraps synchronous and asynchronous tool calls with Tool spans (TYPE=tool). Captures tool name, input, and output.
  • set_llm_context(): Attach a context string to a BaseLanguageModel or RunnableBinding. The middleware reads this at invocation time and sets gen_ai.llm.context on the LLM span.
  • add_span_attributes(**kwargs): Attach custom metadata to a specific LangChain component (model, tool, or retriever). Emitted as fiddler.span.user.{key} only on the span for that component — scoped, unlike add_session_attributes.
  • set_conversation_id(): Re-exported from fiddler_otel for convenience. Propagates conversation ID to all spans in the current execution context.
  • add_session_attributes(): Re-exported from fiddler_otel. Attaches session-level metadata to all spans in the current context.
  • Full async support: awrap_model_call and awrap_tool_call hooks fully support agent.ainvoke().
  • Error handling: Failing LLM or tool spans are marked StatusCode.ERROR with the exception recorded. The root Agent span is always closed cleanly, preventing dangling open spans. Exceptions are re-raised so normal application error handling is unaffected.
  • Multi-agent single-trace nesting: When a sub-agent is invoked from within a delegation tool, its root Agent span is automatically created as a child of the active tool span — the entire multi-agent flow appears in one trace. Top-level agents (no active parent span) start a new trace as usual.
  • Retriever-as-tool: Retrievers wrapped with @tool are automatically traced as TYPE=tool spans.
  • JSONL local capture: Inherited from fiddler-otel. Use jsonl_capture_enabled=True on FiddlerClient. The FIDDLER_JSONL_FILE environment variable overrides the output file path.

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0.1

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0.1.1

March 18, 2026 Pre-release.