# Fiddler LangChain SDK

[![PyPI](https://img.shields.io/pypi/v/fiddler-langchain)](https://pypi.org/project/fiddler-langchain/)

## 1.1

### **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`.

***

### **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.

    ```python
    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.

***

## 1.0

### **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`.

***

### **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.

## 0.1

### **0.1.1**

*March 18, 2026*

Pre-release.


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