Fiddler LangChain SDK

Instrument your LangChain V1 agents built with langchain.agents.create_agent for comprehensive agentic observability. The Fiddler LangChain SDK produces a clean, flat trace hierarchy — agent → LLM calls → tool calls — with no noisy Chain wrappers. One call to FiddlerLangChainInstrumentor.instrument() auto-traces every agent in your application.

Using LangChain prior to v1? The fiddler-langchain SDK requires LangChain v1 (langchain.agents.create_agent API). For applications using earlier LangChain versions or LangGraph workflows, use the Fiddler LangGraph SDK instead — it covers both LangGraph and earlier LangChain-based agents.

What you’ll need

Fiddler account (cloud or on-premises)
Python 3.10-3.14
LangChain V1 application using langchain.agents.create_agent
Fiddler API key and application ID

Quick start

Get monitoring in 4 steps:

# Step 1: Install
pip install fiddler-langchain

# Step 2: Initialize the Fiddler client
import langchain.agents
from langchain_openai import ChatOpenAI
from fiddler_otel import FiddlerClient
from fiddler_langchain import FiddlerLangChainInstrumentor

client = FiddlerClient(
    application_id='your-app-id',  # Must be valid UUID4
    api_key='your-api-key',
    url='https://your-instance.fiddler.ai'
)

# Step 3: Instrument (patches create_agent globally)
FiddlerLangChainInstrumentor(client=client).instrument()

# Step 4: Create your agent — Fiddler middleware is injected automatically
agent = langchain.agents.create_agent(
    model=ChatOpenAI(model='gpt-4o-mini'),
    tools=[...],
    name='my_agent',  # Optional — label this agent in traces
)

result = agent.invoke({'messages': [{'role': 'user', 'content': 'Hello!'}]})

That’s it! Your agent traces are now flowing to Fiddler.

Important: Use langchain.agents.create_agent (the module attribute), not from langchain.agents import create_agent. Call instrument() before importing create_agent if you use the from ... import style so the local name is bound to the patched version.

What gets monitored

Trace hierarchy

Each agent invocation produces a clean, flat trace with no noisy Chain wrappers:

[Span] my_agent           (Agent root - TYPE=agent)
  └── [Span] gpt-4o-mini  (LLM call   - TYPE=llm)
  └── [Span] search_docs  (Tool call  - TYPE=tool)
  └── [Span] gpt-4o-mini  (LLM call   - TYPE=llm)

Captured data

Agent root span:

Agent name and agent ID
Conversation ID (if set via set_conversation_id())

LLM spans (per model invocation):

Model name and provider
System prompt and user prompt (last human message)
Full input message history (gen_ai.input.messages)
LLM completion and output messages (gen_ai.output.messages)
Token usage (input, output, total)
LLM context (if set via set_llm_context())
Available tool definitions

Tool spans (per tool call):

Tool name, input arguments, and output

Application setup

Before instrumenting your application, you must create an application in Fiddler and obtain your Application ID.

1. Create your application in Fiddler

Log in to your Fiddler instance and navigate to GenAI Applications, then click Add Application and follow the onboarding wizard to create your application.

2. Copy your Application ID

After creating your application, copy the Application ID from the GenAI Applications page. This must be a valid UUID4 format (for example, 550e8400-e29b-41d4-a716-446655440000). You’ll need this for initialization.

3. Get your API key

Go to Settings > Credentials and copy your API key. You’ll need this for initialization.

Detailed setup

Installation

pip install fiddler-langchain

Framework Compatibility:

LangChain V1: >= 1.0.0 — agents built with langchain.agents.create_agent
Python: 3.10-3.14
fiddler-otel: >= 1.0.0 (installed automatically)
OpenTelemetry: API and SDK >= 1.27.0 (installed automatically)

Configuration

Direct initialization (Recommended)

import langchain.agents
from fiddler_otel import FiddlerClient
from fiddler_langchain import FiddlerLangChainInstrumentor

client = FiddlerClient(
    application_id='your-app-id',         # Required (UUID4 format)
    api_key='your-api-key',               # Required when otlp_enabled=True (default)
    url='https://your-instance.fiddler.ai'  # Required when otlp_enabled=True (default)
)

instrumentor = FiddlerLangChainInstrumentor(client=client)
instrumentor.instrument()

Using environment variables

import os
import langchain.agents
from fiddler_otel import FiddlerClient
from fiddler_langchain import FiddlerLangChainInstrumentor

client = FiddlerClient(
    application_id=os.getenv('FIDDLER_APPLICATION_ID'),
    api_key=os.getenv('FIDDLER_API_KEY'),
    url=os.getenv('FIDDLER_URL')
)

FiddlerLangChainInstrumentor(client=client).instrument()

Environment Variables Reference:

Variable	Description	Example
`FIDDLER_API_KEY`	Your Fiddler API key	`fid_...`
`FIDDLER_APPLICATION_ID`	Your application UUID4	`550e8400-e29b-41d4-a716-446655440000`
`FIDDLER_URL`	Your Fiddler instance URL	`https://your-instance.fiddler.ai`

Instrumentation methods

The Fiddler LangChain SDK provides two instrumentation approaches:

Approach	Best For	Key API
Auto-Instrumentation	All agents in an application	`FiddlerLangChainInstrumentor`
Manual Middleware	Per-agent control	`FiddlerAgentMiddleware`

Auto-instrumentation

FiddlerLangChainInstrumentor.instrument() monkey-patches langchain.agents.create_agent once. Every subsequent call to create_agent() automatically receives a FiddlerAgentMiddleware. No changes to individual agent creation calls are needed.

import langchain.agents
from langchain_openai import ChatOpenAI
from fiddler_otel import FiddlerClient
from fiddler_langchain import FiddlerLangChainInstrumentor

client = FiddlerClient(application_id='...', api_key='...', url='...')

# Instrument once at startup
instrumentor = FiddlerLangChainInstrumentor(client=client)
instrumentor.instrument()

# All agents created after this point are automatically traced
agent1 = langchain.agents.create_agent(
    model=ChatOpenAI(model='gpt-4o-mini'),
    tools=[search_tool],
    name='search_agent',
)
agent2 = langchain.agents.create_agent(
    model=ChatOpenAI(model='gpt-4o-mini'),
    tools=[booking_tool],
    name='booking_agent',
)

Key behaviors:

Idempotent: Calling instrument() multiple times is safe — it will not create duplicate middleware.
Agent naming: If name='...' is passed to create_agent(), that name is used for the agent in traces. If omitted, no agent name is set and the agent appears without a label in the UI.
Existing middleware preserved: If you pass a FiddlerAgentMiddleware instance manually in middleware=[...], the instrumentor skips injection for that call so your manual configuration is preserved.

Uninstrumenting:

# Restore original create_agent (agents already created keep their middleware)
instrumentor.uninstrument()

Manual middleware

For per-agent control, pass FiddlerAgentMiddleware directly to create_agent() without using the instrumentor:

import langchain.agents
from langchain_openai import ChatOpenAI
from fiddler_otel import FiddlerClient
from fiddler_langchain import FiddlerAgentMiddleware

client = FiddlerClient(application_id='...', api_key='...', url='...')

agent = langchain.agents.create_agent(
    model=ChatOpenAI(model='gpt-4o-mini'),
    tools=[search_tool],
    name='my_agent',
    middleware=[FiddlerAgentMiddleware(client=client, agent_name='my_agent')],
)

Use manual middleware when you want to trace only specific agents, or when you need different configurations per agent.

Advanced usage

Multi-turn conversations

Use set_conversation_id() to link multiple agent invocations into a single conversation in the Fiddler UI. All agents in the application that share the same conversation_id appear together in conversation-level views.

from fiddler_langchain import set_conversation_id
import uuid

# Call once per conversation
set_conversation_id(str(uuid.uuid4()))

# All agent invocations after this carry the same conversation_id
result1 = agent.invoke({'messages': [{'role': 'user', 'content': 'Find hotels in Tokyo.'}]})
result2 = agent.invoke({'messages': [{'role': 'user', 'content': 'Book the cheapest one.'}]})

LLM context

Attach contextual metadata to LLM spans by calling set_llm_context() before the agent runs. The instrumentation reads this value from the model’s metadata at invocation time and records it as gen_ai.llm.context on every LLM span for that model.

import langchain.agents
from langchain_openai import ChatOpenAI
from fiddler_otel import FiddlerClient
from fiddler_langchain import FiddlerLangChainInstrumentor, set_llm_context

client = FiddlerClient(application_id='...', api_key='...', url='...')
FiddlerLangChainInstrumentor(client=client).instrument()

model = ChatOpenAI(model='gpt-4o-mini')
set_llm_context(model, 'User profile: enterprise customer, prefers concise answers')

agent = langchain.agents.create_agent(model=model, tools=[...], name='my_agent')

set_llm_context() accepts both plain model instances (BaseLanguageModel) and RunnableBinding instances (for example, models wrapped with .with_config() or .bind_tools()).

Clearing LLM context for non-RAG steps

In multi-step agent workflows, context set after a RAG retrieval step leaks into subsequent non-RAG LLM calls (tool planning, routing, etc.), causing unintended faithfulness evaluation. Use clear_llm_context() to explicitly remove context before non-RAG steps:

from fiddler_langchain import set_llm_context, clear_llm_context

# After RAG retrieval — attach context for faithfulness evaluation
set_llm_context(model, retrieved_documents)
response = agent.invoke(rag_prompt)  # faithfulness evaluated

# Before non-RAG steps — clear context to skip faithfulness evaluation
clear_llm_context(model)
plan = agent.invoke(planning_prompt)  # no faithfulness evaluation

clear_llm_context(model) is equivalent to set_llm_context(model, None).

Span-level attributes

Use add_span_attributes() to attach custom metadata to a specific LangChain component (model, tool, or retriever). The middleware reads these attributes when creating the span for that component and records them as fiddler.span.user.{key}.

import langchain.agents
from langchain_openai import ChatOpenAI
from langchain_core.tools import tool
from fiddler_otel import FiddlerClient
from fiddler_langchain import FiddlerLangChainInstrumentor, add_span_attributes

client = FiddlerClient(application_id='...', api_key='...', url='...')
FiddlerLangChainInstrumentor(client=client).instrument()

# Add attributes to a specific model — appear only on that model's LLM spans
model = ChatOpenAI(model='gpt-4o-mini')
add_span_attributes(model, model_version='v2', cost_center='cc-123')

# Add attributes to a specific tool — appear only on that tool's spans
@tool
def search_hotels(query: str) -> str:
    """Search for hotels."""
    return f'Hotels for: {query}'

add_span_attributes(search_hotels, category='travel', region='apac')

agent = langchain.agents.create_agent(model=model, tools=[search_hotels], name='my_agent')

Unlike add_session_attributes (which applies to every span in the context), add_span_attributes is scoped to a single component.

Session attributes

Use add_session_attributes() to attach metadata that appears on every span created in the current thread or async coroutine. Use this for user-level or environment-level metadata that applies to the whole session.

import langchain.agents
from fiddler_otel import FiddlerClient
from fiddler_langchain import FiddlerLangChainInstrumentor, add_session_attributes

client = FiddlerClient(application_id='...', api_key='...', url='...')
FiddlerLangChainInstrumentor(client=client).instrument()

# Set once — applies to all spans in this context (emitted as fiddler.session.user.{key})
add_session_attributes('user_id', 'user_12345')
add_session_attributes('environment', 'production')
add_session_attributes('tier', 'premium')

agent = langchain.agents.create_agent(model=ChatOpenAI(model='gpt-4o-mini'), tools=[...], name='my_agent')
agent.invoke({'messages': [{'role': 'user', 'content': 'Hello!'}]})

Retriever instrumentation

The LangChain V1 middleware does not expose a dedicated retriever hook. Following the same convention used in fiddler-langgraph, retrievers are treated as tools. Wrap your retriever with @tool (or use create_retriever_tool) and pass it to create_agent. The middleware’s tool hook captures the retriever call automatically as a TYPE=tool span — with the query as tool_input and the retrieved documents as tool_output.

import langchain.agents
from langchain_core.tools import tool
from fiddler_otel import FiddlerClient
from fiddler_langchain import FiddlerLangChainInstrumentor

client = FiddlerClient(application_id='...', api_key='...', url='...')
FiddlerLangChainInstrumentor(client=client).instrument()

retriever = vector_store.as_retriever()

@tool
def search_docs(query: str) -> str:
    """Search company documents for relevant information."""
    return str(retriever.invoke(query))

agent = langchain.agents.create_agent(
    model=ChatOpenAI(model='gpt-4o-mini'),
    tools=[search_docs],
    name='rag_agent',
)

The resulting trace:

[Span] rag_agent           (Agent root - TYPE=agent)
  └── [Span] gpt-4o-mini   (LLM call  - TYPE=llm)
  └── [Span] search_docs   (Retriever as Tool - TYPE=tool)
  └── [Span] gpt-4o-mini   (LLM call  - TYPE=llm)

Multi-agent setup

With the instrumentor, a single instrument() call patches create_agent so every agent is traced. Pass name='...' to each create_agent() to label agents in traces. When a sub-agent is invoked from within a delegation tool, its root Agent span is automatically created as a child of the tool span — the entire multi-agent flow appears in a single trace. No manual linking is needed: wrap_tool_call attaches the active tool span into the OTel context before invoking the handler, and before_agent detects that active span and nests under it.

import langchain.agents
from langchain_openai import ChatOpenAI
from langchain_core.tools import tool
from fiddler_otel import FiddlerClient
from fiddler_langchain import FiddlerLangChainInstrumentor

client = FiddlerClient(application_id='...', api_key='...', url='...')
FiddlerLangChainInstrumentor(client=client).instrument()

# Sub-agents
flight_agent = langchain.agents.create_agent(
    model=ChatOpenAI(model='gpt-4o-mini'),
    tools=[book_flight],
    name='flight_assistant',
)
hotel_agent = langchain.agents.create_agent(
    model=ChatOpenAI(model='gpt-4o-mini'),
    tools=[search_hotel, book_hotel],
    name='hotel_assistant',
)

# Delegation tools — the tool span becomes the parent of the sub-agent span automatically
@tool
def delegate_to_flight_assistant(task: str) -> str:
    """Delegate a flight booking task to the flight assistant."""
    result = flight_agent.invoke({'messages': [{'role': 'user', 'content': task}]})
    return result['messages'][-1].content

@tool
def delegate_to_hotel_assistant(task: str) -> str:
    """Delegate a hotel search and booking task to the hotel assistant."""
    result = hotel_agent.invoke({'messages': [{'role': 'user', 'content': task}]})
    return result['messages'][-1].content

# Supervisor — invoking this produces one trace containing all agents
supervisor = langchain.agents.create_agent(
    model=ChatOpenAI(model='gpt-4o-mini'),
    tools=[delegate_to_flight_assistant, delegate_to_hotel_assistant],
    name='supervisor',
)

supervisor.invoke({'messages': [{'role': 'user', 'content': 'Book a flight and a hotel in Tokyo.'}]})

Trace output (all agents appear in a single trace):

[Span] supervisor                              (root  - TYPE=agent)
  └── [Span] gpt-4o-mini                       (LLM   - TYPE=llm)
  └── [Span] delegate_to_flight_assistant       (Tool  - TYPE=tool)
       └── [Span] flight_assistant              (Agent - TYPE=agent)
            └── [Span] gpt-4o-mini             (LLM   - TYPE=llm)
            └── [Span] book_flight             (Tool  - TYPE=tool)
  └── [Span] delegate_to_hotel_assistant        (Tool  - TYPE=tool)
       └── [Span] hotel_assistant              (Agent - TYPE=agent)
            └── [Span] gpt-4o-mini             (LLM   - TYPE=llm)
            └── [Span] search_hotel            (Tool  - TYPE=tool)
            └── [Span] book_hotel              (Tool  - TYPE=tool)

set_conversation_id() is useful for linking multiple top-level invocations (e.g., multi-turn conversations) — not for joining sub-agents within a single invocation, since they already share the same trace automatically.

Async agents

The instrumentation fully supports async agents via the awrap_model_call and awrap_tool_call hooks. Use agent.ainvoke() — no additional configuration needed:

import asyncio
import langchain.agents
from langchain_openai import ChatOpenAI
from fiddler_otel import FiddlerClient
from fiddler_langchain import FiddlerLangChainInstrumentor

client = FiddlerClient(application_id='...', api_key='...', url='...')
FiddlerLangChainInstrumentor(client=client).instrument()

agent = langchain.agents.create_agent(
    model=ChatOpenAI(model='gpt-4o-mini'),
    tools=[...],
    name='my_agent',
)

async def main():
    result = await agent.ainvoke({'messages': [{'role': 'user', 'content': 'Hello!'}]})
    print(result['messages'][-1].content)

asyncio.run(main())

The instrumentation automatically uses the async lifecycle hooks when the agent is invoked asynchronously, producing the same span hierarchy as the sync path.

Error handling

If an LLM call or tool call raises an exception, the instrumentation:

Catches the exception and marks the failing span with StatusCode.ERROR
Re-raises the exception so normal error handling in your application is unaffected
Cleanly closes the root agent span — no dangling open spans

try:
    result = agent.invoke({'messages': [{'role': 'user', 'content': 'Hello!'}]})
except Exception as e:
    # The failing LLM or tool span is already marked ERROR in Fiddler
    # The root agent span is closed — all spans are recorded
    raise

This means partial traces are never lost — all spans up to the point of failure are recorded and visible in Fiddler.

Running in AWS SageMaker

Because fiddler-langchain exports traces through FiddlerClient, it inherits the Fiddler OTel SDK’s AWS SageMaker Partner App authentication. Install the sagemaker extra (pip install "fiddler-otel[sagemaker]") and set the AWS_PARTNER_APP_AUTH, AWS_PARTNER_APP_ARN, and AWS_PARTNER_APP_URL environment variables — your instrumentation code is unchanged.

Flush and shutdown

# Sync
client.force_flush()
client.shutdown()

# Async
await client.aflush()
await client.ashutdown()

# Context manager (calls shutdown() on exit)
with FiddlerClient(application_id='...', api_key='...', url='...') as client:
    FiddlerLangChainInstrumentor(client=client).instrument()
    result = agent.invoke({'messages': [...]})

Local debugging

JSONL file capture (save a local copy of spans in addition to Fiddler export): jsonl_capture_enabled=True is additive — spans are saved to a local JSONL file and continue to be exported to Fiddler via OTLP. Setting this to True does not suppress or disable the OTLP export to Fiddler.

client = FiddlerClient(
    application_id='...',
    api_key='...',
    url='...',
    jsonl_capture_enabled=True,       # Saves spans locally; OTLP export to Fiddler still active
    jsonl_file_path='trace_data.jsonl',
)

Override the output file path via environment variable:

FIDDLER_JSONL_FILE=trace_data.jsonl python my_agent.py

Each line in the output file is a JSON object. Fields:

Field	Description
`trace_id`	Trace identifier
`span_id`	Span identifier
`parent_span_id`	Parent span identifier
`span_type`	Span type (`agent`, `llm`, `tool`)
`agent_name`	Name of the agent
`conversation_id`	Conversation identifier
`model_name`	LLM model name
`model_provider`	LLM provider
`llm_input_system`	System prompt
`llm_input_user`	User prompt
`llm_output`	LLM completion text
`llm_context`	Context set via `set_llm_context()`
`llm_token_count_input`	Input token count
`llm_token_count_output`	Output token count
`llm_token_count_total`	Total token count
`gen_ai_input_messages`	Full input message history (JSON)
`gen_ai_output_messages`	Output messages (JSON)
`tool_name`	Tool/function name
`tool_input`	Tool input arguments
`tool_output`	Tool result
`tool_definitions`	Available tool schemas (JSON)

Relationship to fiddler-langgraph

Package	Framework	Instrumentation Approach
`fiddler-langchain`	LangChain V1 (`create_agent`)	`FiddlerLangChainInstrumentor` auto-patches `langchain.agents.create_agent`
`fiddler-langgraph`	LangGraph (`StateGraph.compile()`)	`LangGraphInstrumentor` callback handler

Both packages depend on fiddler-otel for the core FiddlerClient and span wrappers.

What’s next?

Fiddler OTel SDK — For decorator-based instrumentation of custom Python functions
LangGraph SDK — If your application uses LangGraph
Agentic Observability Concepts — Understand the agent lifecycle and monitoring approach

Agentic AI & LLM Frameworks

Data Platforms & Pipelines

ML Platforms & Tools

Cloud Platforms & Deployment

Monitoring & Alerting

Fiddler LangChain SDK

Fiddler LangChain SDK

What you’ll need

Quick start

What gets monitored

Trace hierarchy

Captured data

Application setup

1. Create your application in Fiddler

2. Copy your Application ID

3. Get your API key

Detailed setup

Installation

Configuration

Direct initialization (Recommended)

Using environment variables

Instrumentation methods

Auto-instrumentation

Manual middleware

Advanced usage

Multi-turn conversations

LLM context

Clearing LLM context for non-RAG steps

Span-level attributes

Session attributes

Retriever instrumentation

Multi-agent setup

Async agents

Error handling

Running in AWS SageMaker

Flush and shutdown

Local debugging

Relationship to fiddler-langgraph

What’s next?

​Fiddler LangChain SDK

​What you’ll need

​Quick start

​What gets monitored

​Trace hierarchy

​Captured data

​Application setup

​1. Create your application in Fiddler

​2. Copy your Application ID

​3. Get your API key

​Detailed setup

​Installation

​Configuration

​Direct initialization (Recommended)

​Using environment variables

​Instrumentation methods

​Auto-instrumentation

​Manual middleware

​Advanced usage

​Multi-turn conversations

​LLM context

​Clearing LLM context for non-RAG steps

​Span-level attributes

​Session attributes

​Retriever instrumentation

​Multi-agent setup

​Async agents

​Error handling

​Running in AWS SageMaker

​Flush and shutdown

​Local debugging

​Relationship to fiddler-langgraph

​What’s next?

Fiddler LangChain SDK

What you’ll need

Quick start

What gets monitored

Trace hierarchy

Captured data

Application setup

1. Create your application in Fiddler

2. Copy your Application ID

3. Get your API key

Detailed setup

Installation

Configuration

Direct initialization (Recommended)

Using environment variables

Instrumentation methods

Auto-instrumentation

Manual middleware

Advanced usage

Multi-turn conversations

LLM context

Clearing LLM context for non-RAG steps

Span-level attributes

Session attributes

Retriever instrumentation

Multi-agent setup

Async agents

Error handling

Running in AWS SageMaker

Flush and shutdown

Local debugging

Relationship to fiddler-langgraph

What’s next?