Documentation Index
Fetch the complete documentation index at: https://docs.agentbasis.co/llms.txt
Use this file to discover all available pages before exploring further.
AgentBasis integrates with LangChain via callbacks, providing full visibility into your chains’ execution steps.
Setup
import agentbasis
from agentbasis.frameworks.langchain import get_callback_handler
# Initialize AgentBasis first
agentbasis.init(api_key="your-api-key", agent_id="your-agent-id")
# Get a callback handler
handler = get_callback_handler()
Unlike OpenAI/Anthropic instrumentation which patches globally, LangChain requires explicitly passing the callback handler to your components.
Basic Usage
Pass the handler to your LangChain calls via the config parameter:
from langchain_openai import ChatOpenAI
handler = get_callback_handler()
llm = ChatOpenAI(model="gpt-4")
response = llm.invoke(
"Hello world",
config={"callbacks": [handler]}
)
Using get_callback_config
For convenience, use get_callback_config() to get a pre-configured dict:
from agentbasis.frameworks.langchain import get_callback_config
config = get_callback_config()
response = llm.invoke("Hello world", config=config)
Using instrument (Singleton)
Use instrument() to get a global singleton handler:
from agentbasis.frameworks.langchain import instrument
# Returns the same handler instance every time
handler = instrument()
# Use throughout your application
llm.invoke("Hello", config={"callbacks": [handler]})
Chains
Trace entire chain executions with parent-child relationships:
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from agentbasis.frameworks.langchain import get_callback_config
chain = LLMChain(
llm=llm,
prompt=PromptTemplate.from_template("Answer this: {query}")
)
result = chain.invoke(
{"query": "What is the capital of France?"},
config=get_callback_config()
)
from langchain.agents import create_openai_functions_agent, AgentExecutor
from langchain.tools import tool
from agentbasis.frameworks.langchain import get_callback_handler
@tool
def search(query: str) -> str:
"""Search for information."""
return f"Results for: {query}"
handler = get_callback_handler()
agent_executor = AgentExecutor(
agent=agent,
tools=[search],
callbacks=[handler] # Can also pass to constructor
)
result = agent_executor.invoke(
{"input": "Search for Python tutorials"}
)
Retrievers
RAG retriever operations are traced:
from langchain_community.vectorstores import FAISS
from langchain_openai import OpenAIEmbeddings
from agentbasis.frameworks.langchain import get_callback_config
vectorstore = FAISS.from_texts(["Document 1", "Document 2"], OpenAIEmbeddings())
retriever = vectorstore.as_retriever()
docs = retriever.invoke(
"search query",
config=get_callback_config()
)
Trace Structure
The callback handler creates nested spans showing the full execution tree:
langchain.chain.RetrievalQA
├── langchain.retriever.VectorStoreRetriever
│ └── query: "What is machine learning?"
│ └── documents: [...]
└── langchain.llm.ChatOpenAI
└── model: "gpt-4"
└── prompt: [...]
└── completion: "..."
Captured Data
The integration traces:
| Component | Captured Data |
|---|
| LLM | Model, prompts, completions, tokens, latency |
| Chain | Chain type, inputs, outputs, duration |
| Tool | Tool name, input, output, duration |
| Retriever | Query, retrieved documents, duration |
With User Context
Combine with AgentBasis context for per-user tracing:
import agentbasis
from agentbasis.frameworks.langchain import get_callback_config
# Set user context
agentbasis.set_user("user-123")
agentbasis.set_session("session-456")
# All traces will include user context
result = chain.invoke(
{"query": "Hello"},
config=get_callback_config()
)
