observability.md

Using LangSmith with LangGraph enables robust observability in multi-agent systems, especially when teams (like research teams using tools such as Tavily) are coordinated via a supervisor agent. Observability ensures transparency, debuggability, and traceability of workflows across the graph.

🧠 Why Use Observability in a LangGraph?

Debug execution flows across agents and tools.
Visualize agent decisions, tool calls, and responses.
Track latency, success/failure, and metadata.
Compare behavior across runs or task types.

LangSmith provides real-time and retrospective insight by logging:

Prompt inputs and outputs.
Tool invocations and results.
Decision branching within the graph.

🔁 Sample Team Graph: Research with Tavily + Supervisor

In this example:

A user submits a query.
A Supervisor routes it to a Research Team, which uses Tavily to retrieve information.
Output is returned to the Supervisor for evaluation.

graph TD
  A[User Input] --> B[Supervisor Agent]
  B --> C{Task Type?}
  C -- Research --> D[Research Team]
  D --> E[Tool: Tavily Search]
  E --> F[Research Summary]
  F --> G[Supervisor Eval]
  G --> H[Final Output to User]

🔍 LangSmith Integration Steps

1. Initialize a Tracer for LangSmith

from langsmith import traceable

@traceable(name="research_team")
def research_team(query):
    # Call Tavily, process result
    ...

2. Wrap Supervisor and Agents

@traceable(name="supervisor")
def supervisor_router(user_input):
    if "research" in user_input:
        return research_team(user_input)
    ...

3. Inspect Runs in LangSmith UI

View structured traces with nodes:
- supervisor_router
- research_team
- Tavily tool invocation
Filter by tags, runtime, or errors.

📊 Benefits in a Team-Based LangGraph

Identify slow tools or agents.
Understand decision bottlenecks.
Ensure prompt consistency.
Export trace data for QA or reporting.

LangSmith turns your LangGraph from a black box into a traceable, auditable system — key for reliable, iterative development.

decagondev/observability.md