agentmetrics-langchain 0.2.0

AgentMetrics observability integration for LangChain agents

agentmetrics-langchain

AgentMetrics integration for LangChain. Pass one callback to any chain or agent .invoke() call and every run reports back to your dashboard showing latency, cost, token counts, tool calls, and errors, with no changes to your agent logic.

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Install

pip install agentmetrics-langchain

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Quickstart

from agentmetrics_langchain import AgentMetricsCallback

cb = AgentMetricsCallback(
    agent_id="my-langchain-agent",
    base_url="http://localhost:8099",
)

result = agent.invoke(
    {"input": "What is the weather in Paris?"},
    config={"callbacks": [cb]},
)

cb.flush()

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API

AgentMetricsCallback(agent_id, base_url)

Parameter	Default	Description
agent_id	"langchain-agent"	Label shown in the dashboard
base_url	"http://localhost:8099"	AgentMetrics server address

The callback is a BaseCallbackHandler. Pass it via config={"callbacks": [cb]} on any chain or agent .invoke() call. It tracks the top-level chain only, with nested sub-chains aggregated into the same run.

Supports both OpenAI-style and Anthropic-style token counting from usage_metadata and llm_output.

.flush(timeout=10.0)

Blocks until all in-flight HTTP requests complete. Call before process exit in scripts.

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What gets tracked

Each top-level chain invocation emits one event to /v1/events on completion or error:

Field	Description
status	success or failed
duration_ms	Wall-clock chain duration
input_tokens / output_tokens	Aggregated across all LLM calls in the chain
cache_read_tokens / cache_write_tokens	Cache token counts (Anthropic)
llm_calls	Number of LLM requests in the chain
tool_calls / tool_errors	Tool usage counts
tool_names	Set of tools invoked
model	Model name from the first LLM call
estimated_cost_usd	Computed from token counts and model pricing
error	First 500 chars of the error message on failure

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LangGraph

The callback works with LangGraph graphs the same way:

from langgraph.graph import StateGraph
from agentmetrics_langchain import AgentMetricsCallback

cb  = AgentMetricsCallback(base_url="http://localhost:8099")
app = build_graph().compile()

result = app.invoke(state, config={"callbacks": [cb]})

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License

MIT