Metrics Tracking - OpenHands Docs

Overview

The OpenHands SDK provides metrics tracking at two levels: individual LLM metrics and aggregated conversation-level costs:

You can access detailed metrics from each LLM instance using the llm.metrics object to track token usage, costs, and latencies per API call.
For a complete view, use conversation.conversation_stats to get aggregated costs across all LLMs used in a conversation, including the primary agent LLM and any auxiliary LLMs (such as those used by the context condenser).

Getting Metrics from Individual LLMs

A ready-to-run example is available here!

Track token usage, costs, and performance metrics from LLM interactions:

Accessing Individual LLM Metrics

Access metrics directly from the LLM object after running the conversation:

conversation.run()

assert llm.metrics is not None
print(f"Final LLM metrics: {llm.metrics.model_dump()}")

The llm.metrics object is an instance of the Metrics class, which provides detailed information including:

accumulated_cost - Total accumulated cost across all API calls
accumulated_token_usage - Aggregated token usage with fields like:
- prompt_tokens - Number of input tokens processed
- completion_tokens - Number of output tokens generated
- cache_read_tokens - Cache hits (if supported by the model)
- cache_write_tokens - Cache writes (if supported by the model)
- reasoning_tokens - Reasoning tokens (for models that support extended thinking)
- context_window - Context window size used
costs - List of individual cost records per API call
token_usages - List of detailed token usage records per API call
response_latencies - List of response latency metrics per API call

For more details on the available metrics and methods, refer to the source code.

Ready-to-run Example (LLM metrics)

This example is available on GitHub: examples/01_standalone_sdk/13_get_llm_metrics.py

examples/01_standalone_sdk/13_get_llm_metrics.py

import os

from pydantic import SecretStr

from openhands.sdk import (
    LLM,
    Agent,
    Conversation,
    Event,
    LLMConvertibleEvent,
    get_logger,
)
from openhands.sdk.tool import Tool
from openhands.tools.file_editor import FileEditorTool
from openhands.tools.terminal import TerminalTool


logger = get_logger(__name__)

# Configure LLM
api_key = os.getenv("LLM_API_KEY")
assert api_key is not None, "LLM_API_KEY environment variable is not set."
model = os.getenv("LLM_MODEL", "anthropic/claude-sonnet-4-5-20250929")
base_url = os.getenv("LLM_BASE_URL")
llm = LLM(
    usage_id="agent",
    model=model,
    base_url=base_url,
    api_key=SecretStr(api_key),
)

cwd = os.getcwd()
tools = [
    Tool(name=TerminalTool.name),
    Tool(name=FileEditorTool.name),
]

# Add MCP Tools
mcp_config = {"mcpServers": {"fetch": {"command": "uvx", "args": ["mcp-server-fetch"]}}}

# Agent
agent = Agent(llm=llm, tools=tools, mcp_config=mcp_config)

llm_messages = []  # collect raw LLM messages


def conversation_callback(event: Event):
    if isinstance(event, LLMConvertibleEvent):
        llm_messages.append(event.to_llm_message())


# Conversation
conversation = Conversation(
    agent=agent,
    callbacks=[conversation_callback],
    workspace=cwd,
)

logger.info("Starting conversation with MCP integration...")
conversation.send_message(
    "Read https://github.com/OpenHands/OpenHands and write 3 facts "
    "about the project into FACTS.txt."
)
conversation.run()

conversation.send_message("Great! Now delete that file.")
conversation.run()

print("=" * 100)
print("Conversation finished. Got the following LLM messages:")
for i, message in enumerate(llm_messages):
    print(f"Message {i}: {str(message)[:200]}")

assert llm.metrics is not None
print(
    f"Conversation finished. Final LLM metrics with details: {llm.metrics.model_dump()}"
)

# Report cost
cost = llm.metrics.accumulated_cost
print(f"EXAMPLE_COST: {cost}")

You can run the example code as-is.

The model name should follow the LiteLLM convention: provider/model_name (e.g., anthropic/claude-sonnet-4-5-20250929, openai/gpt-4o). The LLM_API_KEY should be the API key for your chosen provider.

ChatGPT Plus/Pro subscribers: You can use LLM.subscription_login() to authenticate with your ChatGPT account and access Codex models without consuming API credits. See the LLM Subscriptions guide for details.

Using LLM Registry for Cost Tracking

A ready-to-run example is available here!

The LLM Registry allows you to maintain a centralized registry of LLM instances, each identified by a unique usage_id. This is particularly useful for tracking costs across different LLMs used in your application.

How the LLM Registry Works

Each LLM is created with a unique usage_id (e.g., “agent”, “condenser”) that serves as its identifier in the registry. The registry maintains references to all LLM instances, allowing you to:

Register LLMs: Add LLM instances to the registry with llm_registry.add(llm)
Retrieve LLMs: Get LLM instances by their usage ID with llm_registry.get("usage_id")
List Usage IDs: View all registered usage IDs with llm_registry.list_usage_ids()
Track Costs Separately: Each LLM’s metrics are tracked independently by its usage ID

This pattern is essential when using multiple LLMs in your application, such as having a primary agent LLM and a separate LLM for context condensing.

Ready-to-run Example (LLM Registry)

This example is available on GitHub: examples/01_standalone_sdk/05_use_llm_registry.py

examples/01_standalone_sdk/05_use_llm_registry.py

import os

from pydantic import SecretStr

from openhands.sdk import (
    LLM,
    Agent,
    Conversation,
    Event,
    LLMConvertibleEvent,
    LLMRegistry,
    Message,
    TextContent,
    get_logger,
)
from openhands.sdk.tool import Tool
from openhands.tools.terminal import TerminalTool


logger = get_logger(__name__)

# Configure LLM using LLMRegistry
api_key = os.getenv("LLM_API_KEY")
assert api_key is not None, "LLM_API_KEY environment variable is not set."
model = os.getenv("LLM_MODEL", "anthropic/claude-sonnet-4-5-20250929")
base_url = os.getenv("LLM_BASE_URL")

# Create LLM instance
main_llm = LLM(
    usage_id="agent",
    model=model,
    base_url=base_url,
    api_key=SecretStr(api_key),
)

# Create LLM registry and add the LLM
llm_registry = LLMRegistry()
llm_registry.add(main_llm)

# Get LLM from registry
llm = llm_registry.get("agent")

# Tools
cwd = os.getcwd()
tools = [Tool(name=TerminalTool.name)]

# Agent
agent = Agent(llm=llm, tools=tools)

llm_messages = []  # collect raw LLM messages


def conversation_callback(event: Event):
    if isinstance(event, LLMConvertibleEvent):
        llm_messages.append(event.to_llm_message())


conversation = Conversation(
    agent=agent, callbacks=[conversation_callback], workspace=cwd
)

conversation.send_message("Please echo 'Hello!'")
conversation.run()

print("=" * 100)
print("Conversation finished. Got the following LLM messages:")
for i, message in enumerate(llm_messages):
    print(f"Message {i}: {str(message)[:200]}")

print("=" * 100)
print(f"LLM Registry usage IDs: {llm_registry.list_usage_ids()}")

# Demonstrate getting the same LLM instance from registry
same_llm = llm_registry.get("agent")
print(f"Same LLM instance: {llm is same_llm}")

# Demonstrate requesting a completion directly from an LLM
resp = llm.completion(
    messages=[
        Message(role="user", content=[TextContent(text="Say hello in one word.")])
    ]
)
# Access the response content via OpenHands LLMResponse
msg = resp.message
texts = [c.text for c in msg.content if isinstance(c, TextContent)]
print(f"Direct completion response: {texts[0] if texts else str(msg)}")

# Report cost
cost = llm.metrics.accumulated_cost
print(f"EXAMPLE_COST: {cost}")

You can run the example code as-is.

Getting Aggregated Conversation Costs

This example is available on GitHub: examples/01_standalone_sdk/21_generate_extraneous_conversation_costs.py

Beyond individual LLM metrics, you can access aggregated costs for an entire conversation using conversation.conversation_stats. This is particularly useful when your conversation involves multiple LLMs, such as the main agent LLM and auxiliary LLMs for tasks like context condensing.

examples/01_standalone_sdk/21_generate_extraneous_conversation_costs.py

import os

from pydantic import SecretStr
from tabulate import tabulate

from openhands.sdk import (
    LLM,
    Agent,
    Conversation,
    LLMSummarizingCondenser,
    Message,
    TextContent,
    get_logger,
)
from openhands.sdk.tool.spec import Tool
from openhands.tools.terminal import TerminalTool


logger = get_logger(__name__)

# Configure LLM using LLMRegistry
api_key = os.getenv("LLM_API_KEY")
assert api_key is not None, "LLM_API_KEY environment variable is not set."
model = os.getenv("LLM_MODEL", "anthropic/claude-sonnet-4-5-20250929")
base_url = os.getenv("LLM_BASE_URL")

# Create LLM instance
llm = LLM(
    usage_id="agent",
    model=model,
    base_url=base_url,
    api_key=SecretStr(api_key),
)

llm_condenser = LLM(
    model=model,
    base_url=base_url,
    api_key=SecretStr(api_key),
    usage_id="condenser",
)

# Tools
condenser = LLMSummarizingCondenser(llm=llm_condenser, max_size=10, keep_first=2)

cwd = os.getcwd()
agent = Agent(
    llm=llm,
    tools=[
        Tool(
            name=TerminalTool.name,
        ),
    ],
    condenser=condenser,
)

conversation = Conversation(agent=agent, workspace=cwd)
conversation.send_message(
    message=Message(
        role="user",
        content=[TextContent(text="Please echo 'Hello!'")],
    )
)
conversation.run()

# Demonstrate extraneous costs part of the conversation
second_llm = LLM(
    usage_id="demo-secondary",
    model=model,
    base_url=os.getenv("LLM_BASE_URL"),
    api_key=SecretStr(api_key),
)
conversation.llm_registry.add(second_llm)
completion_response = second_llm.completion(
    messages=[Message(role="user", content=[TextContent(text="echo 'More spend!'")])]
)

# Access total spend
spend = conversation.conversation_stats.get_combined_metrics()
print("\n=== Total Spend for Conversation ===\n")
print(f"Accumulated Cost: ${spend.accumulated_cost:.6f}")
if spend.accumulated_token_usage:
    print(f"Prompt Tokens: {spend.accumulated_token_usage.prompt_tokens}")
    print(f"Completion Tokens: {spend.accumulated_token_usage.completion_tokens}")
    print(f"Cache Read Tokens: {spend.accumulated_token_usage.cache_read_tokens}")
    print(f"Cache Write Tokens: {spend.accumulated_token_usage.cache_write_tokens}")

spend_per_usage = conversation.conversation_stats.usage_to_metrics
print("\n=== Spend Breakdown by Usage ID ===\n")
rows = []
for usage_id, metrics in spend_per_usage.items():
    rows.append(
        [
            usage_id,
            f"${metrics.accumulated_cost:.6f}",
            metrics.accumulated_token_usage.prompt_tokens
            if metrics.accumulated_token_usage
            else 0,
            metrics.accumulated_token_usage.completion_tokens
            if metrics.accumulated_token_usage
            else 0,
        ]
    )

print(
    tabulate(
        rows,
        headers=["Usage ID", "Cost", "Prompt Tokens", "Completion Tokens"],
        tablefmt="github",
    )
)

# Report cost
cost = conversation.conversation_stats.get_combined_metrics().accumulated_cost
print(f"EXAMPLE_COST: {cost}")

You can run the example code as-is.

Understanding Conversation Stats

The conversation.conversation_stats object provides cost tracking across all LLMs used in a conversation. It is an instance of the ConversationStats class, which provides the following key features:

Key Methods and Properties

usage_to_metrics: A dictionary mapping usage IDs to their respective Metrics objects. This allows you to track costs separately for each LLM used in the conversation.
get_combined_metrics(): Returns a single Metrics object that aggregates costs across all LLMs used in the conversation. This gives you the total cost of the entire conversation.
get_metrics_for_usage(usage_id: str): Retrieves the Metrics object for a specific usage ID, allowing you to inspect costs for individual LLMs.

# Get combined metrics for the entire conversation
total_metrics = conversation.conversation_stats.get_combined_metrics()
print(f"Total cost: ${total_metrics.accumulated_cost:.6f}")

# Get metrics for a specific LLM by usage ID
agent_metrics = conversation.conversation_stats.get_metrics_for_usage("agent")
print(f"Agent cost: ${agent_metrics.accumulated_cost:.6f}")

# Access all usage IDs and their metrics
for usage_id, metrics in conversation.conversation_stats.usage_to_metrics.items():
    print(f"{usage_id}: ${metrics.accumulated_cost:.6f}")

Next Steps

Context Condenser - Learn about context management and how it uses separate LLMs
LLM Routing - Optimize costs with smart routing between different models

​Overview

​Getting Metrics from Individual LLMs

​Accessing Individual LLM Metrics

​Ready-to-run Example (LLM metrics)

​Using LLM Registry for Cost Tracking

​How the LLM Registry Works

​Ready-to-run Example (LLM Registry)

​Getting Aggregated Conversation Costs

​Understanding Conversation Stats

​Key Methods and Properties

​Next Steps

Overview

Getting Metrics from Individual LLMs

Accessing Individual LLM Metrics

Ready-to-run Example (LLM metrics)

Using LLM Registry for Cost Tracking

How the LLM Registry Works

Ready-to-run Example (LLM Registry)

Getting Aggregated Conversation Costs

Understanding Conversation Stats

Key Methods and Properties

Next Steps