> ## Documentation Index
> Fetch the complete documentation index at: https://developer.watson-orchestrate.ibm.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Context

## Overview

Use context compression to manage long conversation histories by summarizing older messages while preserving recent context.\
This helps maintain performance and stay within model token limits during multi-turn interactions.

### When to use context compression

Use context compression when:

* Conversation history exceeds token limits
* Building agents with extended multi-turn conversations
* Using models with strict context window limits

### Initialize the SDK

Initialize the SDK client before using context compression.

For more information, see [Client](/sdk/client).

### Usage

```python PYTHON [expandable] theme={null}


from ibm_watsonx_orchestrate_sdk import Client
from langchain_core.messages.utils import count_tokens_approximately


def create_agent(config: RunnableConfig):
    # Initialize the client
    execution_context = config.get("configurable", {}).get("execution_context")
    client = Client(execution_context=execution_context)

    def agent_node(state: AgentState):
        messages = state.get("messages", [])
    
        if count_tokens_approximately(messages) > 30000:
            response = client.context.compress(messages=messages)
            messages = compressed_messages
    
        # -----------------------------------
        # Your business logic goes here
        #
        # - Validate inputs
        # - Call tools/models
        # - Process responses
        # - Update state
        # -----------------------------------
    
        return {"messages": [response]}


    # Build the graph
    builder = StateGraph(AgentState)
    builder.add_node("agent", agent_node)
    builder.set_entry_point("agent")
    builder.add_edge("agent", END)
    
    return builder
```

#### API reference

`client.context.compress()`

Compress a conversation history into a summary.

#### Parameters

* messages (`List[Dict[str, Any]]`)
  Required. List of message dictionaries in OpenAI format. Minimum 2 messages.

* model (`str`)
  Optional. Model name used for summarization. Uses the default model if not provided.

#### Returns

A SummarizationResponse object:

* summary (str)Generated summary of the conversation
* original\_message\_count (int)Number of messages summarized
* model\_used (str)Model used for summarization

#### Raises

* ValueError if fewer than 2 messages are provided
* ClientAPIException if the API request fails

### Message format

Messages must follow OpenAI format:

```json JSON [expandable] theme={null}
{
  "role": "user",
  "content": "Message text",
  "name": "optional",
  "tool_calls": [],
  "tool_call_id": "optional",
  "reasoning": "optional"
}
```

#### Supported roles

* `user`
* `assistant`
* `system`
* `tool`

#### Usage guidance

* Pass full conversation history for best summarization
* Use summaries to reduce prompt size before LLM calls
* Combine summaries with recent messages to maintain context continuity

#### What to test

* Compression works with valid message lists
* Fails when fewer than 2 messages are provided
* Summary output is usable in downstream prompts
* Model selection behaves as expected

#### Mental model

* context compression reduces conversation size
* summaries replace older message windows
* recent messages should remain uncompressed
* use compression before LLM calls to control token usage

### References

* [Chat models](/sdk/chat_wxo)
* [Runs-on](/sdk/runs_on)