Compaction ↗

anthropic-platform guide intermediate context-window

Original Documentation

Server-side context compaction for managing long conversations that approach context window limits.

Server-side compaction is the recommended strategy for managing context in long-running conversations and agentic workflows. It handles context management automatically with minimal integration work.

Compaction extends the effective context length for long-running conversations and tasks by automatically summarizing older context when approaching the context window limit. This isn’t just about staying under a token cap. As conversations get longer, models struggle to maintain focus across the full history. Compaction keeps the active context focused and performant by replacing stale content with concise summaries.

For a deeper look at why long contexts degrade and how compaction helps, see Effective context engineering.

This is ideal for:

Chat-based, multi-turn conversations where you want users to use one chat for a long period of time
Task-oriented prompts that require a lot of follow-up work (often tool use) that may exceed the 200K context window

Compaction is currently in beta. Include the beta header compact-2026-01-12 in your API requests to use this feature.

Compaction is eligible for Zero Data Retention (ZDR) arrangements.

Supported models#

Compaction is supported on the following models:

Claude Opus 4.6 (claude-opus-4-6)
Claude Sonnet 4.6 (claude-sonnet-4-6)

How compaction works#

When compaction is enabled, Claude automatically summarizes your conversation when it approaches the configured token threshold. The API:

Detects when input tokens exceed your specified trigger threshold.
Generates a summary of the current conversation.
Creates a compaction block containing the summary.
Continues the response with the compacted context.

On subsequent requests, append the response to your messages. The API automatically drops all message blocks prior to the compaction block, continuing the conversation from the summary.

Flow diagram showing the compaction process: when input tokens exceed the trigger threshold, Claude generates a summary in a compaction block and continues the response with the compacted context

Basic usage#

Enable compaction by adding the compact_20260112 strategy to context_management.edits in your Messages API request.

curl https://api.anthropic.com/v1/messages \
     --header "x-api-key: $ANTHROPIC_API_KEY" \
     --header "anthropic-version: 2023-06-01" \
     --header "anthropic-beta: compact-2026-01-12" \
     --header "content-type: application/json" \
     --data \
'{
    "model": "claude-opus-4-6",
    "max_tokens": 4096,
    "messages": [
        {
            "role": "user",
            "content": "Help me build a website"
        }
    ],
    "context_management": {
        "edits": [
            {
                "type": "compact_20260112"
            }
        ]
    }
}'

import anthropic

client = anthropic.Anthropic()

messages = [{"role": "user", "content": "Help me build a website"}]

response = client.beta.messages.create(
    betas=["compact-2026-01-12"],
    model="claude-opus-4-6",
    max_tokens=4096,
    messages=messages,
    context_management={"edits": [{"type": "compact_20260112"}]},
)

# Append the response (including any compaction block) to continue the conversation
messages.append({"role": "assistant", "content": response.content})


const client = new Anthropic();

const messages = [{ role: "user", content: "Help me build a website" }];

const response = await client.beta.messages.create({
  betas: ["compact-2026-01-12"],
  model: "claude-opus-4-6",
  max_tokens: 4096,
  messages,
  context_management: {
    edits: [{ type: "compact_20260112" }]
  }
});

// Append the response (including any compaction block) to continue the conversation
messages.push({ role: "assistant", content: response.content });

Parameters#

Parameter	Type	Default	Description
`type`	string	Required	Must be `"compact_20260112"`
`trigger`	object	150,000 tokens	When to trigger compaction. Must be at least 50,000 tokens.
`pause_after_compaction`	boolean	`false`	Whether to pause after generating the compaction summary
`instructions`	string	`null`	Custom summarization prompt. Completely replaces the default prompt when provided.

Trigger configuration#

Configure when compaction triggers using the trigger parameter:

response = client.beta.messages.create(
    betas=["compact-2026-01-12"],
    model="claude-opus-4-6",
    max_tokens=4096,
    messages=messages,
    context_management={
        "edits": [
            {
                "type": "compact_20260112",
                "trigger": {"type": "input_tokens", "value": 150000},
            }
        ]
    },
)

const response = await client.beta.messages.create({
  betas: ["compact-2026-01-12"],
  model: "claude-opus-4-6",
  max_tokens: 4096,
  messages,
  context_management: {
    edits: [
      {
        type: "compact_20260112",
        trigger: {
          type: "input_tokens",
          value: 150000
        }
      }
    ]
  }
});

Custom summarization instructions#

By default, compaction uses the following summarization prompt:

You have written a partial transcript for the initial task above. Please write a summary of the transcript. The purpose of this summary is to provide continuity so you can continue to make progress towards solving the task in a future context, where the raw history above may not be accessible and will be replaced with this summary. Write down anything that would be helpful, including the state, next steps, learnings etc. You must wrap your summary in a <summary></summary> block.

You can provide custom instructions via the instructions parameter to replace this prompt entirely. Custom instructions don’t supplement the default; they completely replace it:

response = client.beta.messages.create(
    betas=["compact-2026-01-12"],
    model="claude-opus-4-6",
    max_tokens=4096,
    messages=messages,
    context_management={
        "edits": [
            {
                "type": "compact_20260112",
                "instructions": "Focus on preserving code snippets, variable names, and technical decisions.",
            }
        ]
    },
)

const response = await client.beta.messages.create({
  betas: ["compact-2026-01-12"],
  model: "claude-opus-4-6",
  max_tokens: 4096,
  messages,
  context_management: {
    edits: [
      {
        type: "compact_20260112",
        instructions:
          "Focus on preserving code snippets, variable names, and technical decisions."
      }
    ]
  }
});

Pausing after compaction#

Use pause_after_compaction to pause the API after generating the compaction summary. This allows you to add additional content blocks (such as preserving recent messages or specific instruction-oriented messages) before the API continues with the response.

When enabled, the API returns a message with the compaction stop reason after generating the compaction block:

response = client.beta.messages.create(
    betas=["compact-2026-01-12"],
    model="claude-opus-4-6",
    max_tokens=4096,
    messages=messages,
    context_management={
        "edits": [{"type": "compact_20260112", "pause_after_compaction": True}]
    },
)

# Check if compaction triggered a pause
if response.stop_reason == "compaction":
    # Response contains only the compaction block
    messages.append({"role": "assistant", "content": response.content})

    # Continue the request
    response = client.beta.messages.create(
        betas=["compact-2026-01-12"],
        model="claude-opus-4-6",
        max_tokens=4096,
        messages=messages,
        context_management={"edits": [{"type": "compact_20260112"}]},
    )

let response = await client.beta.messages.create({
  betas: ["compact-2026-01-12"],
  model: "claude-opus-4-6",
  max_tokens: 4096,
  messages,
  context_management: {
    edits: [
      {
        type: "compact_20260112",
        pause_after_compaction: true
      }
    ]
  }
});

// Check if compaction triggered a pause
if (response.stop_reason === "compaction") {
  // Response contains only the compaction block
  messages.push({ role: "assistant", content: response.content });

  // Continue the request
  response = await client.beta.messages.create({
    betas: ["compact-2026-01-12"],
    model: "claude-opus-4-6",
    max_tokens: 4096,
    messages,
    context_management: {
      edits: [{ type: "compact_20260112" }]
    }
  });
}

Enforcing a total token budget#

When a model works on long tasks with many tool-use iterations, total token consumption can grow significantly. You can combine pause_after_compaction with a compaction counter to estimate cumulative usage and gracefully wrap up the task once a budget is reached:

TRIGGER_THRESHOLD = 100_000
TOTAL_TOKEN_BUDGET = 3_000_000
n_compactions = 0

response = client.beta.messages.create(
    betas=["compact-2026-01-12"],
    model="claude-opus-4-6",
    max_tokens=4096,
    messages=messages,
    context_management={
        "edits": [
            {
                "type": "compact_20260112",
                "trigger": {"type": "input_tokens", "value": TRIGGER_THRESHOLD},
                "pause_after_compaction": True,
            }
        ]
    },
)

if response.stop_reason == "compaction":
    n_compactions += 1
    messages.append({"role": "assistant", "content": response.content})

    # Estimate total tokens consumed; prompt wrap-up if over budget
    if n_compactions * TRIGGER_THRESHOLD >= TOTAL_TOKEN_BUDGET:
        messages.append(
            {
                "role": "user",
                "content": "Please wrap up your current work and summarize the final state.",
            }
        )

Working with compaction blocks#

When compaction is triggered, the API returns a compaction block at the start of the assistant response.

A long-running conversation may result in multiple compactions. The last compaction block reflects the final state of the prompt, replacing content prior to it with the generated summary.

{
  "content": [
    {
      "type": "compaction",
      "content": "Summary of the conversation: The user requested help building a web scraper..."
    },
    {
      "type": "text",
      "text": "Based on our conversation so far..."
    }
  ]
}

Passing compaction blocks back#

You must pass the compaction block back to the API on subsequent requests to continue the conversation with the shortened prompt. The simplest approach is to append the entire response content to your messages:

# After receiving a response with a compaction block
messages.append({"role": "assistant", "content": response.content})

# Continue the conversation
messages.append({"role": "user", "content": "Now add error handling"})

response = client.beta.messages.create(
    betas=["compact-2026-01-12"],
    model="claude-opus-4-6",
    max_tokens=4096,
    messages=messages,
    context_management={"edits": [{"type": "compact_20260112"}]},
)

// After receiving a response with a compaction block
messages.push({ role: "assistant", content: response.content });

// Continue the conversation
messages.push({ role: "user", content: "Now add error handling" });

const nextResponse = await client.beta.messages.create({
  betas: ["compact-2026-01-12"],
  model: "claude-opus-4-6",
  max_tokens: 4096,
  messages,
  context_management: {
    edits: [{ type: "compact_20260112" }]
  }
});

When the API receives a compaction block, all content blocks before it are ignored. You can either:

Keep the original messages in your list and let the API handle removing the compacted content
Manually drop the compacted messages and only include the compaction block onwards

Streaming#

When streaming responses with compaction enabled, you’ll receive a content_block_start event when compaction begins. The compaction block streams differently from text blocks. You’ll receive a content_block_start event, followed by a single content_block_delta with the complete summary content (no intermediate streaming), and then a content_block_stop event.

import anthropic

client = anthropic.Anthropic()

with client.beta.messages.stream(
    betas=["compact-2026-01-12"],
    model="claude-opus-4-6",
    max_tokens=4096,
    messages=messages,
    context_management={"edits": [{"type": "compact_20260112"}]},
) as stream:
    for event in stream:
        if event.type == "content_block_start":
            if event.content_block.type == "compaction":
                print("Compaction started...")
            elif event.content_block.type == "text":
                print("Text response started...")

        elif event.type == "content_block_delta":
            if event.delta.type == "compaction_delta":
                print(f"Compaction complete: {len(event.delta.content)} chars")
            elif event.delta.type == "text_delta":
                print(event.delta.text, end="", flush=True)

    # Get the final accumulated message
    message = stream.get_final_message()
    messages.append({"role": "assistant", "content": message.content})


const client = new Anthropic();

const stream = await client.beta.messages.stream({
  betas: ["compact-2026-01-12"],
  model: "claude-opus-4-6",
  max_tokens: 4096,
  messages,
  context_management: {
    edits: [{ type: "compact_20260112" }]
  }
});

for await (const event of stream) {
  if (event.type === "content_block_start") {
    if (event.content_block.type === "compaction") {
      console.log("Compaction started...");
    } else if (event.content_block.type === "text") {
      console.log("Text response started...");
    }
  } else if (event.type === "content_block_delta") {
    if (event.delta.type === "compaction_delta") {
      console.log(`Compaction complete: ${event.delta.content.length} chars`);
    } else if (event.delta.type === "text_delta") {
      process.stdout.write(event.delta.text);
    }
  }
}

// Get the final accumulated message
const message = await stream.finalMessage();
messages.push({ role: "assistant", content: message.content });

Prompt caching#

Compaction works well with prompt caching. You can add a cache_control breakpoint on compaction blocks to cache the summarized content. The original compacted content is ignored.

{
  "role": "assistant",
  "content": [
    {
      "type": "compaction",
      "content": "[summary text]",
      "cache_control": { "type": "ephemeral" }
    },
    {
      "type": "text",
      "text": "Based on our conversation..."
    }
  ]
}

Maximizing cache hits with system prompts#

When compaction occurs, the summary becomes new content that needs to be written to the cache. Without additional cache breakpoints, this would also invalidate any cached system prompt, requiring it to be re-cached along with the compaction summary.

To maximize cache hit rates, add a cache_control breakpoint at the end of your system prompt. This keeps the system prompt cached separately from the conversation, so when compaction occurs:

The system prompt cache remains valid and is read from cache
Only the compaction summary needs to be written as a new cache entry

response = client.beta.messages.create(
    betas=["compact-2026-01-12"],
    model="claude-opus-4-6",
    max_tokens=4096,
    system=[
        {
            "type": "text",
            "text": "You are a helpful coding assistant...",
            "cache_control": {
                "type": "ephemeral"
            },  # Cache the system prompt separately
        }
    ],
    messages=messages,
    context_management={"edits": [{"type": "compact_20260112"}]},
)

const response = await client.beta.messages.create({
  betas: ["compact-2026-01-12"],
  model: "claude-opus-4-6",
  max_tokens: 4096,
  system: [
    {
      type: "text",
      text: "You are a helpful coding assistant...",
      cache_control: { type: "ephemeral" } // Cache the system prompt separately
    }
  ],
  messages,
  context_management: {
    edits: [{ type: "compact_20260112" }]
  }
});

This approach is particularly beneficial for long system prompts, as they remain cached even across multiple compaction events throughout a conversation.

Understanding usage#

Compaction requires an additional sampling step, which contributes to rate limits and billing. The API returns detailed usage information in the response:

{
  "usage": {
    "input_tokens": 45000,
    "output_tokens": 1234,
    "iterations": [
      {
        "type": "compaction",
        "input_tokens": 180000,
        "output_tokens": 3500
      },
      {
        "type": "message",
        "input_tokens": 23000,
        "output_tokens": 1000
      }
    ]
  }
}

The iterations array shows usage for each sampling iteration. When compaction occurs, you’ll see a compaction iteration followed by the main message iteration. The final iteration’s token counts reflect the effective context size after compaction.

The top-level input_tokens and output_tokens do not include compaction iteration usage. They reflect the sum of all non-compaction iterations. To calculate total tokens consumed and billed for a request, sum across all entries in the usage.iterations array.

If you previously relied on usage.input_tokens and usage.output_tokens for cost tracking or auditing, you’ll need to update your tracking logic to aggregate across usage.iterations when compaction is enabled. The iterations array is only populated when a new compaction is triggered during the request. Re-applying a previous compaction block incurs no additional compaction cost, and the top-level usage fields remain accurate in that case.

Combining with other features#

Server tools#

When using server tools (like web search), the compaction trigger is checked at the start of each sampling iteration. Compaction may occur multiple times within a single request depending on your trigger threshold and the amount of output generated.

Token counting#

The token counting endpoint (/v1/messages/count_tokens) applies existing compaction blocks in your prompt but does not trigger new compactions. Use it to check your effective token count after previous compactions:

count_response = client.beta.messages.count_tokens(
    betas=["compact-2026-01-12"],
    model="claude-opus-4-6",
    messages=messages,
    context_management={"edits": [{"type": "compact_20260112"}]},
)

print(f"Current tokens: {count_response.input_tokens}")
print(f"Original tokens: {count_response.context_management.original_input_tokens}")

const countResponse = await client.beta.messages.countTokens({
  betas: ["compact-2026-01-12"],
  model: "claude-opus-4-6",
  messages,
  context_management: {
    edits: [{ type: "compact_20260112" }]
  }
});

console.log(`Current tokens: ${countResponse.input_tokens}`);
console.log(`Original tokens: ${countResponse.context_management.original_input_tokens}`);

Examples#

Here’s a complete example of a long-running conversation with compaction:

import anthropic

client = anthropic.Anthropic()

messages: list[dict] = []


def chat(user_message: str) -> str:
    messages.append({"role": "user", "content": user_message})

    response = client.beta.messages.create(
        betas=["compact-2026-01-12"],
        model="claude-opus-4-6",
        max_tokens=4096,
        messages=messages,
        context_management={
            "edits": [
                {
                    "type": "compact_20260112",
                    "trigger": {"type": "input_tokens", "value": 100000},
                }
            ]
        },
    )

    # Append response (compaction blocks are automatically included)
    messages.append({"role": "assistant", "content": response.content})

    # Return the text content
    return next(block.text for block in response.content if block.type == "text")


# Run a long conversation
print(chat("Help me build a Python web scraper"))
print(chat("Add support for JavaScript-rendered pages"))
print(chat("Now add rate limiting and error handling"))
# ... continue as long as needed


const client = new Anthropic();

const messages: Anthropic.Beta.BetaMessageParam[] = [];

async function chat(userMessage: string): Promise<string> {
  messages.push({ role: "user", content: userMessage });

  const response = await client.beta.messages.create({
    betas: ["compact-2026-01-12"],
    model: "claude-opus-4-6",
    max_tokens: 4096,
    messages,
    context_management: {
      edits: [
        {
          type: "compact_20260112",
          trigger: { type: "input_tokens", value: 100000 }
        }
      ]
    }
  });

  // Append response (compaction blocks are automatically included)
  messages.push({ role: "assistant", content: response.content });

  // Return the text content
  const textBlock = response.content.find((block) => block.type === "text");
  return textBlock?.text ?? "";
}

// Run a long conversation
console.log(await chat("Help me build a Python web scraper"));
console.log(await chat("Add support for JavaScript-rendered pages"));
console.log(await chat("Now add rate limiting and error handling"));
// ... continue as long as needed

Here’s an example that uses pause_after_compaction to preserve the last two messages (one user + one assistant turn) verbatim instead of summarizing them:

import anthropic
from typing import Any

client = anthropic.Anthropic()

messages: list[dict[str, Any]] = []


def chat(user_message: str) -> str:
    messages.append({"role": "user", "content": user_message})

    response = client.beta.messages.create(
        betas=["compact-2026-01-12"],
        model="claude-opus-4-6",
        max_tokens=4096,
        messages=messages,
        context_management={
            "edits": [
                {
                    "type": "compact_20260112",
                    "trigger": {"type": "input_tokens", "value": 100000},
                    "pause_after_compaction": True,
                }
            ]
        },
    )

    # Check if compaction occurred and paused
    if response.stop_reason == "compaction":
        # Get the compaction block from the response
        compaction_block = response.content[0]

        # Preserve the last 2 messages (1 user + 1 assistant turn)
        # by including them after the compaction block
        preserved_messages = messages[-2:] if len(messages) >= 2 else messages

        # Build new message list: compaction + preserved messages
        new_assistant_content = [compaction_block]
        messages_after_compaction = [
            {"role": "assistant", "content": new_assistant_content}
        ] + preserved_messages

        # Continue the request with the compacted context + preserved messages
        response = client.beta.messages.create(
            betas=["compact-2026-01-12"],
            model="claude-opus-4-6",
            max_tokens=4096,
            messages=messages_after_compaction,
            context_management={"edits": [{"type": "compact_20260112"}]},
        )

        # Update our message list to reflect the compaction
        messages.clear()
        messages.extend(messages_after_compaction)

    # Append the final response
    messages.append({"role": "assistant", "content": response.content})

    # Return the text content
    return next(block.text for block in response.content if block.type == "text")


# Run a long conversation
print(chat("Help me build a Python web scraper"))
print(chat("Add support for JavaScript-rendered pages"))
print(chat("Now add rate limiting and error handling"))
# ... continue as long as needed


const client = new Anthropic();

let messages: Anthropic.Beta.BetaMessageParam[] = [];

async function chat(userMessage: string): Promise<string> {
  messages.push({ role: "user", content: userMessage });

  let response = await client.beta.messages.create({
    betas: ["compact-2026-01-12"],
    model: "claude-opus-4-6",
    max_tokens: 4096,
    messages,
    context_management: {
      edits: [
        {
          type: "compact_20260112",
          trigger: { type: "input_tokens", value: 100000 },
          pause_after_compaction: true
        }
      ]
    }
  });

  // Check if compaction occurred and paused
  if (response.stop_reason === "compaction") {
    // Get the compaction block from the response
    const compactionBlock = response.content[0];

    // Preserve the last 2 messages (1 user + 1 assistant turn)
    // by including them after the compaction block
    const preservedMessages = messages.length >= 2 ? messages.slice(-2) : [...messages];

    // Build new message list: compaction + preserved messages
    const messagesAfterCompaction: Anthropic.Beta.BetaMessageParam[] = [
      { role: "assistant", content: [compactionBlock] },
      ...preservedMessages
    ];

    // Continue the request with the compacted context + preserved messages
    response = await client.beta.messages.create({
      betas: ["compact-2026-01-12"],
      model: "claude-opus-4-6",
      max_tokens: 4096,
      messages: messagesAfterCompaction,
      context_management: {
        edits: [{ type: "compact_20260112" }]
      }
    });

    // Update our message list to reflect the compaction
    messages = messagesAfterCompaction;
  }

  // Append the final response
  messages.push({ role: "assistant", content: response.content });

  // Return the text content
  const textBlock = response.content.find((block) => block.type === "text");
  return textBlock?.text ?? "";
}

// Run a long conversation
console.log(await chat("Help me build a Python web scraper"));
console.log(await chat("Add support for JavaScript-rendered pages"));
console.log(await chat("Now add rate limiting and error handling"));
// ... continue as long as needed

Current limitations#

Same model for summarization: The model specified in your request is used for summarization. There is no option to use a different (for example, cheaper) model for the summary.

Next steps#

Explore practical examples and implementations in the cookbook. Learn about context window sizes and management strategies. Explore other strategies for managing conversation context like tool result clearing and thinking block clearing.

Link last verified June 7, 2026. View original ↗

Source: Anthropic Platform Docs

Link last verified: 2026-02-26