GPT-5.2 Compaction API: Scale AI Agents Beyond Context Limits

Your agent is 47 tool calls deep into a complex workflow. It’s been reading files, querying databases, analyzing documents. Then suddenly — it forgets what you asked for in the first place. Sound familiar?

This is the context window problem, and OpenAI’s new Compaction API in GPT-5.2 offers an elegant solution.

📋 TL;DR

What	Details
🎯 Problem	AI agents hit context limits during complex workflows
💡 Solution	Native compression via `/responses/compact` endpoint
📉 Result	150K tokens → ~15K tokens while preserving task context
⚡ When to use	Multi-step agents, 20+ tool calls, iterative tasks

🧠 The Context Window Problem

Modern AI agents are impressive. They can browse the web, execute code, query APIs, and orchestrate complex multi-step workflows. But they all share one fundamental limitation: finite context windows.

Even with 128K or 200K token limits, real-world agentic workflows hit the ceiling faster than you’d expect:

Workflow Type	Typical Token Usage	Risk Level
💬 Simple Q&A	1K–5K tokens	🟢 Low
💻 Code generation with context	10K–30K tokens	🟢 Low
📁 Multi-file refactoring	50K–100K tokens	🟡 Medium
🔍 Research agent (10+ sources)	80K–150K tokens	🟠 High
🤖 Complex agentic workflow (50+ tool calls)	150K–300K+ tokens	🔴 Critical

When you exceed the limit, you have three bad options:

❌ Truncate early messages — lose critical context

❌ Restart the conversation — lose all progress

❌ Implement custom summarization — add latency and lose fidelity

GPT-5.2’s Compaction API introduces a fourth option: ✅ native, loss-aware compression.

🔧 What Is the Compaction API?

The Compaction API (

/responses/compact

) performs intelligent compression on your conversation state. Instead of naive truncation, it:

🔍 Analyzes the full conversation history
🎯 Identifies task-relevant information
🔐 Compresses it into encrypted, opaque tokens
📦 Returns a dramatically smaller payload that preserves semantic meaning

💭 Think of it as a “checkpoint” system for your AI agent. You compress the state at key milestones, then continue with a fresh context window while retaining everything important.

📊 Key Characteristics

Property	Description	Icon
Loss-aware	Prioritizes task-relevant information during compression	🎯
Opaque output	Returns encrypted items — not human-readable	🔐
Model-specific	Currently works with GPT-5.2 and Responses API	🔗
Repeatable	Safe to run multiple times in long sessions	🔄

⚠️ Warning

Compacted items are designed for continuation, not inspection. Don’t try to parse or depend on their internal structure — it may change.

⚙️ How It Works

🔄 Basic Flow

┌─────────────────────┐
│  📚 Conversation    │
│    (150K tokens)    │
└──────────┬──────────┘
           │
           ▼
┌─────────────────────┐
│  🗜️  Compact        │
│     Endpoint        │
└──────────┬──────────┘
           │
           ▼
┌─────────────────────┐
│  📦 Compacted       │
│   (~15K tokens)     │
└──────────┬──────────┘
           │
           ▼
┌─────────────────────┐
│  ▶️  Continue       │
│     Workflow        │
└─────────────────────┘

💻 Code Example

from openai import OpenAI
import json

client = OpenAI()

# 📍 Step 1: Run your agent workflow
response = client.responses.create(
    model="gpt-5.2",
    input=[
        {
            "role": "user",
            "content": "Analyze all Python files in the repository and suggest refactoring opportunities.",
        },
    ]
)

# ... many tool calls later, context is getting large ...

# 📍 Step 2: Compact the conversation state
output_json = [msg.model_dump() for msg in response.output]

compacted = client.responses.compact(
    model="gpt-5.2",
    input=[
        {
            "role": "user", 
            "content": "Analyze all Python files in the repository and suggest refactoring opportunities.",
        },
        output_json[0]  # Include assistant's response
    ]
)

# 📍 Step 3: Continue with compacted state
continuation = client.responses.create(
    model="gpt-5.2",
    input=[
        compacted.output[0],  # Compacted state
        {
            "role": "user",
            "content": "Now implement the top 3 refactoring suggestions.",
        },
    ]
)

🧲 What Gets Preserved?

✅ Prioritized

🎯 User intent — the original task and constraints
🔀 Key decisions — choices made during execution
🔧 Tool results — important outputs from tool calls
📝 State changes — what was modified and where
⚠️ Error context — failures and their resolutions

⬇️ Deprioritized

💭 Verbose intermediate reasoning
🔁 Redundant information
📜 Superseded states (e.g., old file versions)

✅ When to Use Compaction

🟢 Good Use Cases

🔗 Multi-step agent workflows

User request → Plan → Execute step 1 → Execute step 2 → ... → Step N
                                     ↑
                               🗜️ Compact here

Compact after completing major phases (e.g., after research, before implementation).

🔧 Long-running sessions with many tool calls

💡 If your agent makes 20+ tool calls, you’re likely approaching context limits. Compact proactively.

🔄 Iterative refinement tasks

Code review → fixes → re-review → more fixes
           ↑                    ↑
      🗜️ Compact           🗜️ Compact

Each cycle adds tokens. Compact between cycles.

📚 Research and synthesis

Gathering information from multiple sources, then synthesizing. Compact after gathering, before synthesis.

🔴 When NOT to Use

Scenario	Why Not	Icon
Short conversations	Overhead not worth it	💬
Single-turn completions	Nothing to compress	1️⃣
When you need to inspect history	Compacted items are opaque	🔍
Real-time streaming	Adds latency	⏱️
Cross-model continuation	Compacted items are model-specific	🔀

💎 Best Practices

1️⃣ Compact at Milestones, Not Every Turn

# ❌ Bad: Compacting too frequently
for step in workflow_steps:
    response = execute_step(step)
    compacted = client.responses.compact(...)  # Wasteful

# ✅ Good: Compact at logical breakpoints
response = execute_research_phase()
compacted = client.responses.compact(...)  # After major phase
response = execute_implementation_phase(compacted)

2️⃣ Monitor Context Usage Proactively

📊 Pro tip: Don’t wait until you hit the limit. Track token usage and compact when you reach ~70% capacity.

def should_compact(response, threshold=0.7):
    usage = response.usage.total_tokens
    limit = 128000  # or your model's limit
    return usage / limit > threshold

3️⃣ Keep Prompts Consistent When Resuming

⚠️ Behavior can drift if you change your system prompt after compaction. Keep instructions functionally identical.

# ❌ Bad: Changing instructions mid-workflow
system_prompt_v1 = "You are a code reviewer..."
# ... compact ...
system_prompt_v2 = "You are a senior engineer..."  # Different!

# ✅ Good: Consistent instructions
system_prompt = "You are a code reviewer..."
# ... compact ...
# Continue with same system_prompt

4️⃣ Handle Compaction Failures Gracefully

try:
    compacted = client.responses.compact(model="gpt-5.2", input=history)
except Exception as e:
    # 🔄 Fallback: truncate oldest messages
    history = history[-10:]  
    logging.warning(f"Compaction failed, using truncation: {e}")

5️⃣ Don’t Parse Compacted Items

# ❌ Bad: Trying to extract information
compacted_text = compacted.output[0]["content"]
data = json.loads(compacted_text)  # Will fail or break later

# ✅ Good: Treat as opaque
next_input = [compacted.output[0], new_user_message]

🌍 Context Management Across LLM Providers

OpenAI’s Compaction API isn’t just a new feature — it’s a fundamentally different approach to a problem every LLM provider faces.

📊 Provider Comparison

Provider	Context Window	Native Compaction	Strategy
🟢 OpenAI GPT-5.2	128K	✅ Yes	Compress & continue
🟠 Anthropic Claude	200K	❌ No	Larger window + prompt caching
🔵 Google Gemini 2.0	2M	❌ No	Massive window eliminates the problem
🟣 Mistral Large	128K	❌ No	Manual workarounds
⚪ Meta Llama 3	128K	❌ No	Open source — build your own
🟤 Cohere Command R+	128K	❌ No	RAG-first architecture
⚫ xAI Grok	128K	❌ No	Manual workarounds

🎯 Three Approaches to the Context Problem

🗜️ Compress

OpenAI

Native compaction preserves semantic meaning while reducing token count.

Best for: workflows needing continuity without 2M-token pricing

📐 Expand

Google

2M token window means most workflows simply never hit the limit.

Tradeoff: cost per token and latency at scale

📤 Offload

Everyone else

Manual summarization, RAG retrieval, sliding window, custom middleware.

You’re on your own

🏗️ What This Means for Your Architecture

Your Situation	Recommendation
🎯 OpenAI-first?	Use Compaction API — it’s purpose-built for this
🔀 Multi-provider?	Abstract your context management layer. Don’t depend on provider-specific features without a fallback
💰 Cost-sensitive?	Google’s 2M window might be cheaper than repeated compaction calls — benchmark both
🔒 Privacy-critical?	Compacted items are opaque. If you need auditability, consider manual summarization

💡 The trend is clear: context management is becoming a first-class API concern, not just a prompt engineering problem. Expect other providers to follow with similar features.

⚖️ Compaction vs. Alternatives

Approach	Pros	Cons	Best For
🗜️ Compaction API	Native, loss-aware, preserves semantics	Opaque output, GPT-5.2 only	Production agents
📜 Sliding window	Simple to implement	Loses early context entirely	Simple chatbots
📝 Summarization prompt	Transparent, model-agnostic	Adds latency, lossy, costs tokens	Debugging/auditing
🔍 RAG retrieval	Scalable to huge contexts	Requires infrastructure, retrieval errors	Knowledge bases
✂️ Message truncation	Zero overhead	Loses information randomly	Last resort

🎯 For agentic workflows where task continuity matters, Compaction API is currently the cleanest solution — assuming you’re on GPT-5.2.

🛠️ Practical Example: Code Review Agent

Here’s a complete example of a code review agent that uses compaction to handle large repositories:

from openai import OpenAI

client = OpenAI()

def review_repository(repo_path: str):
    """
    🔍 Review all files in a repository, using compaction for scale.
    """
    
    history = [
        {
            "role": "system",
            "content": "You are a senior code reviewer. Analyze code for bugs, security issues, and improvements."
        },
        {
            "role": "user", 
            "content": f"Review the repository at {repo_path}. List all files first."
        }
    ]
    
    files_reviewed = 0
    compaction_interval = 10  # 🗜️ Compact every 10 files
    
    while True:
        response = client.responses.create(
            model="gpt-5.2",
            input=history
        )
        
        # 📝 Add response to history
        history.append(response.output[0].model_dump())
        files_reviewed += 1
        
        # 🗜️ Check if we should compact
        if files_reviewed % compaction_interval == 0:
            print(f"🗜️ Compacting after {files_reviewed} files...")
            
            compacted = client.responses.compact(
                model="gpt-5.2",
                input=history
            )
            
            # 🔄 Reset history with compacted state
            history = [
                history[0],  # Keep system prompt
                compacted.output[0].model_dump()
            ]
        
        # ✅ Check if review is complete
        if is_review_complete(response):
            break
        
        # ➡️ Continue to next file
        history.append({
            "role": "user",
            "content": "Continue to the next file."
        })
    
    return generate_final_report(history)

🎬 Conclusion

The Compaction API solves a real problem that every production AI agent faces: context limits kill complex workflows.

📌 Key Takeaways

#	Takeaway
1️⃣	Use it for long, tool-heavy workflows where losing context means losing progress
2️⃣	Compact at milestones, not every turn
3️⃣	Keep prompts consistent when resuming from compacted state
4️⃣	Treat compacted items as opaque — don’t try to parse them
5️⃣	Have a fallback for when compaction fails

🚀 As AI agents become more capable, they’ll need to handle increasingly complex, multi-hour workflows. Native context management features like Compaction are a step toward making that practical.

📚 Further Reading

Resource	Link
📖 OpenAI Conversation State Guide	platform.openai.com/docs/guides/conversation-state
📖 GPT-5.2 Prompting Guide	cookbook.openai.com/examples/gpt-5/gpt-5-2_prompting_guide
📖 Compact a Response API Reference	platform.openai.com/docs/api-reference/responses/compact

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