Token Budget Management: Stop Drowning Your AI in Context

Every AI API call has a hidden cost multiplier: your context window.

You pay per token. And “tokens” include everything: your prompt, the AI’s response, and all the context you loaded.

If you’re loading 50,000 tokens of memory into every conversation, you’re not just slowing down your agent — you’re burning money.

I learned this the hard way. After running OpenClaw agents 24/7 for two months, my API bills looked like this:

• Month 1: $127
• Month 2: $340
• Month 3: $890 (!!)

What changed?

My MEMORY.md file grew from 5,000 tokens to 60,000 tokens. I was paying for that bloated context on every single API call.

Token budget management isn’t optional. If you’re serious about running persistent AI agents, you need a context budget — and you need to enforce it ruthlessly.

Here’s how.

The Math Behind Token Costs

Let’s break down the actual costs with GPT-4 Turbo pricing (as of Feb 2026):

Seems cheap, right? Let’s see what happens at scale.

Scenario 1: Personal Assistant (Light Use)

• 50 conversations per day
• Average 5,000 tokens context + 500 tokens prompt
• Average 300 tokens response

Daily cost (GPT-4 Turbo):

• Input: 50 × 5,500 tokens × $0.01 / 1K = $2.75/day
• Output: 50 × 300 tokens × $0.03 / 1K = $0.45/day
• Total: $3.20/day = $96/month

Scenario 2: Context Bloat (No Budget Enforcement)

Same usage, but MEMORY.md grows to 60,000 tokens:

• Input: 50 × 60,500 tokens × $0.01 / 1K = $30.25/day
• Output: same $0.45/day
• Total: $30.70/day = $921/month

That’s a 10x cost increase just from loading bloated context.

Scenario 3: Autonomous Agent (Heavy Use)

• 500 API calls per day (monitoring, heartbeats, tasks)
• 10,000 token context per call

Monthly cost: $1,500 - $3,000 depending on model and response length.

Without token budget management, these costs spiral out of control.

What Is Token Budget Management?

Token budget management means:

1. Setting a maximum context size (e.g., 5,000 tokens)
2. Prioritizing what gets loaded (critical > recent > historical)
3. Pruning or archiving low-priority context
4. Enforcing limits before each API call

Think of it like RAM management in software:

• You have limited “working memory” (context window)
• You load only what’s needed for the current task
• Everything else stays in “storage” (searchable but not loaded)

The Three-Tier Context Budget

Here’s the architecture I use:

Tier 1: Core Context (Always Loaded)

Budget: 2,000 tokens
Contents:

• Agent identity (AGENTS.md — who you are, core rules)
• Critical instructions (never send emails without asking, etc.)
• Active project context (what you’re working on right now)

Why always loaded? These are non-negotiable. Without them, the agent loses its identity and breaks fundamental rules.

Tier 2: Working Memory (Session-Specific)

Budget: 3,000 tokens
Contents:

• Recent conversation history (last 10 exchanges)
• Current task context (files being edited, emails being drafted)
• Relevant preferences (loaded based on current task)

Why session-specific? This context is relevant right now but won’t be tomorrow.

Tier 3: Warm Storage (Retrieved On-Demand)

Budget: 0 tokens (not auto-loaded)
Contents:

• Historical memories (older than 7 days)
• Low-frequency facts (referenced less than 3 times/month)
• Archived project context (completed projects)

Why not auto-loaded? This information is searchable via vector DB. It only gets loaded if semantically relevant to the current query.

Total loaded context: 5,000 tokens (Tier 1 + Tier 2)
Total stored context: Unlimited (searchable but not loaded)

How to Enforce Token Budgets (Practical Implementation)

Step 1: Measure Current Context Size

Before every API call, count your tokens:

import tiktoken

def count_tokens(text, model="gpt-4"):
    encoding = tiktoken.encoding_for_model(model)
    return len(encoding.encode(text))

# Load your context
core_context = open("AGENTS.md").read()
memory = open("MEMORY.md").read()

total_tokens = count_tokens(core_context) + count_tokens(memory)
print(f"Total context: {total_tokens} tokens")

If you’re over budget, you need to prune.

Step 2: Rank Context by Priority

Assign priority scores to each memory:

memories = [
    {"text": "User prefers Python over JS", "priority": 0.9, "tokens": 15},
    {"text": "HDFC policy renews April 15", "priority": 0.8, "tokens": 12},
    {"text": "User debugged CSS issue on Feb 10", "priority": 0.2, "tokens": 20},
]

# Sort by priority (descending)
memories.sort(key=lambda m: m["priority"], reverse=True)

Step 3: Load Until Budget Exhausted

TOKEN_BUDGET = 5000
loaded_tokens = count_tokens(core_context)  # Start with Tier 1

loaded_memories = []
for memory in memories:
    if loaded_tokens + memory["tokens"] <= TOKEN_BUDGET:
        loaded_memories.append(memory["text"])
        loaded_tokens += memory["tokens"]
    else:
        break  # Budget exhausted

final_context = core_context + "\n\n" + "\n".join(loaded_memories)

Result: You stay within budget by loading only the highest-priority context.

Step 4: Move Unprioritized Context to Vector DB

Low-priority memories don’t get deleted — they get archived to searchable storage:

from qdrant_client import QdrantClient

client = QdrantClient("localhost", port=6333)

for memory in memories:
    if memory not in loaded_memories:
        # Store in vector DB (searchable but not auto-loaded)
        client.upsert(
            collection_name="archived_memories",
            points=[{
                "id": memory["id"],
                "vector": get_embedding(memory["text"]),
                "payload": {"text": memory["text"], "date": memory["date"]}
            }]
        )

Now you can search archived memories when needed, without burning tokens on every call.

Advanced: Dynamic Budget Allocation

Not all tasks need the same context budget. Here’s how to allocate dynamically:

Task Type → Budget Mapping

Implementation:

def get_context_budget(task_type):
    budgets = {
        "quick_query": 2000,
        "code_review": 8000,
        "email_draft": 5000,
        "research": 15000
    }
    return budgets.get(task_type, 5000)  # Default 5K

Cost-Aware Budget Scaling

If your daily spend is under budget, allow more context. If over budget, tighten limits:

def get_dynamic_budget(base_budget=5000):
    daily_spend = get_api_costs_today()
    
    if daily_spend < 5:  # Under $5/day
        return base_budget * 1.5  # Allow 50% more context
    elif daily_spend > 20:  # Over $20/day
        return base_budget * 0.5  # Cut context in half
    else:
        return base_budget

Common Token Budget Mistakes

Mistake 1: No Measurement

You can’t manage what you don’t measure. If you’re not tracking token counts per call, you’re flying blind.

Fix: Log context size on every API call. Alert when it exceeds budget.

Mistake 2: Loading Everything “Just in Case”

“Better to have too much context than too little” → this is how you get $900 bills.

Fix: Load only what’s proven relevant. Use semantic search for everything else.

Mistake 3: Ignoring System Prompts

Your agent’s identity/instructions count toward the budget. A 5,000-token system prompt leaves no room for actual memories.

Fix: Keep system prompts under 2,000 tokens. Be ruthless with brevity.

Mistake 4: Not Versioning Budgets

If you change your budget rules, you should be able to roll back if performance degrades.

Fix: Track budget changes in git:

config/
  ├─ token-budget-v1.json (original: 10K)
  ├─ token-budget-v2.json (optimized: 5K)
  └─ token-budget-v3.json (task-specific)

Real-World Results: My Budget Optimization Journey

Here’s how I reduced costs by 75% without losing performance:

Before Optimization

• MEMORY.md: 58,000 tokens
• Monthly cost: $890
• Agent performance: Degraded (attention dilution)

After Implementing Budgets

• Core context: 2,000 tokens (identity + rules)
• Working memory: 3,000 tokens (recent + relevant)
• Warm storage: 53,000 tokens (searchable, not loaded)
• Monthly cost: $220
• Agent performance: Improved (sharper focus)

Result: 75% cost reduction + better performance.

Tools for Token Budget Management

1. tiktoken (OpenAI’s Tokenizer)

pip install tiktoken

Count tokens before API calls to stay within budget.

2. LangChain (Context Management)

from langchain.memory import ConversationTokenBufferMemory

memory = ConversationTokenBufferMemory(
    max_token_limit=5000,
    llm=llm
)

3. Qdrant / Pinecone (Vector Storage)

Store archived context in a vector DB for semantic search.

4. MyDeepBrain (Coming Soon)

We’re building automatic token budget enforcement into MyDeepBrain. Set your budget, and the system handles prioritization, pruning, and archiving.

Join the waitlist to get early access.

Key Takeaways

1. Context costs scale linearly — 10x context = 10x cost
2. Set token budgets early — 5,000 tokens is a good starting point
3. Tier your context — core (always), working (session), warm (searchable)
4. Measure before every call — track token counts in logs
5. Prune ruthlessly — if it’s not high-priority, archive it
6. Use semantic search — don’t auto-load everything

Token budget management isn’t just about saving money. It’s about keeping your AI sharp.

A 5,000-token context with high-quality facts beats a 50,000-token context full of noise.

Want automated token budget enforcement? MyDeepBrain handles context prioritization, pruning, and cost optimization out of the box. Join the waitlist.