AI Credits Running Out Too Fast? These Methods Can Help You Save Tokens

When your AI usage intensity is high, you often encounter situations where your credits are insufficient.

Here is a systematic summary of some solutions I have accumulated in actual use.

Each solution can be adopted simultaneously and used in combination for better results.

Usage Technique Level

Start New Sessions Promptly

Large models are inherently stateless; each Q&A session re-inputs the complete conversation history into the model. After multiple rounds of interaction, the context expands dramatically, not only consuming an equivalent amount of Tokens but also potentially hitting context length limits.

Therefore, it is recommended to proactively start a new session in the following two situations: first, when the current task reaches a milestone; second, when starting a new task unrelated to the previous context.

If the current context is close to the length limit and the task is not yet complete, you can first have the current session generate a concise handoff summary. The content should cover: objectives, completed items, key files, unresolved issues, and verification commands. Then continue advancing in a new session based on this summary.

Furthermore, overly long contexts can cause the model's attention to "collapse" (the model ignores key information), degrading output quality. Starting new sessions promptly keeps the context lightweight and clean, helping to improve the AI's output quality.

Solutions for Generating Handoff Summaries

• Option 1: Let the AI generate directly

In the target session, directly ask the AI to generate a handoff summary. An example prompt is as follows.

Please compress the current session into a summary that can be handed off to a new session for continued execution.
Do not write reasoning processes, do not restate irrelevant content, do not fabricate information, mark uncertainties as "To be confirmed", and desensitize sensitive information.

Please include:
1. Objective
2. Completed Items
3. Key Context / Constraints / Decisions
4. Key Files, Paths, and Current Status
5. Unresolved Issues
6. Next Steps
7. Verification Commands
8. Startup Prompt that can be directly copied to the new session

• Option 2: Use the handoff summary skill

Cross-agent/session handoff is a typical scenario, and the industry has provided multiple related Skills to choose from. It is recommended to install and use mattpocock/handoff.

Narrow Down the Problem Before Calling the Model

High consumption is often not because the model is expensive, but because the input is too much.

• Only provide relevant files, relevant functions, and error snippets; for large logs, only capture content near the error.
• Clearly define task boundaries: objectives, non-objectives, acceptance criteria, and files allowed to be modified.
• Require the model to first provide a "plan + list of involved files", and confirm before executing complex changes.
• Avoid unbounded requests like "help me look at the entire project" or "optimize all the code".

Model and Agent Level

Establish Task Tiers and Use Multiple Models in Coordination

Reserve high-tier models for tasks that "require judgment", and leave low-cost models for tasks that are "rule-clear and verifiable". Switch to the appropriate model based on the task type.

Task Type	Recommended Channel
Architecture design, unfamiliar codebases, complex multi-file changes, stubborn Debugging	GPT-5.5 / High-tier Codex
Clear small features, single-file modifications, test supplementation, routine refactoring	GPT-5.4 mini or other cost-effective models
Information retrieval, requirement breakdown, draft generation, code explanation, preliminary solutions, batch execution after a clear solution is established	Domestic models or low-cost models

The pricing difference between GPT 5.5 and 5.4 is 2.5 times, making the model switching effect significant.

Reasonable Agent Configuration

Taking Codex as an example, its "Reasoning" and "Speed" configurations have a huge impact on Token consumption. It is recommended to switch dynamically based on the task type.

The higher the reasoning level, the more reasoning and exploration the model typically invests, potentially generating longer outputs, more tool calls, and retries, so the actual token consumption is often higher.

Tool Level

Headroom

Headroom (a context compression tool), according to official data, can save 60%–95% of Token consumption.

Installation can be completed with just one line of command. For detailed steps and commands, refer to https://github.com/headroomlabs-ai/headroom or https://dashen-tech.com/dev-tools/headroom-llm-token-compression/.

Below are some experience summaries:

1. Common Commands

   • Enable (using Codex as an example): headroom wrap codex
   • Disable: headroom unwrap codex
   • View statistics and savings: headroom perf

2. After executing the wrap command, Headroom will automatically enable the Agent's CLI mode. If you are using a client or editor plugin, after seeing the 8787 port service start successfully, close the command line, and you can use it normally in the client or plugin. The reason is that Headroom modifies the Agent's global configuration, so CLI and client/plugin take effect simultaneously.

3. After enabling Headroom, previous historical sessions will be temporarily invisible, which is equivalent to switching the login method; after executing unwrap, the original sessions will automatically recover.

4. Headroom will automatically install and use rtk and serena. Among them, rtk is used for command compression, and serena is an MCP tool used to understand the codebase and save project memory.

codebase-memory-mcp

codebase-memory-mcp is an MCP service that provides AI with a fast, structured understanding of the codebase. It allows AI to "remember" and understand the structure of the entire codebase like a human, instead of starting from scratch to search file by file each time. Officials claim it can save 120 times the tokens.

Installation and Usage Steps:

1. Global system installation

   npm install -g codebase-memory-mcp
   

2. Configure MCP to your Agent tool

   codebase-memory-mcp install
   

3. Usage

Restart your Agent, switch to the target project, and say "Index this project".

Index this project