| --- |
| tags: |
| - ml-intern |
| --- |
| # Agent Cost Optimizer (ACO) |
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| A universal control layer that reduces total cost of autonomous agent runs while preserving task quality. |
|
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| ## Core Thesis |
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| Most agent cost is wasted through: |
| - Overusing frontier models |
| - Sending huge context every turn |
| - Using tools unnecessarily |
| - Failing and retrying blindly |
| - Ignoring cache boundaries |
| - Using verifiers everywhere instead of selectively |
| - Not learning from previous traces |
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| ACO learns when to spend and when not to spend. |
|
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| ## Architecture |
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| ### 10 Core Modules |
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| 1. **Cost Telemetry Collector** — Structured trace collection with normalized schema |
| 2. **Task Cost Classifier** — Predicts expected cost, risk, model strength needed |
| 3. **Model Cascade Router** — Dynamic model selection (tiny → cheap → medium → frontier → specialist) |
| 4. **Context Budgeter** — Decides what context is needed vs. what can be omitted/summarized/cached |
| 5. **Cache-Aware Prompt Layout** — Optimizes prompt structure for prefix-cache reuse |
| 6. **Tool-Use Cost Gate** — Predicts whether a tool call is worth the cost |
| 7. **Verifier Budgeter** — Selective verification based on risk, confidence, task type |
| 8. **Retry/Recovery Optimizer** — Intelligent failure recovery without blind retry loops |
| 9. **Meta-Tool Miner** — Compresses repeated workflows into reusable deterministic scripts |
| 10. **Early Termination / Doom Detector** — Detects runs unlikely to succeed and stops them |
|
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| ## Installation |
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| ```bash |
| pip install agent-cost-optimizer |
| ``` |
|
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| ## Quick Start |
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| ```python |
| from aco import AgentCostOptimizer |
| |
| optimizer = AgentCostOptimizer.from_config("config.yaml") |
| result = optimizer.optimize(agent_request, run_state) |
| ``` |
|
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| ## Reward Objective |
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|
| ``` |
| cost_adjusted_score = |
| task_success_score |
| + safety_bonus |
| + artifact_completion_bonus |
| + calibration_bonus |
| - model_cost_penalty |
| - tool_cost_penalty |
| - latency_penalty |
| - retry_penalty |
| - unnecessary_verifier_penalty |
| - false_done_penalty |
| - unsafe_cheap_model_penalty |
| - missed_escalation_penalty |
| ``` |
|
|
| ## Benchmarks |
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| - Coding Agent Tasks |
| - Research Agent Tasks |
| - Tool-Use Tasks |
| - Document / Contract / QA Tasks |
| - Long-Horizon Agent Tasks |
|
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| ## License |
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| MIT |
|
|
| <!-- ml-intern-provenance --> |
| ## Generated by ML Intern |
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| This model repository was generated by [ML Intern](https://github.com/huggingface/ml-intern), an agent for machine learning research and development on the Hugging Face Hub. |
|
|
| - Try ML Intern: https://smolagents-ml-intern.hf.space |
| - Source code: https://github.com/huggingface/ml-intern |
|
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| ## Usage |
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| ```python |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
| |
| model_id = "narcolepticchicken/agent-cost-optimizer" |
| tokenizer = AutoTokenizer.from_pretrained(model_id) |
| model = AutoModelForCausalLM.from_pretrained(model_id) |
| ``` |
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| For non-causal architectures, replace `AutoModelForCausalLM` with the appropriate `AutoModel` class. |
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