# GTool: Graph Enhanced Tool Planning with Large Language Model

arXiv: https://arxiv.org/abs/2508.12725 · HTML: https://mlsanity.com/paper/2508.12725/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 40 references that are in the map.
- First posted on arXiv 18 Aug 2025.

## Builds on (most cited first, 30 of 33)

- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023)
- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [Code Llama: Open Foundation Models for Code](https://mlsanity.com/paper/2308.12950/) (2023)
- [Variational Graph Auto-Encoders](https://mlsanity.com/paper/1611.07308/) (2016)
- [HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face](https://mlsanity.com/paper/2303.17580/) (2023)
- [Link Prediction Based on Graph Neural Networks](https://mlsanity.com/paper/1802.09691/) (2018)
- [Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification](https://mlsanity.com/paper/2009.03509/) (2020)
- [Understanding the planning of LLM agents: A survey](https://mlsanity.com/paper/2402.02716/) (2024)
- [GPT4Tools: Teaching Large Language Model to Use Tools via Self-instruction](https://mlsanity.com/paper/2305.18752/) (2023)
- [ToolkenGPT: Augmenting Frozen Language Models with Massive Tools via Tool Embeddings](https://mlsanity.com/paper/2305.11554/) (2023)
- [Tool Learning with Large Language Models: A Survey](https://mlsanity.com/paper/2405.17935/) (2024)
- [Language is All a Graph Needs](https://mlsanity.com/paper/2308.07134/) (2023)
- [MetaTool Benchmark for Large Language Models: Deciding Whether to Use Tools and Which to Use](https://mlsanity.com/paper/2310.03128/) (2023)
- [APIGen: Automated Pipeline for Generating Verifiable and Diverse Function-Calling Datasets](https://mlsanity.com/paper/2406.18518/) (2024)
- [Large Language Models on Graphs: A Comprehensive Survey](https://mlsanity.com/paper/2312.02783/) (2023)
- [TaskMatrix.AI: Completing Tasks by Connecting Foundation Models with Millions of APIs](https://mlsanity.com/paper/2303.16434/) (2023)
- [EASYTOOL: Enhancing LLM-based Agents with Concise Tool Instruction](https://mlsanity.com/paper/2401.06201/) (2024)
- [RestGPT: Connecting Large Language Models with Real-World RESTful APIs](https://mlsanity.com/paper/2306.06624/) (2023)
- [ToolChain*: Efficient Action Space Navigation in Large Language Models with A* Search](https://mlsanity.com/paper/2310.13227/) (2023)
- [Can GNN be Good Adapter for LLMs?](https://mlsanity.com/paper/2402.12984/) (2024)
- [Confucius: Iterative Tool Learning from Introspection Feedback by Easy-to-Difficult Curriculum](https://mlsanity.com/paper/2308.14034/) (2023)
- [Graph Neural Prompting with Large Language Models](https://mlsanity.com/paper/2309.15427/) (2023)
- [ToolNet: Connecting Large Language Models with Massive Tools via Tool Graph](https://mlsanity.com/paper/2403.00839/) (2024)
- [Patton: Language Model Pretraining on Text-Rich Networks](https://mlsanity.com/paper/2305.12268/) (2023)
- [Magnet: Multi-turn Tool-use Data Synthesis and Distillation via Graph Translation](https://mlsanity.com/paper/2503.07826/) (2025)
- [Tool-Planner: Task Planning with Clusters across Multiple Tools](https://mlsanity.com/paper/2406.03807/) (2024)
- [Toolink: Linking Toolkit Creation and Using through Chain-of-Solving on Open-Source Model](https://mlsanity.com/paper/2310.05155/) (2023)
- [Plan-over-Graph: Towards Parallelable LLM Agent Schedule](https://mlsanity.com/paper/2502.14563/) (2025)

## Built on by (most cited first, 10 of 10)

- [The Evolution of Tool Use in LLM Agents: From Single-Tool Call to Multi-Tool Orchestration](https://mlsanity.com/paper/2603.22862/) (2026)
- [EnvFactory: Scaling Tool-Use Agents via Executable Environments Synthesis and Robust RL](https://mlsanity.com/paper/2605.18703/) (2026)
- [PhGPO: Pheromone-Guided Policy Optimization for Long-Horizon Tool Planning](https://mlsanity.com/paper/2602.13691/) (2026)
- [Experience-Evolving Multi-Turn Tool-Use Agent with Hybrid Episodic-Procedural Memory](https://mlsanity.com/paper/2512.07287/) (2025)
- [From Intent to Execution: Composing Agentic Workflows with Agent Recommendation](https://mlsanity.com/paper/2605.03986/) (2026)
- [GRAFT: Graph-Tokenized LLMs for Tool Planning](https://mlsanity.com/paper/2605.11706/) (2026)
- [QueryGraph: Reliable Multi-Tool Query Execution Planning via LLM-Based Graph Generation](https://mlsanity.com/paper/2606.08300/) (2026)
- [Choosing Before Acting: Comparative Value Estimation for Long-Horizon Tool-Use Agents](https://mlsanity.com/paper/2610.02330/) (2026)
- [CIPO: Counterfactual Imagination Policy Optimization for Adaptive Tool Granularity Selection](https://mlsanity.com/paper/2610.04991/) (2026)
- [Topology-Consistent Task Planning over Cellular Workflow Complexes for LLM-based Agents](https://mlsanity.com/paper/2610.07004/) (2026)

## Abstract (arXiv)

Tool planning with large language models (LLMs), referring to selecting, organizing, and preparing the tools necessary to complete a user request, bridges the gap between natural language understanding and task execution. However, current works treat different tools as isolated components and fail to leverage the inherent dependencies of tools, leading to invalid planning results. Since tool dependencies are often incomplete, it becomes challenging for LLMs to accurately identify the appropriate tools required by a user request, especially when confronted with a large toolset. To solve this challenge, we propose textttGTool, which is the first work aiming to enhance the tool planning ability of LLMs under incomplete dependencies. textttGTool constructs a request-specific tool graph to select tools efficiently and generate the texttt<graph token> which provides sufficient dependency information understandable by LLMs. Moreover, a missing dependency prediction task is designed to improve the reliability of textttGTool with incomplete dependencies. Without trimming LLMs, textttGTool can be seamlessly integrated with various LLM backbones without extensive retraining. Extensive experiments show that textttGTool achieves more than 29.6% performance improvements compared with the state-of-the-art (SOTA) baselines with a light-weight (7B) LLM backbone.