| # ToolUse |
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| **Open tools, workflows, models, and experiments for AI systems that can act through software.** |
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| ToolUse is an independent Hugging Face organization focused on the systems that let AI move beyond text generation and interact with the outside world through **tools, APIs, browsers, databases, code, files, and structured actions**. |
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| The goal is simple: |
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| > **Give AI the right tools, make every action inspectable, and measure whether the task was actually completed.** |
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| Tool use is one of the core building blocks of practical AI agents. |
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| ## What Is Tool Use? |
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| Tool use means allowing an AI system to select and execute external capabilities instead of relying only on its internal model knowledge. |
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| A tool can be: |
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| - an API |
| - a calculator |
| - a search engine |
| - a browser |
| - a database |
| - a code interpreter |
| - a file reader |
| - a retrieval system |
| - a business application |
| - a custom function |
| - another model |
| - a structured software action |
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| The model decides **what should happen**. |
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| The tool performs the actual operation. |
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| A robust system then checks whether the result is useful before continuing. |
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| --- |
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| ## Core Areas |
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| ### 🛠️ Function & Tool Calling |
| Projects may explore: |
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| - function calling |
| - structured arguments |
| - tool schemas |
| - tool selection |
| - parameter validation |
| - tool routing |
| - retries |
| - fallback tools |
| - result parsing |
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| ### 🌐 Browser Use |
| Possible workflows include: |
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| - web navigation |
| - information retrieval |
| - multi-step browsing |
| - form interaction |
| - page extraction |
| - website research |
| - browser-based agents |
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| ### 💻 Code Execution |
| AI systems can use code as a tool for: |
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| - calculations |
| - data analysis |
| - transformation |
| - validation |
| - plotting |
| - automation |
| - testing |
| - file generation |
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| ### 🗄️ Databases |
| Possible integrations: |
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| - SQL databases |
| - vector databases |
| - knowledge bases |
| - analytics systems |
| - structured enterprise data |
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| ### 📚 Retrieval |
| Tool-enabled systems may retrieve information from: |
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| - documents |
| - search indexes |
| - APIs |
| - knowledge bases |
| - file repositories |
| - semantic search systems |
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| ### 📂 Files & Documents |
| Possible tools may work with: |
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| - PDFs |
| - spreadsheets |
| - text files |
| - images |
| - structured documents |
| - archives |
| - business files |
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| ### 🔗 APIs |
| Agents may connect to: |
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| - internal APIs |
| - external APIs |
| - SaaS platforms |
| - data providers |
| - enterprise systems |
| - custom services |
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| ### 🤖 Agent Workflows |
| Tool use becomes especially powerful when combined with: |
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| - planning |
| - memory |
| - retrieval |
| - multi-step execution |
| - validation |
| - human approval |
| - observability |
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| --- |
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| ## Possible Spaces |
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| ### 🛠️ Tool Calling Playground |
| Test models on structured tool-selection and function-calling tasks. |
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| ### 🧪 Tool Use Benchmark |
| Measure whether models select the right tool and provide valid arguments. |
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| ### 🌐 Browser Agent Lab |
| Experiment with browser-based research and navigation workflows. |
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| ### 💻 Code Tool Agent |
| Use code execution for calculations, data analysis, and validation. |
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| ### 🗄️ SQL Agent |
| Translate natural-language questions into safe, structured database queries. |
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| ### 📚 Retrieval Tool Agent |
| Combine semantic search, document retrieval, and grounded answering. |
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| ### 🔗 API Agent |
| Explore reliable API selection, parameter generation, and response handling. |
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| ### ✅ Tool Output Validator |
| Check whether tool responses match expected schemas and task requirements. |
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| ### 🔄 Multi-Tool Workflow |
| Combine several tools in a single repeatable task. |
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| ### 📊 Tool Use Analytics |
| Track success rate, latency, retries, errors, and cost across tool-enabled workflows. |
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| --- |
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| ## Why Tool Use Matters |
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| Language models can explain what should happen. |
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| Tools allow them to **do something**. |
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| That distinction is important. |
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| Without tools, a model may only describe: |
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| - how to search |
| - how to calculate |
| - how to query a database |
| - how to modify a file |
| - how to call a service |
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| With tools, the system can potentially perform those steps directly. |
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| A useful pattern is: |
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| **understand → choose tool → validate inputs → execute → inspect result → continue** |
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| --- |
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| ## Tool Selection |
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| One of the hardest problems is not execution — it is choosing the right tool. |
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| A tool-using system should understand: |
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| - when a tool is necessary |
| - which tool is appropriate |
| - which parameters are required |
| - whether the tool succeeded |
| - whether a fallback is needed |
| - when to stop |
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| Good tool use is therefore a reasoning and orchestration problem, not just an API problem. |
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| --- |
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| ## Structured Actions |
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| Reliable tool use depends on structure. |
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| Possible techniques include: |
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| - JSON schemas |
| - typed arguments |
| - constrained decoding |
| - validation |
| - enumerated actions |
| - structured outputs |
| - deterministic parsers |
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| The more consequential the action, the more important strict validation becomes. |
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| --- |
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| ## Evaluation |
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| Tool use should be measured with real tasks. |
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| Useful metrics may include: |
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| - correct tool selection |
| - valid arguments |
| - execution success |
| - task completion |
| - unnecessary tool calls |
| - number of retries |
| - latency |
| - cost |
| - error recovery |
| - safety violations |
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| A system that calls many tools is not necessarily a good agent. |
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| The goal is **successful, efficient, and reliable action**. |
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| ## Safety & Permissions |
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| Tool-enabled AI systems can have real-world impact. |
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| Projects should consider: |
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| - least-privilege access |
| - read vs. write permissions |
| - user confirmation |
| - authentication |
| - secrets management |
| - sandboxing |
| - rate limits |
| - spending limits |
| - audit logs |
| - tool allowlists |
| - action validation |
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| Sensitive or irreversible actions should require stronger controls. |
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| ## Prompt Injection & Tool Security |
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| External content can contain malicious or misleading instructions. |
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| Tool-using systems should be designed to resist: |
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| - prompt injection |
| - data exfiltration |
| - malicious tool arguments |
| - unsafe file access |
| - credential leakage |
| - unauthorized actions |
| - compromised external content |
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| Tool outputs should be treated as **data**, not automatically trusted instructions. |
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| ## Human-in-the-Loop |
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| Not every action should be autonomous. |
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| Useful approval points may include: |
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| - sending messages |
| - publishing content |
| - deleting files |
| - making purchases |
| - modifying accounts |
| - executing financial actions |
| - changing production systems |
| - sharing sensitive data |
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| The correct level of autonomy depends on the risk of the task. |
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| ## Observability |
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| Reliable tool use requires visibility. |
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| Projects may track: |
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| - tool selected |
| - arguments |
| - result |
| - latency |
| - error |
| - retry |
| - model decision |
| - task status |
| - cost |
| - approval events |
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| Good observability makes agent workflows easier to debug and improve. |
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| --- |
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| ## Who Is ToolUse For? |
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| This organization may be useful for: |
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| - AI engineers |
| - agent developers |
| - platform teams |
| - automation builders |
| - API developers |
| - researchers |
| - MLOps teams |
| - enterprise AI teams |
| - tool developers |
| - open-source contributors |
| - students exploring agentic AI |
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| --- |
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| ## Technology Directions |
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| Projects may use: |
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| - Hugging Face Transformers |
| - Hugging Face Spaces |
| - open-weight models |
| - tool calling |
| - structured outputs |
| - JSON Schema |
| - MCP |
| - REST APIs |
| - browsers |
| - SQL |
| - vector databases |
| - Python |
| - JavaScript |
| - agent frameworks |
| - evaluation harnesses |
| - observability systems |
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| No single framework defines good tool use. |
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| Reliability matters more than framework choice. |
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| --- |
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| ## Principles |
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| ### 🎯 Use Tools With Purpose |
| A tool call should move the task toward completion. |
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| ### 🔎 Make Actions Inspectable |
| Users and developers should be able to understand what happened. |
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| ### ✅ Validate Before Execution |
| Inputs should be checked before tools are called. |
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| ### 🔐 Minimize Permissions |
| Agents should only receive the access they actually need. |
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| ### 🧪 Test Real Workflows |
| Benchmarks should reflect meaningful tool-use tasks. |
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| ### ⚡ Prefer Simplicity |
| One reliable tool call is better than an unnecessary ten-step agent loop. |
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| ### 🤖 Keep Models Accountable |
| Model confidence is not proof that an action is correct. |
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| --- |
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| ## Important Notice |
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| The models, Spaces, datasets, and experiments published here are intended for **research, development, education, testing, and technical exploration**. |
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| Unless explicitly stated otherwise, they do not guarantee: |
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| - secure execution |
| - correct tool selection |
| - valid parameters |
| - successful task completion |
| - production reliability |
| - regulatory compliance |
| - safe autonomous behavior |
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| Tool-enabled AI systems can make mistakes with real consequences. |
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| Appropriate validation, permissions, logging, and human oversight should be used for higher-impact tasks. |
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| ## Independent Organization |
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| **ToolUse is an independent Hugging Face community organization.** |
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| It is not an official Hugging Face organization, standards body, infrastructure provider, or certification authority. |
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| The name **ToolUse** describes the organization’s technical focus: |
| building and evaluating AI systems that can reliably interact with external tools. |
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| # ToolUse |
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| **Choose the right tool. Execute reliably. Build AI that can act.** |
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