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# InfCode
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[](https://www.python.org/downloads/) [](https://opensource.org/licenses/MIT)
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**InfCode** is an intelligent Code Agent System developed by [Tokfinity](https://www.tokfinity.com)'s Code Research team, which uses LLMs to automatically analyze and fix Code Repo issues.
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**Project Status:** InfCode has achieved **79.4%** solution rate on the SWE-Bench Verified split, demonstrating the **latest SOTA** performance.
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## 🌟 System Architecture
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<img src="figures/framework.svg" alt="framework" width="100%">
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### Core Modules
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The InfCode adopts a generate-select architectural pattern. Its core modules include the **Patch Generator** and the **Patch Selector**, which are responsible for generating candidate patches and selecting the optimal patch, respectively.
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- **Patch Generator**: Register multiple groups of generators, where each generator is configured with a separate container to generate and repair multiple candidate patches in parallel. Can interact with `File Editor`, `File Search`, `Bash Executor`, `Result Submitter` tools. The Patch Generator runs up to $5$ attempts and gathers all produced patches.
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- **Patch Selector**: Select the best patch from all the candidate patches. Can interact with `File Editor`, `File Search`, `Bash Executor` tools.
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### Other Modules
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In addition to the core modules, InfCode includes several other modules, such as the **Image Builder**, **Tool Executor**, and **LLM API Manager**, which handle image construction, tool execution, and API management, respectively. These modules complement the core modules and enhance the overall system functionality.
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- **Image Builder**: This component is responsible for generating dedicated container images for each example in the benchmark suite. The images are stored locally and reused across iterative runs to improve efficiency.
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- **Tool Executor**: This module is responsible for receiving tool-level instructions, executing the specified commands within the containerized environment, collecting the execution outputs, and transmitting the results back to the tool layer.
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- **LLM API Manager**: Unified LLM API manager. It invokes the LLM API through the completion endpoint to retrieve model-generated responses. It supports integration with OpenAI, OpenRouter, and DeepSeek clients, as well as self-hosted (privately deployed) LLM instances.
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## ✨ Tools Support
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To enable interaction with code repositories, the InfCode is equipped with the following tools:
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- **File Editor**:
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- File Preview (view): Retrieve the content of a specified line range from a file within the container.
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- File Creation (create): Create a new file within the container and populate it with the specified content.
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- File Modification (str_replace): Modify specific portions of a given file within the container by replacing designated content.
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- File Insertion (insert): Insert specified content into a designated position within a target file in the container.
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- **File Searcher**:
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- The LLM outputs a search pattern, which the tool uses to perform file searches within the container.
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- The tool then parses and returns the results from the container.
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> This module is implemented based on `ripgrep`, a high-performance file search utility that outperforms traditional `grep` in speed and supports fuzzy matching for enhanced search flexibility.
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- **Bash Executor**:
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- Execute Bash commands generated by the LLM within the container environment.
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- Parse and return the execution results from the container.
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- **Result Submitter**:
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- After the LLM completes patch generation and testing, it triggers this tool and provides patch generation details.
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- The tool executes `git diff` within the container to obtain and return the patch content.
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## 📖 Quick Start
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### 1. Environment Setup
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- Create a `.env` file in the project root directory and configure the necessary environment variables:
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```bash
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# LLM API configuration
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OPENROUTER_API_KEY=your_openrouter_api_key
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```
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- Install the pip dependencies, Python 3.12 is recommended.
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```bash
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pip install -r requirements.txt
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```
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### 2. Run the System
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#### Batch Execution
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```bash
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python _batch_run.py -c config/batch.yaml --name first -i config/list_all.txt -p 2 --clean
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```
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Explanation:
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- `-c` specifies the batch configuration file (currently minimal)
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- `-name` assigns a name to this batch run
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- `-i` specifies the issue list file
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- `-p` sets concurrency (default 20)
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- `-clean` clears the output directory before running (useful when reusing the same name)
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- `-o` sets the output directory (default: ./batch_out/)
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- `-h` prints help
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#### Evaluation
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```bash
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./eval.sh <run_id>
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```
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Explanation:
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- `run_id` — required parameter identifying this evaluation run
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- The script automatically reads dataset and prediction paths from `config/config.yaml`.
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Since batch_run results are redirected, you may need to manually copy the preds.json file to the path specified in the config.
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- Uses the official SWE-bench evaluation tool to verify generated patches
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- Supports multi-process parallel evaluation (default: 20 workers)
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- Results will be saved in the specified directory, including pass rates, failure reasons, and detailed logs
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### 3. Configuration Files
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The main configurations are in `config/config.yaml`:
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- `providers`: LLM provider settings
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- `runner`: runner settings (concurrency, number of iterations, etc.)
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- `builder`: Docker image build settings
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- `log`: logging settings
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### Notes
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- Ensure Docker is installed and running correctly.
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- Check logs for detailed execution steps.
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- Supports multiple LLM providers, tool formats are auto-selected based on configuration.
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## Contact
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research@tokfinity.com
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## Core Team
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- [Pete Wong](https://petewongggg.github.io/)
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- [Shihao Zheng](https://github.com/kirito41dd)
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- [Hengzhi Zhang](https://henryzhang11.github.io)
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- [Kefan Li](https://lbaf23.github.io)
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- [Qingao Dong](https://qadong.github.io)
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## 📄 License
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This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
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## 🙏 Acknowledgments
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We thank Anthropic for building the [anthropic-quickstart](https://github.com/anthropics/claude-quickstarts) project and bytedance for building the [trae-agent](https://github.com/bytedance/trae-agent) project that served as valuable references for the tool building.
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