Quadropic Syntheo: Redefining SOTA Coding Agents with Pre-Release Configurations (Feb 2025)

How Syntheo’s Agentic Architecture and Context-Aware Reasoning Achieve 56% SWE Bench Lite Accuracy

Introduction: The Rise of Agentic Coding

As AI continues to reshape software engineering, the race to build state-of-the-art (SOTA) coding agents has intensified. Enter Quadropic Syntheo, a pre-release agentic framework that promises to redefine how developers interact with AI. With a 56% accuracy on SWE Bench Lite—a benchmark for real-world software engineering tasks—Syntheo combines deep code analysis, multi-agent collaboration, and seamless integration with modern tools. In this blog post, we’ll dive deeper into its configurations, architecture, and what makes it a game-changer.

Core Architecture: RepoVision and CodeReason

Syntheo’s power lies in its dual-engine design:

1. RepoVision Evolution

RepoVision has undergone significant evolution across three versions, culminating in a highly sophisticated system.

v0.1: Traditional Search Systems

The first iteration of RepoVision was rudimentary, relying on two primary mechanisms:

• RAG (Retrieval-Augmented Generation): A technique where the model retrieves relevant code snippets from a repository before generating responses.
• Grep-like Search: A simple keyword-based search mechanism to locate files or lines containing specific terms.

Example:
Suppose you want to find all occurrences of the calculate_tax function in a large codebase. Using v0.1, RepoVision would:

grep -r "calculate_tax" ./src/  

This command would return all files and lines where calculate_tax appears, but without any contextual understanding of how the function is used or its dependencies.

Limitations:

• Lacks context about relationships between files or functions.
• Struggles with complex queries requiring deeper semantic understanding.

v0.2: Optimized Retrieval with RepoMap

In this version, RepoVision introduced optimizations to retrieve structured information about the codebase. Key features included:

• File Tree Navigation: Efficiently lists directories and files in a hierarchical structure.
• RepoMap Generation: Creates a map of method signatures, class definitions, and function headers across the entire repository, similar to what tools like Aider provide.
• Hyper-Localization: Focuses on retrieving only the most relevant parts of the codebase based on user input.

Example:
If you're working on debugging an issue related to the User class, RepoVision v0.2 might generate a RepoMap that looks like this:

├── src/  
    ├── models.py  
        └── User  
            ├── __init__(self, name, email)  
            ├── save_to_db(self)  
            └── delete_from_db(self)  
    ├── utils.py  
        └── validate_user(user: User) -> bool  

With hyper-localization, it can focus specifically on the models.py file if the query pertains to the User class.

Improvements Over v0.1:

• Provides more control over navigation.
• Offers better context by mapping out relationships between components.

v0.3: Agentic Approach

The latest version of RepoVision takes a fully agentic approach, inspired by Kodu's architecture, which emphasizes modular, context-aware decision-making and dynamic agent interactions. Key enhancements include:

• Dynamic File Tree Selection: The LLM chooses which part of the file tree to explore based on the task at hand, similar to how Kodu dynamically selects modules based on contextual needs.
• Multi-Modal Retrieval Options: Combines Grep-like searches, RepoMaps, and direct access to code by line or header/signature, allowing the agent to flexibly switch between retrieval modes depending on the complexity of the task.
• Enhanced Context Injection via Function, Class, and Method Signatures: Builds upon Aider's RepoMap concept by leveraging function, class, and method signatures along with dependency graphs to inject richer contextual details. This enables the agent to understand not just the code itself but also how different components interact with one another.

Example:
Imagine you need to refactor the save_to_db method in the User class. RepoVision v0.3 presents the following options to the LLM:

1. Retrieve the full file tree:

   ├── src/  
       ├── models.py  
           └── User.save_to_db()  

2. Fetch the exact method definition using its signature:

   def save_to_db(self):  
       # Logic to save user data to the database  
       pass  

3. Perform a targeted Grep search for references to save_to_db:

   grep -r "save_to_db" ./src/  

Additionally, the agent leverages dependency graphs to identify all callers of save_to_db, ensuring that changes are consistent across the codebase. For instance:

Callers of save_to_db():  
├── src/views.py  
    └── create_user_profile(user: User)  
├── src/utils.py  
    └── backup_user_data(user: User)  

Based on these choices, the LLM selects the most appropriate action—e.g., fetching just the method definition for refactoring.

2. CodeReason Evolution

CodeHit Architecture (Legacy System)

Prior to CodeReason, Syntheo relied on a simpler architecture called CodeHit. This system assumed that the context provided by RepoVision was always correct and proceeded directly to apply patches without verification. It required only a single LLM call, but this approach had significant drawbacks:

• Higher Probability of Errors: Without verifying the accuracy of the context, CodeHit often applied incorrect or suboptimal edits.
• No Feedback Loop: Once a patch was applied, there was no mechanism to evaluate its quality or correctness.

Example Workflow:
Continuing with the save_to_db example:

1. RepoVision retrieves the method definition.
2. CodeHit applies a fix without further analysis:

   def save_to_db(self):  
       # Logic to save user data to the database  
       pass  

3. No validation or testing occurs post-edit, leading to potential issues downstream.

CodeReason v0.1: Powered by Deepseek's R1

To address the limitations of CodeHit, CodeReason v0.1 introduces a revolutionary feedback-driven system powered by Deepseek's R1, a cutting-edge LLM that "thinks before it infers." Unlike traditional models that generate outputs directly, Deepseek's R1 employs a deliberate reasoning process to ensure high-quality results. Key innovations include:

• Deliberate Reasoning Before Inference: Instead of immediately applying edits, CodeReason uses Deepseek's R1 to analyze the context retrieved by RepoVision. This ensures that every proposed edit is both accurate and contextually appropriate.
• Real-Time Feedback System: After proposing an edit, CodeReason evaluates its effectiveness using three key parameters:
  1. Localization: How well the edit aligns with the intended scope of the task.
  2. Code Writing Quality: The syntactic and semantic correctness of the generated code.
  3. Problem-Solving Approach: The logical coherence and robustness of the solution.
• Mathematical Feedback Function: These parameters are passed into a specially designed mathematical function that generates a score ranging from -1 (poor) to 1 (excellent). If the score falls below a threshold, CodeReason re-prompts itself to refine the edit iteratively.
• External LLM Actor: An independent external LLM evaluates each parameter independently, ensuring unbiased ratings.

How Deepseek's R1 Works in CodeReason:
Deepseek's R1 is integrated directly into the decision-making pipeline of CodeReason. When presented with a task, it performs a multi-step reasoning process:

1. Context Analysis: Evaluates the context provided by RepoVision to ensure it is accurate and complete.
2. Solution Exploration: Considers multiple potential solutions and their implications.
3. Output Generation: Produces the final edit after thorough deliberation, ensuring high-quality results.

Example Workflow:
Continuing with the save_to_db example:

1. RepoVision retrieves the method definition and identifies potential issues (e.g., missing error handling).
2. CodeReason, powered by Deepseek's R1, proposes a fix:

   def save_to_db(self):  
       try:  
           # Logic to save user data to the database  
           pass  
       except Exception as e:  
           print(f"Error saving to DB: {e}")  

3. The external LLM actor evaluates the edit:
   • Localization Score: 0.9 (highly localized to the save_to_db method)
   • Code Writing Quality Score: 0.85 (syntactically and semantically correct)
   • Problem-Solving Approach Score: 0.8 (robust error-handling logic)
4. The mathematical feedback function computes an overall score depending on whether the edit is finalized and integrated into the codebase.

Localization Misalignment and RepoVision Collaboration

In cases where the localization of the code (i.e., identifying the exact location of the code snippet within the repository) does not align perfectly with the task requirements, CodeReason collaborates with RepoVision to refine the context:

• Re-Querying RepoVision: If the localization score is low (e.g., below 0.5), CodeReason triggers a follow-up query to RepoVision, asking for more precise information. For example:
  • Request: "Provide the exact file and line number where save_to_db is defined."
  • Response:

    File: src/models.py  
    Line: 45  

• Dependency Graph Validation: RepoVision provides a dependency graph to verify whether the identified location is indeed the correct one. For instance:

  Dependencies of save_to_db():  
  ├── src/views.py  
      └── create_user_profile(user: User)  
  ├── src/utils.py  
      └── backup_user_data(user: User)  

  This ensures that the proposed edit aligns with the broader codebase structure.

Step-by-Step Explanation of a Real-World Example

Let’s walk through a detailed example of how Syntheo handles a real-world bug fix, showcasing the roles of RepoVision and CodeReason in identifying and resolving the issue. For this example, we’ll use an imaginary codebase to illustrate the process.

The Problem

In a hypothetical e-commerce platform, there’s a bug in the calculate_discount function located in discounts/utils.py. The function incorrectly applies discounts to products with special pricing rules, leading to incorrect final prices for certain items.

Step 1: Identifying the Issue with RepoVision

• Issue Description:

  <Issue>The calculate_discount function in discounts/utils.py incorrectly applies discounts to products with special pricing rules, leading to incorrect final prices for certain items.</Issue>  

RepoVision Response:

• RepoVision searches the codebase for the calculate_discount function.
• It generates the following RepoMap of the utils.py file:

  ├── src/  
      ├── discounts/  
          └── utils.py  
              └── calculate_discount(product: Product) -> float  

• Results: RepoVision identifies the method in utils.py, ready for further investigation.

Step 2: Reasoning with CodeReason

• CodeReason:
  CodeReason’s Deepseek's R1 is invoked to analyze the problem context and generate a possible solution:

  def calculate_discount(product):  
      if product.special_pricing:  
          return 0.0  
      else:  
          return product.price * 0.9  

  • Mathematical Feedback Evaluation:
    CodeReason performs the necessary feedback evaluation (localization, code quality, problem-solving) and finalizes the fix.

Step 3: Final Evaluation and Patch Integration

• Feedback Scoring:
  After CodeReason generates the fix, it passes the new code through the mathematical feedback system:

  • Localization Score: 1.0 (perfect alignment with the calculate_discount function).
  • Code Writing Quality Score: 0.95 (correct syntax and logic).
  • Problem-Solving Approach Score: 0.9 (accurately addresses the special pricing rule).

  Given the high scores, CodeReason passes the fix for final integration.

• Patch Application:
  CodeReason applies the fix to discounts/utils.py, ensuring the issue is resolved. The updated code looks like this:

  def calculate_discount(product):  
      if product.special_pricing:  
          return 0.0  # Special pricing products get no discount  
      else:  
          return product.price * 0.9  # Regular discount for other products  

Step 4: Real-Time Feedback and Iteration

After the patch is deployed, the system continuously monitors for any further issues. If additional problems arise, CodeReason re-engages with RepoVision for more context and Deepseek's R1 for refined fixes. Additionally, feedback loops are established with the development team to ensure the patch is performing as expected in real-world scenarios.

The Future of Syntheo: Scaling and Enhancing Agentic Intelligence

While Quadropic Syntheo has already demonstrated remarkable accuracy and efficiency on SWE Bench Lite, there’s much more in store for this evolving framework.

1. Multi-Agent Collaboration

Syntheo’s architecture is not limited to a single agent. Future versions will incorporate multi-agent collaboration, where multiple specialized agents work in tandem on complex coding tasks. For instance:

• BugFixer Agent: Focused exclusively on identifying and fixing bugs.
• Refactor Agent: Handles code refactoring and optimizations.
• Test Agent: Specializes in generating and executing unit tests.

By leveraging the strengths of each agent, Syntheo will scale to handle larger and more complex codebases with greater efficiency.

2. Enhanced Context Awareness

As Syntheo progresses, the context-awareness of the agents will improve. Future versions will be able to understand entire workflows, not just individual code snippets. By integrating with external tools like Jira, GitHub, and CI/CD pipelines, Syntheo can better predict development needs, prioritize tasks, and suggest improvements based on the broader project context.

3. Real-Time Collaboration with Developers

Rather than just responding to fixed prompts, Syntheo will enable real-time collaboration between developers and agents. Developers will be able to request suggestions, make adjustments, and see updates instantly. This kind of dynamic interaction promises to enhance productivity and creativity in coding, making Syntheo an indispensable tool for developers.

4. Advanced Reasoning with Knowledge Graphs

The integration of knowledge graphs into the agent's reasoning process will enable deeper understanding and decision-making. These graphs will provide a comprehensive view of the relationships between code, libraries, and dependencies, facilitating more accurate suggestions for complex tasks such as optimizations and architectural changes.

5. Continuous Learning and Adaptation

One of the most promising aspects of Syntheo is its ability to learn from past experiences. The system will leverage machine learning and feedback loops to continuously adapt and improve its reasoning and code generation. As developers use Syntheo, it will gather valuable data on code quality, success rates, and error handling, refining its capabilities over time.

Conclusion: A New Era for Coding Agents

Quadropic Syntheo is setting the stage for a new era in coding automation. By combining advanced architectural design, agentic reasoning, and powerful feedback loops, it offers a level of accuracy and efficiency that pushes the boundaries of what was previously possible. The integration of RepoVision and CodeReason allows for a dynamic and context-aware approach to software development that doesn’t just generate code but understands the broader problem at hand.

With a promising roadmap ahead, Syntheo is poised to become a cornerstone of the software engineering landscape, helping developers tackle the most complex coding challenges with ease and confidence. Whether you’re working on small projects or scaling large applications, Syntheo’s approach promises to deliver powerful insights, efficient solutions, and continuous improvement to your coding workflow.

The future of coding agents has arrived, and with Quadropic Syntheo, the possibilities are limitless.

References:

• Quadropic quadropic.com
• SWE Bench swebench.com
• Aider aider.chat
• Kodu aider.chat
• Agentless GitHub/OpenAutoCoder/Agentless
• Deepseek aider.chat
• Qwen qwenlm.ai
• Anthropic anthropic.com
• OpenAI openai.com

Contributers

• Mohamed Kamran GitHub/MohamedKamran