CodeMode: Domain-Adapted Embeddings for Agentic Codebases

Overview

Large-scale AI software increasingly relies on agentic frameworks such as LangChain, CrewAI, AutoGen, and similar orchestration systems that coordinate multiple agents, tools, and workflows. Traditional code embedding models are not optimized for these modern patterns and often fail to understand agent structure, roles, tools, chaining logic, and interactions between files and modules.

This Innovation Challenge aims to develop an enhanced code embedding model specifically optimized for Agentic AI development. Participants will fine-tune existing code encoders using real-world agentic repositories and documentation, enabling better understanding and retrieval of complex agentic systems. The output will support high-quality similarity search, contextual code reasoning, and question answering on top of modern AI software stacks.

Problem

Current embedding models are trained on general code and natural language, but:

• They lack awareness of agentic frameworks, their syntax, and interactions.

• Traditional chunking splits code without understanding program structure, leading to loss of context.

• Lack of high-quality datasets for question answering about AI agents.

• Retrieval quality degrades when questions involve multi-role behaviors, tool calls, agent messaging, or workflow execution paths.

• Many existing vector models struggle to recognize how different files contribute to a single agent flow.

As a result, developers working on agent-based AI systems face challenges:

• Difficulty querying codebases for behavior explanation

• Poor retrieval accuracy during RAG

• Incomplete answers for code-level debugging

• Limited ability to provide context-aware assistance

This challenge solves that gap.

Proposed Solution

The project will:

1. Select high-performing code embedding models from open-source or commercial ecosystems.

2. Curate training datasets consisting of:

   • Real projects using agentic frameworks

   • Documentation for various AI agent libraries

   • Code Q&A datasets generated from OpenAI and compiled examples

3. Introduce syntax-aware chunking using AST-based parsing so the model sees complete logical structures such as:

   • Agent definitions

   • Tool functions

   • Chains and pipelines

   • Utility methods

4. Fine-tune embeddings to better represent:

   • Agent interactions

   • Framework semantics

   • Execution relationships between modules

5. Train a retrieval-powered QA pipeline, enabling the system to:

   • Embed queries and code

   • Perform similarity matching

   • Provide accurate explanations and answers

By the end, we will deliver a model capable of deeper understanding of modern AI agent frameworks.

Project Goals

Primary Goals

• Produce an embedding model specialized for Agentic AI development.

• Improve retrieval performance for multi-file and multi-agent systems.

• Enable precise question answering over codebases.

Secondary Goals

• Build reusable data pipelines for large-scale code ingestion.

• Produce datasets that can be reused for ongoing research.

• Benchmark multiple embedding models for comparison.

• Deliver well-documented evaluation methodology.

Expected Deliverables

Participants are expected to deliver:

1. Dataset

   • Curated code repositories from agentic libraries

   • Documentation datasets

   • Question–answer pairs for code reasoning

2. Data Processing Pipeline

   • Syntax-aware chunking using ASTs or equivalent

   • Embedding + vector indexing pipeline

3. Fine-Tuned Embedding Model

   • Trained for agentic code similarity search

   • Optimized for question answering

4. Evaluation Benchmarks

   • Retrieval metrics such as Recall@K, MRR, nDCG

   • Human evaluation results

5. Demo

   • Jupyter notebook or simple UI demonstrating:

     • Querying the model

     • Comparing baseline vs improved model retrieval

6. Documentation

   • Architecture diagrams

   • Model training steps

   • Dataset and pipeline details

   • How to reproduce the results

 

Project Timeline 

Sprint 1

Objectives

• Identify top agentic AI frameworks.

• Collect code repositories and documentation.

• Generate initial Q&A pairs using LLMs.

• Set up repository and collaboration tools.

Deliverables

• Initial dataset dump

• Defined evaluation criteria

• First baseline model for comparison

Sprint 2

Objectives

• Implement AST-based or structural chunking for:

  • Classes

  • Methods

  • Chains

  • Agent definitions

• Build embedding + vector storage pipeline.

• Generate larger Q&A dataset using automated prompts.

Deliverables

• Data pipeline for chunking

• First pass of embeddings and searchable index

• Sample semantic search demo

Sprint 3

Objectives

• Fine-tune selected embedding models using:

  • Contrastive learning

  • Supervised QA tasks

  • Pairwise ranking loss

• Compare different embedding baselines.

• Improve performance based on early feedback.

Deliverables

• Fine-tuned model

• Benchmark results (Recall@K, MRR, etc.)

• Comparison with baseline embeddings

Sprint 4

Objectives

• Complete large-scale evaluation.

• Conduct human scoring of retrieval quality.

• Build final demonstration (notebook or small interface).

• Prepare full documentation and final presentation.

Deliverables

• Final trained model

• Evaluation report

• Reproducible demo

• Final project documentation

Who Should Join

This challenge is suited for:

• Machine Learning Engineers

• Data Scientists

• NLP Researchers

• Software Engineers

• AI/ML Students

• MLOps practitioners

• Contributors passionate about AI agent systems

No single participant needs to cover everything—teams will collaborate.

Impact

This challenge will advance the creation of AI models that deeply understand modern agentic codebases, enabling:

• Smarter code search

• More capable development assistants

• Stronger RAG systems for engineering

• Better developer productivity