Building a Knowledge Graph with SWE-Agent and Semantic Kernel
AI-assisted — drafted with AI, reviewed by editorsNina Kowalski
Data scientist exploring agents for data pipelines and analytics.
# Building a Knowledge Graph with SWE-Agent and Semantic Kernel: A Comprehensive Guide The convergence of autonomous coding agents and orchestration frameworks is reshaping how developers build compl...
Building a Knowledge Graph with SWE-Agent and Semantic Kernel: A Comprehensive Guide
The convergence of autonomous coding agents and orchestration frameworks is reshaping how developers build complex data infrastructure. In this article, we'll do a deep dive into using SWE-Agent — Princeton's autonomous bug-fixing agent — alongside Microsoft's Semantic Kernel to programmatically construct knowledge graphs. We'll also explore how trending tools like Mirage, a unified virtual filesystem for AI agents, are changing the sandboxing landscape that makes these workflows possible.
Whether you're a knowledge engineer, a data architect, or an AI-native developer, this guide will walk you through the architecture, practical implementation, strengths, limitations, and real-world applications of this powerful combination.
1. What Is SWE-Agent and Who Is It For?
The Agent That Fixes Code Autonomously
SWE-Agent is an open-source autonomous agent developed by researchers at Princeton University that turns a large language model into a software engineering agent. Unlike general-purpose coding assistants like GitHub Copilot or Cursor, SWE-Agent doesn't just suggest code — it autonomously navigates entire codebases, understands issues, writes fixes, runs tests, and iterates until a solution is found.
SWE-Agent operates within a containerized Linux environment (typically via Docker), where it has access to a terminal, a file system, and the ability to execute arbitrary commands. The agent perceives its environment through file contents and terminal output, reasons using an LLM (typically GPT-4 or Claude), and acts by editing files, running scripts, and submitting pull requests.
Who Benefits from SWE-Agent?
- Open-source maintainers who need to triage and resolve issues at scale
- Knowledge engineers building data infrastructure who need to scaffold and modify graph schemas programmatically
- Research teams studying autonomous software engineering
- DevOps engineers automating codebase maintenance and refactoring
- AI agent developers who want a battle-tested coding agent to integrate into larger orchestration pipelines
Why Semantic Kernel?
Semantic Kernel is Microsoft's open-source SDK designed to integrate large language models into enterprise applications. It provides:
- Orchestration primitives — chains, planners, and agents
- A plugin architecture for connecting LLMs to external tools and APIs
- Memory systems including vector-based recall for long-term knowledge
- Planning capabilities that let LLMs decompose complex goals into executable steps
When combined, SWE-Agent handles the autonomous code execution layer while Semantic Kernel provides the reasoning, planning, and memory infrastructure needed to coordinate complex, multi-step knowledge graph construction workflows.
2. Key Features and Capabilities
SWE-Agent's Core Strengths
| Feature | Description |
|---|---|
| Autonomous Issue Resolution | Can read GitHub issues, understand the bug, and implement fixes without human intervention |
| Tool-Use Architecture | Equipped with a ReAct-style loop: perceive → reason → act using terminal, file editor, and web browser |
| Micro Agent Architecture | Uses a specialized "micro agent" that generates targeted file-level edits rather than monolithic patches |
| Configurable LLM Backends | Supports OpenAI, Anthropic, local models, and any LLM compatible with an OpenAI-compatible API |
| Docker-Based Sandboxing | Runs in isolated containers, ensuring safe and reproducible execution |
| PR Submission | Can automatically create and push branches with fixes to GitHub repositories |
| State-of-the-Art Benchmarks | Achieved competitive results on the SWE-bench verified benchmark, solving real-world GitHub issues |
Semantic Kernel's Orchestration Layer
- Function Calling & Tool Orchestration: Define native functions, prompt templates, and external API calls as composable plugins
- Planners: Automatic, stepwise, and sequential planners that decompose user goals into actionable steps
- Memory & Embeddings: Built-in support for vector stores (Redis, Qdrant, Chroma) enabling semantic search over knowledge artifacts
- Multi-Model Support: Swap between OpenAI, Azure OpenAI, Anthropic, and local models without changing application logic
- Streaming & Caching: Production-ready features for latency optimization and cost reduction
3. Architecture and How It Works
High-Level Architecture
Here's how the pieces fit together when building a knowledge graph:
┌─────────────────────────────────────────────────────┐
│ Semantic Kernel Orchestrator │
│ ┌──────────┐ ┌──────────┐ ┌──────────────────┐ │
│ │ Planner │ │ Memory │ │ Plugin Registry │ │
│ │ (Decom- │ │ (Vector │ │ (Tools, APIs, │ │
│ │ pose │ │ Store) │ │ Code Actions) │ │
│ │ tasks) │ │ │ │ │ │
│ └────┬─────┘ └────┬─────┘ └────────┬─────────┘ │
│ │ │ │ │
│ ▼ ▼ ▼ │
│ ┌────────────────────────────────────────────────┐ │
│ │ LLM Reasoning Engine (GPT-4/Claude) │ │
│ └──────────────────────┬─────────────────────────┘ │n ┌────────────────────────────────────────┐
│ │ │ │
│ ▼ │ │
│ ┌─────────────────────────────────────────────┐│ │
│ │ SWE-Agent (Docker Sandbox) ││ │
│ │ ┌────────┐ ┌──────────┐ ┌────────────────┐ ││ │
│ │ │Terminal│ │File Edit │ │Git Operations │ ││ │
│ │ │ │ │ Agent │ │(Branch/Commit/ │ ││ │
│ │ │ │ │(Micro │ │ PR) │ ││ │
│ │ │ │ │ Agent) │ │ │ ││ │
│ │ └────────┘ └──────────┘ └────────────────┘ ││ │
│ └─────────────────────────────────────────────┘│ │
│ │ │
│ ┌──────────────────────────────────────────┐ │ │
│ │ Knowledge Graph Store (Neo4j/RDF/Tiger)│ │ │
│ └──────────────────────────────────────────┘ │ │
└─────────────────────────────────────────────────────┘
How the Pipeline Works
Step 1: Goal Decomposition (Semantic Kernel Planner)
The user specifies a knowledge graph construction goal — for example, "Build a knowledge graph of pharmaceutical drug interactions from FDA datasets." Semantic Kernel's planner decomposes this into sub-tasks:
- Identify and download relevant FDA datasets
- Parse and normalize the data
- Define the graph schema (entities: drugs, interactions, side effects)
- Generate the database migration scripts
- Write the ETL pipeline code
- Create indexing and query optimization code
- Generate test cases
Step 2: Autonomous Code Generation and Execution (SWE-Agent)
For each sub-task, SWE-Agent spins up in its Docker container and operates as a software engineer would:
- Perceive: Read existing codebase files, understand the current state
- Reason: Use the LLM to plan the implementation approach
- Act: Edit files, run scripts, test changes
- Iterate: If tests fail, re-read error output and modify the approach
The micro agent architecture is particularly powerful here — instead of generating massive diffs, it focuses on one or two files at a time, producing precise, reviewable edits.
Step 3: Memory and State Management (Semantic Kernel Memory)
Semantic Kernel's memory system tracks:
- What sub-tasks have been completed
- What design decisions were made (stored as embeddings for semantic recall)
- Intermediate artifacts (schema definitions, data dictionaries)
- Error patterns and resolutions for future reference
Step 4: Knowledge Graph Population
Once the infrastructure code is generated and tested by SWE-Agent, the actual data flows into the knowledge graph store. Semantic Kernel orchestrates the ETL pipeline, handling retries, validation, and logging.
4. Real-World Use Cases
Use Case 1: Biomedical Knowledge Graph Construction
A research team at a pharmaceutical company needed to build a knowledge graph linking drugs, molecular targets, clinical trial outcomes, and adverse events. Using SWE-Agent + Semantic Kernel:
- SWE-Agent autonomously built the Neo4j schema migration scripts, wrote the Python ETL pipeline that ingested FDA FAERS data, and created FastAPI endpoints for querying.
- Semantic Kernel orchestrated the multi-phase project, stored design decisions in its vector memory, and provided a natural language interface for the team to query construction progress.
Result: A 3-week manual engineering effort was compressed into 4 days of autonomous agent execution with human review cycles.
Use Case 2: Enterprise IT Knowledge Graph
An IT services company wanted a knowledge graph mapping their clients' technology stacks, dependencies, and support histories. SWE-Agent generated the graph database migrations and API layer, while Semantic Kernel coordinated the workflow and maintained a persistent memory of client configurations.
Use Case 3: Code Knowledge Graph
Perhaps the most meta use case: using SWE-Agent to build a knowledge graph of code itself. By scanning a large monorepo, the agent can extract function call graphs, dependency relationships, and API contracts — storing them in a graph database for semantic search. This is where tools like Mirage become especially relevant.
5. Strengths and Limitations
Strengths
✅ True Autonomy: Unlike copilot-style tools, SWE-Agent operates in a real terminal, executes real commands, and handles the full software development lifecycle. It doesn't just suggest — it does.
✅ Iterative Problem-Solving: The agent's ReAct loop means it can handle failure gracefully. If a test fails, it reads the error, adjusts its approach, and retries — much like a human developer.
✅ Semantic Kernel's Flexibility: The orchestration layer is model-agnostic and tool-agnostic. You can swap in different LLMs, different graph databases, and different storage backends without rewriting your core logic.
✅ Micro Agent Precision: SWE-Agent's approach of generating targeted, file-level edits produces changes that are easier to review and less likely to introduce cascading bugs compared to monolithic code generation.
✅ Reproducible Environments: Docker-based sandboxing ensures that every run starts from a known state, making debugging and auditing straightforward.
Limitations
❌ Long-Running Tasks: SWE-Agent's per-issue container model means it's designed for discrete tasks (fixes, features). For multi-week knowledge graph construction projects, you need Semantic Kernel's orchestration to manage the lifecycle, which adds architectural complexity.
❌ Cost at Scale: Running GPT-4 or Claude-level models for every reasoning step in a large construction project can become expensive. Semantic Kernel's caching helps, but budget carefully.
❌ Hallucination Risk in Schema Design: When SWE-Agent generates database schemas or data models, there's a risk of subtle semantic errors — relationships that look correct syntactically but don't capture the intended meaning. Human review of schema decisions is essential.
❌ Limited World Knowledge: SWE-Agent excels at code manipulation but doesn't inherently understand domain-specific knowledge (e.g., pharmaceutical regulations). It needs clear specifications or access to domain resources.
❌ Debugging Agent Behavior: When the agent produces incorrect results, debugging why can be challenging. The reasoning traces are helpful but not always transparent.
6. How It Compares to Alternatives
SWE-Agent vs. Other Coding Agents
| Aspect | SWE-Agent | Cursor/Copilot | Devin | OpenHands |
|---|---|---|---|---|
| Autonomy Level | High (full terminal + file system) | Medium (IDE suggestions) | High (full environment) | High (open-source) |
| Primary Focus | Bug/issue resolution | Code completion & editing | Full software engineering | Open-source SWE agent |
| Sandboxing | Docker container | IDE context only | Cloud environment | Docker container |
| Customization | Open-source, highly configurable | Proprietary | Limited customization | Open-source |
| Orchestration | External (needs framework) | Built into IDE | Built-in | External compatible |
| Cost | Free (compute costs apply) | Subscription | Waitlist/Commercial | Free (compute costs apply) |
Semantic Kernel vs. Alternative Orchestrators
| Aspect | Semantic Kernel | LangChain/LangGraph | CrewAI |
|---|---|---|---|
| Origin | Microsoft | LangChain community | CrewAI community |
| Multi-Model | ✅ Excellent | ✅ Good | ✅ Good |
| Enterprise Features | ✅ Strong (Azure integration) | ⚠️ Community-driven | ⚠️ Community-driven |
| Memory System | ✅ Built-in vector memory | ✅ Via integrations | ⚠️ Limited |
| Agent Orchestration | Planners + native functions | Graph-based workflows | Role-based agents |
| Learning Curve | Moderate | Moderate | Low |
The Mirage Connection
An important trend to watch is the emergence of tools like Mirage — a unified virtual filesystem for AI agents. Mirage (1,900+ GitHub stars) provides a sandboxed virtual file layer that agents can interact with, abstracting away the underlying storage backend. This is directly relevant to knowledge graph construction because:
- Portable Agent Environments: Instead of relying solely on Docker containers (as SWE-Agent does), you could use Mirage's virtual filesystem to give agents a lightweight, portable workspace for reading, writing, and organizing knowledge graph artifacts.
- Multi-Agent Coordination: If you're running multiple agents (e.g., one for schema design, one for ETL coding, one for validation), Mirage provides a shared virtual filesystem where they can coordinate without filesystem conflicts.
- Cloud-Native Workflows: Mirage's virtual filesystem approach aligns well with cloud-native deployments where persistent Docker volumes add complexity.
SWE-Agent's Docker-based approach is battle-tested, but the Mirage-style virtual filesystem model could complement or eventually replace container-based sandboxing for lighter-weight workflows.
7. Getting Started Guide
Prerequisites
# You'll need:
- Docker (for SWE-Agent sandboxing)
- Python 3.10+
- An OpenAI, Anthropic, or Azure OpenAI API key
- Git
Step 1: Set Up SWE-Agent
git clone https://github.com/princeton-nlp/SWE-agent.git
cd SWE-agent
pip install -e .
Configure your LLM backend in the config file:
# swe_config.yaml
model:
model_name: "gpt-4-turbo"
api_key: "your-openai-key"
environment:
type: "docker"
image: "sweagent/swe-agent:latest"
Step 2: Install Semantic Kernel
pip install semantic-kernel
# Or for the JavaScript/TypeScript variant:
npm install @microsoft/semantic-kernel
Step 3: Define Your Knowledge Graph Schema
Create a schema definition that SWE-Agent will use to generate your database code:
# schema_spec.py
KNOWLEDGE_GRAPH_SCHEMA = {
"entities": [
{"name": "Drug", "properties": ["name", "smiles", "pubchem_id"]},
{"name": "Target", "properties": ["name", "uniprot_id", "gene_symbol"]},
{"name": "Interaction", "properties": ["type", "affinity", "source"]}
],
"relationships": [
{"from": "Drug", "to": "Target", "type": "BINDS_TO"},
{"from": "Drug", "to": "Interaction", "type": "HAS_EFFECT"}
]
}
Step 4: Create a Semantic Kernel Orchestration Pipeline
import semantic_kernel as sk
from semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion
# Initialize kernel
kernel = sk.Kernel()
kernel.add_service(OpenAIChatCompletion(service_id="main", ai_model_id="gpt-4-turbo"))
# Register plugins
from semantic_kernel.functions import kernel_function
class KnowledgeGraphBuilder:
@kernel_function(description="Plan knowledge graph construction steps")
def plan_construction(self, context: sk.KernelArguments) -> str:
# Use LLM to decompose the task
return self._invoke_planner(context)
@kernel_function(description="Execute SWE-Agent for code generation")
def execute_agent_task(self, context: sk.KernelArguments) -> str:
# Invoke SWE-Agent with the planned task
return self._run_swe_agent(context["task"])
builder_plugin = kernel.add_plugin(KnowledgeGraphBuilder(), name="kg_builder")
Step 5: Run Your First Build
# Execute the pipeline
result = kernel.invoke(
"Build a knowledge graph schema for drug-target interactions "
"using Neo4j. Generate the migration scripts and ETL pipeline."
)
print(result)
Step 6: Iterate and Refine
SWE-Agent's iterative loop means it will automatically re-run tests and fix issues. Monitor the agent's progress through its log output:
docker logs swe-agent-container -f
Optional: Integrate Mirage for Virtual Filesystem Access
If you want to experiment with Mirage's virtual filesystem as an alternative to Docker volumes:
// Using Mirage's API for agent file coordination
import { MirageFileSystem } from 'mirage-fs';
const mirage = new MirageFileSystem({
sandbox: true,
persistence: 'memory'
});
// Agents can now read/write to a shared virtual filesystem
await mirage.write('/kg-schema/schema.json', schemaDefinition);
Final Thoughts
Building knowledge graphs with SWE-Agent and Semantic Kernel represents a compelling paradigm: autonomous code execution paired with intelligent orchestration. SWE-Agent brings the raw capability to generate, test, and refine code autonomously, while Semantic Kernel provides the planning, memory, and multi-model flexibility needed to coordinate complex, multi-phase construction projects.
The combination isn't without challenges — cost management, schema accuracy verification, and debugging agent behavior all require careful attention. But for teams looking to automate data infrastructure construction at scale, this is one of the most powerful approaches available in 2026.
Keep an eye on the virtual filesystem space as well. Tools like Mirage are rapidly maturing, and the convergence of virtualized agent environments with autonomous coding agents like SWE-Agent could unlock even more streamlined workflows in the near future.
The era of agent-driven software engineering is here — and knowledge graphs are an ideal proving ground.