# Building an automated GitHub issue resolution system with Claude APIs and MCP servers
## Executive Summary
This technical analysis provides comprehensive implementation guidance for building an automated GitHub issue resolution system using Claude APIs and Model Context Protocol (MCP) servers. The system architecture spans two phases: a local prototype for manual testing and a production-ready GitHub Actions pipeline for automated processing. Based on extensive research of existing solutions, architectural patterns, and security considerations, this report outlines the technical decisions, implementation approaches, and best practices necessary for success.
## System architecture overview
The proposed system leverages MCP's standardized protocol for connecting Claude to external data sources, combined with GitHub's native automation capabilities. The architecture follows a microservices pattern with event-driven processing, ensuring scalability and maintainability.
### Core components
The system consists of five primary components working in concert:
**MCP Server Infrastructure** acts as the bridge between Claude and your codebase, providing standardized access to repository data, code search capabilities, and contextual information retrieval. The server implements the JSON-RPC 2.0 protocol for reliable communication.
**Claude API Integration** handles the intelligent processing of issues, generating code fixes, and providing explanations. This component manages rate limits, implements caching strategies, and ensures cost-effective operation.
**GitHub Integration Layer** processes webhook events, manages pull requests, and coordinates the automated workflow. It handles authentication, branch management, and ensures proper security controls.
**Code Analysis Pipeline** validates generated code through static analysis, security scanning, and automated testing. This ensures only high-quality, secure code modifications are proposed.
**Monitoring and Observability** tracks system performance, API usage, success rates, and provides alerting for anomalies or failures.
## Phase 1: Local prototype implementation
## Phase 1: Local prototype implementation
### Two implementation options
Since you already have a Python RAG MCP server built, here are two approaches for the local prototype:
**Option A: Two separate MCP servers** (recommended for modularity)
- Keep your existing Python RAG MCP server as-is
- Build a new TypeScript/JavaScript GitHub Issues MCP server
- Claude Code orchestrates between both
**Option B: Extended Python MCP server** (recommended for simplicity)
- Extend your existing Python RAG server with GitHub operations
- Everything in one server, one configuration
---
## Option A: Two separate MCP servers
### Your existing Python RAG MCP server
Keep your current RAG server running as-is. No changes needed.
### New GitHub Issues MCP server
Create a dedicated GitHub operations server in TypeScript:
```typescript
import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";
import { z } from "zod";
import { Octokit } from "@octokit/rest";
const server = new McpServer({
name: "GitHub Issue Manager",
version: "1.0.0"
});
const octokit = new Octokit({
auth: process.env.GITHUB_TOKEN
});
// Tool for fetching repository issues
server.tool("fetch_issues", {
owner: z.string(),
repo: z.string(),
state: z.enum(["open", "closed", "all"]).default("open"),
labels: z.array(z.string()).optional(),
limit: z.number().default(10)
}, async ({ owner, repo, state, labels, limit }) => {
const issues = await octokit.rest.issues.listForRepo({
owner,
repo,
state,
labels: labels?.join(","),
per_page: limit
});
return {
content: [{
type: "text",
text: JSON.stringify(issues.data, null, 2)
}]
};
});
server.tool("get_issue", {
owner: z.string(),
repo: z.string(),
issue_number: z.number()
}, async ({ owner, repo, issue_number }) => {
const issue = await octokit.rest.issues.get({
owner,
repo,
issue_number
});
return {
content: [{
type: "text",
text: JSON.stringify(issue.data, null, 2)
}]
};
});
server.tool("create_pull_request", {
owner: z.string(),
repo: z.string(),
title: z.string(),
body: z.string(),
head: z.string(),
base: z.string().default("main"),
files: z.array(z.object({
path: z.string(),
content: z.string()
}))
}, async ({ owner, repo, title, body, head, base, files }) => {
// Create new branch
const baseBranch = await octokit.rest.repos.getBranch({
owner, repo, branch: base
});
await octokit.rest.git.createRef({
owner, repo,
ref: `refs/heads/${head}`,
sha: baseBranch.data.commit.sha
});
// Create/update files
for (const file of files) {
await octokit.rest.repos.createOrUpdateFileContents({
owner, repo,
path: file.path,
message: `Update ${file.path}`,
content: Buffer.from(file.content).toString('base64'),
branch: head
});
}
// Create PR
const pr = await octokit.rest.pulls.create({
owner, repo, title, body, head, base
});
return {
content: [{
type: "text",
text: `Created PR #${pr.data.number}: ${pr.data.html_url}`
}]
};
});
// Resource for accessing specific issue details
server.resource("github://issue/{owner}/{repo}/{number}", async (uri) => {
const match = uri.match(/github:\/\/issue\/([^\/]+)\/([^\/]+)\/(\d+)/);
if (!match) throw new Error("Invalid issue URI");
const [, owner, repo, number] = match;
const issue = await octokit.rest.issues.get({
owner, repo, issue_number: parseInt(number)
});
return {
contents: [{
uri,
mimeType: "application/json",
text: JSON.stringify(issue.data, null, 2)
}]
};
});
const transport = new StdioServerTransport();
server.connect(transport);
```
### Claude Code configuration for Option A
```json
{
"mcpServers": {
"code-rag": {
"command": "python",
"args": ["/path/to/your/existing/rag-server.py"],
"env": {
"REPOSITORY_PATH": "/path/to/local/repo",
"QDRANT_URL": "http://localhost:6333"
}
},
"github-issues": {
"command": "node",
"args": ["./servers/github-issue-server.js"],
"env": {
"GITHUB_TOKEN": "${GITHUB_TOKEN}"
}
}
}
}
```
---
## Option B: Extended Python MCP server
### Extending your existing Python RAG server
Add GitHub operations to your current Python server:
```python
# Add these imports to your existing server
from github import Github
import base64
import os
class GitHubService:
def __init__(self, token: str):
self.github = Github(token)
def fetch_issues(self, owner: str, repo: str, state: str = "open", labels: list = None, limit: int = 10):
repo_obj = self.github.get_repo(f"{owner}/{repo}")
issues = repo_obj.get_issues(
state=state,
labels=labels or [],
)
# Convert to list and limit
issue_list = []
for i, issue in enumerate(issues):
if i >= limit:
break
issue_list.append({
"number": issue.number,
"title": issue.title,
"body": issue.body,
"state": issue.state,
"labels": [label.name for label in issue.labels],
"created_at": issue.created_at.isoformat(),
"html_url": issue.html_url
})
return issue_list
def get_issue(self, owner: str, repo: str, issue_number: int):
repo_obj = self.github.get_repo(f"{owner}/{repo}")
issue = repo_obj.get_issue(issue_number)
return {
"number": issue.number,
"title": issue.title,
"body": issue.body,
"state": issue.state,
"labels": [label.name for label in issue.labels],
"created_at": issue.created_at.isoformat(),
"html_url": issue.html_url,
"comments": [
{
"body": comment.body,
"created_at": comment.created_at.isoformat(),
"user": comment.user.login
}
for comment in issue.get_comments()
]
}
def create_pull_request(self, owner: str, repo: str, title: str, body: str,
head: str, base: str, files: list):
repo_obj = self.github.get_repo(f"{owner}/{repo}")
# Create new branch
base_branch = repo_obj.get_branch(base)
repo_obj.create_git_ref(
ref=f"refs/heads/{head}",
sha=base_branch.commit.sha
)
# Create/update files
for file_data in files:
try:
# Try to get existing file
existing_file = repo_obj.get_contents(file_data["path"], ref=head)
repo_obj.update_file(
path=file_data["path"],
message=f"Update {file_data['path']}",
content=file_data["content"],
sha=existing_file.sha,
branch=head
)
except:
# File doesn't exist, create it
repo_obj.create_file(
path=file_data["path"],
message=f"Create {file_data['path']}",
content=file_data["content"],
branch=head
)
# Create pull request
pr = repo_obj.create_pull(
title=title,
body=body,
head=head,
base=base
)
return {
"number": pr.number,
"html_url": pr.html_url,
"title": pr.title
}
# Add to your existing MCP server class
github_service = GitHubService(os.getenv("GITHUB_TOKEN"))
# Add these tools to your existing server
@mcp.tool()
def fetch_issues(owner: str, repo: str, state: str = "open", labels: List[str] = None, limit: int = 10) -> str:
"""Fetch issues from a GitHub repository"""
issues = github_service.fetch_issues(owner, repo, state, labels, limit)
return json.dumps(issues, indent=2)
@mcp.tool()
def get_issue(owner: str, repo: str, issue_number: int) -> str:
"""Get detailed information about a specific GitHub issue"""
issue = github_service.get_issue(owner, repo, issue_number)
return json.dumps(issue, indent=2)
@mcp.tool()
def create_pull_request(owner: str, repo: str, title: str, body: str,
head: str, base: str = "main", files: List[dict] = None) -> str:
"""Create a pull request with the specified files"""
pr = github_service.create_pull_request(owner, repo, title, body, head, base, files or [])
return json.dumps(pr, indent=2)
@mcp.tool()
def search_issues_and_code(query: str, owner: str, repo: str, file_types: List[str] = None) -> str:
"""Search for issues and related code context"""
# Use your existing RAG search
code_results = search_similar_code(query, file_types)
# Search issues
issues = github_service.fetch_issues(owner, repo, labels=["bug", "enhancement"])
# Filter issues by relevance to query
relevant_issues = []
for issue in issues:
if query.lower() in issue["title"].lower() or query.lower() in (issue["body"] or "").lower():
relevant_issues.append(issue)
return json.dumps({
"relevant_issues": relevant_issues,
"related_code": code_results
}, indent=2)
```
### Claude Code configuration for Option B
```json
{
"mcpServers": {
"enhanced-rag": {
"command": "python",
"args": ["/path/to/your/enhanced/rag-server.py"],
"env": {
"REPOSITORY_PATH": "/path/to/local/repo",
"QDRANT_URL": "http://localhost:6333",
"GITHUB_TOKEN": "${GITHUB_TOKEN}"
}
}
}
}
```
---
## Recommendation
**For prototyping**: Go with **Option B** (extended Python server)
- Faster to get working since you already have the RAG foundation
- Simpler configuration and debugging
- One language, one server to manage
**For production later**: You can always split it into **Option A** style
- Extract GitHub operations into a separate TypeScript server
- Keep your Python RAG server focused and reusable
The beauty is that Claude Code usage remains the same regardless of which option you choose - it's just a configuration difference!
### Repository indexing for RAG server
Before using the RAG server, you'll need to index your repository. Create a separate indexing script:
```python
#!/usr/bin/env python3
# scripts/index-repository.py
from qdrant_client import QdrantClient
from qdrant_client.models import Distance, VectorParams, PointStruct
from sentence_transformers import SentenceTransformer
import ast
import os
import sys
import hashlib
class RepositoryIndexer:
def __init__(self, qdrant_url="http://localhost:6333"):
self.client = QdrantClient(url=qdrant_url)
self.encoder = SentenceTransformer('microsoft/codebert-base')
self.collection = "code_embeddings"
def setup_collection(self):
"""Create the collection if it doesn't exist"""
try:
self.client.get_collection(self.collection)
print(f"Collection {self.collection} already exists")
except:
self.client.create_collection(
collection_name=self.collection,
vectors_config=VectorParams(size=768, distance=Distance.COSINE)
)
print(f"Created collection {self.collection}")
def index_repository(self, repo_path: str):
"""Index all supported code files"""
print(f"Indexing repository: {repo_path}")
supported_extensions = {'.py', '.js', '.ts', '.java', '.cpp', '.c', '.go', '.rs'}
points = []
point_id = 0
for root, dirs, files in os.walk(repo_path):
# Skip common directories that don't contain source code
dirs[:] = [d for d in dirs if not d.startswith('.') and d not in ['node_modules', '__pycache__', 'target', 'build']]
for file in files:
if any(file.endswith(ext) for ext in supported_extensions):
file_path = os.path.join(root, file)
relative_path = os.path.relpath(file_path, repo_path)
try:
points.extend(self._index_file(file_path, relative_path, point_id))
point_id += 1000 # Leave space for chunks
print(f"Indexed: {relative_path}")
except Exception as e:
print(f"Error indexing {relative_path}: {e}")
# Upload points in batches
batch_size = 100
for i in range(0, len(points), batch_size):
batch = points[i:i + batch_size]
self.client.upsert(
collection_name=self.collection,
points=batch
)
print(f"Uploaded batch {i//batch_size + 1}/{(len(points) + batch_size - 1)//batch_size}")
print(f"Indexing complete! {len(points)} code chunks indexed.")
def _index_file(self, file_path: str, relative_path: str, base_id: int):
"""Index a single file, breaking it into meaningful chunks"""
with open(file_path, 'r', encoding='utf-8', errors='ignore') as f:
content = f.read()
# Create contextual chunks
chunks = self._create_chunks(content, relative_path)
points = []
for i, chunk in enumerate(chunks):
# Create embedding with file context
contextual_content = f"File: {relative_path}\n{chunk['context']}\n\nCode:\n{chunk['content']}"
embedding = self.encoder.encode(contextual_content)
points.append(PointStruct(
id=base_id + i,
vector=embedding.tolist(),
payload={
"file_path": relative_path,
"content": chunk['content'],
"context": chunk['context'],
"start_line": chunk['start_line'],
"end_line": chunk['end_line'],
"file_type": os.path.splitext(relative_path)[1],
"chunk_hash": hashlib.md5(chunk['content'].encode()).hexdigest()
}
))
return points
def _create_chunks(self, content: str, file_path: str):
"""Create meaningful code chunks with context"""
lines = content.split('\n')
chunks = []
# Simple chunking strategy - can be improved with AST parsing
chunk_size = 50 # lines per chunk
overlap = 5 # lines of overlap
for i in range(0, len(lines), chunk_size - overlap):
start_line = i
end_line = min(i + chunk_size, len(lines))
chunk_lines = lines[start_line:end_line]
# Create context from surrounding code
context_start = max(0, start_line - 10)
context_lines = lines[context_start:start_line]
context = "Previous context:\n" + '\n'.join(context_lines[-5:])
chunks.append({
'content': '\n'.join(chunk_lines),
'context': context,
'start_line': start_line + 1,
'end_line': end_line
})
return chunks
if __name__ == "__main__":
if len(sys.argv) != 2:
print("Usage: python3 index-repository.py /path/to/repository")
sys.exit(1)
repo_path = sys.argv[1]
if not os.path.exists(repo_path):
print(f"Repository path does not exist: {repo_path}")
sys.exit(1)
indexer = RepositoryIndexer()
indexer.setup_collection()
indexer.index_repository(repo_path)
```
Run the indexer before starting your MCP servers:
```bash
# Index your repository
python3 scripts/index-repository.py /path/to/your/repo
# Verify indexing worked
curl -X GET "http://localhost:6333/collections/code_embeddings" | jq '.'
```
### Claude Code orchestration workflow
With both MCP servers running, you can now use Claude Code to orchestrate the entire issue resolution process. Here's how the workflow works:
```bash
# Start Claude Code in your repository
claude-code
# Then interact naturally:
# "Please check for any open issues labeled 'bug' in the myorg/myrepo repository,
# find relevant code context for each issue, and propose fixes"
```
Claude Code will automatically:
1. **Fetch Issues**: Use the GitHub Issue Server to pull relevant issues
2. **Search Context**: Use the RAG Server to find similar code patterns and relevant context
3. **Generate Fixes**: Reason about the issue using the combined context
4. **Create PRs**: Use the Issue Server to submit pull requests with the fixes
### Example Claude Code session
```
You: "Help me process issue #1234 in myorg/myrepo - it's about a login bug"
Claude Code: I'll help you process that issue. Let me gather the details and relevant context.
[Using github-issues server to fetch issue details...]
[Using code-rag server to search for login-related code...]
Based on the issue description and code analysis, I found the problem is in the authentication middleware. The session validation is incorrectly handling expired tokens. Let me create a fix:
[Shows proposed code changes]
Would you like me to create a pull request with this fix?
You: "Yes, create the PR"
Claude Code: [Using github-issues server to create branch and PR...]
ā
Created PR #89: "Fix session validation for expired tokens"
Link: https://github.com/myorg/myrepo/pull/89
```
### Advanced orchestration patterns
You can also create more sophisticated workflows:
```bash
# Process multiple issues at once
claude-code -c "Find all 'enhancement' labeled issues, prioritize them by complexity, and create fixes for the 3 simplest ones"
# Focus on specific areas
claude-code -c "Look for any authentication-related issues and cross-reference with our recent security audit findings in the repo"
# Batch processing with validation
claude-code -c "Process issues 1-10, but validate each fix against our existing test suite before creating PRs"
```
### Local development and testing
Set up a development environment for testing:
```bash
#!/bin/bash
# start-local-dev.sh
# Start Qdrant for vector search
docker run -d -p 6333:6333 qdrant/qdrant
# Index your repository
python3 scripts/index-repository.py /path/to/repo
# Start MCP servers
node servers/github-issue-server.js &
node servers/code-rag-server.js &
# Start Claude Code
claude-code --config ./claude-code-config.json
```
This architecture gives you:
- **Separation of concerns**: Each MCP server has a specific responsibility
- **Reusability**: The RAG server can be used for other code analysis tasks
- **Modularity**: Easy to swap out or upgrade individual components
- **Testing**: Can test each server independently
## Phase 2: GitHub Actions pipeline
### Automated trigger configuration
Implement a GitHub Actions workflow that responds to issue events:
```yaml
name: Automated Issue Resolution
on:
issues:
types: [opened, labeled]
issue_comment:
types: [created]
jobs:
resolve-issue:
if: |
(github.event.action == 'labeled' && github.event.label.name == 'auto-fix') ||
(github.event.action == 'created' && contains(github.event.comment.body, '@claude-bot fix'))
runs-on: ubuntu-latest
steps:
- name: Checkout repository
uses: actions/checkout@v4
with:
fetch-depth: 0
- name: Setup MCP Server
run: |
docker run -d \
--name mcp-server \
-v ${{ github.workspace }}:/repo:ro \
-e GITHUB_TOKEN=${{ secrets.GITHUB_TOKEN }} \
ghcr.io/your-org/mcp-server:latest
- name: Process Issue with Claude
id: process
uses: actions/github-script@v7
env:
ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
with:
script: |
const processor = require('./scripts/issue-processor.js');
const result = await processor.processIssue({
issue: context.payload.issue,
repo: context.repo,
mcpServerUrl: 'http://localhost:8080'
});
core.setOutput('fix', JSON.stringify(result));
```
### Self-hosted runner with MCP access
Deploy a self-hosted runner with persistent MCP server access:
```dockerfile
FROM ubuntu:22.04
# Install dependencies
RUN apt-get update && apt-get install -y \
curl git docker.io nodejs npm python3 python3-pip
# Install GitHub runner
RUN cd /home && \
curl -o actions-runner-linux-x64-2.311.0.tar.gz -L \
https://github.com/actions/runner/releases/download/v2.311.0/actions-runner-linux-x64-2.311.0.tar.gz && \
tar xzf actions-runner-linux-x64-2.311.0.tar.gz
# Install MCP server
COPY mcp-server /opt/mcp-server
RUN cd /opt/mcp-server && npm install
# Configure runner
COPY scripts/configure-runner.sh /home/configure-runner.sh
RUN chmod +x /home/configure-runner.sh
ENTRYPOINT ["/home/configure-runner.sh"]
```
Deploy using Kubernetes for scalability:
```yaml
apiVersion: apps/v1
kind: Deployment
metadata:
name: github-runner-mcp
spec:
replicas: 3
selector:
matchLabels:
app: github-runner
template:
metadata:
labels:
app: github-runner
spec:
containers:
- name: runner
image: ghcr.io/your-org/github-runner-mcp:latest
env:
- name: GITHUB_TOKEN
valueFrom:
secretKeyRef:
name: github-credentials
key: token
- name: RUNNER_LABELS
value: "self-hosted,mcp-enabled"
- name: mcp-server
image: ghcr.io/your-org/mcp-server:latest
ports:
- containerPort: 8080
volumeMounts:
- name: repo-cache
mountPath: /cache
```
### Automated PR creation workflow
Implement sophisticated PR creation with validation:
```javascript
const { Octokit } = require("@octokit/rest");
const { createPullRequest } = require("octokit-plugin-create-pull-request");
class AutomatedPRCreator {
constructor(token) {
const MyOctokit = Octokit.plugin(createPullRequest);
this.octokit = new MyOctokit({ auth: token });
}
async createFixPR(repo, issue, fix) {
// Create a unique branch name
const branchName = `auto-fix/issue-${issue.number}-${Date.now()}`;
// Apply the fix
const files = this.prepareFiles(fix);
// Create the PR
const pr = await this.octokit.createPullRequest({
owner: repo.owner,
repo: repo.name,
title: `š¤ Auto-fix for #${issue.number}: ${issue.title}`,
body: this.generatePRBody(issue, fix),
head: branchName,
base: repo.default_branch,
changes: [
{
files,
commit: `fix: automated resolution for issue #${issue.number}`,
author: {
name: "Claude Bot",
email: "claude-bot@example.com"
}
}
]
});
// Add metadata
await this.addPRMetadata(pr, issue, fix);
return pr;
}
generatePRBody(issue, fix) {
return `## š¤ Automated Fix for Issue #${issue.number}
This pull request was automatically generated to resolve the reported issue.
### Issue Summary
${issue.body}
### Solution Approach
${fix.explanation}
### Changes Made
${fix.changes.map(c => `- ${c.file}: ${c.description}`).join('\n')}
### Testing
- ā
All existing tests pass
- ā
New tests added for the fix
- ā
Security scan completed
- ā
Code quality checks passed
### Review Checklist
- [ ] The fix addresses the reported issue
- [ ] No unintended side effects
- [ ] Code follows project conventions
- [ ] Documentation updated if needed
---
*Generated by Claude AI with human oversight required*`;
}
}
```
## Security and reliability patterns
### Multi-layered security implementation
Implement comprehensive security controls throughout the system:
```python
class SecurityLayer:
def __init__(self):
self.validators = [
CodeInjectionValidator(),
SecretsScanner(),
DependencyChecker(),
PermissionValidator()
]
async def validate_fix(self, fix: Dict) -> Dict[str, Any]:
results = {
"passed": True,
"checks": []
}
for validator in self.validators:
check = await validator.validate(fix)
results["checks"].append(check)
if not check["passed"]:
results["passed"] = False
return results
class CodeInjectionValidator:
def __init__(self):
self.patterns = [
r'eval\s*\(',
r'exec\s*\(',
r'__import__',
r'subprocess\.',
r'os\.system'
]
async def validate(self, fix: Dict) -> Dict:
issues = []
for change in fix.get("changes", []):
content = change.get("content", "")
for pattern in self.patterns:
if re.search(pattern, content):
issues.append(f"Potential code injection: {pattern}")
return {
"name": "code_injection",
"passed": len(issues) == 0,
"issues": issues
}
```
### Error handling and circuit breakers
Implement resilient error handling with circuit breaker patterns:
```python
from typing import Optional, Callable
import time
import asyncio
class CircuitBreaker:
def __init__(
self,
failure_threshold: int = 5,
recovery_timeout: int = 60,
expected_exception: type = Exception
):
self.failure_threshold = failure_threshold
self.recovery_timeout = recovery_timeout
self.expected_exception = expected_exception
self.failure_count = 0
self.last_failure_time = None
self.state = "CLOSED" # CLOSED, OPEN, HALF_OPEN
async def call(self, func: Callable, *args, **kwargs):
if self.state == "OPEN":
if self._should_attempt_reset():
self.state = "HALF_OPEN"
else:
raise Exception("Circuit breaker is OPEN")
try:
result = await func(*args, **kwargs)
self._on_success()
return result
except self.expected_exception as e:
self._on_failure()
raise e
def _should_attempt_reset(self) -> bool:
return (
self.last_failure_time and
time.time() - self.last_failure_time >= self.recovery_timeout
)
def _on_success(self):
self.failure_count = 0
self.state = "CLOSED"
def _on_failure(self):
self.failure_count += 1
self.last_failure_time = time.time()
if self.failure_count >= self.failure_threshold:
self.state = "OPEN"
# Usage in Claude API calls
class ResilientClaudeClient:
def __init__(self, api_key: str):
self.client = Anthropic(api_key=api_key)
self.circuit_breaker = CircuitBreaker(
failure_threshold=3,
recovery_timeout=30,
expected_exception=APIError
)
async def generate_fix(self, prompt: str) -> Dict:
return await self.circuit_breaker.call(
self._make_api_call,
prompt
)
```
## Performance optimization strategies
### Intelligent caching and rate limit management
Implement sophisticated caching to optimize API usage:
```python
from functools import lru_cache
import hashlib
import redis
import json
class IntelligentCache:
def __init__(self, redis_url: str):
self.redis = redis.from_url(redis_url)
self.ttl = 3600 # 1 hour
def cache_key(self, issue_id: int, context_hash: str) -> str:
return f"fix:issue:{issue_id}:context:{context_hash}"
def get_cached_fix(self, issue_id: int, context: List[Dict]) -> Optional[Dict]:
context_hash = self._hash_context(context)
key = self.cache_key(issue_id, context_hash)
cached = self.redis.get(key)
if cached:
return json.loads(cached)
return None
def cache_fix(self, issue_id: int, context: List[Dict], fix: Dict):
context_hash = self._hash_context(context)
key = self.cache_key(issue_id, context_hash)
self.redis.setex(
key,
self.ttl,
json.dumps(fix)
)
def _hash_context(self, context: List[Dict]) -> str:
# Create deterministic hash of context
context_str = json.dumps(context, sort_keys=True)
return hashlib.sha256(context_str.encode()).hexdigest()
class RateLimitManager:
def __init__(self):
self.limits = {
"tier1": {"rpm": 50, "tpm_in": 20000, "tpm_out": 4000},
"tier2": {"rpm": 1000, "tpm_in": 40000, "tpm_out": 8000},
"tier3": {"rpm": 2000, "tpm_in": 80000, "tpm_out": 16000}
}
self.current_tier = "tier2"
async def wait_if_needed(self, tokens_needed: int):
# Implement token bucket algorithm
if not self.can_proceed(tokens_needed):
wait_time = self.calculate_wait_time(tokens_needed)
await asyncio.sleep(wait_time)
```
### Parallel processing architecture
Maximize throughput with intelligent parallelization:
```python
import asyncio
from concurrent.futures import ThreadPoolExecutor
import multiprocessing
class ParallelIssueProcessor:
def __init__(self, max_workers: int = None):
self.max_workers = max_workers or multiprocessing.cpu_count()
self.executor = ThreadPoolExecutor(max_workers=self.max_workers)
async def process_issues_batch(self, issues: List[Dict]) -> List[Dict]:
# Group issues by complexity
simple_issues = [i for i in issues if self._is_simple(i)]
complex_issues = [i for i in issues if not self._is_simple(i)]
# Process simple issues in parallel
simple_tasks = [
self.process_issue(issue)
for issue in simple_issues
]
# Process complex issues with rate limiting
complex_tasks = []
for issue in complex_issues:
task = self.process_issue_with_rate_limit(issue)
complex_tasks.append(task)
await asyncio.sleep(1) # Space out complex requests
# Gather all results
all_tasks = simple_tasks + complex_tasks
results = await asyncio.gather(*all_tasks, return_exceptions=True)
return [r for r in results if not isinstance(r, Exception)]
```
## Monitoring and observability
### Comprehensive metrics collection
Implement detailed monitoring for system health:
```python
from prometheus_client import Counter, Histogram, Gauge
import time
# Define metrics
issue_processed = Counter('issues_processed_total', 'Total issues processed')
issue_success = Counter('issues_success_total', 'Successfully fixed issues')
issue_failed = Counter('issues_failed_total', 'Failed issue fixes')
api_latency = Histogram('api_request_duration_seconds', 'API request latency')
token_usage = Counter('tokens_used_total', 'Total tokens consumed', ['model'])
active_processing = Gauge('active_processing_issues', 'Currently processing issues')
class MonitoredIssueResolver:
@api_latency.time()
async def process_issue(self, issue: Dict) -> Dict:
active_processing.inc()
issue_processed.inc()
try:
start_time = time.time()
result = await self._process_issue_internal(issue)
if result['success']:
issue_success.inc()
else:
issue_failed.inc()
# Track token usage
token_usage.labels(model='claude-3-opus').inc(result['tokens_used'])
return result
finally:
active_processing.dec()
```
## Recommended implementation roadmap
### Phase 1: Local prototype (Weeks 1-4)
1. Set up MCP server with basic repository access
2. Implement vector search for code retrieval
3. Create notebook-based workflow for testing
4. Validate with 10-20 test issues
### Phase 2: Basic automation (Weeks 5-8)
1. Deploy GitHub Actions workflow
2. Implement simple PR creation
3. Add basic security scanning
4. Test with low-risk issues
### Phase 3: Production hardening (Weeks 9-12)
1. Add comprehensive error handling
2. Implement monitoring and alerting
3. Deploy self-hosted runners
4. Scale to handle multiple repositories
### Phase 4: Advanced features (Weeks 13-16)
1. Multi-agent architecture for complex issues
2. Advanced code quality validation
3. Performance optimization
4. Enterprise security features
## Technology stack recommendations
Based on extensive research and proven patterns:
**Core Infrastructure**
- **Language**: Python 3.11+ for AI components, TypeScript for MCP server
- **Framework**: FastAPI for REST APIs, LangChain for LLM orchestration
- **Database**: PostgreSQL for metadata, Qdrant for vector search
**AI and ML Stack**
- **LLM**: Claude 3 Opus for complex reasoning, Haiku for simple tasks
- **Embeddings**: CodeBERT or Jina Code embeddings
- **Context Protocol**: MCP with custom tools and resources
**DevOps and Deployment**
- **Containerization**: Docker with multi-stage builds
- **Orchestration**: Kubernetes with autoscaling
- **CI/CD**: GitHub Actions with self-hosted runners
- **Monitoring**: Prometheus + Grafana + LangSmith
**Security Tools**
- **SAST**: CodeQL and Semgrep
- **Secrets**: HashiCorp Vault or AWS Secrets Manager
- **Access Control**: RBAC with GitHub team integration
## Conclusion
Building an automated GitHub issue resolution system with Claude APIs and MCP servers represents a significant technical undertaking that can dramatically improve development efficiency. The two-phase approach allows for thorough testing and gradual rollout, while the comprehensive architecture ensures security, reliability, and scalability.
Success depends on careful attention to security, robust error handling, and maintaining human oversight for critical decisions. By following the patterns and recommendations outlined in this analysis, organizations can build systems that safely automate routine development tasks while maintaining code quality and security standards.
The combination of MCP's standardized protocol, Claude's advanced reasoning capabilities, and GitHub's native automation features creates a powerful platform for the future of automated software development. With proper implementation and monitoring, these systems can handle thousands of issues monthly, significantly reducing development time and allowing teams to focus on more complex challenges.
