Documentation Index
Fetch the complete documentation index at: https://docs.upsonic.ai/llms.txt
Use this file to discover all available pages before exploring further.
Overview
Build an autonomous agent that connects to GitHub, analyzes repository data — issues, pull requests, commit activity — and generates structured reports in your workspace.
Prerequisites
Environment Variables
# .env
GITHUB_PERSONAL_ACCESS_TOKEN=ghp_xxxxxxxxxxxxxxxxxxxx
ANTHROPIC_API_KEY=your-anthropic-key-here
Installation
# With uv (recommended)
uv pip install upsonic python-dotenv
# With pip
pip install upsonic python-dotenv
Example: Repository Issue Analysis
The agent fetches open issues from a data science repository, categorizes them by label, and writes a summary report to the workspace.
import os
from dotenv import load_dotenv
from upsonic import AutonomousAgent, Task
from upsonic.tools.mcp import MCPHandler
load_dotenv()
github_handler = MCPHandler(
command="npx -y @modelcontextprotocol/server-github",
env={
"GITHUB_PERSONAL_ACCESS_TOKEN": os.getenv("GITHUB_PERSONAL_ACCESS_TOKEN")
},
timeout_seconds=60,
)
agent = AutonomousAgent(
model="anthropic/claude-sonnet-4-6",
workspace="./reports",
)
task = Task(
description="""
Fetch the open issues from 'pandas-dev/pandas' repository (per_page=10).
Categorize each issue by label (bug, enhancement, etc.) and write
a short summary report to 'pandas_issues_report.md'.
""",
tools=[github_handler],
)
agent.print_do(task)
./reports/pandas_issues_report.md:
# Pandas Open Issues Report
## Issue Summary
Total issues analyzed: 10
## By Category
| Label | Count |
|-------------|-------|
| Bug | 4 |
| Enhancement | 3 |
| Docs | 2 |
| Regression | 1 |
## Issue Details
| # | Title | Labels |
|-------|--------------------------------|-------------|
| 12345 | DataFrame.merge produces ... | Bug |
| ... | ... | ... |
Example: Compare Contributor Activity
The agent fetches recent commits from a repository and summarizes contributor activity.
import os
from dotenv import load_dotenv
from upsonic import AutonomousAgent, Task
from upsonic.tools.mcp import MCPHandler
load_dotenv()
github_handler = MCPHandler(
command="npx -y @modelcontextprotocol/server-github",
env={
"GITHUB_PERSONAL_ACCESS_TOKEN": os.getenv("GITHUB_PERSONAL_ACCESS_TOKEN")
},
timeout_seconds=60,
)
agent = AutonomousAgent(
model="anthropic/claude-sonnet-4-6",
workspace="./reports",
)
task = Task(
description="""
Fetch the last 30 commits from 'scikit-learn/scikit-learn' and write
a contributor activity summary to 'sklearn_contributors.md' with
a table of authors ranked by commit count.
""",
tools=[github_handler],
)
agent.print_do(task)
| Tool | Description |
|---|
list_issues | List issues in a repository |
create_issue | Create a new issue |
get_pull_request | Get PR details and diff |
search_repositories | Search for repositories |
get_file_contents | Read file contents from a repo |
list_commits | List recent commits |
Security Notes
- Use a fine-grained personal access token with only the permissions your agent needs.
- Store your token in
.env — never hardcode it.
- For production, use GitHub Apps with scoped installation tokens instead of personal access tokens.
