> ## 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. # Git Changelog Writer > Use Upsonic's Sequential Team with two agents to turn raw git log output into a ready-to-post Twitter/X update — Tech Lead summarizes commits, Growth Hacker writes the tweet. No glue code; context flows automatically. This example demonstrates how to use an **Upsonic Team** in **sequential mode** to turn raw `git log --oneline` output into a developer-native Twitter/X post. Two agents run in a pipeline: Agent A distills commits into a technical summary; Agent B turns that summary into a single, post-ready tweet. Context passes from A → B automatically via `mode="sequential"` — no variable passing or glue code. ## Overview Upsonic framework provides a **Team** abstraction for multi-agent workflows. This example showcases: 1. **Sequential Team** — `mode="sequential"` runs agents in order and injects each agent's output into the next agent's context. 2. **Tech Lead Agent** — Reads raw commit messages, filters out `chore`/`docs`, and produces a concise technical summary of user-facing changes. 3. **Growth Hacker Agent** — Takes the summary and writes a single Twitter/X post with strict tone and format rules (no hashtags, no corporate language, minimal emoji). 4. **No Glue Code** — You don't pass strings or parse outputs between agents; the framework handles context handover. The pipeline: * **Input**: A string of `git log --oneline` (mock in script; replace with real `git log` later). * **Output**: The final tweet from `tasks[-1].response`. ## Project Structure ``` git_changelog_writer/ ├── main.py # Sequential Team pipeline (agents + tasks) ├── README.md # Quick start and notes ``` ### Environment Variables Configure the OpenAI models used by both agents: ```bash theme={null} # Required: OpenAI API key (Tech Lead uses gpt-5-mini, Growth Hacker uses gpt-4o) export OPENAI_API_KEY="your_openai_api_key_here" ``` Or use a `.env` file in the project root. ## Installation ```bash theme={null} # From repo root uv sync # Or from this example directory cd examples/multi_agent/git_changelog_writer uv sync ``` ## Usage ### Run the pipeline ```bash theme={null} uv run main.py ``` Or from repo root: ```bash theme={null} uv run examples/multi_agent/git_changelog_writer/main.py ``` The script uses mock commit data (`RAW_COMMITS`). To use real git output, replace `RAW_COMMITS` with the result of `git log --oneline -n 5` (or pipe it in). ## How It Works | Component | Description | | --------------- | ------------------------------------------------------------------------------------- | | Tech Lead | Summarizes raw commits; ignores chore/docs; explains what changed and why users care | | Growth Hacker | Writes a single Twitter/X post from the summary; dev tone, no hashtags, minimal emoji | | Sequential Team | Runs tasks in order; each task's response becomes context for the next | | Output | Final tweet from `tasks[-1].response` | ### Example Output **Agent A (Tech Lead)** produces a technical summary: ``` Summary of User-Facing Changes: 1. New Feature: Dark Mode Support - What Changed: The user interface now offers dark mode support. - Why Users Care: More comfortable for extended usage and low-light environments. 2. New Feature: Smart Caching Layer - What Changed: A smart caching layer was introduced to optimize performance. - Why Users Care: Dashboard load times are 3x faster. 3. Bug Fix: Database Connection Timeout - What Changed: Resolved a timeout issue under heavy load. - Why Users Care: Improved reliability during peak usage. 4. Bug Fix: Real-Time Notifications - What Changed: Fixed a race condition affecting notifications. - Why Users Care: Notifications are now delivered accurately and timely. ``` **Agent B (Growth Hacker)** turns it into a tweet: ``` Dark mode now available across our UI. 🌙 UI enhancements + performance upgrades with these latest updates. - 🌙 Dark Mode support - 🚀 Dashboard 3x speed boost with smart caching - 🔧 Database timeout fix for heavy loads - 🛠️ Real-time notifications now more reliable Changelog: [link] ``` Programmatic access to the final result: ```python theme={null} team.print_do(tasks) print(tasks[-1].response) # The final tweet ``` ## Complete Implementation ### main.py ```python theme={null} """ I Stopped Writing Changelogs. Here's What Writes Them Now. Two agents, one pipeline: feed in `git log --oneline`, get back a ready-to-post tweet. Agent A reads the commits and pulls out what matters. Agent B turns that into something you'd actually post. The context flows from A → B automatically. No variable passing. No glue code. Just `mode="sequential"`. """ from upsonic import Agent, Task, Team # ── Mock Input ─────────────────────────────────────────────────────────── # Simulate `git log --oneline -n 5` — swap this for real git log later. RAW_COMMITS = """ a1b2c3d feat: added dark mode support across the entire UI b2c3d4e feat: introduced smart caching layer — 3x faster dashboard loads c3d4e5f Merge pull request #42 from team/perf-improvements d4e5f6a fix: database connection timeout on heavy load e5f6a7b fix: resolved race condition in real-time notifications """ # ── Agent A — The Tech Lead ────────────────────────────────────────────── tech_lead = Agent( model="openai/gpt-5-mini", name="Tech Lead", role="Technical Summarizer", goal="Distill raw commit messages into a clear, developer-friendly summary that highlights user-facing value.", instructions=( "You will receive a batch of raw git commit messages. " "Ignore anything tagged 'chore' or 'docs' — those are housekeeping. " "Focus on 'feat' and 'fix' entries. For each one, explain: " "(1) what changed, and (2) why an end-user should care. " "Be concise but technical enough for a developer audience." ), ) # ── Agent B — The Growth Hacker ────────────────────────────────────────── growth_hacker = Agent( model="openai/gpt-4o", name="Growth Hacker", role="Developer Social Media Writer", goal="Turn technical summaries into dev-native Twitter/X posts that feel like a smart engineer sharing something useful, not a marketing department announcing it.", instructions=( "You will receive a technical summary produced by the previous agent. " "Write a SINGLE Twitter/X post (NOT a thread). Follow these rules strictly:\n\n" "TONE:\n" "- Write like a developer talking to other developers. Direct, no fluff.\n" "- Confident but understated. Never hype or exaggerate.\n" "- NEVER use phrases like 'Exciting news!', 'We're thrilled', 'Game-changer', " "'Just dropped', or any corporate marketing language.\n" "- Short punchy sentences. Let line breaks do the work.\n\n" "STRUCTURE (follow this layout):\n" "- Line 1: A calm, confident hook (one sentence). Can end with a single emoji like a rocket.\n" "- Blank line.\n" "- 2-3 short lines of context explaining what shipped and why it matters.\n" "- Blank line.\n" "- A bullet list of the key changes. Use - or emoji bullets (one emoji per line, e.g. a rocket, wrench, zap). " "Keep each bullet to one short line.\n" "- Blank line.\n" "- CTA: 'Link in the comments' or 'Changelog: [link]'\n\n" "EMOJI RULES:\n" "- Use emojis SPARINGLY and only with PURPOSE.\n" "- OK: single emoji at end of hook line, or one emoji per bullet as a visual marker.\n" "- NEVER: emoji chains, mid-sentence emojis, or more than one emoji in a row.\n" "- When in doubt, skip the emoji.\n\n" "ABSOLUTE DON'TS:\n" "- No hashtags (no #ProductUpdates, #TechNews, etc.).\n" "- No numbered thread format (no 1/3, 2/3, 3/3).\n" "- No 'we're excited/thrilled/proud' language.\n" "- Do NOT invent features. Only use what the technical summary provides.\n\n" "REFERENCE EXAMPLES (match this energy):\n\n" "Example A:\n" "We wrote a Medium post about agent standardization.\n\n" "Teams waste weeks starting agent projects, adding safety layers, " "and maintaining & deploying everything.\n\n" "Upsonic fixes this: same structure, same API, shared safety.\n\n" "Read how. Link in the comments.\n\n" "Example B:\n" "Upsonic v0.71.0 is live!\n" "We shipped these in this version:\n" "- Culture System & CultureManager\n" "- Simulation System (+built-in scenarios)\n" "- Smoke Test Makefile\n" "- Open-source friendly README\n" "- Direct LLM call metrics bug fix\n" "Link in the comments\n" ), ) # ── The Sequential Team ───────────────────────────────────────────────── team = Team( agents=[tech_lead, growth_hacker], mode="sequential", ) # ── Tasks ──────────────────────────────────────────────────────────────── tasks = [ Task( description=( f"Analyze the following raw git commit messages and produce " f"a concise technical summary of the meaningful, user-facing changes.\n\n" f"Raw commits:\n{RAW_COMMITS}" ), agent=tech_lead ), Task( description=( "Using the technical summary from the previous step, " "create a Twitter/X thread (3 tweets)" "that announce this week's product updates." ), agent=growth_hacker ), ] team.print_do(tasks) print(tasks[-1].response) ``` ### Two-agent pipeline * **Tech Lead**: Filters commits, focuses on feat/fix, outputs a structured technical summary. * **Growth Hacker**: Consumes that summary and applies strict tone/structure rules to produce one tweet. ### Extensibility You can add more agents (e.g. Editor, Translator) to the `agents` list and add corresponding `Task` entries. Context still flows in order; no extra glue code. ### Cost Roughly on the order of a few cents per run (Tech Lead on gpt-5-mini, Growth Hacker on gpt-4o). Adjust models in each `Agent()` if you want to trade cost vs. quality. ## Repository View the full example: [Git Changelog Writer](https://github.com/Upsonic/Examples/tree/master/examples/multi_agent/git_changelog_writer)