Docker

No git clone needed — the image is published on Docker Hub:

docker run -d --restart unless-stopped \
  --name gogogot \
  -e TELEGRAM_BOT_TOKEN=... \
  -e TELEGRAM_OWNER_ID=... \
  -e GOGOGOT_PROVIDER=anthropic \
  -e ANTHROPIC_API_KEY=... \
  -e GOGOGOT_MODEL=claude-sonnet-4-6 \
  -v ./data:/data \
  -v ./work:/work \
  octagonlab/gogogot:latest

The image supports linux/amd64 and linux/arm64 and ships with a full Ubuntu environment (bash, git, Python, Node.js, ripgrep, sqlite, postgresql-client, and more).

curl -O https://raw.githubusercontent.com/aspasskiy/GoGogot/main/deploy/docker-compose.yml

# Create .env with your keys
cat > .env <<EOF
TELEGRAM_BOT_TOKEN=...
TELEGRAM_OWNER_ID=...
GOGOGOT_PROVIDER=anthropic
ANTHROPIC_API_KEY=...
GOGOGOT_MODEL=claude-sonnet-4-6
EOF

docker compose up -d

Requires Go 1.25+:

make generate          # fetch OpenRouter model catalog
go run ./cmd

Choosing a Model

Set GOGOGOT_PROVIDER, GOGOGOT_MODEL, and the corresponding API key. The agent will not start without all three.

| Provider | GOGOGOT_PROVIDER | API key env | Example GOGOGOT_MODEL | |---|---|---|---| | Anthropic | anthropic | ANTHROPIC_API_KEY | claude-sonnet-4-6, claude-opus-4-6, claude-haiku-4-5 | | OpenAI | openai | OPENAI_API_KEY | gpt-4o, gpt-4.1, gpt-5.4, o3, o4-mini | | OpenRouter | openrouter | OPENROUTER_API_KEY | deepseek/deepseek-v3.2, google/gemini-3-flash-preview |

Model metadata (context window, vision support, pricing) is stored in JSON catalogs under llm/catalog/ — just edit the JSON to add or update models.

With OpenRouter you can also pass any slug directly, e.g. GOGOGOT_MODEL=moonshotai/kimi-k2.5.

Short Aliases

For convenience, short aliases are supported as GOGOGOT_MODEL values:

| Alias | Resolves to | |---|---| | claude | claude-sonnet-4-6 | | openai | openai/gpt-5-nano | | deepseek | deepseek/deepseek-v3.2 | | gemini | google/gemini-3-pro-preview | | llama | meta-llama/llama-4-maverick | | qwen | qwen/qwen3.5-397b-a17b | | minimax | minimax/minimax-m2.5 | | kimi | moonshotai/kimi-k2.5 |

Browse all available models: Anthropic | OpenAI | OpenRouter | Benchmarks: PinchBench

Features

27 built-in tools across 10 categories:

• Telegram — multi-chat, attachments, typing indicators
• System — bash, read/write/edit files, system info
• Web — Brave search, fetch pages, HTTP requests, downloads
• Identity — persistent soul.md / user.md, auto-evolving
• Memory — persistent markdown notes the agent manages itself
• Skills — reusable procedural knowledge
• Task planning — session-scoped checklist for multi-step work
• Scheduling — cron-based self-scheduling, persisted across restarts
• Compaction — automatic context compression near token limits
• Multi-model — Anthropic, OpenAI, or any OpenRouter model
• Observability — compact info-level iteration logs; full request/response dumps at trace level (LOG_LEVEL=debug)

Use Cases

• Daily digest — "Find top 5 AI news, summarize each in 2 sentences, send me every morning at 9:00"
• Report generation — "Download sales data from this URL, calculate totals by region, generate a PDF report"
• File processing — "Take these 12 screenshots, merge them into a single PDF, and send the file back"
• Market research — "Search the web for pricing of competitors X, Y, Z and make a comparison table"
• Server monitoring — "Check disk and memory usage every hour, alert me if anything exceeds 80%"
• Data extraction — "Fetch this webpage, extract all email addresses and phone numbers into a CSV"
• Routine automation — "Every Friday at 18:00, pull this week's git commits and send me a changelog summary"

How It Works

The entire agent is a for loop. Call the LLM, execute tool calls, feed results back, repeat:

func (a *Agent) Run(ctx context.Context, input []ContentBlock) error {
    a.messages = append(a.messages, userMessage(input))

    for {
        resp, err := a.llm.Call(ctx, a.messages, a.tools)
        if err != nil {
            return err
        }
        a.messages = append(a.messages, resp)

        if len(resp.ToolCalls) == 0 {
            break
        }

        results := a.executeTools(resp.ToolCalls)
        a.messages = append(a.messages, results)
    }
    return nil
}

Everything else — memory, scheduling, compaction, identity — is just tools the LLM can call inside this loop.

Extending

GoGogot is designed to be extended without frameworks or plugin registries:

• Adding a new LLM backend (implement one Backend interface method)
• Adding a new transport like Discord or Slack (implement Channel + Replier — 3 + 6 methods)
• Adding custom models by editing JSON catalogs in llm/catalog/

License

MIT