Setup

1. Create an Inngest Account

1. Sign up at app.inngest.com
2. Go to Settings → Keys and copy your:
   • Event Key (for sending events)
   • Signing Key (for authenticating your worker)

2. Configure and Run

git clone https://github.com/inngest/utah
cd utah
npm install # or pnpm
cp .env.example .env

Edit .env with your keys:

ANTHROPIC_API_KEY=sk-ant-...
INNGEST_EVENT_KEY=...
INNGEST_SIGNING_KEY=signkey-prod-...

Then add the environment variables for your channel(s) — see setup guides below.

Start the worker:

# Production mode (connects to Inngest Cloud via WebSocket)
npm start

# Development mode (uses local Inngest dev server)
npx inngest-cli@latest dev &
npm run dev

On startup, the worker automatically sets up webhooks and transforms for each configured channel.

Channels

The agent supports multiple messaging channels. Each channel has its own setup guide:

• Telegram — Fully automated setup. Just add your bot token and run.
• Slack — Requires creating a Slack app and configuring Event Subscriptions.

Project Structure

src/
├── worker.ts                  # Entry point — connect() or serve()
├── client.ts                  # Inngest client
├── config.ts                  # Configuration from env vars
├── agent-loop.ts              # Core think → act → observe cycle
├── setup.ts                   # Channel setup orchestration
├── lib/
│   ├── llm.ts                 # pi-ai wrapper (multi-provider: Anthropic, OpenAI, Google)
│   ├── tools.ts               # Tool definitions (TypeBox schemas) + execution
│   ├── context.ts             # System prompt builder with workspace file injection
│   ├── session.ts             # JSONL session persistence
│   ├── memory.ts              # File-based memory system (daily logs + distillation)
│   └── compaction.ts          # LLM-powered conversation summarization
├── functions/
│   ├── message.ts             # Main agent function (singleton + cancelOn)
│   ├── send-reply.ts          # Channel-agnostic reply dispatch
│   ├── acknowledge-message.ts # Message acknowledgment (typing indicator, etc.)
│   ├── heartbeat.ts           # Cron-based memory maintenance
│   └── failure-handler.ts     # Global error handler with notifications
└── channels/
    ├── types.ts               # ChannelHandler interface
    ├── index.ts               # Channel registry
    ├── setup-helpers.ts       # Inngest REST API helpers for webhook setup
    └── <channel-name>/        # A channel implementation (see README for setup)
        ├── handler.ts         # ChannelHandler implementation
        ├── api.ts             # API client
        ├── setup.ts           # Webhook setup automation
        ├── transform.ts       # Webhook transform
        └── format.ts          # Formatting for channel messages
workspace/                       # Agent workspace (persisted across runs)
├── SOUL.md                    # Agent personality and behavioral guidelines
├── USER.md                    # User information
├── MEMORY.md                  # Long-term memory (agent-writable)
├── memory/                    # Daily logs (YYYY-MM-DD.md, auto-managed)
└── sessions/                  # JSONL conversation files (gitignored)

How It Works

The Agent Loop

The core is a while loop where each iteration is an Inngest step:

1. Think — step.run("think") calls the LLM via pi-ai's complete()
2. Act — if the LLM wants tools, each tool runs as step.run("tool-read")
3. Observe — tool results are fed back into the conversation
4. Repeat — until the LLM responds with text (no tools) or max iterations

Inngest auto-indexes duplicate step IDs in loops (think:0, think:1, etc.), so you don't need to track iteration numbers in step names.

Event-Driven Composition

One incoming message triggers multiple independent functions:

| Function | Purpose | Config | | ------------------------ | ---------------------------------------- | ----------------------------------------- | | agent-handle-message | Run the agent loop | Singleton per chat, cancel on new message | | acknowledge-message | Show "typing..." immediately | No retries (best effort) | | send-reply | Format and send the response | 3 retries, channel dispatch | | agent-heartbeat | Distill daily logs into long-term memory | Cron (every 30 min) | | global-failure-handler | Catch errors, notify user | Triggered by inngest/function.failed |

Workspace Context Injection

The agent reads markdown files from the workspace directory and injects them into the system prompt:

| File | Purpose | | ----------- | ---------------------------------------------------------- | | SOUL.md | Agent personality, behavioral guidelines, tone, boundaries | | USER.md | Info about the user (name, timezone, preferences) | | MEMORY.md | Curated long-term memory (agent-writable) |

Edit these files to customize your agent's personality and knowledge. The agent can also update MEMORY.md using the write tool to remember things across conversations.

Memory System

The agent has a two-tier memory system:

• Daily logs (workspace/memory/YYYY-MM-DD.md) — append-only notes written via the remember tool during conversations
• Long-term memory (workspace/MEMORY.md) — curated summary distilled from daily logs by the heartbeat function

The agent-heartbeat function runs on a cron schedule (default: every 30 minutes). It checks if daily logs have accumulated enough content, then uses the LLM to distill them into MEMORY.md. Old daily logs are pruned after a configurable retention period (default: 30 days).

Conversation Compaction

Long conversations get summarized automatically so the agent doesn't lose context or hit token limits:

• Token estimation: Uses a chars/4 heuristic to estimate conversation size
• Threshold: Compaction triggers when estimated tokens exceed 80% of the configured max (150K)
• LLM summarization: Old messages are summarized into a structured checkpoint (goals, progress, decisions, next steps)
• Recent messages preserved: The most recent ~20K tokens of conversation are kept verbatim
• Persisted: The compacted session replaces the JSONL file, so it survives restarts

Compaction runs as an Inngest step (step.run("compact")), so it's durable and retryable.

Context Pruning

Long tool results bloat the conversation context and cause the LLM to lose focus. The agent uses two-tier pruning:

• Soft trim: Tool results over 4K chars get head+tail trimmed (first 1,500 + last 1,500 chars)
• Hard clear: When total old tool content exceeds 50K chars, old results are replaced entirely
• Budget warnings: System messages are injected when iterations are running low

Adding New Channels

The agent is channel-agnostic. Each channel implements a ChannelHandler interface (src/channels/types.ts) with methods for sending replies, acknowledging messages, and setup. Each channel directory follows the same structure:

src/channels/<name>/
├── handler.ts      # ChannelHandler implementation (sendReply, acknowledge)
├── api.ts          # API client for the channel's platform
├── setup.ts        # Webhook setup automation
├── transform.ts    # Plain JS transform for Inngest webhook
└── format.ts       # Markdown → channel-specific format conversion

To add Discord, WhatsApp, or any other channel:

1. Create a new directory under src/channels/ following the structure above
2. Implement the ChannelHandler interface in handler.ts
3. Write a webhook transform that converts the channel's payload to agent.message.received
4. Register the channel in src/channels/index.ts

The agent loop, reply dispatch, and acknowledgment functions are all channel-agnostic — no changes needed outside src/channels/.

Key Inngest Features Used

• **[connect()](https://www.inngest.com/doc