deepline-gtm-agent

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deepline-gtm-agent

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GTM automation agent powered by Deepline + Deep Agents

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# deepline-gtm-agent

Open-source GTM chat agent powered by [Deepline](https://deepline.com). The default path is Deepline v2 native agent/chat: your app brokers Slack, REST, and web chat requests while Deepline handles tool routing, enrichment, research, CRM actions, and provider-specific workflows through the v2 API.

**API portal:** [code.deepline.com](https://code.deepline.com) - create your `DEEPLINE_API_KEY` there.

## Quickstart

```bash
cd managed_agent
pip install -r requirements.txt

export DEEPLINE_API_KEY=dlp_...

python server.py     # starts REST, web chat, and Slack endpoints on :8000
```

Open `http://localhost:8000` for web chat, or call the REST API:

```bash
curl -X POST http://localhost:8000/chat \
  -H "Content-Type: application/json" \
  -d '{"message": "find emails for 5 VP Sales at fintech companies"}'
```

## What it does

The agent handles common GTM workflows with Deepline's v2 tool catalog and API:

| Workflow | Example prompt |
|---|---|
| Contact enrichment | "Find the email for Jane Smith at Acme" |
| Prospect search | "Find 10 VP Sales at B2B SaaS companies, 200-500 employees, US" |
| Account research | "Research stripe.com and summarize GTM-relevant signals" |
| Email verification | "Is jsmith@acme.com safe to send?" |
| LinkedIn resolution | "Find the LinkedIn URL for Tom Nguyen at Notion" |
| CRM and outreach | "Create a HubSpot contact" or "show my Lemlist campaigns" |

Responses should include sources, provider outcomes, and a clear next step. The agent should state data gaps instead of inventing missing emails, titles, or company facts.

## Eve reference implementation

This repo now includes an additive Eve reference implementation in [`eve_agent/`](eve_agent/). It preserves the Deepline v2 execution backend while using Eve for durable sessions, local HTTP, evals, and fast Vercel deployment.

Use it when you want an out-of-the-box deployable agent path:

```bash
cd eve_agent
npm install
npm run link
# set DEEPLINE_API_KEY in Vercel env or local .env.local
npm run dev
npm run smoke -- --host http://127.0.0.1:3000
```

See [`eve_agent/README.md`](eve_agent/README.md) for the full local, eval, and Vercel flow.

## Architecture

```
Slack / REST / Web UI
      |
      v
FastAPI broker
      |
      v
Deepline v2 agent/chat + SDK/API
      |
      v
Deepline integrations, enrichment providers, CRM, outreach, and research tools
```

Configure access with environment variables and call the Deepline v2 SDK/API directly. Managed sessions should not depend on local Deepline CLI state.

## Boundary

This repo should stay thin. It owns:

- REST, web chat, and Slack transport
- Slack request verification and formatting
- optional bearer auth for public chat endpoints
- CORS and deployment setup checks
- prompt/tool bounds that steer requests into Deepline

Deepline API owns provider routing, plays, workflows, enrichment, CRM/outreach actions, credentials, billing, run state, and workflow observability. Do not copy those systems into this agent.

## Hermes Compatibility

This repo also includes `hermes-agent-pack/`, the compatibility layer for running the Deepline GTM agent inside Hermes on a persistent Sprite/Fly-style workspace.

Use it when Hermes is the operator interface and Deepline is the GTM execution, logging, workflow, and observability layer. The pack makes the Hermes setup explicit:

- pruned Deepline context, claims, exclusions, and Jai voice rules
- Hermes prompts and skills for one primary `deepline-gtm-agent`
- bounded subagent workflows for sales, account research, CRM hygiene, AgentMail, proof review, and workflow specs
- split marketing specialists for content, campaign planning, and proof/claims review
- Telegram, AgentMail, connector, and `spawn-k2qb` setup docs
- the HTML deck for the Hermes AI marketing team call recording

Start with [`hermes-agent-pack/README.md`](hermes-agent-pack/README.md), then run [`hermes-agent-pack/prompts/00_seed_hermes.md`](hermes-agent-pack/prompts/00_seed_hermes.md) in Hermes.

Run the shared eval suite against a Hermes profile with:

```bash
python tests/run_evals.py \
  --hermes-command "deeplinegtm -z" \
  --output tmp/hermes-eval-results.json
```

For the Sprite-hosted profile:

```bash
python tests/run_evals.py \
  --hermes-command "sprite exec -s spawn-k2qb -- deeplinegtm -z" \
  --output tmp/hermes-sprite-eval-results.json
```

## Interfaces

### Web chat

Run `python managed_agent/server.py` and open `http://localhost:8000`.

### Setup checks

Use `/doctor` to verify non-secret deployment configuration:

```bash
curl http://localhost:8000/doctor
```

The response reports missing auth, wildcard CORS, Slack setup, and unsafe local/live-write combinations without returning API keys or tokens.

### REST

```bash
curl -X POST http://localhost:8000/chat \
  -H "Content-Type: application/json" \
  -d '{"message": "Research rippling.com"}'
```

With endpoint protection enabled:

```bash
curl -X POST http://localhost:8000/chat \
  -H "Authorization: Bearer your-api-key" \
  -H "Content-Type: application/json" \
  -d '{"message": "Find 3 VP Sales in the US"}'
```

### Slack

Set `SLACK_BOT_TOKEN` and `SLACK_SIGNING_SECRET`, then DM the bot or mention it in a channel. See [SETUP.md](SETUP.md).

### SDK/API

Use `DEEPLINE_API_KEY` for Deepline v2 API calls. Keep API keys in environment variables or your deployment secret store.

```python
import os
import httpx

resp = httpx.post(
    "https://code.deepline.com/api/v2/integrations/apollo_search_people/execute",
    headers={"Authorization": f"Bearer {os.environ['DEEPLINE_API_KEY']}"},
    json={"payload": {"job_title": "VP Sales", "limit": 5}},
    timeout=60,
)
resp.raise_for_status()
print(resp.json())
```

For full chat behavior, use the v2 agent/chat SDK or API from the broker layer instead of shelling out to local CLI state.

## Deploy

See [SETUP.md](SETUP.md) for Railway and Slack setup. Required production variables:

| Variable | Required | Description |
|---|---|---|
| `DEEPLINE_API_KEY` | Yes | Deepline v2 API key |
| `PORT` | Yes | Usually `8000` |
| `API_KEY` | Optional | Protects `/chat` endpoints with bearer auth |
| `CORS_ORIGINS` | Optional | Comma-separated allowed origins; empty disables browser CORS |
| `SLACK_BOT_TOKEN` | For Slack | Slack bot token |
| `SLACK_SIGNING_SECRET` | For Slack | Slack request signing secret |
| `REDIS_URL` | Optional | Persistent Slack thread history |

`ANTHROPIC_API_KEY`, `MANAGED_AGENT_ID`, and `MANAGED_ENVIRONMENT_ID` are only needed for the optional Anthropic Managed Agent shell in `managed_agent/setup.py`; they are not required for the default native Deepline v2 broker.

For the Eve on Vercel path, see [`eve_agent/README.md`](eve_agent/README.md) and the Vercel section in [SETUP.md](SETUP.md).

## Legacy self-hosted agent

The root Python package contains a legacy self-hosted agent path for local experimentation. It is not the recommended deployment path. New deployments should use the v2 native agent/chat flow above.

## License

MIT

deepline-gtm-agent

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