# AgentLens — Marketing Master Plan
> Koordiniert aus 5 Spezialisten-Agenten | Stand: April 2026
> Sprache: English (marketing-ready) | Deutsche Kommentare wo hilfreich

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## 1. Executive Summary

AgentLens is an LLM observability platform targeting the gap between "too basic" (Helicone) and "too expensive" (Arize AI). The product is live, v0.2.0 is on PyPI, and the LLM Judge is active — meaning the technical foundation is ready to sell.

**Overall Strategy:** Distribution-first, community-led growth. No paid ads. Start by owning search terms ("langsmith alternative", "helicone alternative"), then activate communities (HN, Reddit, Discord), then scale with Product Hunt after hitting 200 signups.

**The 3 Core Bets:**
1. **SEO Arbitrage** — "langsmith alternative" and "helicone alternative" are high-intent, underserved keywords. Two comparison pages can drive organic signups within 30 days.
2. **Community Trust** — ML Engineers and AI startup CTOs don't buy from ads. They buy from GitHub stars, HN posts, and peer recommendations. Show up where they are.
3. **Cold Outreach Precision** — 3 targeted email sequences (ML Engineers, Startup CTOs, Enterprise Teams) with personalization. No spray-and-pray.

**Top 3 Priorities (in order):**
1. Capture emails NOW — every visitor without a capture mechanism is a lost lead
2. Publish comparison landing pages — highest-ROI SEO play available
3. Show HN post — single highest-leverage community action available

---

## 2. Prioritized Action List — "Was tue ich als erstes?"

<!-- WICHTIG: Diese Liste ist nach Impact/Aufwand sortiert. Nicht überspringen, nicht umordnen. -->

### TODAY — Tag 1 (do these before anything else)

| # | Action | Why | Time |
|---|--------|-----|------|
| 1 | **Add email capture to README and docs** | Every GitHub visitor = lost if no capture. Add a simple Tally/Typeform "Get notified" link. | 30 min |
| 2 | **Buy domain agentlens.dev or agentlens.io** | Required before any external linking. Current state: no owned web presence. | 10 min |
| 3 | **Post "Show HN: AgentLens — LLM observability with built-in LLM Judge"** | Highest single-action leverage. HN brings ML Engineers who are exactly ICP #1. | 45 min |
| 4 | **Rewrite GitHub README top section** | First impression for every inbound. Replace generic description with problem-first hook. | 45 min |
| 5 | **Set up a free PostHog or Plausible instance** | You can't optimize what you can't see. Track: page views, README clicks, PyPI installs. | 20 min |

---

### THIS WEEK — Woche 1 (Days 2–7)

| # | Action | Output | Owner |
|---|--------|--------|-------|
| 1 | Publish "LangSmith Alternative" landing page | SEO page targeting "langsmith alternative" keyword | Page live |
| 2 | Publish "Helicone Alternative" comparison page | SEO page targeting "helicone alternative" keyword | Page live |
| 3 | Post to r/MachineLearning (value-first, not promo) | Community awareness + backlinks | Post live |
| 4 | Post to r/LangChain and r/LocalLLaMA | Community awareness | Posts live |
| 5 | Send cold email sequence to ML Engineers (Batch 1: 20 contacts from GitHub) | First outreach replies | 20 emails sent |
| 6 | Write and publish Blog Post #1: "Production LLM Debugging Guide" | SEO + thought leadership | Post live |
| 7 | Post 3 LinkedIn posts (already written by Agent 2) | Personal brand + B2B reach | 3 posts published |

---

### THIS MONTH — Woche 2–4

<!-- Diese Aktionen bauen aufeinander auf. Woche 2 zuerst, dann 3, dann 4. -->

**Week 2:**
- [ ] Publish Blog Post #2: "AgentLens vs LangSmith vs Helicone — Honest Comparison"
- [ ] Launch Twitter/X account, post 15-tweet launch thread (written by Agent 3)
- [ ] Submit to 5 developer newsletters (Tldr.tech, Bytes.dev, Cooper Press, AI Weekly, Import AI)
- [ ] Post in LangChain Discord, HuggingFace Discord with template from Agent 3
- [ ] Start cold email Sequence 2: AI Startup CTOs (LinkedIn + GitHub research)

**Week 3:**
- [ ] A/B test Hero section: launch Problem-Led variant ("Your LLM Is Failing. You Just Can't See It.")
- [ ] Implement 5 Quick Win CRO fixes from Agent 4 (social proof, single CTA, pricing table)
- [ ] Send cold email Sequence 3: Enterprise Teams (10–15 targets)
- [ ] Reach out to 3 newsletter owners for feature/sponsorship swap
- [ ] Publish one "how to" tutorial targeting "llm monitoring python" keyword

**Week 4:**
- [ ] Hit 200-signup threshold → schedule Product Hunt launch
- [ ] Write Product Hunt assets (3 taglines, description, first comment — all ready from Agent 3)
- [ ] Prepare upvote coordination: personal network, existing users, communities
- [ ] Create 3 testimonial-request emails to early users (templates from Agent 4)
- [ ] Set up referral loop: "refer a teammate, get 1 month free"

---

### ONGOING — Kontinuierlich

- **Weekly:** 1 LinkedIn post (educational, not promotional), 1 GitHub issue engagement
- **Bi-weekly:** 1 blog post (SEO-targeted), check keyword rankings
- **Monthly:** Review KPI dashboard (see Section 6), adjust outreach sequences
- **Always:** Reply to every GitHub issue within 24h — this is free marketing for a dev tool

---

## 3. Positionierung & Messaging

<!-- Das ist das Herzstück. Alle externen Texte müssen von hier abgeleitet sein. -->

### Master Positioning Statement

> "AgentLens is the LLM observability platform for teams that have outgrown Helicone but can't justify Arize pricing. Framework-agnostic, with a built-in LLM Judge for automated quality evaluation — so you stop flying blind in production."

### Top 3 Taglines

1. **"Your LLM Is Failing. You Just Can't See It."** *(Problem-Led — highest urgency, recommended)*
2. **"LLM Observability That Doesn't Cost Enterprise Money"** *(Value-Led — for price-sensitive CTOs)*
3. **"See Every Token. Judge Every Response. Fix What's Breaking."** *(Feature-Led — for technical audience)*

### Per-ICP Headlines

| ICP | Headline | Subheadline |
|-----|----------|-------------|
| Solo ML Engineer (€299) | "Debug LLM calls in minutes, not hours" | "Full trace visibility + built-in LLM Judge. Framework-agnostic. 5-minute setup." |
| AI Startup CTO (€999) | "Your LLM costs are a black box. AgentLens opens it." | "Monitor cost, quality, and latency across your entire AI stack — without enterprise pricing." |
| Enterprise AI Team (€2.999) | "Production-grade LLM observability with audit trails" | "SOC2-ready logging, compliance-friendly, integrates with your existing stack." |

### Killer Lines vs. Each Competitor

| Competitor | Our Kill Line |
|------------|---------------|
| **LangSmith** | "LangSmith is great if you're all-in on LangChain. We work with everything." |
| **Helicone** | "Helicone logs your requests. AgentLens tells you if the responses are actually good." |
| **Arize AI** | "Arize starts at $1,000+/month. We start at €299." |
| **Langfuse** | "Langfuse is open-source and requires self-hosting. AgentLens is managed — deploy in 5 minutes." |
| **W&B** | "W&B is a training tool. AgentLens is built for production inference." |
| **Traceloop** | "Similar traces. We add LLM Judge on top — automated quality scoring built in." |
| **OpenLIT** | "OpenLIT is good for metrics. AgentLens gives you metrics + quality evaluation in one platform." |

---

## 4. Channel Strategie

### Owned Channels (bauen, nicht mieten)

| Asset | Priority | Action |
|-------|----------|--------|
| **Domain + Landing Page** | P0 — today | Buy domain, set up comparison pages |
| **GitHub Repository** | P0 — today | Rewrite README, add email capture, star-baiting section |
| **Blog / Docs** | P1 — week 1 | Publish 2 comparison posts, set up SEO basics |
| **Email List** | P1 — week 1 | Tally form → Mailchimp/ConvertKit, start capturing now |

**Do NOT invest in:** Custom video content, podcast, paid newsletter — too slow for current stage.

### Rented Channels (2–3 max, high-frequency)

| Channel | Frequency | Content Type |
|---------|-----------|--------------|
| **Twitter/X** | 3–4x/week | Short technical threads, product updates, debug tips |
| **LinkedIn** | 2x/week | Longer-form POV posts, ICP-targeted (CTOs + ML leads) |
| **Reddit** | 1–2x/week | Value-first comments + occasional posts in target subs |

**Subs to be active in:** r/MachineLearning, r/LangChain, r/LocalLLaMA, r/mlops, r/SideProject

### Borrowed Channels — Top 5 Opportunities

<!-- Diese haben den höchsten Hebel mit niedrigsten Kosten -->

| # | Opportunity | How | Effort |
|---|-------------|-----|--------|
| 1 | **AI newsletter features** (Tldr.tech, The Batch, Import AI) | Submit product brief + story angle | Low — 2h |
| 2 | **GitHub influencer outreach** (ML Engineers with 1k+ followers) | "Built this for people like you" email | Medium — 1 day |
| 3 | **LangChain/LlamaIndex blog guest post** | Pitch: "How to debug LangChain in production" | Medium — 2 days |
| 4 | **Dev.to / Hashnode cross-posts** | Re-publish blog posts with canonical URL | Low — 1h per post |
| 5 | **YouTube tutorial collab** (AI/ML channels with 10k–100k subs) | Offer free account + demo script | High effort, high return |

---

## 5. 30-Tage Launch Calendar (komprimiert)

```
WEEK 1: Foundation
──────────────────
Day 1:  Buy domain | Email capture in README | Show HN post | README rewrite
Day 2:  Set up landing page (agentlens.dev)
Day 3:  Publish "LangSmith Alternative" page + submit to Google Search Console
Day 4:  Publish "Helicone Alternative" page
Day 5:  Post Blog #1 (Production LLM Debugging Guide)
Day 6:  Reddit posts (r/MachineLearning + r/LangChain)
Day 7:  Cold email batch 1 — 20 ML Engineers from GitHub

WEEK 2: Content & Community
────────────────────────────
Day 8:  LinkedIn posts x3 (schedule for Mon/Wed/Fri)
Day 9:  Twitter/X launch thread (15 tweets, queued)
Day 10: Discord outreach (LangChain + HuggingFace + AI Discord)
Day 11: Blog #2 (Comparison post — AgentLens vs LangSmith vs Helicone)
Day 12: Cold email batch 2 — 15 AI Startup CTOs
Day 13: Submit to 5 developer newsletters
Day 14: Review Week 1 KPIs → adjust messaging if needed

WEEK 3: Conversion & Optimization
───────────────────────────────────
Day 15: A/B test Hero Section live (Problem-Led variant)
Day 16: Implement CRO Quick Wins (5 fixes from Agent 4)
Day 17: Cold email batch 3 — 10 Enterprise targets
Day 18: Newsletter follow-ups (check open rates, 2nd touch)
Day 19: Testimonial request emails to first users
Day 20: Dev.to cross-post of both blog articles
Day 21: Review signups — are we at 100+?

WEEK 4: Launch Prep
────────────────────
Day 22: Write Product Hunt Hunter intro + Schedule PH launch (if 200 signups)
Day 23: Upvote coordination prep (email list + Twitter DMs)
Day 24: Referral loop setup ("refer a teammate = 1 month free")
Day 25: Final CRO pass on pricing page
Day 26: Cold email Sequence 2 follow-up (touch 2 for CTO sequence)
Day 27: Product Hunt launch (target: #1 Product of the Day)
Day 28: Post-PH follow-up: thank voters, offer trials, capture replies
Day 29: HN comment section monitoring + engage
Day 30: Month 1 review — MRR, signups, CAC, conversion rate audit
```

---

## 6. KPI Dashboard

<!-- Täglich tracken: nur 4–5 Metriken, nicht 20. Fokus schlägt Vollständigkeit. -->

### Daily Tracking (5 minutes max)

- PyPI installs (check via `pip-stats` or libraries.io)
- GitHub stars (delta)
- Email signups (neue pro Tag)
- HN / Reddit referral traffic
- Reply rate on cold emails

### Weekly Targets

| Metric | Week 1 | Week 4 | Month 3 |
|--------|--------|--------|---------|
| Email signups | 50 | 200 | 1,000 |
| GitHub stars | +50 | +200 | +1,000 |
| PyPI installs (weekly) | 100 | 500 | 2,000 |
| Paying customers | 0 | 5 | 30 |
| MRR | €0 | €2,500 | €15,000 |
| Blog organic traffic | — | 500 visits | 5,000 visits |
| Cold email reply rate | — | 8%+ | — |

### Red Flags — sofort handeln wenn:

- Week 1 signups < 20 → README/landing page ist nicht klar genug, rewrite hero section
- HN post < 20 upvotes in first 2h → kommentiere mit more context, ask community
- Cold email reply rate < 3% → subject lines neu schreiben (A/B test)
- PyPI installs stagnieren → activation problem, fix onboarding docs

---

## 7. Content-Bibliothek Index

<!-- Was bereits fertig ist und wo es liegt (oder wo es liegen sollte) -->

### Ready to Publish — Sofort verwendbar

| Asset | Status | Location / Notes |
|-------|--------|------------------|
| Blog Post #1: Production LLM Debugging Guide | Written | Saved in Agent 2 output — publish to blog |
| Blog Post #2: Comparison Post (vs LangSmith/Helicone) | Written | Saved in Agent 2 output — publish to blog |
| 5 LinkedIn Posts | Written | Agent 2 output — schedule in buffer/hypefury |
| GitHub README Enhancement | Spec written | Agent 2 — implement manually in README.md |
| Product Hunt: 3 Taglines | Written | Agent 3 output |
| Product Hunt: 3 Descriptions | Written | Agent 3 output |
| Product Hunt: First Comment + Hunter Intro | Written | Agent 3 output |
| 10-Tweet PH Launch Thread | Written | Agent 3 output |
| 15-Tweet Twitter Launch Thread | Written | Agent 3 output |
| HN Show HN post (title + body + self-comment) | Written | Agent 3 output — post today |
| Reddit posts x3 (ML, LangChain, LocalLLaMA) | Written | Agent 3 output |
| Discord/Slack outreach templates x3 | Written | Agent 3 output |
| Landing Page Hero — 3 A/B variants | Written | Agent 4 output |
| Pricing Table Copy + 8-question FAQ | Written | Agent 4 output |
| 3 Testimonial Request Templates | Written | Agent 4 output |
| Top 5 CRO Quick Wins | Spec written | Agent 4 output — implement on landing page |
| Cold Email Sequence #1: ML Engineers (3 emails) | Written | Agent 5 output |
| Cold Email Sequence #2: AI Startup CTOs (4 emails) | Written | Agent 5 output |
| Cold Email Sequence #3: Enterprise Teams (4 emails) | Written | Agent 5 output |

### Still Needed

| Asset | Priority | Estimated effort |
|-------|----------|-----------------|
| agentlens.dev landing page (actual HTML) | P0 | 1–2 days (use Framer or Webflow) |
| Email capture form (Tally or Typeform) | P0 | 30 min |
| "LangSmith Alternative" SEO page | P1 | 2h writing + 30 min publish |
| "Helicone Alternative" SEO page | P1 | 2h writing + 30 min publish |
| PyPI page optimization (description, keywords) | P2 | 1h |
| Demo video / GIF for README | P2 | 2–3h |

---

## 8. Outreach Playbook

<!-- Wer kontaktieren, in welcher Reihenfolge, was sagen -->

### Already Contacted (do NOT re-approach cold)

- Comet ML
- OpenLIT
- Arize AI
- Traceloop
- Pydantic
- TensorZero
- BerriAI

**Next step for these:** If no reply in 14 days, send a value-first follow-up (share a blog post relevant to them, not a pitch).

### Outreach Priority Queue

**Tier 1 — This Week (highest conversion probability)**

| Target | Channel | Approach |
|--------|---------|----------|
| ML Engineers on GitHub (repos using LangChain/OpenAI) | Cold email (Sequence 1) | "I noticed you're building with [framework] — here's how to debug it in production" |
| Solo developers posting in r/MachineLearning | Reddit DM after value comment | Comment first, DM second — never DM cold |
| AI Startup CTOs on LinkedIn (seed-stage, AI-first) | LinkedIn connection + cold email | Sequence 2 — focus: cost black box + silent failures |

**Tier 2 — Week 2**

| Target | Channel | Approach |
|--------|---------|----------|
| Developer newsletter editors (Tldr.tech, Bytes.dev) | Email pitch | "New open-source LLM observability tool — reader-relevant" |
| ML influencers (Twitter/X, 5k–50k followers) | Twitter DM or email | Offer free Team plan for honest review/mention |
| LangChain community contributors | GitHub + Discord | "Built something that complements LangChain" — add value first |

**Tier 3 — Month 2**

| Target | Channel | Approach |
|--------|---------|----------|
| Enterprise AI Teams (Fortune 500 AI divisions) | Sequence 3 cold email | Compliance + audit angle, not cost |
| Conference speakers (NeurIPS, MLConf, AI Summit) | LinkedIn + email | Request 5-min slot or podcast appearance |
| YC AI companies (current/recent batch) | YC directory + LinkedIn | "Built for founders like you" |

### Outreach Rules (nicht verhandeln)

1. **Never pitch first.** Lead with value — a useful insight, a relevant article, a specific observation about their work.
2. **Personalize the first line.** {{specificDetail}} must be real. "I saw your repo does X" not "I noticed you work in AI."
3. **One ask per email.** Not "check out our tool AND give feedback AND share it." One clear CTA.
4. **Follow up max 2x.** Three touches max. After that, move on.

---

## 9. Quick Wins — Was in 24h erledigt werden kann

<!-- Maximaler Impact, minimaler Aufwand. Keine Ausreden. -->

These 5 actions can be completed today and will have compounding effects within 7 days:

### Quick Win #1 — Email Capture in README (30 min, P0)
Add this to the top of README.md below the headline:
```
📬 **Get updates:** [Join the waitlist](https://tally.so/your-form) — be first to know about new features.
```
Why it matters: GitHub is getting traffic now. Without capture, every visitor is gone forever.

### Quick Win #2 — Show HN Post (45 min, P0)
Title: `Show HN: AgentLens – LLM observability with built-in LLM Judge (framework-agnostic)`
Post in the morning (9–10am EST, 15–16 Uhr DE) for maximum visibility.
Self-comment ready from Agent 3 — post it immediately after the main post.

### Quick Win #3 — PyPI Description Update (20 min, P1)
Update `pyproject.toml` description and keywords to include:
- "llm observability", "llm monitoring", "langsmith alternative", "openai tracing"
This gets indexed by PyPI search and Google within days.

### Quick Win #4 — GitHub Topics (10 min, P1)
Add these topics to the GitHub repo:
`llm-observability`, `llm-monitoring`, `langchain`, `openai`, `tracing`, `mlops`, `python`
GitHub search will surface the repo for these terms immediately.

### Quick Win #5 — Hero Section Swap on Landing Page (1h, P1)
Replace current hero with Problem-Led variant:
- **Headline:** "Your LLM Is Failing. You Just Can't See It."
- **Subheadline:** "AgentLens gives you full trace visibility, cost tracking, and automated quality scoring — for every LLM call in production."
- **CTA:** "Start Free" (single button, no secondary CTA above fold)

---

## Appendix: ICP Summary

| ICP | Plan | Price | Pain Point | Key Message |
|-----|------|-------|------------|-------------|
| Solo ML Engineer | Starter | €299/mo | "I can't debug production failures fast enough" | Fast setup, full traces, no devops needed |
| AI Startup CTO | Team | €999/mo | "I don't know if my LLM quality is regressing" | Cost visibility + LLM Judge = ship with confidence |
| Enterprise AI Team | Scale | €2,999/mo | "We need audit trails for compliance" | SOC2-aligned, team permissions, retention policies |

---

## Appendix: Competitor Quick Reference

| Competitor | Weakness | Our counter |
|------------|----------|-------------|
| LangSmith | LangChain-only, clunky UX | Framework-agnostic, cleaner DX |
| Helicone | No quality evaluation, basic features | Built-in LLM Judge, richer traces |
| Arize AI | $1,000+/month, complex setup | Startup pricing, 5-min setup |
| Langfuse | Self-hosted complexity | Managed service, no infra overhead |
| W&B | Training-focused, not inference | Production-first, inference observability |
| Traceloop | Traces only | Traces + quality scoring in one tool |
| OpenLIT | Metrics-heavy, evaluation lacking | Metrics + evaluation combined |

---

*Plan erstellt: April 2026 | Nächste Review: Mai 2026 | Verantwortlich: Soufiane Azzaoui*
*AgentLens v0.2.0 — live on PyPI | 36 Marketing Skills aktiviert*
