OpenClaw + Gemma 4 E2B + Agentic RAG — Full Integration

Build a personal AI assistant that answers on Telegram/WhatsApp/CLI using Gemma 4 E2B, and delegates research-heavy questions to your local Agentic RAG API.

What you end up with

1. OpenClaw Gateway — always-on control plane (daemon)
2. gemma4:e2b — conversational model with tools + optional vision
3. agentic-rag skill — shells out to rag_query.sh → POST /predict on LitServe
4. qwen-agentic-rag — CrewAI Researcher + Writer + Qdrant (and optional Firecrawl)

This integration uses one Ollama model everywhere: gemma4:e2b for OpenClaw chat and for the CrewAI RAG agents.

Architecture

flowchart LR
    A[Channels] -->|user message| B[OpenClaw Gateway]
    B -->|gemma4:e2b plans| C[agentic-rag skill]
    C -->|rag_query.sh POST /predict| D[LitServe API]
    D --> E[Researcher Agent]
    E --> F[Vector DB Tool]
    E --> G[Firecrawl Search]
    E --> H[Writer Agent]
    H --> I[JSON Response]
    F --> J[(Qdrant)]
    G --> K[Firecrawl API]
    B --> L[(Ollama gemma4:e2b)]
    E --> L
    H --> L
    I --> C
    C --> B
    B -->|reply| A

Flow:

1. User messages OpenClaw on Telegram, WhatsApp, or CLI
2. gemma4:e2b decides whether to call the agentic-rag skill
3. Skill runs rag_query.sh → LitServe POST /predict
4. CrewAI Researcher picks Qdrant or Firecrawl; Writer drafts the answer
5. JSON returns through the skill → OpenClaw → the same channel

---

Prerequisites

Requirement	Check
Node 22.12+ or 24 (OpenClaw will not run on Node 20)	node -v
Ollama	ollama -v
Python 3.10+	python3 --version
curl + jq	curl --version && jq --version
Completed qwen-agentic-rag once	vector DB built

---

Part 1 — Agentic RAG API

If you already finished the Qwen Agentic RAG tutorial, start the server only:

ollama pull gemma4:e2b
cd guides/qwen-agentic-rag
source .venv/bin/activate
cp ../openclaw-gemma-rag/env.rag.example .env   # sets OLLAMA_MODEL=ollama/gemma4:e2b
# First time only:
# pip install -r requirements.txt && python setup_vectordb.py
python server.py

Default URL: http://127.0.0.1:8001 (PORT in .env).

Verify:

python client.py --query "What is cross-validation?"
# or
curl -sS -X POST http://127.0.0.1:8001/predict \
  -H 'Content-Type: application/json' \
  -d '{"query":"What is cross-validation?"}' | jq -r .output

Keep this terminal open. First crew run may take several minutes.

---

Part 2 — Pull Gemma 4 E2B

ollama pull gemma4:e2b
ollama run gemma4:e2b "Reply in one sentence: what is Gemma 4?"

Recommended sampling (Ollama may already apply defaults): temperature=1, top_p=0.95, top_k=64.

---

Part 3 — Install OpenClaw

Node version (required)

OpenClaw needs Node >= 22.12. If node -v shows v20, switch with nvm (you may already have 22 installed):

cd guides/openclaw-gemma-rag
source ./use-node22.sh   # uses .nvmrc → 22.22.3
node -v                # must be v22.12.0 or higher

Optional — make Node 22 the default in new terminals:

nvm alias default 22

npm install -g openclaw@latest
openclaw onboard --install-daemon

Follow prompts for workspace, auth, and optional channels. See Getting started.

Set the primary model:

export OLLAMA_API_KEY="ollama-local"
openclaw models list --provider ollama
openclaw models set ollama/gemma4:e2b

Config snippet

Copy fields from config/openclaw.snippet.json5 in this guide into ~/.openclaw/openclaw.json.

Critical points:

• baseUrl: http://127.0.0.1:11434 — no /v1 suffix
• api: "ollama" — native tool calling
• agents.defaults.model.primary: "ollama/gemma4:e2b"

Restart:

openclaw gateway restart
openclaw gateway status

---

Part 4 — Install the agentic-rag skill

From this guide directory:

cd guides/openclaw-gemma-rag
chmod +x install-skill.sh skills/agentic-rag/scripts/*.sh
./install-skill.sh

This copies to ~/.openclaw/workspace/skills/agentic-rag/.

Alternative (if your CLI supports it):

openclaw skills install ./guides/openclaw-gemma-rag/skills/agentic-rag --global

Enable in config:

{
  skills: {
    entries: {
      "agentic-rag": {
        enabled: true,
        env: { RAG_API_URL: "http://127.0.0.1:8001" },
      },
    },
  },
}

Optional allowlist so only this skill is injected:

{
  agents: {
    defaults: {
      skills: ["agentic-rag"],
    },
  },
}

Restart the gateway after skill or config changes.

Skill behavior

The skill teaches OpenClaw to run:

~/.openclaw/workspace/skills/agentic-rag/scripts/rag_query.sh "user question"

That POSTs to LitServe and prints the crew answer. The Gemma model decides when to use the skill; the RAG crew uses the same OLLAMA_MODEL=ollama/gemma4:e2b from guides/qwen-agentic-rag/.env (see env.rag.example).

---

Part 5 — End-to-end test

CLI (no channel)

openclaw agent --message "Using the agentic RAG knowledge base: explain cross-validation in 3 bullets." --thinking low

Watch the gateway logs — you should see an exec invoking rag_query.sh.

Manual script test

export RAG_API_URL=http://127.0.0.1:8001
./skills/agentic-rag/scripts/rag_query.sh "What is regularization?"

Health check

./skills/agentic-rag/scripts/rag_health.sh

---

Part 6 — Connect a channel (optional)

Example: Telegram

1. Create a bot via @BotFather
2. During openclaw onboard or openclaw configure, add the Telegram channel token
3. Keep DM pairing enabled (dmPolicy: "pairing") until you trust exposure
4. Approve yourself: openclaw pairing approve telegram <code>

Send: "Search the ML FAQ: what is gradient descent?"

Flow: Telegram → Gateway → Gemma → agentic-rag skill → RAG API → reply on Telegram.

Channel docs: OpenClaw Channels.

---

Security checklist

• Treat inbound DMs as untrusted — keep pairing on for production-adjacent setups
• exec (used by the RAG skill) is powerful — do not expose the gateway to the public internet without Security and Exposure runbook
• Run openclaw doctor after config changes
• RAG API binds to localhost by default — keep it that way

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Troubleshooting

Symptom	Fix
connection refused on :8001	Start python server.py in qwen-agentic-rag
RAG very slow	Normal on laptop; reduce parallel Ollama loads
OpenClaw ignores RAG	Confirm skill installed, enabled: true, gateway restarted; ask explicitly to "use agentic RAG"
ollama/gemma4:e2b not found	ollama pull gemma4:e2b; check openclaw models list
Tool calling errors	Ensure api: "ollama" and no /v1 on baseUrl
openclaw requires Node >=22.12.0	Run source guides/openclaw-gemma-rag/use-node22.sh or nvm use 22 before any openclaw command
OOM on 16GB Mac	Only run gemma4:e2b; quit other Ollama models (ollama ps)
Skill curl fails	brew install jq or apt install jq

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What's next

• Add your own documents in guides/qwen-agentic-rag/rag_code.py and re-run setup_vectordb.py
• Publish a second OpenClaw skill for Gradio (ui.py) health checks
• Route work vs personal agents with multi-agent routing
• Share this guide on the ecosystem docs site

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Summary

Component	You run
Ollama	gemma4:e2b (chat + RAG)
RAG	guides/qwen-agentic-rag/server.py
OpenClaw	openclaw gateway (daemon)
Skill	agentic-rag → rag_query.sh → /predict

You now have a local-first assistant: Gemma for conversation, CrewAI RAG for grounded ML research — no cloud LLM required for either layer.