Autonomous Local Agents

Run your OpenClaw/Hermes agent and ByteRover memory system entirely on local models using LM Studio - no cloud API keys required.

This guide walks through running both OpenClaw and ByteRover CLI on local LLMs using LM Studio.

Tested Configuration

Component	Version / Details
Machine	Mac M4 Pro, RAM 24GB
LM Studio	0.4.9
OpenClaw	2026.4.12
ByteRover CLI	3.3.0

NOTE

This is an experimental setup. ByteRover and autonomous agents can run with OpenClaw on a Apple RAM 24GB machine, but for production usage we recommend at least an Apple M4 with RAM 48GB.

Step 1 — Download the Models

Search for and download both GGUF files directly from LM Studio's Discover tab.

Step 1: Download Gemma 4 E4B for OpenClaw

Search for unsloth/gemma-4-E4B-it-GGUF and download gemma-4-E4B-it-UD-Q4_K_XL.gguf.

Step 2: Download Qwen3.5-9B for ByteRover

Search for unsloth/Qwen3.5-9B-GGUF and download Qwen3.5-9B-Q4_K_S.gguf.

NOTE

On a 24 GB machine, both models fit in memory simultaneously. Gemma 4 E4B at Q4 uses ~8.7 GB and Qwen3.5-9B at Q4 uses ~10.5 GB.

Step 2 — Load Both Models in LM Studio

LM Studio serves all loaded models from a single endpoint at http://localhost:1234/v1. Load both models before starting the server.

Step 1: Open My Models

Go to the Models tab. You should see both downloaded models listed.

Step 2: Load Gemma 4 E4B

Click on gemma-4-E4B-it-UD-Q4_K_XL.gguf and click Load. Note the API Identifier — LM Studio assigns it google/gemma-4-e4b. This is the model ID you will use in OpenClaw's config.

Step 3: Load Qwen3.5-9B

Click on Qwen3.5-9B-Q4_K_S.gguf.gguf and click Load. The API Identifier will be qwen3.5-9b.

Step 4: Verify both models are ready

Open the Developer tab. Both models should show READY status, reachable at http://127.0.0.1:1234.

Confirm with:

bash

curl http://localhost:1234/v1/models

The response should list both google/gemma-4-e4b and qwen3.5-9b.

Step 3 — Configure Your Agent

Both OpenClaw and Hermes use the same local provider setup. Pick the agent you are using.

OpenClaw

Run the OpenClaw onboard wizard:

bash

openclaw onboard

Step 1: Select Custom Provider

When prompted for Model/auth provider, scroll down and select Custom Provider.

Step 2: Enter the endpoint details

Fill in the following when prompted:

Field	Value
API Base URL	`http://localhost:1234/v1`
API Key	(leave blank)
Endpoint compatibility	`OpenAI-compatible`
Model ID	`google/gemma-4-e4b`
Model alias	`google-gemma-4-e4b`

The wizard verifies the endpoint and reports Verification successful.

Step 3: Start OpenClaw and verify

Launch OpenClaw. It will use google/gemma-4-e4b served by LM Studio at localhost:1234.

WARNING

Context limit — OpenClaw works normally 50,000 tokens and above. To update this, edit openclaw.json manually, then run openclaw gateway restart to apply changes.

Resulting openclaw.json config

The wizard writes the following into ~/.openclaw/openclaw.json. You can also add this manually:

json

{
  "models": {
    "mode": "merge",
    "providers": {
      "custom-localhost-1234": {
        "baseUrl": "http://localhost:1234/v1",
        "api": "openai-completions",
        "models": [
          {
            "id": "google/gemma-4-e4b",
            "name": "gemma-4-E4B-it (Local)",
            "contextWindow": 50000,
            "maxTokens": 50000,
            "input": ["text"],
            "cost": { "input": 0, "output": 0, "cacheRead": 0, "cacheWrite": 0 },
            "reasoning": false
          }
        ]
      }
    }
  },
  "agents": {
    "defaults": {
      "model": {
        "primary": "custom-localhost-1234/google/gemma-4-e4b"
      },
      "models": {
        "custom-localhost-1234/google/gemma-4-e4b": {
          "alias": "google-gemma-4-e4b"
        }
      }
    }
  }
}

Hermes

Run the Hermes model setup wizard:

bash

hermes setup model

Step 1: Select Custom Provider

When prompted for the model provider, scroll down and select Custom Provider (any OpenAI or Anthropic compatible endpoint).

Step 2: Enter the endpoint details

Fill in the following when prompted:

Field	Value
API Base URL	`http://localhost:1234/v1`
API Key	(leave blank)
Endpoint compatibility	`OpenAI-compatible`
Model ID	`google/gemma-4-e4b`

The wizard confirms the endpoint and model are configured.

Step 3: Configure context length

Hermes will prompt you to set the context length. Set it to match the model's context window (65000 tokens for Gemma 4 E4B because hermes agent required minimum 64000 tokens).

Step 4: Start Hermes and verify

Launch Hermes. It will use google/gemma-4-e4b served by LM Studio at localhost:1234.

Step 4 — Configure ByteRover CLI

Connect ByteRover to the same local endpoint and select the Qwen model.

TUI

Step 1: Open the providers command

In the ByteRover TUI, type /providers and press Enter.

Step 2: Select OpenAI Compatible

Scroll to OpenAI Compatible and press Enter. This covers LM Studio, Ollama, and any other OpenAI-compatible local server.

Step 3: Enter the base URL

When prompted, enter http://localhost:1234/v1 and press Enter. Leave the API key blank.

Step 4: Select the Qwen model

From the model list, select qwen3.5-9b (128K ctx).

CLI

Step 1: Connect the local provider

bash

brv providers connect openai-compatible --base-url http://localhost:1234/v1

Step 2: Switch to the Qwen model

bash

brv model switch qwen3.5-9b

Step 5 — Verify ByteRover Is Working

Run a quick curate command to confirm ByteRover is using the local Qwen model.

Step 1: Run a curate command

/curate "caching algorithm list: lru, lfu, fifo"

Step 2: Confirm it processes with the local model

ByteRover sends the request to Qwen3.5-9B on LM Studio. You can watch the LM Studio Developer tab update in real time.

Step 3: Review the results

ByteRover returns structured knowledge extracted from the curate request.

The context tree is updated with new memory files you can inspect directly.

Step 6 — Enable ByteRover Memory Integration

Connect your agent to ByteRover for persistent memory across sessions.

OpenClaw Integration — Configure ByteRover as the context engine for OpenClaw
Hermes Integration — Configure ByteRover as the memory provider for Hermes

Reference

LLM Providers — Connect an external provider or use the built-in LLM
Onboard Context — Learn how to seed your context tree with existing knowledge
Reference — Configuration details, troubleshooting, and advanced topics
Local & Cloud — Exploring local & cloud options

Autonomous Local Agents ​

Tested Configuration ​

Step 1 — Download the Models ​

Step 1: Download Gemma 4 E4B for OpenClaw ​

Step 2: Download Qwen3.5-9B for ByteRover ​

Step 2 — Load Both Models in LM Studio ​

Step 1: Open My Models ​

Step 2: Load Gemma 4 E4B ​

Step 3: Load Qwen3.5-9B ​

Step 4: Verify both models are ready ​

Step 3 — Configure Your Agent ​

Step 1: Select Custom Provider ​

Step 2: Enter the endpoint details ​

Step 3: Start OpenClaw and verify ​

Resulting openclaw.json config ​

Step 1: Select Custom Provider ​

Step 2: Enter the endpoint details ​

Step 3: Configure context length ​

Step 4: Start Hermes and verify ​

Step 4 — Configure ByteRover CLI ​

Step 1: Open the providers command ​

Step 2: Select OpenAI Compatible ​

Step 3: Enter the base URL ​

Step 4: Select the Qwen model ​

Step 1: Connect the local provider ​

Step 2: Switch to the Qwen model ​

Step 5 — Verify ByteRover Is Working ​

Step 1: Run a curate command ​

Step 2: Confirm it processes with the local model ​

Step 3: Review the results ​

Step 6 — Enable ByteRover Memory Integration ​

Reference ​

Autonomous Local Agents

Tested Configuration

Step 1 — Download the Models

Step 1: Download Gemma 4 E4B for OpenClaw

Step 2: Download Qwen3.5-9B for ByteRover

Step 2 — Load Both Models in LM Studio

Step 1: Open My Models

Step 2: Load Gemma 4 E4B

Step 3: Load Qwen3.5-9B

Step 4: Verify both models are ready

Step 3 — Configure Your Agent

Step 1: Select Custom Provider

Step 2: Enter the endpoint details

Step 3: Start OpenClaw and verify

Resulting openclaw.json config

Step 1: Select Custom Provider

Step 2: Enter the endpoint details

Step 3: Configure context length

Step 4: Start Hermes and verify

Step 4 — Configure ByteRover CLI

Step 1: Open the providers command

Step 2: Select OpenAI Compatible

Step 3: Enter the base URL

Step 4: Select the Qwen model

Step 1: Connect the local provider

Step 2: Switch to the Qwen model

Step 5 — Verify ByteRover Is Working

Step 1: Run a curate command

Step 2: Confirm it processes with the local model

Step 3: Review the results

Step 6 — Enable ByteRover Memory Integration

Reference