As far as I can tell Its all setup correctly. What am I doing wrong?

[nova patio]

A couple key things going on here:

1. ollama serve “address already in use” is normal
   That error just means Ollama is already running and already bound to 127.0.0.1:11434 (often as a background service). You don’t need to start it again.

Quick checks:

ollama ps
ollama list
curl http://127.0.0.1:11434/api/tags
curl http://127.0.0.1:11434/v1/models

2. The model won’t load into RAM until something actually calls it
   OpenClaw doesn’t “preload” models just because the gateway UI is up. RAM will spike only when you run an actual completion/chat request.

Force a real request via OpenClaw:

openclaw chat --model ollama/deepseek-r1:14b "hello"

(or run ollama run deepseek-r1:14b "hello" and watch RAM / ollama ps)

3. Make sure the model is actually pulled
   OpenClaw won’t download it for you. Verify:

ollama pull deepseek-r1:14b
ollama list

4. If it still doesn’t work, we need the probe/log signal
   Please paste:

openclaw --version

openclaw status --deep

and then run:

openclaw logs --follow

in one terminal while you run the openclaw chat --model ... "hello" command in another. The logs will show if it’s failing to reach Ollama, model name mismatch, wrong config file, etc.

Docs (worth skimming, they match your config style exactly):

• https://docs.openclaw.ai/providers/ollama
• https://docs.openclaw.ai/gateway/troubleshooting

[sly ginkgo]

ollama list
NAME ID SIZE MODIFIED
deepseek-r1:14b c333b7232bdb 9.0 GB 4 hours ago
lfm2.5-thinking:latest 95bd9d45385f 731 MB 20 hours ago
dan@dan-Lenovo-B40-45:~$ curl http://127.0.0.1:11434/api/tags
{"models":[{"name":"deepseek-r1:14b","model":"deepseek-r1:14b","modified_at":"2026-02-06T14:49:31.476969265Z","size":8988112209,"digest":"REMOVED","details":{"parent_model":"","format":"gguf","family":"qwen2","families":["qwen2"],"parameter_size":"14.8B","quantization_level":"Q4_K_M"}},{"name":"lfm2.5-thinking:latest","model":"lfm2.5-thinking:latest","modified_at":"2026-02-05T22:51:17.961651811Z","size":731163903,"digest":"REMOVED","details":{"parent_model":"","format":"gguf","family":"lfm2","families":["lfm2"],"parameter_size":"1.2B","quantization_level":"Q4_K_M"}}]}dan@dan-Lenovo-B40-45:~$ curl http://127.0.0.1:11434/v1/mcurl http://127.0.0.1:11434/v1/models
{"object":"list","data":[{"id":"deepseek-r1:14b","object":"model","created":1770389371,"owned_by":"library"},{"id":"lfm2.5-thinking:latest","object":"model","created":1770331877,"owned_by":"library"}]}
dan@dan-Lenovo-B40-45:~$ openclaw chat --model ollama/deepseek-r1:14b "hello"
error: unknown command 'chat'
dan@dan-Lenovo-B40-45:~$ ollama run deepseek-r1:14b "hello"
Hello! How can I assist you today? 😊
dan@dan-Lenovo-B40-45:~$ ollama ps
NAME ID SIZE PROCESSOR CONTEXT UNTIL
deepseek-r1:14b c333b7232bdb 9.3 GB 100% CPU 4096 4 minutes from now

[nova patio]

Yep — Ollama is 100% working on your box. The missing piece is: OpenClaw hasn’t actually made a request to Ollama yet, so nothing would “load into RAM” from OpenClaw’s side.

Also: my bad — there is no openclaw chat command (that’s not a thing in current CLI), so that test won’t work.

Fastest way to force OpenClaw to hit Ollama

Run a single agent turn (no delivery, just to test the model call):

openclaw agent --session-id test --message "hello from openclaw"

While that runs, in another terminal:

ollama ps

If OpenClaw is successfully calling Ollama, you’ll see the model pop up in ollama ps (like it did with ollama run).

Docs: https://docs.openclaw.ai/cli/agent

If that errors, paste these 3 outputs

openclaw --version
openclaw models list
openclaw logs --follow

Common failure mode here: some Ollama models don’t support tool-calling, and OpenClaw will try to send tools → you’ll see a clear error in logs. If that happens, easiest sanity check is to temporarily switch to a known tool-capable Ollama model or let OpenClaw auto-discover (it filters to tool-capable models): https://docs.openclaw.ai/providers/ollama

[sly ginkgo]

openclaw agent --session-id test --message "hello from openclaw"

🦞 OpenClaw 2026.2.3-1 (d84eb46) — Ah, the fruit tree company! 🍎

│
◇
400 registry.ollama.ai/library/deepseek-r1:14b does not support tools

[nova patio]

That error is the whole story:

deepseek-r1:14b does not support tools

OpenClaw always sends the “tools” schema (so it can do exec/browser/etc). Your Ollama model rejects any request that includes tools, so OpenClaw can’t use deepseek-r1:14b as an agent model right now. You didn’t misconfigure Ollama — you picked a model that can chat, but not tool-call.

Fix options

Option A (recommended): let OpenClaw auto-discover only tool-capable Ollama models

Auto-discovery filters to models that report tool support, so you won’t hit this.

1. Remove the explicit ollama provider block from config:

openclaw config unset models.providers.ollama

2. Ensure Ollama is enabled for OpenClaw (any value works):

export OLLAMA_API_KEY="ollama-local"

3. Restart gateway, then list models:

openclaw models list

Pick one of the listed ollama/... models and set it as primary.

Option B: keep explicit provider config, but switch to a tool-capable model

Pull a known tool-capable model in Ollama (example from docs):

ollama pull llama3.3

Then point OpenClaw at it:

openclaw config set agents.defaults.model.primary "ollama/llama3.3"

Re-test

openclaw agent --session-id test --message "hello from openclaw"

Docs: https://docs.openclaw.ai/providers/ollama (see the “auto-discover tool-capable models” bit)

[sly ginkgo]

what models can I use that will run in 14.6gb ram that supprots tool calls

[nova patio]

You need an Ollama model that advertises tools support. deepseek-r1:14b doesn’t, so OpenClaw will always fail (because it sends a tools schema on agent turns).

I can’t give you a “definitive list of models” here (it changes fast + depends on your Ollama build), but you can reliably find ones that work on your machine like this:

1) Rule of thumb for 14.6 GB RAM

Pick something small:

• usually ~3B–8B at Q4 quantization
• aim for an ollama list SIZE <= ~6–8 GB (Windows + background stuff will eat the rest)
• bigger models might start, but you’ll OOM once context/KV cache grows

2) Let OpenClaw tell you if a model supports tools (fast test)

Try your other installed model first:

openclaw config set agents.defaults.model.primary "ollama/lfm2.5-thinking:latest"
openclaw gateway restart
openclaw agent --session-id test --message "hello"

• If it works → you’re done.
• If you get the same kind of error (“does not support tools”) → that model also can’t be used for OpenClaw.

3) Check tool support via Ollama directly (optional, but useful)

Ollama exposes tool capability via /api/show (what OpenClaw uses under the hood). Run:

curl http://127.0.0.1:11434/api/show -d '{"name":"MODELNAME"}'

Look in the JSON for something indicating tools capability (word “tools” / “capabilities” containing tools).

4) Best next step: ask for a current “tool-capable + 16GB” pick in #models

That channel has up-to-date “this works today in Ollama” answers. (Tool support is the gotcha, not OpenClaw.)

Once you find one model name that supports tools, paste it here and I’ll tell you the exact OpenClaw config line(s) to switch over cleanly.

[sly ginkgo]

this is my config now.
"meta": {
"lastTouchedVersion": "2026.2.3-1",
"lastTouchedAt": "2026-02-06T19:21:38.654Z"
},
"wizard": {
"lastRunAt": "2026-02-06T19:21:38.628Z",
"lastRunVersion": "2026.2.3-1",
"lastRunCommand": "onboard",
"lastRunMode": "local"
},
"models": {
"providers": {
"ollama": {
"baseUrl": "http://127.0.0.1:11434/v1",
"apiKey": "ollama-local",
"api": "openai-completions",
"models": [
{
"id": "lfm2.5-thinking:1.2b",
"name": "lfm2.5-thinking:1.2b",
"reasoning": false,
"input": [
"text"
],
"cost": {
"input": 0,
"output": 0,
"cacheRead": 0,
"cacheWrite": 0
},
"contextWindow": 128000,
"maxTokens": 8192
}
]
}
}
},
"agents": {
"defaults": {
"model": {
"primary": "ollama/lfm2.5-thinking:1.2b"
},
"models": {
"ollama": {},
"ollama/lfm2.5-thinking:1.2b": {}
},
"maxConcurrent": 4,
"subagents": {
"maxConcurrent": 8
},
"workspace": "/home/dan/.openclaw/workspace"
}
},
"messages": {
"ackReactionScope": "group-mentions"
},
"commands": {
"native": "auto",
"nativeSkills": "auto"
},
"gateway": {
"port": 18789,
"mode": "local",
"bind": "loopback",
"auth": {
"mode": "token",
"token":"
},
"tailscale": {
"mode": "off",
"resetOnExit": false
}
},
"skills": {
"install": {
"nodeManager": "npm"
}
}
}

[nova patio]

Two things jump out in that config:

1. Your model name doesn’t match what you actually have installed in Ollama
   Earlier your ollama list showed:

• lfm2.5-thinking:latest

…but your config references:

• lfm2.5-thinking:1.2b (and ollama/lfm2.5-thinking:1.2b)

If Ollama doesn’t have that exact tag, OpenClaw will fail or behave weirdly.

Fix (pick one):

• Change config to use the tag you do have:

openclaw config set agents.defaults.model.primary "ollama/lfm2.5-thinking:latest"

and also update the provider model id to lfm2.5-thinking:latest if you’re keeping an explicit models list.

• OR actually pull the exact tag you configured:

ollama pull lfm2.5-thinking:1.2b

2. Your pasted JSON looks truncated / invalid
   This part is broken in the snippet (probably redaction/copy-paste, but double-check the real file):

"token":"

If the actual config file is malformed JSON/JSON5, the gateway may ignore it / fall back / error on startup.

---

Now re-test tool support (this is the real gate)

Run:

openclaw agent --session-id test --message "hello"

If you still get a “does not support tools” error, then lfm2.5-thinking also isn’t tool-capable in your Ollama build, and you’ll need a different Ollama model (or use auto-discovery which filters to tool-capable models):

Docs: https://docs.openclaw.ai/providers/ollama

If you paste the exact output of that openclaw agent ... command (and/or the relevant openclaw logs --follow lines right after), I can tell you the next move immediately.

[sly ginkgo]

dan@dan-Lenovo-B40-45:~$ ollama list
NAME ID SIZE MODIFIED
lfm2.5-thinking:1.2b 95bd9d45385f 731 MB 16 minutes ago
deepseek-r1:14b c333b7232bdb 9.0 GB 5 hours ago
lfm2.5-thinking:latest 95bd9d45385f 731 MB 21 hours ago

[nova patio]

Perfect — so lfm2.5-thinking:1.2b is installed (and :latest points to the same model ID).

Next step is simply to see whether it supports tools (because OpenClaw always sends tools):

1. Restart OpenClaw gateway (so it definitely picks up the config change):

openclaw gateway restart

2. Run a test agent turn:

openclaw agent --session-id test --message "hello"

3. While that runs, check if Ollama loaded it:

ollama ps

What we expect

• If it works, you’ll see lfm2.5-thinking:1.2b in ollama ps and OpenClaw will reply normally.
• If it fails with something like:
  400 ... does not support tools
  then that model also can’t be used for OpenClaw (same issue as deepseek-r1).

If you paste the output of the openclaw agent ... run (just the error line), I’ll tell you the exact fix path. If it’s the tools error again, the cleanest solution is using Ollama auto-discovery (OpenClaw filters to tool-capable models automatically): https://docs.openclaw.ai/providers/ollama

[sly ginkgo]

it just seems to be hanging on waiting for agent reply

[nova patio]

If it’s sitting on “waiting for agent reply”, the gateway accepted the request — it’s either:

1. actually running the model call (slow / stuck), or
2. waiting on some internal step (tool/approval/streaming parse issue).

Let’s narrow it down in 30 seconds:

A) While it’s “hanging”, does Ollama show the model running?

In another terminal:

ollama ps

• If you see lfm2.5-thinking:1.2b there → OpenClaw did hit Ollama, and we’re waiting on the response.
• If you don’t → OpenClaw isn’t reaching Ollama (then logs will say why).

B) Get the one thing that matters: live logs while you reproduce

Terminal 1:

openclaw logs --follow

Terminal 2 (force a short, deterministic prompt + short timeout):

openclaw agent --session-id test --message "Reply with exactly: ok" --timeout 60

Then paste the last ~30 lines from the log terminal right after you run it (wrap in a code block if you can).

C) Quick sanity: confirm what model OpenClaw thinks it’s using

openclaw models status --plain

Once I see (1) whether ollama ps shows the model and (2) what the logs say, we’ll know if this is just first-load latency vs a tool-calling/streaming incompat with that specific Ollama model.

[sly ginkgo]

openclaw models status --plain

🦞 OpenClaw 2026.2.3-1 (d84eb46)
I read logs so you can keep pretending you don't have to.

ollama/lfm2.5-thinking:1.2b give me a sec on the other log wait for me to give it you

[nova patio]

👍 Cool — models status confirms OpenClaw is actually targeting ollama/lfm2.5-thinking:1.2b.

When you grab the logs, the most useful thing is:

1. Start log tail:

openclaw logs --follow

2. In another terminal run a short test (so we don’t wait forever):

openclaw agent --session-id test --message "Reply with exactly: ok" --timeout 60

3. While it’s “waiting for agent reply”, also run:

ollama ps

Then paste:

• the ollama ps output (during the hang)
• the last ~30 lines from openclaw logs --follow right after the timeout/hang

(Feel free to redact tokens/IDs; don’t redact the actual error text.)

[sly ginkgo]

openclaw agent --session-id test --message "Reply with exactly: ok" --timeout 60

🦞 OpenClaw 2026.2.3-1 (d84eb46)
If it works, it's automation; if it breaks, it's a "learning opportunity."

│
◇
Gateway agent failed; falling back to embedded: Error: gateway timeout after 90000ms
Gateway target: ws://127.0.0.1:18789
Source: local loopback
Config: /home/dan/.openclaw/openclaw.json
Bind: loopback
19:41:15 [diagnostic] lane task error: lane=main durationMs=11358 error="Error: session file locked (timeout 10000ms): pid=10480 /home/dan/.openclaw/agents/main/sessions/test.jsonl.lock"
19:41:15 [diagnostic] lane task error: lane=session:test durationMs=11373 error="Error: session file locked (timeout 10000ms): pid=10480 /home/dan/.openclaw/agents/main/sessions/test.jsonl.lock"
FailoverError: session file locked (timeout 10000ms): pid=10480 /home/dan/.openclaw/agents/main/sessions/test.jsonl.lock

19:40:22 info ollama/lfm2.5-thinking:1.2b
19:41:02 error Gateway agent failed; falling back to embedded: Error: gateway timeout after 90000ms
Gateway target: ws://127.0.0.1:18789
Source: local loopback
Config: /home/dan/.openclaw/openclaw.json
Bind: loopback
19:41:03 debug diagnostic {"subsystem":"diagnostic"} lane enqueue: lane=session:test queueSize=1
19:41:03 debug diagnostic {"subsystem":"diagnostic"} lane dequeue: lane=session:test waitMs=32 queueSize=0
19:41:03 debug diagnostic {"subsystem":"diagnostic"} lane enqueue: lane=main queueSize=1
19:41:03 debug diagnostic {"subsystem":"diagnostic"} lane dequeue: lane=main waitMs=4 queueSize=0
19:41:04 debug agent/embedded {"subsystem":"agent/embedded"} embedded run start: runId=test sessionId=test provider=ollama model=lfm2.5-thinking:1.2b thinking=off messageChannel=unknown
19:41:15 error diagnostic {"subsystem":"diagnostic"} lane task error: lane=main durationMs=11358 error="Error: session file locked (timeout 10000ms): pid=10480 /home/dan/.openclaw/agents/main/sessions/test.jsonl.lock"
19:41:15 error diagnostic {"subsystem":"diagnostic"} lane task error: lane=session:test durationMs=11373 error="Error: session file locked (timeout 10000ms): pid=10480 /home/dan/.openclaw/agents/main/sessions/test.jsonl.lock"
19:41:15 error FailoverError: session file locked (timeout 10000ms): pid=10480 /home/dan/.openclaw/agents/main/sessions/test.jsonl.lock

[nova patio]

That hang isn’t Ollama/model-related — it’s your session storage lock.

This line is the culprit:

session file locked (timeout 10000ms): pid=10480 ... /sessions/test.jsonl.lock

Meaning: some OpenClaw process with PID 10480 is holding the lock for the session id test, so the gateway can’t write to that session file → request never completes → you hit the gateway timeout.

Fix it (safe + quick)

1. See what PID 10480 is

ps -p 10480 -o pid,cmd

• If it’s an openclaw/node process that’s stuck, stop it (or reboot as a blunt fix).
• If ps shows no such process, the lock file is stale.

2. Make sure you’re not running 2 gateways

openclaw gateway status

3. If the PID is gone, delete the stale lock

rm -f /home/dan/.openclaw/agents/main/sessions/test.jsonl.lock

4. Restart the gateway

openclaw gateway restart

5. Don’t reuse --session-id test while debugging
   Use a new one each run:

openclaw agent --session-id test2 --message "Reply with exactly: ok" --timeout 60

If you paste the output of ps -p 10480 -o pid,cmd + openclaw gateway status, I can tell you exactly whether it’s a stuck process vs stale lock vs two gateways fighting.

Those logs confirm it: you’re not “hanging on Ollama” — you’re deadlocked on a stuck session lock file:

session file locked ... pid=10480 ... /sessions/test.jsonl.lock

So the gateway (and then the embedded fallback) can’t write to the session transcript → it never completes → you hit the 90s gateway timeout.

Fix (in order)

1. See what PID 10480 is

ps -p 10480 -o pid,cmd

2. If it’s an openclaw/node process, stop it
   (Do this first if it exists)

openclaw gateway stop
# if that doesn’t stop it, then:
kill -TERM 10480

3. If PID 10480 does NOT exist anymore, it’s a stale lock → delete the lock file

rm -f /home/dan/.openclaw/agents/main/sessions/test.jsonl.lock

4. Restart the gateway

openclaw gateway restart

5. Re-test but use a fresh session id

(just to avoid any leftover “test” state while debugging)

openclaw agent --session-id test2 --message "Reply with exactly: ok" --timeout 60

If it still locks again immediately, paste:

• output of ps -p 10480 -o pid,cmd
• openclaw gateway status

That’ll tell us if you’ve got two gateways running or one stuck process constantly holding the session file.

[sly ginkgo]

ps -p 10480 -o pid,cmd
PID CMD
10480 openclaw-gateway 🦞 OpenClaw 2026.2.3-1 (d84eb46)
Pairing codes exist because even bots believe in consent—and good security hygiene.

│
◇
Service: systemd (enabled)
File logs: /tmp/openclaw/openclaw-2026-02-06.log
Command: /usr/bin/node /home/dan/.npm-global/lib/node_modules/openclaw/dist/index.js gateway --port 18789
Service file: ~/.config/systemd/user/openclaw-gateway.service
Service env: OPENCLAW_GATEWAY_PORT=18789

Config (cli): ~/.openclaw/openclaw.json
Config (service): ~/.openclaw/openclaw.json

Gateway: bind=loopback (127.0.0.1), port=18789 (service args)
Probe target: ws://127.0.0.1:18789
Dashboard: http://127.0.0.1:18789/
Probe note: Loopback-only gateway; only local clients can connect.

Runtime: running (pid 10480, state active, sub running, last exit 0, reason 0)
RPC probe: ok

Listening: 127.0.0.1:18789
Troubles: run openclaw status
Troubleshooting: https://docs.openclaw.ai/troubleshooting

[nova patio]

Nice, that confirms the lock holder: PID 10480 is your running openclaw-gateway.

So what happened is: the gateway grabbed the session lock for test.jsonl at some point and never released it (or there’s still an in-flight run on that same session). Any new request to the same session will sit until the lock timeout, then fail.

Fix: restart the gateway + clear the stale session lock

Do this:

openclaw gateway stop
ps -p 10480 # should show nothing now
rm -f /home/dan/.openclaw/agents/main/sessions/test.jsonl.lock
openclaw gateway start

(Equivalent systemd way is fine too: systemctl --user restart openclaw-gateway.)

Re-test (use a NEW session id)

Don’t reuse test right away:

openclaw agent --session-id test2 --message "Reply with exactly: ok" --timeout 60

If the lock comes back again

Then something is keeping a run “stuck” inside the gateway. Paste:

• openclaw logs --follow (the lines around when the lock file is created)
• ls -la /home/dan/.openclaw/agents/main/sessions/ | tail -n 20

and we’ll see if it’s a repeated lock bug vs a runaway concurrent run.

[sly ginkgo]

openclaw agent --session-id test2 --message "Reply with exactly: ok" --timeout 60

🦞 OpenClaw 2026.2.3-1 (d84eb46)
Welcome to the command line: where dreams compile and confidence segfaults.

│
◇
completed

9:47:43 debug agent/embedded {"subsystem":"agent/embedded"} embedded run prompt start: runId=498677c1-1115-456f-a395-8092436a6184 sessionId=test2
19:47:43 debug agent/embedded {"subsystem":"agent/embedded"} embedded run agent start: runId=498677c1-1115-456f-a395-8092436a6184
19:48:14 debug plugins {"subsystem":"plugins"} plugin CLI register skipped (memory-core): command already registered (memory)
19:48:43 warn agent/embedded {"subsystem":"agent/embedded"} embedded run timeout: runId=498677c1-1115-456f-a395-8092436a6184 sessionId=test2 timeoutMs=60000
19:48:43 debug agent/embedded {"subsystem":"agent/embedded"} embedded run prompt end: runId=498677c1-1115-456f-a395-8092436a6184 sessionId=test2 durationMs=60016
19:48:43 debug diagnostic {"subsystem":"diagnostic"} session state: sessionId=test2 sessionKey=unknown prev=processing new=idle reason="run_completed" queueDepth=0
19:48:43 debug diagnostic {"subsystem":"diagnostic"} run cleared: sessionId=test2 totalActive=0
19:48:43 debug agent/embedded {"subsystem":"agent/embedded"} embedded run done: runId=498677c1-1115-456f-a395-8092436a6184 sessionId=test2 durationMs=60473 aborted=true
19:48:43 debug diagnostic {"subsystem":"diagnostic"} lane task done: lane=main durationMs=60480 active=0 queued=0
19:48:43 debug diagnostic {"subsystem":"diagnostic"} lane task done: lane=session:test2 durationMs=60494 active=0 queued=0
19:48:43 info No reply from agent.
19:48:43 info complete

9:51:10 info gateway/ws {"subsystem":"gateway/ws"} webchat connected conn=1dc952d5-fb4c-4848-90b9-ea593b30c86f remote=127.0.0.1 client=openclaw-control-ui webchat vdev
19:51:13 info gateway/ws {"subsystem":"gateway/ws"} ⇄ res ✓ chat.send 63ms runId=fc39b368-78cf-4574-9da9-1751fb0e973f conn=1dc952d5…c86f id=bd83d3e8…bba2
19:51:13 debug diagnostic {"subsystem":"diagnostic"} lane enqueue: lane=session:agent:main:main queueSize=1
19:51:13 debug diagnostic {"subsystem":"diagnostic"} lane dequeue: lane=session:agent:main:main waitMs=25 queueSize=0
19:51:13 debug diagnostic {"subsystem":"diagnostic"} lane enqueue: lane=main queueSize=1
19:51:13 debug diagnostic {"subsystem":"diagnostic"} lane dequeue: lane=main waitMs=26 queueSize=0
19:51:14 debug agent/embedded {"subsystem":"agent/embedded"} embedded run start: runId=fc39b368-78cf-4574-9da9-1751fb0e973f sessionId=aa844741-4abd-4700-ab31-a0003f594325 provider=ollama model=lfm2.5-thinking:1.2b thinking=off messageChannel=webchat
19:51:14 debug diagnostic {"subsystem":"diagnostic"} session state: sessionId=aa844741-4abd-4700-ab31-a0003f594325 sessionKey=unknown prev=idle new=processing reason="run_started" queueDepth=0
19:51:14 debug diagnostic {"subsystem":"diagnostic"} run registered: sessionId=aa844741-4abd-4700-ab31-a0003f594325 totalActive=1
19:51:14 debug agent/embedded {"subsystem":"agent/embedded"} embedded run prompt start: runId=fc39b368-78cf-4574-9da9-1751fb0e973f sessionId=aa844741-4abd-4700-ab31-a0003f594325
19:51:14 warn agent/embedded {"subsystem":"agent/embedded"} Removed orphaned user message to prevent consecutive user turns. runId=fc39b368-78cf-4574-9da9-1751fb0e973f sessionId=aa844741-4abd-4700-ab31-a0003f594325
19:51:14 debug agent/embedded {"subsystem":"agent/embedded"} embedded run agent start: runId=fc39b368-78cf-4574-9da9-1751fb0e973f

im trying the web interface again

the web interface is just hanging

Krill?

total 96
drwxrwxr-x 2 dan dan 4096 Feb 6 19:51 .
drwxrwxr-x 4 dan dan 4096 Feb 5 23:01 ..
-rw-rw-r-- 1 dan dan 10890 Feb 6 19:00 01c5eae7-b5b4-4dc7-b57f-444b60e2940f.jsonl
-rw-rw-r-- 1 dan dan 15467 Feb 6 18:07 52c066e5-e463-4830-8ad3-358a7cd7d6e8.jsonl
-rw-rw-r-- 1 dan dan 4118 Feb 6 19:51 aa844741-4abd-4700-ab31-a0003f594325.jsonl
-rw-rw-r-- 1 dan dan 61 Feb 6 19:51 aa844741-4abd-4700-ab31-a0003f594325.jsonl.lock
-rw-rw-r-- 1 dan dan 24196 Feb 6 15:51 f6e5e683-8e8e-432e-bb0d-29f04c45d932.jsonl
-rw-rw-r-- 1 dan dan 1776 Feb 6 15:52 fce18c17-415a-42b4-bedc-633699bd62c7.jsonl
-rw------- 1 dan dan 10004 Feb 6 19:51 sessions.json
-rw-rw-r-- 1 dan dan 2040 Feb 6 19:39 test.jsonl

19:57:48 debug agent/embedded {"subsystem":"agent/embedded"} embedded run prompt end: runId=fc39b368-78cf-4574-9da9-1751fb0e973f sessionId=aa844741-4abd-4700-ab31-a0003f594325 durationMs=393497
19:57:48 debug diagnostic {"subsystem":"diagnostic"} session state: sessionId=aa844741-4abd-4700-ab31-a0003f594325 sessionKey=unknown prev=processing new=idle reason="run_completed" queueDepth=0
19:57:48 debug diagnostic {"subsystem":"diagnostic"} run cleared: sessionId=aa844741-4abd-4700-ab31-a0003f594325 totalActive=0
19:57:48 debug agent/embedded {"subsystem":"agent/embedded"} embedded run done: runId=fc39b368-78cf-4574-9da9-1751fb0e973f sessionId=aa844741-4abd-4700-ab31-a0003f594325 durationMs=394453 aborted=true
19:57:48 debug diagnostic {"subsystem":"diagnostic"} lane task done: lane=main durationMs=394470 active=0 queued=0
19:57:48 debug diagnostic {"subsystem":"diagnostic"} lane task done: lane=session:agent:main:main durationMs=394542 active=0 queued=0
19:57:50 debug diagnostic {"subsystem":"diagnostic"} lane enqueue: lane=session:agent:main:main queueSize=1

19:57:50 debug diagnostic {"subsystem":"diagnostic"} lane dequeue: lane=session:agent:main:main waitMs=7 queueSize=0
19:57:50 debug diagnostic {"subsystem":"diagnostic"} lane enqueue: lane=main queueSize=1
19:57:50 debug diagnostic {"subsystem":"diagnostic"} lane dequeue: lane=main waitMs=6 queueSize=0
19:57:50 debug agent/embedded {"subsystem":"agent/embedded"} embedded run start: runId=1823272c-de24-44b0-be6f-8656a4350578 sessionId=aa844741-4abd-4700-ab31-a0003f594325 provider=ollama model=lfm2.5-thinking:1.2b thinking=off messageChannel=webchat
19:57:50 debug diagnostic {"subsystem":"diagnostic"} session state: sessionId=aa844741-4abd-4700-ab31-a0003f594325 sessionKey=unknown prev=idle new=processing reason="run_started" queueDepth=0
19:57:50 debug diagnostic {"subsystem":"diagnostic"} run registered: sessionId=aa844741-4abd-4700-ab31-a0003f594325 totalActive=1
19:57:50 debug agent/embedded {"subsystem":"agent/embedded"} embedded run prompt start: runId=1823272c-de24-44b0-be6f-8656a4350578 sessionId=aa844741-4abd-4700-ab31-a0003f594325
19:57:50 warn agent/embedded {"subsystem":"agent/embedded"} Removed orphaned user message to prevent consecutive user turns. runId=1823272c-de24-44b0-be6f-8656a4350578 sessionId=aa844741-4abd-4700-ab31-a0003f594325
19:57:50 debug agent/embedded {"subsystem":"agent/embedded"} embedded run agent start: runId=1823272c-de24-44b0-be6f-8656a4350578

[nova patio]

Hell yes — that means the whole pipeline is working now (gateway ⇄ Ollama ⇄ session write). The “hang” was just that stale/stuck lock on the test session.

A couple quick takeaways so it doesn’t bite you again:

• Avoid reusing the same --session-id while debugging. If something gets wedged, a new session id sidesteps it.
• If you ever see *.jsonl.lock timeouts again, the fix is the same: restart gateway + remove the stale lock file (only after stopping the process).

Now you should be able to open the dashboard (http://127.0.0.1:18789/) and actually chat normally.

If you want, run one more “real” test:

openclaw agent --session-id test3 --message "Give me 3 bullet points about the moon."

If everything is good now, please mark the solution in Discord (right-click the message → Apps → ✅ Mark Solution) so it gets indexed.

[nova patio]

What you’re seeing now is not a lock problem anymore — it’s the model run timing out / producing no usable reply, so the web UI just sits there waiting.

Key lines from what you pasted:

• embedded run start ... model=lfm2.5-thinking:1.2b
• then nothing for ages
• later: embedded run done ... durationMs=394453 aborted=true
• and in the CLI run you had: embedded run timeout ... timeoutMs=60000 + No reply from agent.

So OpenClaw is calling Ollama, but the request either:

1. takes too long on your machine, or
2. returns output OpenClaw can’t use (e.g. “thinking only”, malformed tool output, etc.)

1) Check if Ollama is actually busy during the hang

While the web UI is “hanging”, run:

ollama ps

If it shows the model loaded and crunching → it’s just slow / stuck in generation.

2) Isolate “Ollama OpenAI API” vs “OpenClaw”

Run this (this is basically what OpenClaw is relying on):

curl http://127.0.0.1:11434/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model":"lfm2.5-thinking:1.2b",
"messages":[{"role":"user","content":"Reply with exactly: ok"}],
"max_tokens":16,
"stream":false
}'

• If this hangs / is super slow, it’s an Ollama+model performance/compat issue (not OpenClaw).
• If this returns instantly, but OpenClaw still hangs, then we dig into OpenClaw parsing/tooling.

3) Give local models more time (web UI + agent runs)

Your webchat run is getting aborted after ~6–7 minutes. For slow CPUs, bump the agent timeout:

openclaw config set agents.defaults.timeoutSeconds 1800
openclaw gateway restart

(Then retry the web UI.)

4) The “Removed orphaned user message…” warning

That’s fallout from earlier runs timing out (you end up with consecutive user messages and the gateway cleans it up). Once you get a model that reliably responds, that warning usually disappears. You can also start a fresh chat session in the UI to clear the messy state.

If you paste:

• output of the curl /v1/chat/completions test above (even if it’s slow)
• plus ollama ps while it’s hanging

…I can tell you immediately whether you should (a) just increase timeouts, or (b) switch models because this one isn’t behaving with the OpenAI-compatible endpoint/tooling.

You’ve got two separate issues in those logs:

1) Web UI “hang” = the model run is timing out / getting aborted

This line is the giveaway:

• embedded run done ... durationMs=394453 aborted=true

So the gateway did start the run, but it never got a usable final answer back before it was aborted (either timeout or the request got canceled).

Also this explains the warning:

• Removed orphaned user message...
  That happens when you hit send again while the previous run never completed cleanly.

Quick checks

While the web UI is “hanging”, run:

ollama ps

If you see the model running, it’s genuinely still generating (slow).

2) Your CLI “completed” doesn’t mean you got a reply

In your earlier log you also had:

• embedded run timeout ... timeoutMs=60000
• No reply from agent.
• then CLI prints completed

So “completed” here is basically “the command finished”, not “the model answered”.

To see the actual result, run:

openclaw agent --session-id test2 --message "Reply with exactly: ok" --timeout 60 --json

and you’ll see whether there’s any assistant text coming back.

---

Fixes to try (in order)

A) Give slow local models more time

Bump the agent timeout and restart:

openclaw config set agents.defaults.timeoutSeconds 1800
openclaw gateway restart

B) Reduce how long the model is allowed to ramble

Right now you’ve got maxTokens: 8192 for that tiny model. If it “thinks forever”, it can just chew CPU for ages.

Try something like 256–512 for now:

openclaw config set models.providers.ollama.models[0].maxTokens 512 --json
openclaw gateway restart

C) Sanity-check Ollama’s OpenAI endpoint directly

This tells us if it’s an Ollama/model hang or an OpenClaw parsing/agent thing:

curl http://127.0.0.1:11434/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model":"lfm2.5-thinking:1.2b",
"messages":[{"role":"user","content":"Reply with exactly: ok"}],
"max_tokens":16,
"stream":false
}'

If that is slow/hangs too → it’s the model/runtime. If that returns fast → we focus on OpenClaw behavior.

---

If you paste:

1. ollama ps while the web UI is hanging, and
2. the output of that curl test (or say “it hangs”)

…I can tell you exactly whether to keep tuning timeouts/token limits or whether this specific model is just a bad fit for tool-calling agent use.

[sly ginkgo]

20:05:44 debug diagnostic {"subsystem":"diagnostic"} lane enqueue: lane=session:agent:main:main queueSize=1
20:05:44 debug diagnostic {"subsystem":"diagnostic"} lane dequeue: lane=session:agent:main:main waitMs=82 queueSize=0
20:05:44 debug diagnostic {"subsystem":"diagnostic"} lane enqueue: lane=main queueSize=1
20:05:44 debug diagnostic {"subsystem":"diagnostic"} lane dequeue: lane=main waitMs=7 queueSize=0
20:05:44 debug agent/embedded {"subsystem":"agent/embedded"} embedded run start: runId=07e1200f-f56c-4dd3-a17d-0ebc38b8abf4 sessionId=aa844741-4abd-4700-ab31-a0003f594325 provider=ollama model=lfm2.5-thinking:1.2b thinking=off messageChannel=webchat
20:05:45 debug diagnostic {"subsystem":"diagnostic"} session state: sessionId=aa844741-4abd-4700-ab31-a0003f594325 sessionKey=unknown prev=idle new=processing reason="run_started" queueDepth=0
20:05:45 debug diagnostic {"subsystem":"diagnostic"} run registered: sessionId=aa844741-4abd-4700-ab31-a0003f594325 totalActive=1
20:05:45 debug agent/embedded {"subsystem":"agent/embedded"} embedded run prompt start: runId=07e1200f-f56c-4dd3-a17d-0ebc38b8abf4 sessionId=aa844741-4abd-4700-ab31-a0003f594325
20:05:45 warn agent/embedded {"subsystem":"agent/embedded"} Removed orphaned user message to prevent consecutive user turns. runId=07e1200f-f56c-4dd3-a17d-0ebc38b8abf4 sessionId=aa844741-4abd-4700-ab31-a0003f594325
20:05:45 debug agent/embedded {"subsystem":"agent/embedded"} embedded run agent start: runId=07e1200f-f56c-4dd3-a17d-0ebc38b8abf4

20:06:43 info gateway/ws {"subsystem":"gateway/ws"} ⇄ res ✓ logs.tail 98ms conn=f764bb8e…aa31 id=229c2cbc…01fe
20:06:46 info gateway/ws {"subsystem":"gateway/ws"} ⇄ res ✓ logs.tail 180ms conn=0910a652…727d id=6516ccd4…74c9

20:08:36 info gateway/ws {"subsystem":"gateway/ws"} ⇄ res ✓ node.list 80ms conn=129b7014…cc2e id=a1b19589…8eb1

[nova patio]

Those lines show the run is starting fine (embedded run agent start…) but then there’s no “prompt end / run done” afterwards — that’s why the web UI looks like it’s hanging: it’s waiting for the model to finish.

With a tiny local model like lfm2.5-thinking:1.2b, the most common cause is: the model is getting overwhelmed by OpenClaw’s tool schema + prompt, and it either takes forever or never produces a clean final answer.

Quick fix to make WebChat responsive (shrink tool schema + cap output)

Try these config tweaks:

openclaw config set agents.defaults.timeoutSeconds 900 --json
openclaw config set 'agents.defaults.models["ollama/lfm2.5-thinking:1.2b"].params.maxTokens' 256 --json

# For Ollama specifically: restrict tools so the prompt isn’t huge
openclaw config set tools.byProvider.ollama.profile "minimal"
openclaw gateway restart

Then in the web UI, start a fresh session (or type /reset) and try: “Reply with exactly: ok”.

Sanity-check whether it’s Ollama itself hanging (super important)

Run this directly (no OpenClaw involved):

curl http://127.0.0.1:11434/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model":"lfm2.5-thinking:1.2b",
"messages":[{"role":"user","content":"Reply with exactly: ok"}],
"max_tokens":16,
"stream":false
}'

• If this also hangs/slow → it’s the model/runtime side.
• If this is fast but OpenClaw still hangs → we’ll tune OpenClaw tool policy/profile next (or switch to auto-discovery so it picks a tool-capable model that behaves better).

Paste either the curl output or just “curl returns fast / curl hangs” and we’ll do the next adjustment.

[nova patio]

Those ⇄ res ✓ logs.tail ... lines are good news but they’re not the failure — that’s just the Control UI polling the gateway for logs (it proves the UI is connected and the gateway is responsive).

The actual problem is still: the Ollama run starts, but never produces a usable “assistant reply” before it gets aborted/times out, so the web UI sits there waiting.

1) First: confirm whether Ollama itself is hanging on the OpenAI endpoint

Run this and tell me if it returns quickly or hangs:

time curl http://127.0.0.1:11434/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model":"lfm2.5-thinking:1.2b",
"messages":[{"role":"user","content":"Reply with exactly: ok"}],
"max_tokens":16,
"stream":false
}'

• If this hangs/slow → it’s the model/runtime side (Ollama + that model via OpenAI API).
• If this is fast → OpenClaw’s prompt/tooling is what’s tipping it over.

2) If curl is fast: shrink OpenClaw’s tool schema for Ollama + cap output

This makes “webchat” much less heavy:

openclaw config set tools.byProvider.ollama.profile "minimal"
openclaw config set 'agents.defaults.models["ollama/lfm2.5-thinking:1.2b"].params.maxTokens' 256 --json
openclaw gateway restart

Then refresh the web UI and try again.

3) If the web UI is “hanging”, check if the model is actually running

While it’s hanging:

ollama ps

If ollama ps shows nothing, OpenClaw is stuck before/at the HTTP call. If it shows the model, it’s genuinely still generating.

Reply with: (a) “curl fast” or “curl hangs” + (b) the ollama ps output during a hang, and we’ll know the next move immediately.

⇄ res ✓ node.list is the same kind of “UI is alive and the gateway answered” signal as logs.tail — it’s not an error, just the Control UI calling the gateway.

So we’re still missing the one datapoint that tells us why the chat run hangs:

Please run these two and tell me what happens

1) Does Ollama’s OpenAI endpoint return quickly?

time curl http://127.0.0.1:11434/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model":"lfm2.5-thinking:1.2b",
"messages":[{"role":"user","content":"Reply with exactly: ok"}],
"max_tokens":16,
"stream":false
}'

2) While the web UI is “hanging”, what does Ollama say?

ollama ps

Meanwhile, one change that usually helps tiny local models

Cap OpenClaw output tokens (prevents endless thinking / long gen):

openclaw config set 'agents.defaults.models["ollama/lfm2.5-thinking:1.2b"].params.maxTokens' 256 --json
openclaw gateway restart

Once you answer “curl fast / curl hangs” + paste ollama ps, we’ll know whether to keep tuning OpenClaw’s tool/prompt size or whether this model just doesn’t behave well with Ollama’s /v1 API.

[sly ginkgo]

time curl http://127.0.0.1:11434/v1/chat/completions
-H "Content-Type: application/json"
-d '{
"model":"lfm2.5-thinking:1.2b",
"messages":[{"role":"user","content":"Reply with exactly: ok"}],
"max_tokens":16,
"stream":false
}'
{"id":"chatcmpl-118","object":"chat.completion","created":1770409099,"model":"lfm2.5-thinking:1.2b","system_fingerprint":"fp_ollama","choices":[{"index":0,"message":{"role":"assistant","content":"","reasoning":"Okay, let me see what the user is asking here. They"},"finish_reason":"length"}],"usage":{"prompt_tokens":14,"completion_tokens":16,"total_tokens":30}}

real 3m26.962s
user 0m0.020s
sys 0m0.015s

[nova patio]

That curl output explains the whole “web UI hangs” thing.

What it means

1. It’s extremely slow on your hardware

• 16 tokens took 3m27s → ~0.08 tokens/sec.
• OpenClaw’s real prompt is way bigger than your 14‑token curl test (system prompt + tool schema + history), so a single reply can easily take many minutes. The UI looks “stuck” but it’s just waiting.

2. The model is returning output in message.reasoning, with empty message.content

"content": "",
"reasoning": "Okay, let me see..."

Most clients (including OpenClaw in many paths) treat content as the actual reply and ignore reasoning → so you can end up with “No reply from agent” / apparent hangs.

So you’re not misconfigured — this specific model + Ollama’s OpenAI-compat response shape + slowness is the issue.

What to do next (practical)

A) Increase the agent timeout (otherwise it’ll always abort mid-generation):

openclaw config set agents.defaults.timeoutSeconds 1800 --json
openclaw gateway restart

B) Use a model that returns non-empty message.content in that curl test
Keep using this exact test on candidates. If content is empty (and only reasoning fills), it’s a bad fit for OpenClaw right now.

C) Reduce prompt/tool bloat for Ollama (helps tiny models):

openclaw config set tools.byProvider.ollama.profile "minimal"
openclaw gateway restart

(That won’t give you a “full tool-using assistant”, but it’s a good sanity check to get the web UI responsive.)

One important reality check

With speeds like this, even if we make it “work”, it’s going to feel painfully slow as a chat assistant. If you want snappy replies, you’ll either need a faster local setup (GPU / better CPU) or a cloud model.

If you tell me whether any model you have returns a normal content quickly via that curl test, I’ll help you wire that one into OpenClaw cleanly.

[sly ginkgo]

i set timeout to 18000 here log 20:49:17 debug agent/embedded {"subsystem":"agent/embedded"} embedded run agent end: runId=ceead6d7-88e6-4337-b775-6a51820eb47f
20:49:17 debug agent/embedded {"subsystem":"agent/embedded"} embedded run prompt end: runId=ceead6d7-88e6-4337-b775-6a51820eb47f sessionId=aa844741-4abd-4700-ab31-a0003f594325 durationMs=905976
20:49:17 debug diagnostic {"subsystem":"diagnostic"} session state: sessionId=aa844741-4abd-4700-ab31-a0003f594325 sessionKey=unknown prev=processing new=idle reason="run_completed" queueDepth=0
20:49:17 debug diagnostic {"subsystem":"diagnostic"} run cleared: sessionId=aa844741-4abd-4700-ab31-a0003f594325 totalActive=0
20:49:17 debug agent/embedded {"subsystem":"agent/embedded"} embedded run done: runId=ceead6d7-88e6-4337-b775-6a51820eb47f sessionId=aa844741-4abd-4700-ab31-a0003f594325 durationMs=906795 aborted=false
20:49:17 debug diagnostic {"subsystem":"diagnostic"} lane task done: lane=main durationMs=906810 active=0 queued=0
20:49:17 debug diagnostic {"subsystem":"diagnostic"} lane task done: lane=session:agent:main:main durationMs=906838 active=0 queued=0
trying tool call minimal next wait for me to try before answering

[nova patio]

That log is actually good: the run finished successfully (aborted=false). It just took ~906 seconds (~15 minutes):

• durationMs=905976 / 906795

So the “web UI is hanging” is basically: your local model is that slow with an agent-sized prompt (system prompt + tools schema + history).

Also, with “thinking” models, you can end up with mostly reasoning and little/no final content, which can look like “no reply”.

Make WebChat behave better (2 quick tweaks)

1. Show reasoning in the UI (so you see something even if the model sticks it in thinking):

• In WebChat, send:
• /reasoning on
• and also: /think off (optional, but can reduce the endless “thinking” behavior)

Docs: https://docs.openclaw.ai/tools/thinking

2. Shrink the prompt + cap generation for Ollama
   This reduces tool-schema bloat and stops super long rambles:

openclaw config set tools.byProvider.ollama.profile "minimal"
openclaw config set 'agents.defaults.models["ollama/lfm2.5-thinking:1.2b"].params.maxTokens' 256 --json
openclaw gateway restart

Reality check (so expectations match)

Your curl test earlier was 3m27s for 16 tokens. With full OpenClaw prompts, 10–20 minutes per message is unfortunately plausible on that CPU.

If after “minimal tools” it’s still painfully slow, the practical path is either:

• a faster local machine (or GPU box), or
• a cloud model for the main agent and keep Ollama for experiments.

When you try the “minimal tools” run, paste whether WebChat shows an answer bubble (or only a “Reasoning:” bubble). That tells us which side we’re fighting: speed vs “reasoning-only / empty content”.

[sly ginkgo]

20:53:00 debug skills {"subsystem":"skills"} {"rawName":"skill-creator","sanitized":"/skill_creator"} Sanitized skill command name "skill-creator" to "/skill_creator".
20:53:01 debug diagnostic {"subsystem":"diagnostic"} lane enqueue: lane=session:agent:main:main queueSize=1
20:53:01 debug diagnostic {"subsystem":"diagnostic"} lane dequeue: lane=session:agent:main:main waitMs=11 queueSize=0
20:53:01 debug diagnostic {"subsystem":"diagnostic"} lane enqueue: lane=main queueSize=1
20:53:01 debug diagnostic {"subsystem":"diagnostic"} lane dequeue: lane=main waitMs=5 queueSize=0
20:53:01 debug agent/embedded {"subsystem":"agent/embedded"} embedded run start: runId=b81973cc-4329-4664-9a4a-d1505d65fe07 sessionId=7d1db71d-7de7-4d62-86f0-35f358803e14 provider=ollama model=lfm2.5-thinking:1.2b thinking=off messageChannel=webchat
20:53:01 debug diagnostic {"subsystem":"diagnostic"} session state: sessionId=7d1db71d-7de7-4d62-86f0-35f358803e14 sessionKey=unknown prev=idle new=processing reason="run_started" queueDepth=0
20:53:01 debug diagnostic {"subsystem":"diagnostic"} run registered: sessionId=7d1db71d-7de7-4d62-86f0-35f358803e14 totalActive=1
20:53:01 debug agent/embedded {"subsystem":"agent/embedded"} embedded run prompt start: runId=b81973cc-4329-4664-9a4a-d1505d65fe07 sessionId=7d1db71d-7de7-4d62-86f0-35f358803e14
20:53:01 debug agent/embedded {"subsystem":"agent/embedded"} embedded run agent start: runId=b81973cc-4329-4664-9a4a-d1505d65fe07

[nova patio]

That log line about skill-creator being sanitized to /skill_creator is normal — it just means the skill command name got normalized for slash-command use.

But the important part is right after:

• embedded run start ... model=lfm2.5-thinking:1.2b
• prompt start
• agent start

…and then (in what you pasted) there’s no “agent end / run done” yet. So it’s doing the same thing as before: it’s running, it’s just slow.

Two quick notes:

1) Don’t try “skill-creator” on this model

That skill wants the agent to do a lot of structured work and likely call tools. On a model that takes minutes for a 16-token response, it’ll feel totally dead.

2) If you want WebChat to feel responsive, keep the request tiny

Try in WebChat:

• /reasoning on (so you can at least see thinking progress if it happens)
• then ask something extremely small like: Reply with exactly: ok

And in parallel check:

ollama ps

If it’s loaded and chewing CPU, it’s just taking time.

Best practical move

Use a faster model for the main agent (even a small tool-capable one), and keep lfm2.5-thinking:1.2b only for experiments. With your current speeds, WebChat will always look like it’s hanging for anything non-trivial.

If you want, paste ollama ps while it’s “stuck” and I’ll tell you whether it’s actively generating or wedged.

[sly ginkgo]

I think my hardware isn't good enoughits an old laptop lenovo B40-45 with an amd a6

[nova patio]

Yeah — with an AMD A6-era laptop CPU, that curl result you got (3m27s for 16 tokens) is exactly what “not enough compute for local LLMs” looks like. OpenClaw itself is fine; the local model runtime is just crawling.

What you can realistically do

Option A (most practical): use a cloud model for the agent
Keep OpenClaw + Gateway on the laptop, but point the model to a hosted provider (OpenAI/Anthropic/MiniMax/etc). That gives you normal latency and full tool-calling.

Option B: keep Ollama but accept very limited local models
If you insist on local-only:

• stick to tiny models (1–3B) and small contexts
• cap output tokens hard (e.g. 128–256)
• use “minimal tools” so the prompt isn’t enormous

Concretely:

openclaw config set tools.byProvider.ollama.profile "minimal"

openclaw config set agents.defaults.timeoutSeconds 1800 --json
openclaw config set 'agents.defaults.models["ollama/lfm2.5-thinking:1.2b"].params.maxTokens' 128 --json
openclaw gateway restart

But even then: it’ll still feel sluggish.

Option C: move Ollama to a better machine
Run Ollama on a desktop/GPU box and point OpenClaw at it via models.providers.ollama.baseUrl (LAN IP), keeping the gateway on your laptop.

If you tell me your preference (A cloud, B local-only, or C remote Ollama), I’ll give you the exact next steps for that path.

[sly ginkgo]

the thing i when I run the model on its own in terminal on ollama it runs fine and gives me a quick respone so it should work with openclaw?

[nova patio]

Not necessarily. ollama run … being fast doesn’t guarantee OpenClaw will be fast (or even get a reply), because OpenClaw is doing a different kind of request:

Why it’s different

1. OpenClaw uses Ollama’s OpenAI-compatible API (/v1/...)
   Your own test showed the difference clearly:

• ollama run ... → “feels quick”
• curl /v1/chat/completions ... → took 3m26s and returned:
• message.content: "" (empty!)
• message.reasoning: "Okay, let me see..."

OpenClaw generally needs actual content to send back as the assistant message. If the model mostly fills a reasoning field (and leaves content empty), it can look like “hanging” / “no reply”.

2. OpenClaw sends a much bigger prompt
   Even with “thinking off”, OpenClaw includes:

• system prompt (agent instructions)
• tool schemas (even if you don’t end up using tools)

• session history

That can turn a “quick terminal prompt” into a way heavier request.

What to do

A) Make Ollama requests smaller for OpenClaw (strongly recommended):

openclaw config set tools.byProvider.ollama.profile "minimal"
openclaw config set 'agents.defaults.models["ollama/lfm2.5-thinking:1.2b"].params.maxTokens' 128 --json
openclaw config set agents.defaults.timeoutSeconds 1800 --json
openclaw gateway restart

B) In WebChat, enable reasoning so you can see what it’s doing:
Send:

• /reasoning on

(That way, if the model insists on putting everything into “reasoning”, you’ll at least see output.)

C) If /v1/chat/completions keeps returning empty content, switch models
That’s a model/Ollama-compat behavior thing. Best place to get current “works with tools + OpenAI-compat + small RAM” recommendations is #models (it changes fast).

If you paste one more /v1/chat/completions result after you cap maxTokens to 128 (especially whether content is still empty), I can tell you if this model is usable at all with OpenClaw or if it’s a dead end.

[sly ginkgo]

already done A. already done B it shows nothing. C im not sure what model to use a there all a bt big for my computer

[nova patio]

Got it — then you’ve basically hit a model compatibility wall, not a config wall.

Why you’re seeing “nothing” in OpenClaw (even with /reasoning on)

Your own /v1/chat/completions test returned this shape:

• message.content = empty
• message.reasoning = contains the text

OpenClaw (and a lot of OpenAI-compatible clients) primarily display content. If the model puts everything into a nonstandard reasoning field, OpenClaw can end up with “no reply” even though Ollama did generate something.

Also: that same test took 3m26s for 16 tokens, so even when it does work, WebChat will look “hung” for ages.

The practical options

Option 1: Use Ollama models that return actual content via /v1/...

This is the key requirement for OpenClaw.

Do this loop for any candidate model you try:

time curl http://127.0.0.1:11434/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model":"<candidate>",
"messages":[{"role":"user","content":"Reply with exactly: ok"}],
"max_tokens":16,
"stream":false
}'

If content is non-empty and it returns in reasonable time, it’s a viable OpenClaw model.

For “which model”, I don’t want to guess names and waste your time — ask in #models with:

• your RAM (14.6GB)
• CPU (AMD A6)
• requirement: “must work through Ollama /v1/chat/completions and return non-empty content + support tools”

They’ll give you current, known-good picks.

Option 2: Run OpenClaw in “chat-only” mode (disable tools), so tool support stops blocking you

This can let models work that otherwise error on tools. Tradeoff: you lose exec/browser/etc.

In config, set:

{
tools: { deny: ["*"] }
}

Restart gateway. (This is only worth it if you just want a basic chat UI.)

Option 3: Cloud model for the agent (recommended on that laptop)

Tool-calling + decent latency basically requires more compute than that A6 can deliver locally.

---

If you want, paste the output of:

openclaw models list

(after you try auto-discovery or after you pull one more model) and I’ll tell you which entries are most likely to work based on what OpenClaw sees.

[sly ginkgo]

it these a terminal command to set tools to deny im still leraning hw to do things im a bit of a noob

how do I add the tools deny to my config. I have the config open where do I add it how is it ment to look?

[nova patio]

Yep — you can do it from the terminal with openclaw config set.

To deny all tools globally (chat-only mode):

openclaw config set tools.deny '["*"]' --json
openclaw gateway restart

To undo later (go back to normal tools):

openclaw config unset tools.deny
openclaw gateway restart

If you want a “mostly chat” mode but still allow some safe stuff, you can deny only the heavy ones (example):

openclaw config set tools.deny '["browser","canvas","exec","process","nodes","cron"]' --json
openclaw gateway restart

After you do the deny-all, try WebChat again — if it starts responding, that confirms the model was choking on the tool schema / tool-calling expectations.

Add it as a top-level block (same level as "models", "agents", "gateway").

Example (deny all tools):

{
// ... your existing config ...

"tools": {
"deny": ["*"]
}

// ... rest of config ...
}

That’s it.

Then restart the gateway:

openclaw gateway restart

If you prefer “deny only the heavy stuff” instead of everything

{
"tools": {
"deny": ["browser", "canvas", "exec", "process", "nodes", "cron"]
}
}

(Still top-level.)

If you want, paste your config (redact tokens) and I’ll point to the exact spot to insert it so the commas/braces are correct.

[sly ginkgo]

when i try it with ollama launch openclaw i get this in terminal when i send a message in webchat 2026-02-06T21:45:45.425Z [browser/service] Browser control service ready (profiles=2)
2026-02-06T21:45:56.194Z [ws] webchat connected conn=88720e94-e5a9-493a-9a06-38d46012c6de remote=127.0.0.1 client=openclaw-control-ui webchat vdev
2026-02-06T21:45:56.451Z [ws] ⇄ res ✓ node.list 190ms conn=88720e94…c6de id=3c157ffe…0635
2026-02-06T21:45:56.459Z [ws] ⇄ res ✓ device.pair.list 194ms conn=88720e94…c6de id=e399f14b…bdce
2026-02-06T21:45:56.656Z [ws] ⇄ res ✓ chat.history 375ms conn=88720e94…c6de id=283c2ee7…c480
2026-02-06T21:46:05.012Z [agent/embedded] Removed orphaned user message to prevent consecutive user turns. runId=15c29b52-31d9-4aeb-b908-8c94a704fba4 sessionId=7d1db71d-7de7-4d62-86f0-35f358803e14
2026-02-06T21:47:04.199Z [ws] ⇄ res ✓ chat.abort 50ms conn=88720e94…c6de id=12288898…c62a
2026-02-06T21:47:27.245Z [agent/embedded] Removed orphaned user message to prevent consecutive user turns. runId=7ac389d9-3049-4c7c-82ca-1c8b3fd62cd7 sessionId=7d1db71d-7de7-4d62-86f0-35f358803e14

[nova patio]

Those lines are basically “Control UI connected and is talking to the gateway” — nothing there is a failure.

The one line that matters is:

[agent/embedded] Removed orphaned user message to prevent consecutive user turns...

That means: in that WebChat session, OpenClaw already had a previous user message “in flight” (or left behind after an abort/timeout), so when you send another message it drops one to avoid user → user with no assistant reply in between. From your side it looks like “I sent a message and nothing happens”.

Fix (fast)

1. In the WebChat box, send:

• /reset (or /new if you have that set up)

2. Then send one tiny test message and don’t send anything else / don’t hit abort:

• Reply with exactly: ok

Given your earlier benchmark (/v1/chat/completions taking minutes), you may need to wait a while even for a tiny message.

If you want to “unstick” it from the terminal

Restart the gateway (clears any stuck in-flight run state):

openclaw gateway restart

Verify your “deny all tools” actually applied

openclaw config get tools.deny

You should see ["*"].

If after a /reset you still get “nothing” (no reply ever), then it’s back to the model behavior we saw: your model is super slow and sometimes returns empty content via /v1, which can lead to “no visible reply” in OpenClaw. In that case, the real fix is switching to a different Ollama model that returns normal content in the /v1/chat/completions test.

[sly ginkgo]

mistral 8b is what i will try next

[nova patio]

Good plan — and the right way to test it is to make sure it behaves via Ollama’s /v1/chat/completions endpoint (since that’s what OpenClaw uses).

1) Pull it

ollama pull mistral:8b

(If that tag doesn’t exist in your Ollama, ollama list will show the exact name you got.)

2) First do the “does it return real content?” test

time curl http://127.0.0.1:11434/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model":"mistral:8b",
"messages":[{"role":"user","content":"Reply with exactly: ok"}],
"max_tokens":16,

"stream":false
}'

You want to see non-empty:

• choices[0].message.content

If content is empty again (or it’s insanely slow), it’s not going to feel good in OpenClaw.

3) If the curl test looks good, point OpenClaw at it

openclaw config set agents.defaults.model.primary "ollama/mistral:8b"
openclaw gateway restart

4) Then test via OpenClaw

openclaw agent --session-id testm --message "Reply with exactly: ok" --timeout 120

One warning: on that AMD A6 laptop, 8B might be painfully slow (and may push RAM once context grows). But the curl test will tell us quickly if it’s at least producing proper content instead of “reasoning-only”.

[sly ginkgo]

ollama launch openclaw

Launching OpenClaw with ministral-3:3b...
2026-02-06T22:54:51.003Z [canvas] host mounted at http://127.0.0.1:18789/__openclaw__/canvas/ (root /home/dan/.openclaw/canvas)
2026-02-06T22:54:51.345Z [heartbeat] started
2026-02-06T22:54:51.370Z [gateway] agent model: ollama/ministral-3:3b
2026-02-06T22:54:51.378Z [gateway] listening on ws://127.0.0.1:18789 (PID 21062)
2026-02-06T22:54:51.384Z [gateway] listening on ws://[::1]:18789
2026-02-06T22:54:51.406Z [gateway] log file: /tmp/openclaw/openclaw-2026-02-06.log
2026-02-06T22:54:51.453Z [browser/service] Browser control service ready (profiles=2)
2026-02-06T22:54:51.603Z [ws] webchat connected conn=93c19a68-98f4-45f8-a537-c3dbe3750087 remote=127.0.0.1 client=openclaw-control-ui webchat vdev
2026-02-06T22:54:51.746Z [ws] ⇄ res ✓ node.list 67ms conn=93c19a68…0087 id=8644d1c6…0b6d
2026-02-06T22:54:51.755Z [ws] ⇄ res ✓ device.pair.list 78ms conn=93c19a68…0087 id=316dafc1…f75c
2026-02-06T22:54:51.945Z [ws] ⇄ res ✓ chat.history 265ms conn=93c19a68…0087 id=f2008168…758c
2026-02-06T22:55:03.612Z [agent/embedded] Removed orphaned user message to prevent consecutive user turns. runId=ad958217-1f0d-43ca-b186-3ae68ad88d39 sessionId=7d1db71d-7de7-4d62-86f0-35f358803e14

[nova patio]

That line isn’t a crash — it’s a cleanup warning:

Removed orphaned user message to prevent consecutive user turns

It happens when that WebChat session previously got aborted / timed out, leaving an extra user message in the session history. When you send a new message, OpenClaw drops the orphan so the transcript is valid again.

Quick fix (no terminal)

In the WebChat UI, send:

• /reset

Then send one small test like:

• Reply with exactly: ok

(And don’t hit abort while it’s running.)

Hard reset (terminal, wipes just that broken WebChat session history)

This deletes only the session file shown in the log (7d1db71d-...). You’ll lose that session’s chat history, but it will stop the orphan-message loop:

openclaw gateway stop
rm -f ~/.openclaw/agents/main/sessions/7d1db71d-7de7-4d62-86f0-35f358803e14.jsonl*
openclaw gateway start

Also: disable heartbeats on that laptop (they can kick off runs + slow everything)

openclaw config set agents.defaults.heartbeat.every "0m"
openclaw gateway restart

If after /reset you still get “nothing”, paste the next lines after you send a message — specifically whether you see embedded run start and whether there’s a later embedded run done.

[sly ginkgo]

I just dont think its going to work on this laptop

[nova patio]

Yeah — I think that’s the right conclusion for “local Ollama as the agent brain” on that AMD A6 laptop. OpenClaw itself runs fine, but the model side is either:

• too slow (minutes for tiny outputs once you include agent prompts), and/or
• not OpenAI-compat in the way OpenClaw needs (empty content, tool quirks, etc.)

If you still want OpenClaw on this machine, here are the workable setups

1) Cloud model (recommended)
Run the gateway on the laptop, but use a hosted provider for the model. That gives you normal speed + proper tool calling.

2) Remote Ollama
Run Ollama on a stronger machine (desktop/GPU box/server) and point OpenClaw to that baseUrl over LAN/Tailscale.

3) “Chat-only local” (toy mode)
Deny tools and use a tiny local model. This can work for simple chat, but it’s not going to be a real assistant.

If you tell me which of those 3 you want, I’ll give you the exact config snippet + commands to switch over cleanly. And if you go with cloud, I’ll keep it minimal and beginner-friendly.

[sly ginkgo]

3 please

[nova patio]

Cool — “chat‑only local” is doable. Goal: make OpenClaw stop sending the big tool schema, so your tiny local model has a chance.

Step 1) Deny all tools (terminal command)

openclaw config set tools.deny '["*"]' --json
openclaw gateway restart

Step 2) Pick your tiny model as the default

(Use whichever is currently working best for you in ollama run.)

Example for ministral-3:3b:

openclaw config set agents.defaults.model.primary "ollama/ministral-3:3b"
openclaw gateway restart

Step 3) Cap output tokens (prevents long “thinking forever”)

openclaw config set 'agents.defaults.models["ollama/ministral-3:3b"].params.maxTokens' 128 --json
openclaw gateway restart

Step 4) Disable heartbeats (saves CPU / avoids surprise runs)

openclaw config set agents.defaults.heartbeat.every "0m"
openclaw gateway restart

Use it

Open the dashboard and use WebChat. Start with tiny prompts like:

• Reply with exactly: ok
• Say hi in one sentence

Undo later (bring tools back)

openclaw config unset tools.deny
openclaw gateway restart

If after doing Step 1+2 WebChat still “hangs”, run this once and paste the time + whether content is empty:

time curl http://127.0.0.1:11434/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{"model":"ministral-3:3b","messages":[{"role":"user","content":"Reply with exactly: ok"}],"max_tokens":16,"stream":false}'

[sly ginkgo]

im going to delete this chat hread as it wont really help anyone

[nova patio]

Up to you, but it actually will help people — this thread uncovered 3 super common gotchas:

• Ollama model must support tools (or you need to deny tools).
• “Web UI hanging” can be session lock / orphaned user message state.
• Some Ollama /v1/chat/completions responses put output in reasoning / empty content, which makes clients look broken.

If you keep it, it’s a great “why local models feel broken” reference.

If you still want to clean it up for others: maybe just edit the last post with a short summary + the final working commands (deny tools + small model + disable heartbeat).

[sly ginkgo]

is there any information in this thread that exposes my token or anything that can be used against me in any way

[nova patio]

From what you’ve pasted in this thread, I don’t see any usable secret:

• Your gateway.auth.token was shown as REMOVED / blank (not the real value).
• OLLAMA_API_KEY="ollama-local" is a dummy (not a real secret).
• The logs you pasted don’t include a tokenized dashboard URL or API keys.

What is exposed (but not “hack me” level):

• your local username (/home/dan/...)
• your machine model/CPU + RAM
• session IDs / run IDs (not really security-sensitive)
• that the gateway is loopback-only (127.0.0.1), which is good.

If you want to be 100% safe anyway (recommended)

Rotate the gateway token (takes 30 seconds):

NEW_TOKEN="$(openssl rand -hex 32)"
openclaw config set gateway.auth.token "$NEW_TOKEN"
openclaw gateway restart

Then don’t paste the output of openclaw dashboard --no-open publicly (it can include an auth link).

If at any point you pasted the actual token (a long random string) or a dashboard URL containing it, then yes—delete that message and rotate the token as above.

[sly ginkgo]

as far as i know i didnt post the token can you read the chat thread from the start and double check for me please

[nova patio]

I just re-checked the entire thread contents (searched through it for the usual “oops I pasted it” patterns like gateway.auth.token, OPENCLAW_GATEWAY_TOKEN, token, dashboard, and http://127.0.0.1:18789).

I do not see your actual gateway token anywhere.

What I do see:

• Your config paste where the token was REMOVED or blank/truncated ("token":" with nothing after it).
• A plain dashboard URL like http://127.0.0.1:18789/ without any ?token=... / auth query params.

So you’re fine based on what’s in the thread.

If you want “paranoid safe” anyway, rotate it once (even if it wasn’t leaked):

NEW_TOKEN="$(openssl rand -hex 32)"
openclaw config set gateway.auth.token "$NEW_TOKEN"
openclaw gateway restart

And avoid pasting the output of openclaw dashboard --no-open publicly (that one can include an auth link).

[sly ginkgo]

how do i set it as solved