Providers
Inferrs
inferrs can serve local models behind an OpenAI-compatible /v1 API. OpenClaw works with inferrs through the generic openai-completions path.
| Property | Value |
|---|---|
| Provider id | inferrs (custom; configure under models.providers.inferrs) |
| Plugin | none — inferrs is not a bundled OpenClaw provider plugin |
| Auth env var | Optional. Any value works if your inferrs server has no auth |
| API | OpenAI-compatible (openai-completions) |
| Suggested base URL | http://127.0.0.1:8080/v1 (or wherever your inferrs server lives) |
Getting started
Start inferrs with a model
inferrs serve google/gemma-4-E2B-it \
--host 127.0.0.1 \
--port 8080 \
--device metal
Verify the server is reachable
curl http://127.0.0.1:8080/health
curl http://127.0.0.1:8080/v1/models
Add an OpenClaw provider entry
Add an explicit provider entry and point your default model at it. See the full config example below.
Full config example
This example uses Gemma 4 on a local inferrs server.
{
agents: {
defaults: {
model: { primary: "inferrs/google/gemma-4-E2B-it" },
models: {
"inferrs/google/gemma-4-E2B-it": {
alias: "Gemma 4 (inferrs)",
},
},
},
},
models: {
mode: "merge",
providers: {
inferrs: {
baseUrl: "http://127.0.0.1:8080/v1",
apiKey: "inferrs-local",
api: "openai-completions",
models: [
{
id: "google/gemma-4-E2B-it",
name: "Gemma 4 E2B (inferrs)",
reasoning: false,
input: ["text"],
cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0 },
contextWindow: 131072,
maxTokens: 4096,
compat: {
requiresStringContent: true,
},
},
],
},
},
},
}
Advanced configuration
Why requiresStringContent matters
Some inferrs Chat Completions routes accept only string
messages[].content, not structured content-part arrays.
compat: {
requiresStringContent: true
}
OpenClaw will flatten pure text content parts into plain strings before sending the request.
Gemma and tool-schema caveat
Some current inferrs + Gemma combinations accept small direct
/v1/chat/completions requests but still fail on full OpenClaw agent-runtime
turns.
If that happens, try this first:
compat: {
requiresStringContent: true,
supportsTools: false
}
That disables OpenClaw's tool schema surface for the model and can reduce prompt pressure on stricter local backends.
If tiny direct requests still work but normal OpenClaw agent turns continue to
crash inside inferrs, the remaining issue is usually upstream model/server
behavior rather than OpenClaw's transport layer.
Manual smoke test
Once configured, test both layers:
curl http://127.0.0.1:8080/v1/chat/completions \
-H 'content-type: application/json' \
-d '{"model":"google/gemma-4-E2B-it","messages":[{"role":"user","content":"What is 2 + 2?"}],"stream":false}'
openclaw infer model run \
--model inferrs/google/gemma-4-E2B-it \
--prompt "What is 2 + 2? Reply with one short sentence." \
--json
If the first command works but the second fails, check the troubleshooting section below.
Proxy-style behavior
inferrs is treated as a proxy-style OpenAI-compatible /v1 backend, not a
native OpenAI endpoint.
- Native OpenAI-only request shaping does not apply here
- No
service_tier, no Responsesstore, no prompt-cache hints, and no OpenAI reasoning-compat payload shaping - Hidden OpenClaw attribution headers (
originator,version,User-Agent) are not injected on custominferrsbase URLs
Troubleshooting
curl /v1/models fails
inferrs is not running, not reachable, or not bound to the expected
host/port. Make sure the server is started and listening on the address you
configured.
messages[].content expected a string
Set compat.requiresStringContent: true in the model entry. See the
requiresStringContent section above for details.
Direct /v1/chat/completions calls pass but openclaw infer model run fails
Try setting compat.supportsTools: false to disable the tool schema surface.
See the Gemma tool-schema caveat above.
inferrs still crashes on larger agent turns
If OpenClaw no longer gets schema errors but inferrs still crashes on larger
agent turns, treat it as an upstream inferrs or model limitation. Reduce
prompt pressure or switch to a different local backend or model.