Models / Providers
Inferrs
inferrs can serve local models behind an
OpenAI-compatible /v1 API. Genesis works with inferrs through the generic
openai-completions path.
inferrs is currently best treated as a custom self-hosted OpenAI-compatible
backend, not a dedicated Genesis provider plugin.
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 Genesis 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.
If Genesis runs fail with an error like:
messages[1].content: invalid type: sequence, expected a string
set compat.requiresStringContent: true in your model entry.
compat: {
requiresStringContent: true
}
Genesis 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 Genesis agent-runtime
turns.
If that happens, try this first:
compat: {
requiresStringContent: true,
supportsTools: false
}
That disables Genesis's tool schema surface for the model and can reduce prompt pressure on stricter local backends.
If tiny direct requests still work but normal Genesis agent turns continue to
crash inside inferrs, the remaining issue is usually upstream model/server
behavior rather than Genesis'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}'
genesis 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 Genesis 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 genesis 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 Genesis 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.
For general help, see Troubleshooting and FAQ.
Related
-
Local models Running Genesis against local model servers.
-
Gateway troubleshooting Debugging local OpenAI-compatible backends that pass probes but fail agent runs.
-
Model selection Overview of all providers, model refs, and failover behavior.