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A local AI agent that cannot reach its model usually has one of four separate problems: the model server is unreachable, the endpoint or authentication mode is wrong, the requested model identifier is unavailable, or the machine cannot load the model with its current resources. Diagnose those layers in that order. From the same machine, container, user account, and network namespace that runs the agent, test the Ollama endpoint, confirm the API path, compare the exact model tag with ollama list, refresh the host integration, and read server logs. Only after connectivity and model availability are proven should you investigate GPU, memory, context-window, or driver limits.
What does a local AI agent need in order to reach its model?
The agent is a client; Ollama or another model server is the service. The client must be able to reach the server address, speak the protocol expected at that address, request a model name that the server actually has, and leave enough CPU, RAM, VRAM, and context capacity for inference. A generic message such as model unavailable can hide any one of those failures.
| Layer | What must be true | Typical evidence |
|---|---|---|
| Reachability | The agent runtime can open the server host and port. | A curl request from the agent’s own environment returns HTTP output instead of a connection error. |
| Protocol and authentication | The base URL uses the native or OpenAI-compatible path, and credentials match local or cloud mode. | Local requests work without a key; cloud requests use the required key. |
| Model availability | The exact model identifier, including its tag, is installed and spelled correctly. | ollama list contains the identifier used by the agent. |
| Inference resources | The server can load the model and process the requested context and output. | ollama ps and logs show CPU/GPU placement, loading, or memory errors. |
Do not change all four layers at once. Each has a different test, and changing hardware cannot fix a wrong port or model tag.
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How do I run the three-minute connection diagnostic?
Use this sequence without skipping ahead. Run every command from the same environment that launches the agent; a successful test in a different terminal, account, machine, or container does not prove that the agent can connect.
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- Test the server endpoint. For Ollama, run
curl http://localhost:11434/v1/modelsfor the OpenAI-compatible route orcurl http://localhost:11434/api/tagsfor the native route. A response proves that something answered at that address; a connection-refused or timeout result means you must fix the service, host, port, or network scope first. - Check that Ollama is running. On Linux, start it with
ollama servewhen it is not already managed by a service. For a container deployment, check the container state and its published port. Inspect logs before repeatedly restarting. - Compare the configured base URL and protocol. Use the native
http://localhost:11434/apibase for Ollama’s native API, orhttp://localhost:11434/v1for clients that expect OpenAI-compatible endpoints. Do not append both paths or point a local client at a cloud URL by accident. - Verify the model identifier. Run
ollama list, then compare the returned name and tag character-for-character with the agent setting. If it is absent, install it withollama pull <model>. - Refresh discovery in the host application. If a picker is empty, confirm the service and model list first, then run the integration’s refresh command and inspect its diagnose command or output channel.
- Read the server logs. Use
journalctl -u ollama --no-pagerfor a Linux systemd installation,docker logs <container-name>for a container, or the platform-specific Ollama log directory for a desktop installation. SetOLLAMA_DEBUG=1for additional detail. - Investigate resources only after the preceding checks pass. Run
ollama psto see whether the model loaded and whether execution is on CPU, GPU, or split across both. Then address drivers, memory, context, or container GPU access indicated by the evidence.
How do I test whether the agent can actually reach Ollama?
Run the test from the agent’s process environment, not merely from your interactive shell. The endpoint must resolve to the same host, port, and network namespace that the agent uses.
Interpret the curl result
- Connection refused: Nothing is accepting connections at that host and port, or a container port is not exposed. Start or restart the server, correct the address, and check container networking.
- Timeout: The route may be blocked, the server may be bound only to another interface, or the remote/container path is wrong. Test from the same namespace and inspect service logs.
- HTTP output listing models: Reachability is working. Continue with protocol, model ID, and resource checks instead of changing network settings.
- Works in a terminal but fails in the agent: Compare the agent’s environment variables, user account, proxy settings, working container, and network namespace with the shell where curl succeeded.
Docker’s local-model guidance summarizes the first check as ensuring that the model server is running and accessible. A curl from another machine or container is not a valid substitute for that check.
Which Ollama URL and protocol should I configure?
Choose one API mode and keep its URL, authentication, and client expectations consistent. The following values are the documented Ollama combinations.
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|---|---|---|---|---|
| Native local API | http://localhost:11434/api | No API key | ollama list; use the exact name and tag |
Local machine or a namespace that can reach its localhost |
| OpenAI-compatible local API | http://localhost:11434/v1 | No API key for local calls | ollama list; map the client model field to that ID |
Local machine or an explicitly reachable host |
| Ollama cloud API | https://ollama.com/api or https://ollama.com/v1 | API key required | The model identifier available to the cloud account | Internet connection and the cloud service |
Ollama states that its local API at http://localhost:11434 does not require authentication. A 401 response usually means that a cloud endpoint was selected without its bearer key, or that local and cloud settings have been mixed. Conversely, adding a cloud key to a local-only configuration does not repair a wrong host or path.
Some clients call the configured value a base URL and append /chat/completions or another route themselves. Enter the documented base exactly as the client expects; supplying http://localhost:11434/v1 to an OpenAI-compatible client is different from supplying the native http://localhost:11434/api base to an Ollama-native client.
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Why does the agent say the model is missing when Ollama is reachable?
A reachable server can still return 404 or model-not-found when the requested identifier is absent or differs by one character. Tags are part of the identifier, so compare the complete value rather than only the family name.
Verify and install the exact ID
- Run
ollama listin the account that owns the local model store. - Copy the returned name and tag into the agent configuration without changing punctuation, capitalization, or the tag suffix.
- If the model is not listed, run
ollama pull <model>and wait for the download to finish. - Repeat the endpoint test and then make one small agent request. A successful server response with a missing model is an availability problem, not a network problem.
Do not infer model availability from a model picker alone. Pickers can have stale discovery state; the server’s own list is the authoritative local check.
How do I make a host application rediscover local models?
Integrations that populate a model picker maintain their own discovery state. The Ollama VS Code integration uses http://127.0.0.1:11434 by default, which is equivalent to the local service only when the extension and server share the same host context.
- Confirm that Ollama is running and that
ollama listshows at least one model. - In VS Code, run the Ollama integration’s model refresh command.
- Run its diagnose command and open the output channel if the list remains empty.
- Compare the extension’s endpoint with the address that succeeded in your curl test. A service inside a different container or on another machine needs a reachable host address rather than that container’s own localhost.
The integration documentation’s first remedy is simply to make sure Ollama is running. Refreshing before fixing a stopped service only refreshes an empty result.
Which logs reveal the real cause?
Read the server log at the time of a failed request. Generic client errors often omit the distinction between a bind failure, permission issue, missing file, GPU initialization error, and out-of-memory condition.
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| Deployment | Log command or location | Look for |
|---|---|---|
| Linux systemd | journalctl -u ollama --no-pager |
Bind errors, service restarts, permissions, model-file failures, GPU setup, and memory exhaustion |
| Container | docker logs <container-name> |
Port binding, volume permissions, missing files, GPU passthrough, and process exits |
| Desktop installation | Platform-specific Ollama log directory | Startup, model loading, driver, and out-of-memory messages |
Set OLLAMA_DEBUG=1 when the normal log does not identify the failure. Capture the timestamp, endpoint, model ID, and the first relevant error line; later warnings can be consequences of the original fault.
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How do remote and containerized agents reach a local model?
In a remote or containerized setup, localhost belongs to the process’s own network namespace. The agent can reach an Ollama server on another machine only when that server listens on an address reachable from the agent and the route and port are allowed.
Check the network namespace
- Execute the same curl command inside the agent container or on the remote host where the agent runs.
- Use the server’s reachable hostname or IP instead of assuming that
localhostrefers to the host computer. - Confirm that the service port is published and that firewall or container-network rules allow the connection.
- When Ollama must listen beyond its default local address, configure
OLLAMA_HOST, then reload or restart the service so the new binding takes effect.
If model downloads must pass through a proxy, Ollama documents HTTPS_PROXY. It warns that setting HTTP_PROXY can interrupt client connections, so do not set both casually. Proxy settings for downloading are not a replacement for making the inference endpoint reachable.
What do 401, 404, and connection-refused errors mean?
Match the observed symptom to one test and one corrective action instead of treating every failure as a broken model.
| Symptom | Most likely cause | Test | Fix |
|---|---|---|---|
connection refused |
Server stopped, wrong host or port, or inaccessible container | curl http://localhost:11434/v1/models from the agent runtime |
Start or restart the server; correct host and port; check container networking |
401 or authentication error |
Cloud endpoint without a key, or mixed local/cloud modes | Compare the configured URL with the API mode | Use localhost without a key, or the cloud URL with its required bearer key |
404 or model not found |
Wrong model name/tag or model not pulled | ollama list |
Use the exact listed ID or run ollama pull |
| Model absent from a picker | Integration discovery cache or server state | Refresh and diagnose the integration; confirm the service and model list | Fix the server first, then refresh or restart the integration |
| Slow response or load failure | GPU, VRAM, RAM, context, or driver problem | ollama ps; inspect logs and GPU visibility |
Use a smaller model or lower token/context settings, or fix drivers and container access |
| Works in shell but not in agent | Different environment, user, container, or proxy | Repeat curl from the agent process environment | Align endpoint, environment variables, credentials, and network namespace |
When is the problem a GPU, memory, or context limit?
Resource troubleshooting starts only after the endpoint responds and the model ID is valid. Run ollama ps while making a request. It shows whether the model is loaded and whether Ollama is using the CPU, GPU, or a split of both.
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Check the evidence before changing settings
- GPU initialization errors: inspect the installed driver and whether the container can see the GPU. Discovery or loading cannot succeed if the server process has no usable device.
- Out-of-memory messages: try a smaller model or reduce
max_tokens. A model can be installed successfully yet fail when a request needs more memory. - Slow or split execution: use
ollama psto confirm placement, then measure whether CPU fallback or VRAM pressure is the bottleneck before buying hardware. - Context errors: change context settings only after measuring the limit. Ollama’s FAQ lists a default context window of 2,048 tokens, while the Ollama VS Code integration recommends at least 64k context for its local-model workflow when that setting is needed. Those are different defaults and recommendations, not contradictory connection requirements.
As a capacity reference, Ollama’s quickstart describes an example model download of about 7.2 GB and recommends 8 GB of available VRAM for that example. Those figures describe that example, not a requirement for every model. A confirmed memory or GPU bottleneck justifies a configuration or hardware change; a connection-refused error does not.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should I compare native, OpenAI-compatible, local, and cloud setups?
Record the same five values for each setup: URL, authentication requirement, exact model identifier, network namespace, and the observed log error. This prevents a configuration copied from one mode from being mistaken for another.
| Comparison axis | Native local | OpenAI-compatible local | Cloud | Remote or containerized local |
|---|---|---|---|---|
| URL | http://localhost:11434/api |
http://localhost:11434/v1 |
https://ollama.com/api or /v1 |
Reachable server host with the selected /api or /v1 path |
| Authentication | None for local calls | None for local calls | API key required | Depends on whether the target is local or cloud |
| Model ID | Exact value from ollama list |
Exact value from ollama list |
Cloud-available identifier | Exact value on the server reached by the agent |
| Namespace | Agent and server share localhost | Agent and server share localhost | Internet route to the service | Agent container or host must route to the server; configure OLLAMA_HOST when required |
| First log check | Service startup and bind errors | Service startup and route errors | Authentication and HTTP response | Port publishing, route, volume, and GPU-access errors |
What should I change first, and what should I leave alone?
Change the smallest setting that matches the evidence. This order avoids turning a simple endpoint mistake into a longer outage.
- Correct the host, port, and API path after testing with curl.
- Fix local versus cloud authentication mode; do not add keys to a local URL merely because the client displays an API-key field.
- Install or rename the configured model to match the exact server identifier.
- Refresh the host integration after the server and model list are correct.
- Adjust
max_tokens, model size, or context only when logs andollama psshow a resource limit. - Change drivers, container GPU access, or hardware only when the corresponding error is present.
After each change, repeat the one test associated with that layer. A clean endpoint response, an exact model match, and a successful small request are stronger evidence than a picker that merely repopulates.
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- The exact URL and API path used by the agent.
- Whether the target is native local, OpenAI-compatible local, cloud, remote, or containerized.
- The complete model name and tag returned by
ollama list. - The curl command and response status run from the agent’s environment.
- The output of
ollama ps, including CPU/GPU placement. - The first relevant line from systemd, container, or desktop logs, with
OLLAMA_DEBUG=1output when enabled. - Whether the failure is connection refused, timeout, 401, 404, picker absence, slow inference, or an out-of-memory/load error.
This record separates a networking defect from an authentication mismatch, an unavailable model, and a resource failure even when the agent reports all of them as cannot connect.
Frequently Asked Questions
Does a local Ollama request need an API key?
No. Requests to the local service at http://localhost:11434 are unauthenticated. An API key belongs to the cloud endpoints; receiving 401 from a local configuration usually indicates that the client is actually targeting a cloud URL or mixing the two modes.
Why can a model download succeed but inference still fail?
Downloading proves that the files were retrieved, not that the running server can load them for the agent’s request. A later GPU initialization, VRAM, RAM, context, permission, or driver error can prevent loading; check ollama ps and the server log at request time.
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Is increasing the context window a fix for connection refused?
No. Context size affects memory and inference capacity after the server has accepted the request. Prove reachability with curl and correct the host, port, and network namespace before changing context settings.
Why does localhost work on my computer but not inside the agent container?
Each container has its own network namespace, so its localhost normally refers to the container rather than the host running Ollama. Test from inside the agent container and configure a reachable host address, published port, and, when needed, an Ollama binding through OLLAMA_HOST.
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