Prime Agent Ollama 2026: Fix Connection Failures

A current Ollama FAQ states that its default context window is 4,096 tokens unless you change it through configuration or request options. (Ollama context and performance FAQ)

Model visible but tasks fail → test API compatibility and model capability before reinstalling.

Model missing or connection refused → isolate Ollama service, network address, models.json, API route, and model ID in that order.

Last updated August 11, 2026. Configuration details were checked against the current Prime Agent repository documentation and Ollama API and troubleshooting documentation.

This guide is for you if Prime Agent does not show an Ollama model, if a configured model produces connection errors, or if a local model handles chat but fails during file edits and coding workflows. It also covers teams running Prime Agent remotely while Ollama stays on a separate host.

Start with a five-layer fault timeline

Do not begin by reinstalling Prime Agent, Ollama, or the model. Reinstallation rarely fixes a wrong address, an invalid JSON file, a mismatched model ID, or an unsupported request field.

Use this timeline:

1. Service layer: Is Ollama running, and does it have the target model?
2. Address layer: Can the Prime Agent process reach the Ollama host and port?
3. Configuration layer: Is Prime Agent reading the expected models.json file?
4. API layer: Does the backend accept the roles, reasoning controls, streaming fields, and token fields Prime Agent sends?
5. Capability layer: Can the selected model follow tools, edit files, execute commands, and continue a long task reliably?

Prime Agent officially supports custom providers through ~/.prime/agent/models.json. Its current documentation shows Ollama configured through an OpenAI-compatible endpoint and identifies openai-completions as the compatible API type for this setup. (Prime Agent custom models documentation)

The important distinction is simple: a successful chat response proves only that one request returned text. It does not prove that the entire agent loop works.

Confirm the Ollama service before touching Prime Agent

Start with the backend. Run these commands on the machine where Ollama is supposed to run:

ollama list
curl http://127.0.0.1:11434/api/tags

Expected result:

• ollama list shows the exact model you intend to configure.
• /api/tags returns JSON containing the model name.
• The response is immediate rather than a timeout or connection refusal.

If ollama list is empty, the model is not installed. Install or pull the model before debugging Prime Agent:

ollama pull <model-id>
ollama list

Use the exact identifier returned by Ollama. Tags and suffixes matter. For example, model-a:7b, model-a:latest, and a custom name created with ollama cp are different identifiers from an API client's perspective.

Next, test the OpenAI-compatible route that Prime Agent will use:

curl http://127.0.0.1:11434/v1/models

Ollama documents /v1/models as part of its OpenAI-compatible API. Its examples also use /v1/chat/completions with a required but ignored API key value. (Ollama OpenAI compatibility documentation)

Then send the smallest chat request:

curl http://127.0.0.1:11434/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer ollama" \
  -d '{
    "model": "<model-id>",
    "messages": [
      {"role": "user", "content": "Reply with the word READY."}
    ],
    "stream": false
  }'

A valid JSON response with assistant text moves you to the next layer. A 404 usually points to the wrong route or an incorrect base path. A connection refusal points to the service, listener, host, container, or firewall. A model-not-found response means the API is reachable but the requested ID does not exist in that Ollama instance.

Ollama's troubleshooting documentation recommends checking server logs rather than relying only on the client error:

cat ~/.ollama/logs/server.log

For a Linux service, use:

journalctl -u ollama --no-pager --follow --pager-end

For a container, inspect its standard output and error streams. (Ollama troubleshooting documentation)

Check the address Prime Agent can actually reach

localhost describes the network namespace of the process making the request. It does not automatically mean “the computer where Ollama is installed.”

Test from the same environment that runs Prime Agent. If Prime Agent runs inside a container, execute the curl test inside that container. Testing from your laptop proves only that your laptop can reach Ollama.

For a remote setup, first identify whether Ollama listens only on loopback. Then verify the route from the Prime Agent host:

curl http://<ollama-host>:11434/api/tags
curl http://<ollama-host>:11434/v1/models

Do not expose an unauthenticated local model endpoint to the public internet. Use a private network, a secure tunnel, or an authenticated reverse proxy. The endpoint may accept prompts, code, file contents, and tool-related data.

A useful diagnostic split is:

• Timeout: routing, firewall, security group, or dead tunnel.
• Connection refused: the host is reachable, but no service accepts the port.
• HTTP 404: the server answered, but the route is wrong.
• HTTP 400: the route works, but the request shape is rejected.
• HTTP 500 or 503: inspect Ollama logs and resource pressure.

The response status and both sides' logs are stronger evidence than the wording shown in the Prime Agent interface.

Repair models.json and reload the model catalog

Prime Agent's current custom model documentation places the file at:

~/.prime/agent/models.json

The minimal Ollama structure is:

{
  "providers": {
    "ollama": {
      "baseUrl": "http://127.0.0.1:11434/v1",
      "api": "openai-completions",
      "apiKey": "ollama",
      "models": [
        { "id": "<model-id>" }
      ]
    }
  }
}

The API key is required by the client configuration, but Ollama ignores its value for this local endpoint. Prime Agent's documentation also states that the model ID is the identifier passed to the API. (Prime Agent provider configuration reference)

Validate the JSON before opening Prime Agent:

python -m json.tool ~/.prime/agent/models.json >/dev/null

If the command exits without a parse error, inspect the actual file:

cat ~/.prime/agent/models.json

Check these points individually:

• The file is under the user account that launches Prime Agent.
• The top-level key is providers.
• The provider contains a models array.
• Each model has an id.
• The model ID exactly matches ollama list.
• api is openai-completions.
• baseUrl ends at /v1, not /v1/chat/completions.
• There are no trailing commas or copied comments.
• The configured provider name is the one you select in Prime Agent.

Prime Agent's current documentation says the file reloads when you open /model, so editing the file does not necessarily require a full restart.

If the model still does not appear, separate the symptoms:

Do not use a guessed model ID simply because the display name looks similar. The API receives the configured id, not the friendly label.

Adjust compatibility one field at a time

A visible model with a failed request usually means the catalog is working. The next question is whether the backend accepts the request fields Prime Agent sends.

The current Prime Agent model documentation lists compatibility controls for fields such as:

• supportsDeveloperRole
• supportsReasoningEffort
• supportsUsageInStreaming
• maxTokensField
• supportsStrictMode
• tool-result message requirements

Provider-level compatibility applies to every model under that provider. Model-level compatibility can override it for one model.

Start with the smallest compatibility block:

{
  "providers": {
    "ollama": {
      "baseUrl": "http://127.0.0.1:11434/v1",
      "api": "openai-completions",
      "apiKey": "ollama",
      "compat": {
        "supportsDeveloperRole": false,
        "supportsReasoningEffort": false
      },
      "models": [
        { "id": "<model-id>" }
      ]
    }
  }
}

Why these two fields matter:

• Some OpenAI-compatible servers do not accept a developer role. Prime Agent can send the system instruction as a system message when supportsDeveloperRole is disabled.
• Some servers reject reasoning_effort, even if they accept ordinary chat requests.
• A streaming request can fail because of usage metadata, not because the model itself is unavailable.
• Token field differences can produce a 400 response even when the endpoint and model are correct.

Change one compatibility field at a time. After each edit:

1. Open /model.
2. Select the same model.
3. Send a short prompt.
4. Repeat the smallest curl request if the error concerns the API.
5. Save the exact response status and error body.

Do not disable every compatibility option at once. That removes evidence and can hide the field that actually caused the rejection.

Ollama documents support for streaming, tools, reasoning controls, and usage fields on its OpenAI-compatible chat endpoint, but support at the endpoint level does not guarantee that every model or client combination behaves identically.

Test coding capability as a separate milestone

A common failure pattern is:

The model is visible, answers “hello,” and then fails when Prime Agent must inspect a repository or edit a file.

Treat this as a capability test, not an immediate connectivity bug.

Use a staged sequence:

1. Read: Ask Prime Agent to inspect one known text file and summarize a specific line.
2. Plan: Ask for a two-step change without allowing edits.
3. Small edit: Ask it to add one harmless comment or change one isolated value.
4. Verify: Ask it to show the diff.
5. Command: Run a safe test command with a clear expected result.
6. Recovery: Introduce a controlled failing test and ask the agent to diagnose it.

Stop if the model claims that it changed a file but the diff is empty. Do not accept invented tool output as success. Verify the filesystem and command result independently.

A local model can be excellent for summarization and still be unreliable for programmatic coding. Prime Agent is designed for coding and long-running work, including file operations, shell commands, persistent sessions, and background execution. (Prime Agent repository overview)

That raises the capability requirement beyond ordinary chat.

If the same failure appears across clean repositories and repeated prompts, first try a model intended for coding or tool use. If the stronger model still fails at the same request field, return to the compatibility layer. Do not label the behavior a confirmed Prime Agent or Ollama bug without a reproducible request and logs.

Trace stalls, broken streams, and resource exhaustion

When a task pauses, build a timeline rather than restarting blindly.

Record:

• The last successful Prime Agent action.
• Whether Ollama loaded the model at that moment.
• The request duration and response status.
• Context size or task length at the point of failure.
• Concurrent subagents or requests.
• Host memory, CPU, and GPU utilization.
• Whether the terminal disconnected.
• Whether the Prime Agent worker and Ollama process remained alive.

Use:

ollama ps

This command shows which models are loaded and whether they are using CPU, GPU, or a split of both.

Typical causes include:

• The model is repeatedly unloaded and reloaded.
• The context grows until memory pressure becomes severe.
• Several subagents compete for one local model server.
• The host begins swapping.
• The stream is rejected because of unsupported usage metadata.
• A remote tunnel drops while the local process continues.
• The terminal disconnects, but the user assumes the agent stopped.

Prime Agent's long-running documentation states that daemon-backed workers can continue after the client detaches and can later be reattached. (Prime Agent long-running agent documentation) Verify this behavior in your deployment instead of assuming it. Run:

prime-agent status
prime-agent agents

Then disconnect and reconnect intentionally. Confirm that the session, worker, model process, and task state all remain available.

If Ollama returns a 503, inspect load and concurrency before changing the model configuration. Overload responses can occur when too many requests are sent to the local service. (Ollama FAQ on server load)

Apply the minimum acceptance sequence

A repaired configuration should pass these milestones in order:

• Discovery: The expected provider and model appear in /model.
• Backend: /v1/models returns the configured model ID.
• Basic response: A non-streaming chat request returns valid JSON.
• Streaming: A short streamed request completes without malformed chunks.
• File access: Prime Agent reads the intended file.
• File mutation: A small edit appears in the real filesystem and diff.
• Command execution: A safe command runs and its output is interpreted correctly.
• Recovery: A controlled failure leads to a verified correction.
• Continuity: A detached session can be reattached if long-running execution is required.

Store the following with the result:

• Prime Agent version.
• Ollama version.
• Model ID.
• Operating system.
• Whether the process runs on a host, container, or remote machine.
• A redacted models.json summary.
• The exact baseUrl.
• Response status and error body.
• Prime Agent log location.
• Ollama log location.
• The first milestone that failed.

This record prevents the team from repeating the same configuration edits without learning whether the failure is service, network, configuration, API, capability, or resources.

For a stable remote workflow, compare three deployment choices:

If you need a regional Mac environment, review Kvmkit's Mac Mini rental options in the US East region or compare another location through the Kvmkit Mac rental directory. Choose based on session continuity, network path, data handling, and the time you need the environment—not simply on whether the local model starts.

FAQ

The questions below cover the remaining search paths without treating community reports as universal defects.

Choose the environment after the fault is proven

If the five-layer timeline ends with a malformed JSON file or incorrect baseUrl, keep the current machine and fix the configuration. If it ends with a model that cannot reliably follow tools, replace the model or narrow the task. If it ends with memory pressure, repeated reloads, terminal dependence, or an unstable remote route, changing the execution environment is usually more effective than making more provider edits.

Your current local setup may be cheaper for quick prompts, but it can suffer from limited memory, sleep or logout interruptions, single-user access, and unpredictable performance during concurrent work. A generic remote setup may avoid local resource limits, but it adds firewall, authentication, latency, and session-lifecycle work.

For temporary Prime Agent testing, long coding runs, or a controlled Mac-based environment, renting a Mac through Kvmkit can provide a cleaner separation between your daily workstation and the agent workload. It does not remove the need to validate the model, API compatibility, or security boundaries, but it can remove the repeated failure caused by an overloaded laptop or a terminal that cannot stay attached.

Use the current setup when the workload is short and interactive. Move to an independent or rented Mac environment when resource stability, remote persistence, or team access matters more than keeping everything on one machine.

FAQ