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What Hermes Agent and Ollama each do
Hermes Agent and Ollama are complementary parts of one setup, not competing applications. Hermes is the agent runtime from Nous Research. It provides the behavior that plans work, invokes tools, maintains cross-session memory, and can create skills automatically; the current package includes more than 70 bundled skills. Ollama is the local model server. It loads a model on your computer and exposes an OpenAI-compatible API that Hermes can call.
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That division explains why a normal chat test can be misleading. A model may answer questions fluently yet fail when Hermes asks it to edit a file, run a terminal command, or browse. Those actions require structured tool calls. Hermes supplies the tools and orchestration, but the Ollama model must know how to emit the tool-call format that Hermes expects.
Local inference keeps the model execution on your machine. It does not automatically make every Hermes feature offline: web search, browser automation, remote MCP servers, hosted memory, speech services, messaging gateways, and cloud fallbacks can still communicate over the network. Treat the model server and the complete agent deployment as separate privacy boundaries.
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The two ways to connect Hermes to Ollama
| Approach | Best for | What it does | Trade-off |
|---|---|---|---|
ollama launch hermes |
Most users and first installations | Installs Hermes if needed, lets you choose local or cloud inference, configures Ollama, and can offer gateway setup | Fewer opportunities to choose every setting manually |
Manual hermes setup |
Users who need a particular model, endpoint, context size, or privacy policy | You install and test Ollama, pull a model, then select Custom Endpoint in Hermes | More steps, but clearer diagnostics and repeatability |
Both approaches ultimately use Ollama’s OpenAI-compatible endpoint. For a local server, the base URL is http://127.0.0.1:11434/v1 (the equivalent http://localhost:11434/v1 also works in the documented manual flow), and a local API key is not required.
Check hardware before downloading a model
Model size determines whether an agent feels usable. The current Nous Research guide lists these capacity targets:
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| Resource | Documented figure | How to interpret it |
|---|---|---|
| System memory for 3B models | 8 GB minimum | A lower-bound starting point for a small model, not a guarantee of comfortable multitasking |
| System memory for 27B or larger models | 32 GB or more recommended | The practical target for larger, more capable local models |
| Free storage | 5 GB minimum | Enough for a minimal installation and one small model; model files and caches consume additional space |
| Storage for multiple models | 30 GB or more recommended | Allows room for several model variants and a 64K-context copy |
| GPU | NVIDIA GPU with at least 8 GB VRAM recommended | Improves inference speed when Ollama can offload work to the GPU |
CPU-only operation is supported, but it is slower. Nous Research gives guide examples of roughly 10 tokens per second for a 9B model on a modern eight-core CPU and roughly 2–5 tokens per second for a 31B model on CPU. These are illustrative figures from the guide, not universal benchmarks: processor, memory bandwidth, quantization, context length, and GPU offload all change the result.
Reserve extra memory for the operating system, Hermes, Ollama, and the context itself. A model that technically fits may still swap to disk or become unresponsive when a long conversation, file contents, or browser output is added.
Fast setup with ollama launch hermes
Use this route when you want Ollama to coordinate the initial configuration.
- Install Ollama. On supported Unix-like systems, the official manual command is
curl -fsSL https://ollama.com/install.sh | sh. Use the platform-appropriate installer on other systems. - Launch Hermes through Ollama. Run
ollama launch hermesin a terminal. - Choose the inference location. When prompted, select a local Ollama model if the model should run on your computer. A cloud choice changes the privacy and availability assumptions.
- Allow the installer to configure Hermes. The launcher can install Hermes when it is missing and set the Ollama endpoint for you.
- Decide whether to configure gateways now. You can defer messaging integrations until the local command-line workflow works.
- Start an interactive session. Run
hermesif the launcher does not open it automatically. - Perform an action that needs a tool. Ask Hermes to list a directory, read a README, or create a small script. A greeting alone is not a sufficient test.
If the launcher reports an endpoint problem, stop and verify Ollama independently with the checks in the next section. Re-running Hermes setup cannot fix a server that is not listening.
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Manual setup, one verified layer at a time
1. Confirm that Ollama is installed and serving
Run the version command first:
ollama --versionThen ask the local API for its model list:
curl http://localhost:11434/api/tagsA working server returns a response containing the models already installed. If the connection is refused, start Ollama using your operating system’s normal application or service procedure, then repeat the request. Do not change Hermes settings until this endpoint responds.
2. Pull a model that can call tools
For the documented example, download the tool-capable model with:
ollama pull gemma4:31bDownload time and disk usage depend on the model package and your connection. Keep enough storage for a second, long-context variant if you need to create one later.
3. Test the OpenAI-compatible chat endpoint
Before involving Hermes, verify that Ollama can answer through the endpoint Hermes will use:
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curl http://localhost:11434/v1/chat/completions
-H "Content-Type: application/json"
-d '{"model":"gemma4:31b","messages":[{"role":"user","content":"Reply with one short sentence confirming that you are ready."}]}'You should receive a normal chat-completions response. This check proves connectivity and model selection; it does not yet prove that structured tool calls work.
4. Point Hermes at Ollama
Start the configuration wizard:
hermes setup- Select Custom Endpoint.
- Enter
http://localhost:11434/v1as the base URL. You can usehttp://127.0.0.1:11434/v1if you prefer the explicit loopback address. - Leave the API-key field empty for local Ollama. If the wizard requires text, use a harmless placeholder such as
no-key; Ollama does not authenticate local requests with a real key. - Select the model you pulled, such as
gemma4:31b. - Save the configuration and exit the wizard.
5. Run an action, not just a conversation
Launch Hermes:
hermesUse a disposable directory for the first test and request a concrete operation: list its files, read a README, or create a tiny script. Watch for a structured tool invocation and the resulting output. If Hermes merely prints the command it would have run, or displays raw JSON instead of executing the action, continue with the model and tool-call checks below.
Choose a model for agent work, not just chat
The decisive question is whether the model supports structured tool calls. The official Hermes guidance distinguishes the following examples:
| Model example | Role in the guide | Expected fit for Hermes actions |
|---|---|---|
gemma4:31b |
Tool-capable example | Suitable starting point for file, terminal, and browsing tasks, subject to your hardware |
gemma2:27b |
Conversational example | Not presented as a reliable tool-calling choice |
gemma2:9b |
Conversational example | May answer quickly but may return prose instead of structured calls |
llama3.2:3b |
Lightweight conversational example | Useful for chat experiments; not presented as a tool-calling model |
Smaller models reduce download size, RAM pressure, and latency. The trade-off is reliability: a chat model can understand your request yet fail to produce the exact function-call structure required to perform it. A larger tool-capable model costs more memory, storage, and waiting time but is a better fit for agent actions.
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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsDo not infer tool support from a model’s general intelligence or from a successful greeting. Test the exact operation you care about and keep the model name and context configuration together when you document your setup.
Give Hermes the required context window
Current Nous Research documentation says Hermes needs at least 64,000 tokens for agentic work with tools. Long tasks consume context with instructions, tool schemas, file contents, command output, and conversation history. A model that starts with a smaller context may be rejected or may lose important earlier information.
The documented Ollama approach is to create a model variant with a 64K context in a Modelfile:
FROM gemma4:31b
PARAMETER num_ctx 64000Save the file as Modelfile in a working directory, then create a new Ollama model name:
ollama create hermes-gemma4-31b-64k -f ModelfileRun ollama list to confirm the new name, then repeat hermes setup and select hermes-gemma4-31b-64k as the model. The model still needs enough RAM and storage to hold the longer context. Increasing num_ctx does not make a small machine faster; it increases the amount of state the runtime may need to manage.
Tune slow responses and long-running sessions
Increase Hermes’ API timeout for CPU inference
Large local models can take longer than a hosted request, especially on CPU. For slow inference, set the Hermes environment variable HERMES_API_TIMEOUT=1800 in ~/.hermes/.env. This is an environment variable, not a config.yaml key. Restart Hermes after changing the file so the new value is loaded.
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Keep a frequently used model loaded
Ollama may unload an idle model, causing the next gateway message to wait through model loading again. For an always-on gateway, configure a 24-hour Ollama keep-alive where your Ollama integration exposes that setting. This reduces cold starts at the cost of keeping model memory occupied. On a shared computer, a shorter lifetime may be preferable so other applications can reclaim RAM.
Separate latency causes before changing settings
- First response is slow, later responses are faster: model loading or GPU initialization is likely contributing.
- Every response is slow: check CPU-only execution, GPU offload, model size, and context length.
- Short prompts work but long tasks stall: inspect context size and memory pressure before raising the timeout further.
- Responses stop midway: check whether the model has reached a context or output limit rather than assuming a network failure.
Verify structured tool calls correctly
Use a progression that isolates each layer:
- Server test:
curl http://localhost:11434/api/tagsreturns the local model list. - Chat test: the
/v1/chat/completionsrequest returns a normal response from the selected model. - Hermes connection test: the wizard saves the Custom Endpoint without an authentication error.
- Tool test: Hermes lists files, reads a README, or creates a script and reports the actual result.
- Destructive-action test: only after the disposable-directory test succeeds should you permit edits in an important project.
Models without tool-call support produce plain text instead of structured function calls. Raw JSON printed in the conversation, a prose description of an unexecuted command, or a claim that a file was changed when it was not are all failures of the tool path. Switch to a model documented as tool-capable, confirm that Hermes is pointed at that exact model name, and repeat the disposable test.
Troubleshoot by symptom
“Connection refused” or an empty model list
- Start Ollama and rerun
curl http://localhost:11434/api/tags. - Confirm that the base URL in Hermes includes
/v1; the tags check itself uses/api/tagsand is a separate endpoint. - Check that no other service has replaced the expected local port.
- Only after the API responds, rerun
hermes setup.
The model name is rejected
- Run
ollama listand copy the model name exactly, including its tag. - Pull the model again if it does not appear locally.
- If you created a 64K variant, select the new name rather than the original model.
Hermes says the context is too small
Create or select an Ollama model configured with PARAMETER num_ctx 64000. A model’s nominal size, such as 9B or 31B, does not tell you its context capacity. Verify the variant you selected in Hermes is the one you created for the required context.
Hermes answers but does not act
Check tool support first. The conversational examples in the official guide are not presented as reliable tool-calling models. Then repeat a simple file operation in a disposable directory. If the model emits raw JSON or prose, it is not completing the structured call that Hermes needs.
Requests time out
- Set
HERMES_API_TIMEOUT=1800in~/.hermes/.envfor slow CPU inference. - Inspect RAM pressure, GPU offload, model size, and context length.
- Use a smaller model only if it still supports the tools your task requires; faster chat output is not a substitute for agent capability.
Add messaging gateways only after the CLI works
Once local terminal tasks pass, run hermes gateway setup to connect supported channels such as Telegram, Discord, Slack, WhatsApp, Signal, or email. Configure one gateway at a time and send a harmless test message before enabling unattended work. A gateway adds another authentication surface, another source of instructions, and potentially another network path.
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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteKeep the first gateway session conservative: restrict the directories Hermes may modify, avoid destructive commands, and require confirmation for actions that affect accounts or production systems. The local model may be private while the message text, attachments, speech processing, remote tools, or gateway service still travel outside the machine.
Understand the privacy boundary
| Component or feature | What local Ollama covers | What may still use the network |
|---|---|---|
| Model inference | The selected Ollama model runs on the local computer | None for the inference request itself when configured locally |
| Web search and browser automation | Not made offline by Ollama | Websites, search providers, and browser targets |
| Remote MCP and hosted memory | Not hosted by the local model server | Remote tool or memory services |
| Speech and messaging gateways | Not covered by local text inference | Speech providers and the selected gateway networks |
| Cloud fallback | Not local when enabled | The configured cloud provider |
Before calling the deployment private, list the tools Hermes is allowed to use, identify which ones leave the machine, and disable cloud fallback if your policy requires local-only inference. Local execution is a strong boundary for the model, not a blanket guarantee for every input and output handled by the agent.
A practical decision framework
Use the following choices when adapting the setup to your computer:
- You want the fewest steps: start with
ollama launch hermes, accept local inference, and postpone gateways. - You need deterministic configuration: use manual setup, test
/api/tagsand/v1/chat/completions, then select Custom Endpoint. - You need file or terminal automation: choose a model documented as tool-capable and validate a real tool call.
- You have limited RAM: use the smallest model that demonstrably supports your required tools, reduce concurrent applications, and expect higher latency.
- You have 32 GB or more of RAM and adequate storage: a 27B-plus model is more practical, though generation speed still depends on GPU and CPU capacity.
- You need long, multi-step work: build a model variant with at least 64,000 tokens of context and allow a longer API timeout.
- You need an always-on assistant: configure a 24-hour keep-alive only after confirming the machine can reserve that memory continuously.
Keep the installation reproducible
Record the Ollama version, exact model tag, whether you created a 64K Modelfile variant, the Hermes endpoint, and the value of HERMES_API_TIMEOUT. Keep the Modelfile beside your setup notes so you can recreate the same context configuration after reinstalling Ollama. When changing models, repeat the chat test and the disposable tool test; a successful previous model does not prove that a replacement emits compatible calls.
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Bottom line
ollama launch hermes is the shortest current route, while manual setup gives you precise control: verify Ollama, pull a tool-capable model, test the OpenAI-compatible endpoint, choose Custom Endpoint in hermes setup, and validate an actual tool action. For dependable agentic work, plan around at least 64K tokens of context, enough RAM and storage for the model, and a longer timeout on CPU-only systems. Keep gateways, web tools, remote services, and cloud fallbacks separate from the local-inference decision so you know exactly what remains on your machine.
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