**Nerve is a tool that creates stateful agents with any LLM — without writing a single line of code.** Agents created with Nerve are capable of both planning _and_ enacting step-by-step whatever actions are required to complete a user-defined task. This is done by dynamically updating the system prompt with new information gathered during previous actions, making the agent stateful across multiple inferences. - 🧠 **Automated Problem Solving:** Nerve provides a standard library of actions the agent uses autonomously to inform and enhance its performance. These include identifying specific goals required to complete the task, devising and revising a plan to achieve those goals, and creating and recalling memories comprised of pertinent information gleaned during previous actions. - 🧑‍💻 **User-Defined Agents:** Agents are defined using a standard YAML template. _The sky is the limit!_ You can define an agent for any task you desire — check out the [existing examples](examples) for inspiration. - 🛠️ **Universal Tool Calling:** Nerve will automatically detect if the selected model natively supports function calling. If not, it will provide a compatibility layer that empowers the LLM to perform function calling anyway. - 🤖 **Works with any LLM:** Nerve is an LLM-agnostic tool. - 💯 **Zero Code:** The project's main goal and core difference with other tools is to allow the user to instrument smart agents without writing code.

## LLM Support Nerve features integrations for any model accessible via the following providers: | Name | API Key Environment Variable | Generator Syntax | |----------|----------------------------|------------------| | **Ollama** | - | `ollama://llama3@localhost:11434` | | **Groq** | `GROQ_API_KEY` | `groq://llama3-70b-8192` | | **OpenAI**¹ | `OPENAI_API_KEY` | `openai://gpt-4` | | **Fireworks** | `LLM_FIREWORKS_KEY` | `fireworks://llama-v3-70b-instruct` | | **Huggingface**² | `HF_API_TOKEN` | `hf://tgi@your-custom-endpoint.aws.endpoints.huggingface.cloud` | | **Anthropic** | `ANTHROPIC_API_KEY` | `anthropic://claude` | | **Nvidia NIM** | `NIM_API_KEY` | `nim://nvidia/nemotron-4-340b-instruct` | | **DeepSeek** | `DEEPSEEK_API_KEY` | `deepseek://deepseek-chat` | | **xAI** | `XAI_API_KEY` | `xai://grok-beta` | | **Mistral.ai** | `MISTRAL_API_KEY` | `mistral://mistral-large-latest` | | **Novita** | `NOVITA_API_KEY` | `novita://meta-llama/llama-3.1-70b-instruct` | ¹ **o1-preview and o1 models do not support function calling directly** and do not support a system prompt. Nerve will try to detect this and fallback to user prompt. It is possible to force this behaviour by adding the `--user-only` flag to the command line. ² Refer to [this document](https://huggingface.co/blog/tgi-messages-api#using-inference-endpoints-with-openai-client-libraries) for how to configure a custom Huggingface endpoint. ## Installing with Cargo ```sh cargo install nerve-ai ``` ## Installing from DockerHub A Docker image is available on [Docker Hub](https://hub.docker.com/r/evilsocket/nerve): In order to run it, keep in mind that you'll probably want the same network as the host in order to reach the OLLAMA server, and remember to share in a volume the tasklet files: ```sh docker run -it --network=host -v ./examples:/root/.nerve/tasklets evilsocket/nerve -h ``` An example with the `ssh_agent` tasklet via an Ollama server running on localhost: ```sh docker run -it --network=host \ -v ./examples:/root/.nerve/tasklets \ evilsocket/nerve -G "ollama://llama3@localhost:11434" -T ssh_agent -P'find which process is consuming more ram' ``` ## Building from sources To build from source: ```sh cargo build --release ``` Run a tasklet with a given OLLAMA server: ```sh ./target/release/nerve -G "ollama://@:11434" -T /path/to/tasklet ``` ## Building with Docker ```sh docker build . -t nerve ``` ## Example Let's take a look at the `examples/ssh_agent` example tasklet (a "tasklet" is a YAML file describing a task and the instructions): ```yaml # If this block is not specified, the agent will be able to access all of the # standard function namespaces. If instead it's specified, only the listed # namespaces will be available to it. Use it to limit what the agent can do. using: # the agent can save and recall memories - memory # the agent can update its own goal - goal # the agent can set the task as completed or impossible autonomously - task # the agent can create an action plan for the task - planning # give the agent a sense of time - time # agent background story system_prompt: > You are a senior developer and computer expert with years of linux experience. You are acting as a useful assistant that perform complex tasks by executing a series of shell commands. # agent specific goal, leave empty to ask the user #prompt: > # find which process is using the most RAM # optional rules to add to the basic ones guidance: - Always assume you start in a new /bin/bash shell in the user home directory. - Prefer using full paths to files and directories. - Use the /tmp directory for any file write operations. - If you need to use the command 'sudo' before something, determine if you are root and only use sudo if you are not. # optional global action timeout timeout: 120s # the agent toolbox functions: # divided in namespaces - name: Commands actions: - name: ssh # explains to the model when to use this action description: "To execute a bash command on the remote host via SSH:" # provides an example payload to the model example_payload: whoami # optional action timeout timeout: 30s # each action is mapped to a custom command # strings starting with $ have to be provided by the user # here the command is executed via ssh with a timeout of 15 seconds # IMPORTANT: this assumes the user can connect via ssh key and no password. tool: ssh $SSH_USER_HOST_STRING ``` In this example we created an agent with the default functionalities that is also capable of executing any ssh command on a given host by using the "tool" we described to it. In order to run this tasklet, you'll need to define the `SSH_USER_HOST_STRING` variable, therefore you'll run for instance (see the below section on how to build Nerve): ```sh nerve -G "ollama://llama3@localhost:11434" \ -T /path/to/ssh_agent \ -DSSH_USER_HOST_STRING=user@example-ssh-server-host ``` You can also not specify a `prompt` section in the tasklet file, in which case you can dynamically pass it via command line via the `-P`/`--prompt` argument: ```sh nerve -G "ollama://llama3@localhost:11434" \ -T /path/to/ssh_agent \ -DSSH_USER_HOST_STRING=user@example-ssh-server-host \ -P 'find which process is using the most RAM' ``` You can find more tasklet examples in the `examples` folder, feel free to send a PR if you create a new cool one! :D ### Robopages Nerve can use functions from a [robopages server](https://github.com/dreadnode/robopages-cli). In order to do so, you'll need to pass its address to the tool via the `-R`/`--robopages` argument: ```sh nerve -G "openai://gpt-4o" \ -T /path/to/tasklet \ -R "localhost:8000" ``` To import only a subset of tools: ```sh nerve -G "openai://gpt-4o" \ -T /path/to/tasklet \ -R "localhost:8000/cybersecurity/reverse-engineering" ``` ## License Nerve is released under the GPL 3 license. To see the licenses of the project dependencies, install cargo license with `cargo install cargo-license` and then run `cargo license`. [](https://star-history.com/#evilsocket/nerve&Date)