Chatnik

Python package that provides Command Line Interface (CLI) scripts for conversing with persistent Large Language Model (LLM) personas.

"Chatnik" uses files of the host Operating System (OS) to maintain persistent interaction with multiple LLM chat objects.

"Chatnik" can be seen as a package that "moves" the LLM-chat objects interaction system of the Python package "JupyterChatbook", [AAp3], into typical OS shell interaction. (I.e. an OS shell is used instead of a Jupyter notebook.)

There are several consequences of this approach:

Multiple LLMs and LLM providers can be used
The chat messages can use the provided by the package "LLMPrompts", [AAp2]:
- Prompts collection
- Prompt spec DSL and related prompt expansion
Easy access to OS shell functionalities

Remark: This Python package is a translation of the Raku package "Chatnik", [AAp4]. The corresponding CLI scripts of the Raku package use kebab-case, i.e. llm-chat and llm-chat-meta. In addition, the Raku package provides the "umbrella" CLI chatnik.

Installation

From PyPI.org:

pip3 install Chatnik

From GitHub:

pip install -e git+https://github.com/antononcube/Python-Chatnik.git#egg=Python-Chatnik

LLM access setup

There are several options for using LLMs with this package:

Install and run Ollama
Run a llamafile / LLaMA model
Have programmatic access to LLMs of service providers like OpenAI or Gemini
For the corresponding setup see "LLMFunctionObjects", [AAp1]

Basic usage examples

The prompts used in the examples are provided by the Python package "LLMPrompts", [AAp2]. Since many of the prompts of that package have dedicated pages at the Wolfram Prompt Repository (WPR) the examples use WPR reference links.

A few turns chat

The script llm_chat is used to create and chat with LLM personas (chat objects):

Create and chat with an LLM persona named "yoda1" (using the Yoda chat persona):

llm_chat -i=yoda1 --prompt @Yoda hi who are you

Continue the conversation with "yoda1":

llm_chat -i=yoda1 since when do you use a green light saber

Remark: The message input for llm_chat can be given in quotes. For example: llm_chat 'Hi, again!' -i=yoda1.

Apply prompt(s) to shell pipeline output

Summarize a file using the prompt "Summarize":

cat README.md | llm_chat --prompt=@Summarize

Summarize a file and then translate it to another language using the prompt "Translate":

cat README.md | llm_chat --prompt=@Summarize | llm_chat -i=rt --prompt='!Translate|Russian'

Remark: The second llm_chat invocation has to use different chat object identifier because the default chat object, with identifier "NONE", is already primed with the prompt "Summary".

Chat objects management

The CLI script llm_chat_meta can be used to view and manage the chat objects used by "Chatnik". Here is its usage message:

llm_chat_meta --help

List all chat objects ("chats" and "personas" are synonyms to "list"):

llm_chat_meta list --format=json

Here we see the messages of "yoda1":

llm_chat_meta messages -i yoda1

Here we clear the messages:

llm_chat_meta clear -i yoda1

Advanced usage examples

Asking for a result in specific format

llm_chat -i=beta --model=ollama::gemma3:12b 'What are the populations of the Brazilian states? #NothingElse|"JSON data frame"'

Make a request, echo, and place in clipboard

llm_chat -i=unix '@CodeWriterX|Shell macOS list of files echo the result and copy to clipboard.' | tee /dev/tty | pbcopy

#  ls | tee >(pbcopy)

Remark: Instead of ... | tee /dev/tty | pbcopy the pipeline command ... | tee >(pbcopy) can be also used.

Make a mind-map of a file

Consider the task of making an (LLM derived) mind map over a certain document. (Say, this REDME.) There are several ways to do that.

1

Put file's content to be the positional input argument
Use the prompt "MermaidDiagram" in --prompt

llm_chat -i=mmd "$(cat README.md)" --model=ollama::gemma4:26b --prompt=@MermaidDiagram

2

Put file's content to be the positional input argument
Expand the prompt "manually" via llm_prompt provided by "LLMPrompts", [AAp2]

llm_chat -i=mmd "$(cat README.md)" --model=ollama::gemma4:26b --prompt="$(llm_prompt 'MermaidDiagram'  below)"

Remark: This example shows another computation result can be used as a prompt. I.e. no need to rely on the automatic prompt expansion.

3

Give the prompt "MermaidDiagram" as input
Put file's content to be the value of --prompt
- Put additional prompting for further interaction

llm_chat -i=mmd @MermaidDiagram --model=ollama::gemma4:26b --prompt="FOCUS TEXT START:: $(cat README.md) ::END OF FOCUS TEXT. If it is not clear which text to use, use FOCUS TEXT."

This command allows to do further tasks with the file content as context. For example:

llm_chat -i=mmd '!ThinkingHatsFeedback'

Result

The commands above produce results similar to this diagram:

mindmap
  root("Chatnik")
    Purpose
      Python package
      CLI for LLM personas
      Persistent interaction via OS files
    Features
      Multiple LLM providers
      LLM Prompts integration
      OS shell access
    LLM Access
      Ollama
      Llamafile
      Service Providers
        OpenAI
        Gemini
        MistralAI
    Scripts
      llm_chat
      llm_chat_meta
        List chats
        Manage messages
        Delete chats
    Installation
      Zef Ecosystem
      GitHub

Render Markdown results with dedicated programs

Get feedback on a text with the prompt "ThinkingHatsFeedback":

cat README.md | llm_chat -i=th --prompt="$(llm-prompt ThinkingHatsFeedback 'the TEXT is GIVEN BELOW.' --format=Markdown)" --model=ollama::gemma4:26b

Remark: By default the prompt "ThinkingHatsFeedback" gives the hat-feedback table in JSON format. (Currently) the prompt expansion does not handle named parameters, hence, llm-prompt is used to specify the Markdown format for that table.

Get the LLM (chat object) answer -- via llm_chat_meta -- put into a temporary file and "system open" that file:

tmpfile="$TMPDIR/llmans.md"; llm_chat_meta -i=th last-message > "$tmpfile"; open "$tmpfile"

The command above works on macOS. On Linux instead of explicitly creating a file in the temporary dictory, the argument --suffix can be passed to mktemp. For example:

tmpfile=$(mktemp --suffix=".md"); llm_chat_meta -i=th last-message > "$tmpfile"; open "$tmpfile"

Tabulate the LLM personas summary

If the text browser w3m and the Raku package "Data::Translators" are installed, the following pipeline can be used to tabulate the summary the LLM personas:

llm_chat_meta list --format=json | data-translation | w3m -T text/html -dump -cols 120

Customization

Default model

Default model can be specified with the env variable CHATNIK_DEFAULT_MODEL. For example:

export CHATNIK_DEFAULT_MODEL=ollama::gemma4:26b

Remove with unset CHATNIK_DEFAULT_MODEL.

Pre-defined LLM personas

Use defined LLM personas are specified with JSON file with a content like this:

[
    {
	"chat-id": "raku",
	"conf": "ChatGPT",
	"prompt": "@CodeWriterX|Raku",
	"model": "gpt-4o",
	"max-tokens": 4096,
	"temperature": 0.4
    }
]

(See such a file here.)

The LLM personas JSON file can be specified with the OS environmental variables CHATNIK_LLM_PERSONAS_CONF or PYTHON_CHATBOOK_LLM_PERSONAS_CONF -- the former has precedence over the latter.

To load the predefined LLM personas use the command:

llm_chat_meta load-llm-personas

Remark: Snake_case CLI commands are also allowed, e.g., llm_chat_meta load_llm_personas.

Implementation details

Architectural design

Here is a flowchart that describes the interaction between the host Operating System and chat objects database:

flowchart LR
    OpenAI{{OpenAI}}
    Gemini{{Gemini}}
    Ollama{{Ollama}}
    LLMFunc[[LLM::Functions]]
    LLMProm[[LLM::Prompts]]
    CODBOS[(Chat objects<br>file)]
    CODB[(Chat objects)]
    PDB[(Prompts)]
    CCommand[/Chat command/]
    CCommandOutput[/Chat result/]
    CIDQ{Chat ID<br>specified?}
    CIDEQ{Chat ID<br>exists in DB?}
    IngestCODB[Chat objects file<br>ingestion]
    UpdateCODB[Chat objects file<br>update]
    RECO[Retrieve existing<br>chat object]
    COEval[Message<br>evaluation]
    PromParse[Prompt<br>DSL spec parsing]
    KPFQ{Known<br>prompts<br>found?}
    PromExp[Prompt<br>expansion]
    CNCO[Create new<br>chat object]
    CIDNone["Assume chat ID<br>is 'NONE'"] 
    subgraph "OS Shell"    
        CCommand
        CCommandOutput
    end
    subgraph OS file system
        CODBOS
    end
    subgraph PromptProc[Prompt processing]
        PDB
        LLMProm
        PromParse
        KPFQ
        PromExp 
    end
    subgraph LLMInteract[LLM interaction]
      COEval
      LLMFunc
      Gemini
      OpenAI
      Ollama
    end
    subgraph Chatnik backend
        IngestCODB
        CODB
        CIDQ
        CIDEQ
        CIDNone
        RECO
        CNCO
        UpdateCODB
        PromptProc
        LLMInteract
    end
    CCommand --> IngestCODB
    CODBOS -.-> IngestCODB 
    UpdateCODB -.-> CODBOS 
    IngestCODB -.-> CODB
    IngestCODB --> CIDQ
    CIDQ --> |yes| CIDEQ
    CIDEQ --> |yes| RECO
    RECO --> PromParse
    COEval --> CCommandOutput
    CIDEQ -.- CODB
    CIDEQ --> |no| CNCO
    LLMFunc -.- CNCO -.- CODB
    CNCO --> PromParse --> KPFQ
    KPFQ --> |yes| PromExp
    KPFQ --> |no| COEval
    PromParse -.- LLMProm 
    PromExp -.- LLMProm
    PromExp --> COEval 
    LLMProm -.- PDB
    CIDQ --> |no| CIDNone
    CIDNone --> CIDEQ
    COEval -.- LLMFunc
    COEval --> UpdateCODB
    LLMFunc <-.-> OpenAI
    LLMFunc <-.-> Gemini
    LLMFunc <-.-> Ollama

    style PromptProc fill:DimGray,stroke:#333,stroke-width:2px
    style LLMInteract fill:DimGray,stroke:#333,stroke-width:2px

Here is the corresponding UML Sequence diagram:

sequenceDiagram
    participant CCommand as Chat command
    participant IngestCODB as Chat objects file ingestion
    participant CODBOS as Chat objects file
    participant CODB as Chat objects
    participant CIDQ as Chat ID specified?
    participant CIDEQ as Chat ID exists in DB?
    participant RECO as Retrieve existing chat object
    participant PromParse as Prompt DSL spec parsing
    participant KPFQ as Known prompts found?
    participant PromExp as Prompt expansion
    participant COEval as Message evaluation
    participant CCommandOutput as Chat result
    participant CNCO as Create new chat object
    participant CIDNone as Assume chat ID is NONE
    participant UpdateCODB as Chat objects file update
    participant LLMFunc as LLM Functions
    participant LLMProm as LLM Prompts

    CCommand->>IngestCODB: Chat command
    CODBOS--)IngestCODB: Chat objects file
    IngestCODB--)CODB: Chat objects
    IngestCODB->>CIDQ: Chat ID specified?
    CIDQ-->>CIDEQ: Yes
    CIDQ-->>CIDNone: No
    CIDNone->>CIDEQ: Assume chat ID is NONE
    CIDEQ-->>RECO: Yes
    CIDEQ-->>CNCO: No
    CIDEQ--)CODB: Chat objects
    RECO->>PromParse: Prompt DSL spec parsing
    PromParse--)LLMProm: LLM Prompts
    CNCO--)LLMFunc: LLM Functions
    CNCO--)CODB: Chat objects
    CNCO->>PromParse: Prompt DSL spec parsing
    PromParse->>KPFQ: Known prompts found?
    KPFQ-->>PromExp: Yes
    KPFQ-->>COEval: No
    PromExp--)LLMProm: LLM Prompts
    PromExp->>COEval: Message evaluation
    COEval--)LLMFunc: LLM evaluator invocation
    LLMFunc--)COEval: Evaluation result
    COEval->>UpdateCODB: Chat objects file update
    COEval->>CCommandOutput: Chat result

Persistent chat objects

Using a JSON file for keeping the chat objects database is a fairly straightforward idea. Efficiency considerations for "using the OS to manage the database" are probably can not that important because LLMs invocation is (much) slower in comparison.

Remark: The following quote is attributed to Ken Thompson about UNIX:

We have persistent objects, they're called files.

References

Articles, blog posts

[AA1] Anton Antonov, "Chatnik: LLM Host in the Shell — Part 1: First Examples & Design Principles", (2026), RakuForPrediction at WordPress.

Packages

[AAp1] Anton Antonov, LLMFunctionObjects, Python package, (2023-2026), GitHub/antononcube. (PyPI.org page.)

[AAp2] Anton Antonov, LLMPrompts, Python package, (2023-2025), GitHub/antononcube. (PyPI.org page.)

[AAp3] Anton Antonov, JupyterChatbook, Python package, (2023-2026), GitHub/antononcube. (PyPI.org page.)

[AAp4] Anton Antonov, Chatnik, Raku package, (2026), GitHub/antononcube.

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Chatnik

Installation

LLM access setup

Basic usage examples

A few turns chat

Apply prompt(s) to shell pipeline output

Chat objects management

Advanced usage examples

Asking for a result in specific format

Make a request, echo, and place in clipboard

Make a mind-map of a file

1

2

3

Result

Render Markdown results with dedicated programs

Tabulate the LLM personas summary

Customization

Default model

Pre-defined LLM personas

Implementation details

Architectural design

Persistent chat objects

References

Articles, blog posts

Packages

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README-work.md

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README-work.md

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Chatnik

Installation

LLM access setup

Basic usage examples

A few turns chat

Apply prompt(s) to shell pipeline output

Chat objects management

Advanced usage examples

Asking for a result in specific format

Make a request, echo, and place in clipboard

Make a mind-map of a file

1

2

3

Result

Render Markdown results with dedicated programs

Tabulate the LLM personas summary

Customization

Default model

Pre-defined LLM personas

Implementation details

Architectural design

Persistent chat objects

References

Articles, blog posts

Packages