Chatnik 0.1.0

CLI scripts for conversing with persistent LLM personas.

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):

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

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

# Yoda, I am. Jedi Master, wise and old. Help you, I can. What seek you, hmm?

2. Continue the conversation with "yoda1":

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

# Green, my lightsaber is, yes. Jedi Consulars often wield green, they do. Focus on the Force and wisdom, they represent. Since my early days as a Jedi, carry green, I have. Strong in the Force, it is. Hmmm.

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

Remark: The script chatnik can be used instead of llm_chat.

Apply prompt(s) to shell pipeline output

Summarize a file using the prompt "Summarize":

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

# Chatnik is a Python package that provides CLI scripts for persistent interaction with multiple LLM personas using OS files, translating the Raku package Chatnik into Python and enabling use of multiple LLMs and prompt collections from LLMPrompts. It includes scripts like `llm_chat` for chatting with LLM personas and `llm_chat_meta` for managing chat objects, supports various LLM providers, and allows advanced usage such as formatted output, clipboard integration, and mind-mapping. The package stores chat objects in a JSON file, supports customization through environment variables, and integrates with OS shell functionalities for flexible and persistent LLM interactions.

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'

# Chatnik — это пакет Python, который предоставляет CLI-скрипты для постоянного взаимодействия с несколькими персонажами LLM с использованием файлов ОС, переводя пакет Raku Chatnik на Python и обеспечивая использование нескольких провайдеров LLM и расширений подсказок из LLMPrompts. Он предлагает команды, такие как `llm_chat` для общения и `llm_chat_meta` для управления объектами чата, поддерживает расширенные сценарии использования, такие как форматированный вывод, интеграция с буфером обмена и майнд-мэппинг, а также позволяет настраивать работу через переменные окружения и предопределённые персонажи LLM. Архитектура пакета основана на хранении объектов чата в JSON-файле, управляемом ОС, с бэкендом, который обрабатывает парсинг подсказок, их расширение, вызов LLM и сохранение объектов чата.

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

# usage: llm_chat_meta [-h] [-i CHAT_ID] [--all] [-n N] [--index INDEX]
#                      [--format FORMAT]
#                      command
# 
# Meta processing of persistent LLM-chat objects.
# 
# positional arguments:
#   command               Command, one of: card, clear, delete, file, first-
#                         message, last-message, list, load-llm-personas,
#                         message, messages.
# 
# options:
#   -h, --help            show this help message and exit
#   -i, --id, --chat-id CHAT_ID
#   --all
#   -n N
#   --index INDEX
#   --format FORMAT

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

llm_chat_meta list --format=json

# [{"chat-id": "NONE", "context": "Summarize the following text using exactly 3 sentences. Do not add details or editorialize.\n\nThe text to summarize is:\n\n", "messages": 2, "llm-configuration": {"name": "chatgpt", "model": "gpt-4.1-mini"}}, {"chat-id": "rt", "context": "Translate the following text into Russia. Respond with only the translated text. Do not include any explanation or summary.\n\nn", "messages": 2, "llm-configuration": {"name": "chatgpt", "model": "gpt-4.1-mini"}}, {"chat-id": "yoda1", "context": "You are Yoda. \nRespond to ALL inputs in the voice of Yoda from Star Wars. \nBe sure to ALWAYS use his distinctive style and syntax. Vary sentence length.\n", "messages": 4, "llm-configuration": {"name": "chatgpt", "model": "gpt-4.1-mini"}}]

Here we see the messages of "yoda1":

llm_chat_meta messages -i yoda1

# 0 : {"content": "hi who are you", "role": "user", "timestamp": 1777776637.491467}
# 1 : {"content": "Yoda, I am. Jedi Master, wise and old. Help you, I can. What seek you, hmm?", "role": "assistant", "timestamp": 1777776639.539155}
# 2 : {"content": "since when do you use a green light saber", "role": "user", "timestamp": 1777776640.151364}
# 3 : {"content": "Green, my lightsaber is, yes. Jedi Consulars often wield green, they do. Focus on the Force and wisdom, they represent. Since my early days as a Jedi, carry green, I have. Strong in the Force, it is. Hmmm.", "role": "assistant", "timestamp": 1777776641.5899498}

Here we clear the messages:

llm_chat_meta clear -i yoda1

# Cleared the messages of chat object yoda1.

Remark: Calling the script chatnik with the command meta has the same effect as llm_chat_meta. For example, chatnik meta clear -i yoda1 can be used instead of the previous command.

---

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"' 

# ```json
# [
#   {
#     "State": "Acre",
#     "Population": 878578
#   },
#   {
#     "State": "Alagoas",
#     "Population": 3432783
#   },
#   {
#     "State": "Amapá",
#     "Population": 846746
#   },
#   {
#     "State": "Amazonas",
#     "Population": 4287179
#   },
#   {
#     "State": "Bahia",
#     "Population": 14744216
#   },
#   {
#     "State": "Ceará",
#     "Population": 9187103
#   },
#   {
#     "State": "Distrito Federal",
#     "Population": 3477520
#   },
#   {
#     "State": "Espírito Santo",
#     "Population": 3790777
#   },
#   {
#     "State": "Goiás",
#     "Population": 7049167
#   },
#   {
#     "State": "Maranhão",
#     "Population": 7480937
#   },
#   {
#     "State": "Mato Grosso",
#     "Population": 3567237
#   },
#   {
#     "State": "Mato Grosso do Sul",
#     "Population": 3033651
#   },
#   {
#     "State": "Minas Gerais",
#     "Population": 21523251
#   },
#   {
#     "State": "Pará",
#     "Population": 8717926
#   },
#   {
#     "State": "Paraíba",
#     "Population": 4116775
#   },
#   {
#     "State": "Paraná",
#     "Population": 11527436
#   },
#   {
#     "State": "Pernambuco",
#     "Population": 9615933
#   },
#   {
#     "State": "Piauí",
#     "Population": 6576496
#   },
#   {
#     "State": "Rio de Janeiro",
#     "Population": 17425727
#   },
#   {
#     "State": "Rio Grande do Norte",
#     "Population": 3507013
#   },
#   {
#     "State": "Rio Grande do Sul",
#     "Population": 11366663
#   },
#   {
#     "State": "Rondônia",
#     "Population": 1127557
#   },
#   {
#     "State": "Roraima",
#     "Population": 517096
#   },
#   {
#     "State": "Santa Catarina",
#     "Population": 7149582
#   },
#   {
#     "State": "São Paulo",
#     "Population": 46278787
#   },
#   {
#     "State": "Sergipe",
#     "Population": 2300427
#   },
#   {
#     "State": "Tocantins",
#     "Population": 1572922
#   }
# ]
# ```

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

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

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

2

1. Put file's content to be the positional input argument
2. 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

1. Give the prompt "MermaidDiagram" as input
2. 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

# ┌────────────────────┬───────────────────────────────────────────────────────────────────────────────┬───────┬────────┐
# │ llm-configuration  │                                    context                                    │chat-id│messages│
# ├────────────────────┼───────────────────────────────────────────────────────────────────────────────┼───────┼────────┤
# │┌─────┬────────────┐│                                                                               │       │        │
# ││model│gpt-4.1-mini││Summarize the following text using exactly 3 sentences. Do not add details or  │       │        │
# │├─────┼────────────┤│editorialize. The text to summarize is:                                        │NONE   │2       │
# ││name │chatgpt     ││                                                                               │       │        │
# │└─────┴────────────┘│                                                                               │       │        │
# ├────────────────────┼───────────────────────────────────────────────────────────────────────────────┼───────┼────────┤
# │┌──────┬───────────┐│                                                                               │       │        │
# ││model │gemma3:12b ││                                                                               │       │        │
# │├──────┼───────────┤│                                                                               │beta   │2       │
# ││ name │ollama     ││                                                                               │       │        │
# │└──────┴───────────┘│                                                                               │       │        │
# ├────────────────────┼───────────────────────────────────────────────────────────────────────────────┼───────┼────────┤
# │┌─────┬────────────┐│                                                                               │       │        │
# ││model│gpt-4.1-mini││Translate the following text into Russia. Respond with only the translated     │       │        │
# │├─────┼────────────┤│text. Do not include any explanation or summary. n                             │rt     │2       │
# ││name │chatgpt     ││                                                                               │       │        │
# │└─────┴────────────┘│                                                                               │       │        │
# ├────────────────────┼───────────────────────────────────────────────────────────────────────────────┼───────┼────────┤
# │┌─────┬────────────┐│                                                                               │       │        │
# ││model│gpt-4.1-mini││You are Yoda. Respond to ALL inputs in the voice of Yoda from Star Wars. Be    │       │        │
# │├─────┼────────────┤│sure to ALWAYS use his distinctive style and syntax. Vary sentence length.     │yoda1  │0       │
# ││name │chatgpt     ││                                                                               │       │        │
# │└─────┴────────────┘│                                                                               │       │        │
# └────────────────────┴───────────────────────────────────────────────────────────────────────────────┴───────┴────────┘

---

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

---

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.