I Built multi-ai-cli to Kill Browser Tab Hell: True Multi-LLM Orchestration + Blackboard Memory in Your Terminal (v0.13.0)

# Example: Mapping Engines to Agents

ENGINE.openai_main = gpt-4o

ENGINE.local_coder = qwen2.5-coder:14b  # Yes, Ollama works perfectly!



AGENT.gpt.architect = openai_main

AGENT.local.code = local_coder

% @sequence -e

[*] Editor prompt captured (182 chars, 13 lines):

--- Preview ---

[

  @gpt Remember exactly this token: ZEBRA-9182

||

  @gemini Remember exactly this token: ZEBRA-9182

]

-> @scrub gpt ->

[

  @gpt What is the token?

||

  @gemini What is the token?

]

--- End Preview ---

[*] Sequence Execution: 3 steps detected.

==================================================

[*] Executing Step 1/3 [PARALLEL: 2 tasks]...

    Task 1: @gpt Remember exactly this token: ZEBRA-9182

    Task 2: @gemini Remember exactly this token: ZEBRA-9182



--- GPT ---

Okay — I’ll remember this token exactly:



ZEBRA-9182





--- Gemini ---

I have memorized the token exactly: **ZEBRA-9182**. 



Just let me know whenever you need me to recall it!



[✓] Step 1/3 completed (all parallel tasks done).

--------------------------------------------------

[*] Executing Step 2/3...

    Command: @scrub gpt

[*] GPT memory scrubbed.

[✓] Step 2/3 completed successfully.

--------------------------------------------------

[*] Executing Step 3/3 [PARALLEL: 2 tasks]...

    Task 1: @gpt What is the 'token?'

    Task 2: @gemini What is the 'token?'



--- GPT ---

Which token do you mean?



If you mean:

- API token: I can’t see your secrets or account tokens.

- A “token” in text/LLMs: it’s a chunk of text a model processes, often a word or part of a word.

- Auth/session token in an app: it’s a credential used to prove identity.



Tell me the context and I’ll answer precisely.





--- Gemini ---

The token is ZEBRA-9182.



[✓] Step 3/3 completed (all parallel tasks done).

==================================================

[✓] Sequence Execution complete. All 3 steps succeeded.

% 

% @sequence -e

[*] Editor prompt captured (225 chars, 7 lines):

--- Preview ---



@sh "echo '<p>Hello World</p>'" -w raw.html

->

@gpt "Extract the text from this HTML" -r raw.html -w text.txt

->

@claude "Translate this text into Japanese" -r text.txt

->

@gemini "Translate this text into French" -r text.txt

--- End Preview ---

[*] Sequence Execution: 4 steps detected.

==================================================

[*] Executing Step 1/4...

    Command: @sh 'echo '"'"'<p>Hello World</p>'"'"'' -w raw.html

[*] @sh: Executing: echo '<p>Hello World</p>'

[✓] @sh: SUCCESS (exit code: 0, 12.0ms)

--- stdout ---

<p>Hello World</p>

--- end stdout ---

[*] @sh: Artifact saved to 'raw.html' (format: text).

[✓] Step 1/4 completed successfully.

--------------------------------------------------

[*] Executing Step 2/4...

    Command: @gpt 'Extract the text from this HTML' -r raw.html -w text.txt

[*] Result saved to 'text.txt' (mode: raw).                                                                            

[✓] Step 2/4 completed successfully.

--------------------------------------------------

[*] Executing Step 3/4...

    Command: @claude 'Translate this text into Japanese' -r text.txt



--- Claude ---

--- [File: text.txt] ---

こんにちは世界

--- [End of File: text.txt] ---



[✓] Step 3/4 completed successfully.

--------------------------------------------------

[*] Executing Step 4/4...

    Command: @gemini 'Translate this text into French' -r text.txt



--- Gemini ---

Bonjour le monde



[✓] Step 4/4 completed successfully.

==================================================

[✓] Sequence Execution complete. All 4 steps succeeded.

% 

# Fetch code from GitHub, run parallel reviews, and merge the results

@sequence

-> @github.file --repo "myproj/repo" --path "app.py" -w code.md

-> [ @claude.review "Find bugs" -r code.md -w claude_review.md

  || @gemini.plan "Optimization ideas" -r code.md -w gemini_opt.md

  || @gpt.code "Add test cases" -r code.md -w tests.py ]

-> @gpt "Merge the 3 reviews above and create the final version" \

   -r claude_review.md -r gemini_opt.md -r tests.py -w final.py

# Example 1: Preprocess text before passing it to an AI agent

@sh "cat raw.html | sed 's/<[^>]*>//g'" -w text.txt



# Example 2: Inspect local project files or command output

@sh "ls -la src"

@sh "git diff --stat"



# Example 3: Run local scripts or test suites seamlessly within your workflow

@sh "python scripts/build_index.py"

@sh "pytest tests/"

# Example 4: Run linters and let Claude fix the errors

@sh "flake8 app.py" -w lint.md

->

@claude "Fix all these lint errors" -r lint.md -r app.py -w fixed.py

git clone [email protected]:ashiras/multi-ai-cli.git

cd multi-ai-cli

uv sync                     # Install dependencies

uv run multi-ai --version   # Verify the installation



# Run it like this from now on!

# uv run multi-ai "@gpt Hello world"

# Example for macOS / Linux

curl -L -o multi-ai https://github.com/ashiras/multi-ai-cli/releases/download/v0.13.0/multi-ai

chmod +x multi-ai

sudo mv multi-ai /usr/local/bin/   # Or to ~/bin/ etc.

multi-ai --version