ollama-agentic 1.0.2

A beautiful, agentic CLI for Ollama — run local LLMs with auto tool-calling, memory, and more

ollama-agentic

A beautiful, agentic terminal interface for Ollama — run local LLMs with auto tool-calling, long-term memory, git integration, concurrent subagents, and semantic code search.

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⚠️ Requirement: Ollama must be installed first

This CLI is a frontend for Ollama. It will not work without Ollama installed and running on your machine.

1. Download and install Ollama from ollama.com/download
2. Start it: ollama serve (or open the Ollama desktop app)
3. Pull a model: ollama pull mistral or ollama pull llama3.1:8b

Then install and launch this CLI:

pip install ollama-agentic
ollama-cli

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Features

• ⚡ Auto mode — model autonomously calls tools to complete tasks (/auto)
• 🐝 Swarm agents — /swarm splits complex tasks across parallel background agents
• 🔍 Semantic code search (RAG) — AST-aware local codebase indexing, no API needed
• 🌿 Git integration — /git status, diff, log, commit (with AI messages), branch, stash
• 🔁 Iterative debug loop — /run file.py auto-fixes errors until code passes
• 📋 Plan executor — /plan <goal> breaks goals into typed steps and executes them
• 🧠 Long-term memory — /remember stores facts that persist across sessions
• ⬇️ Arrow-key model picker — /install lets you browse and download 25+ models
• 🔧 Agent tools — /shell, /file, /fetch, /ls inject real context into chats
• 💾 Conversation saving — /save and /load persist chats as JSON
• 🎭 Personas — save and load system prompt presets
• 🆚 Compare mode — run the same prompt through two models side by side

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Usage

ollama-cli                       # start chatting
ollama-cli --model qwen2.5:7b    # start with a specific model
ollama-cli --auto                # start in autonomous agent mode
ollama-cli --compare             # compare two models side by side

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Commands

Chat & Navigation

Command	Description
/cls	Clear screen (keep context)
/clear	Clear conversation and screen
Ctrl+L	Clear screen
/retry	Regenerate last response
/tokens	Toggle token count display

Models

Command	Description
/model	Switch active model (arrow-key picker)
/current	Show currently active model
/install	Browse & install models from catalogue
/models	List all installed models
/compare	Compare two models side by side

Agentic

Command	Description
/auto	Toggle autonomous tool-calling mode
/plan <goal>	Break a goal into steps and execute
/run <file.py>	Run code, auto-fix errors in a loop
/swarm <task>	Decompose task across parallel background agents
/swarm-status	Check swarm progress
/swarm-status full	See full output from each agent

Git

Command	Description
/git	Show git status
/git diff	Show unstaged diff, inject into context
/git diff staged	Show staged diff
/git log	Recent commits with timestamps
/git branch	List branches
/git branch <n>	Switch branch
/git commit	Stage and commit (AI message option)
/git stash	Stash changes

RAG — Semantic Code Search

Command	Description
/rag	Show index status
/rag index	Incremental index of project
/rag index full	Wipe and rebuild index
/rag search <query>	Semantic search over codebase
/rag auto	Toggle auto-inject relevant chunks into every chat
/rag clear	Wipe the index

Memory

Command	Description
/remember <fact>	Store a fact in long-term memory
/memories	List all stored memories
/forget <id>	Delete a memory by ID

Context Injection

Command	Description
/file <path>	Load a file into context
/shell <cmd>	Run a shell command, inject output
/fetch <url>	Fetch a webpage into context
/ls <path>	Inject a directory listing
/context	View or clear active injections

Conversations & Personas

Command	Description
/save <n>	Save conversation
/load <n>	Load conversation
/list	List saved conversations
/system <prompt>	Set a system prompt
/persona <n>	Load a saved persona
/personas	List saved personas
/save-persona <n>	Save current system prompt as persona

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Swarm Agents

/swarm decomposes a complex task into independent subtasks and runs them as parallel agents in the background. You keep using the CLI while they work.

you › /swarm research React Server Components vs traditional SSR
you › /swarm-status          # check mid-task
you › /swarm-status full     # read each agent's full output

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RAG — Semantic Code Search

Run from inside any git repo. Uses AST-aware chunking for Python and sliding-window chunking for all other languages. Embeddings run fully offline via sentence-transformers.

RAG dependencies are optional — the CLI works fine without them:

pip install lancedb sentence-transformers tree-sitter tree-sitter-python

you › /rag index             # index your project (~seconds)
you › /rag search auth flow  # semantic search
you › /rag auto              # auto-inject relevant chunks into every chat

The index lives in .ollama_rag/ inside your project. Only changed files are re-indexed on subsequent runs.

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Agent Mode

Toggle with /auto or launch with --auto. The model calls tools, reads results, and loops until the task is done.

⚡ you › look at main.py and find any bugs
⚡ you › write a web scraper for hacker news and run it
⚡ you › set up a basic Flask app in this folder

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Config & Data

Path	Description
~/.ollama_cli_config.json	Settings (model, auto mode, etc)
~/.ollama_cli_history	Input history
~/.ollama_cli_memory.json	Long-term memories
~/.ollama_cli_saves/	Saved conversations
~/.ollama_cli_personas/	Saved personas
.ollama_rag/	RAG vector index (per project, inside project root)

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Requirements

• Python 3.10+
• macOS, Linux, or Windows
• Ollama installed and running — ollama.com/download

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Roadmap

• Project memory — /understand deep-reads your codebase and stores structured knowledge
• MCP server support — connect to filesystem, GitHub, Postgres, browser tools
• TUI dashboard — split-pane interface with live swarm agent view
• API key integrations — Claude, OpenAI, Gemini, Groq as model backends

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Contributing

PRs and issues welcome at github.com/Akhil123454321/ollama-cli. Keep changes focused and include tests where appropriate.

License

MIT — see LICENSE