bog-agents-cli 0.7.3

A coding agent in your terminal. 50+ commands, any LLM provider, persistent memory, git workflow, code review, plan mode, remote sandboxes, and CI/CD automation. One install, no code required.

Bog Agents CLI

A coding agent that lives in your terminal. Point it at the work, step back, let it run.

No scaffolding. No config ceremony. One install and you've got file access, a shell, git, code review, planning, sub-agents — the whole outfit. Works with any LLM that does tool calls: Anthropic, OpenAI, Bedrock, Google, Ollama, and a dozen others.

Built on the Bog Agents SDK and LangGraph. MIT.

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Install

pip install bog-agents-cli

pip install 'bog-agents-cli[anthropic]'      # Claude
pip install 'bog-agents-cli[bedrock]'        # AWS Bedrock
pip install 'bog-agents-cli[ollama]'         # Local models, no key
pip install 'bog-agents-cli[all-providers]'  # Everything

Or with uv:

uv tool install 'bog-agents-cli[anthropic]'

First run

bog-agents

If there's a key in your environment — ANTHROPIC_API_KEY, OPENAI_API_KEY, AWS creds, anything — it finds them and gets moving. No key, no problem: the setup wizard handles the introductions in about thirty seconds.

bog-agents -M claude-sonnet-4-6
bog-agents -M gpt-4o
bog-agents -M ollama:llama3              # local, free
bog-agents -M bedrock_converse:anthropic.claude-sonnet-4-6

Something feeling off? Ask it.

bog-agents --doctor

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What it does

Runs in a real TUI. Streaming tokens, syntax highlighting, inline diffs, approve tools one-by-one or not at all. Terminal only — no browser, no Electron, no nonsense.

Keeps state between runs. Every session is a thread you can come back to. Memory, summaries, labels, and per-project context persist in ~/.bog-agents/.

Scripts cleanly. -n, -p, --json, --no-stream, and proper exit codes make it a tool you can pipe, cron, and drop into CI without regret.

Separates concerns. Named agents each get their own prompt, memory, skills, and thread history. A researcher, a reviewer, a debugger — all on the same install.

Scales out. Remote sandboxes for isolated work. MCP for external tools. An HTTP server mode when something else needs to drive.

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Slash commands

Hit / in an interactive session and autocomplete shows you everything. The commands that carry the most weight:

Command	What it does
/model	Switch LLM mid-session
/plan	Read-only mode. Agent scouts the territory without touching anything
/effort	Reasoning depth: low, medium, high, max
/review	Review staged changes, a commit, or specific files
/diff	Show pending file changes as unified diffs
/compact	Trim conversation context when it gets heavy
/cost	Token usage, cost estimate, budget
/context	Context-window usage with a breakdown
/remember	Persist an insight to agent memory across sessions
/agent	Spawn and manage parallel agent threads
/background	Queue local work and watch it from the side
/dashboard	Live multi-agent snapshot
/worktree	Isolated git worktrees for parallel streams
/resume	Resume latest, specific, or tagged threads
/threads	Browse and manage past conversations
/recommend	Persona-based code review
/onboard	Walk a new codebase with you
/mcp	Show active MCP servers and tools
/plugin	Install, list, enable, disable extensions
/remote	Submit, track, and stop remote tasks
/doctor	Health check: Python, packages, keys, tools, sandboxes
/profile	Switch configuration presets
/session	Label, tag, summarize, and export a thread
/keybindings	Show bindings or the config path
/clear	Start a fresh thread
/quit	Hang up your hat

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Non-interactive mode

Where the automation lives. One command, one task, exit code tells the story.

# Basic task — no shell, safe by default
bog-agents -n 'Summarize the README'

# Grant shell access with a curated allow-list
bog-agents -n 'Run the test suite' --shell-allow-list recommended

# Specific commands only
bog-agents -n 'Search logs for errors' --shell-allow-list cat,grep,find

# Unrestricted shell — trusted environments only
bog-agents -n 'Fix the failing tests and commit' --shell-allow-list all

# Clean output for piping
bog-agents -p 'Explain this code' < my_file.py
bog-agents -p 'Write a code review' < pr_diff.patch | tee review.md

# Machine-readable
bog-agents -n 'List all TODO comments' --json

# Fix an issue and open a PR in one shot
bog-agents -n 'Fix issue #42' --pr --shell-allow-list all

# Draft PR against a specific branch
bog-agents -n 'Add dark mode' --pr --pr-base develop --pr-draft --shell-allow-list all

Exit codes: 0 success, 1 error, 130 interrupted.

Shell access is off by default. Three ways to turn it on:

• --shell-allow-list recommended — curated safe commands (ls, cat, grep, find, wc, more)
• --shell-allow-list ls,cat,grep — roll your own
• --shell-allow-list all — no guardrails

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Threads and memory

Come back to what you were working on.

bog-agents -r              # Latest thread
bog-agents -r abc123       # Specific thread
bog-agents threads list    # See 'em all
bog-agents threads delete abc123

Persistent memory lives in ~/.bog-agents/<agent>/AGENTS.md. Use /remember to add a note the agent should carry forward. Use /session to attach labels, tags, project names, and summaries to the current thread so you can find it later.

Project-level memory lives in .bog-agents/AGENTS.md at your repo root — check it in, and every teammate on this codebase gets the same context when they fire up the CLI.

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Skills and extensions

Teach the agent something once, reuse it forever. A skill is a SKILL.md manifest plus whatever scripts and prompts it needs. Extensions bundle skills and slash commands together.

bog-agents skills list
bog-agents skills create              # Scaffold a new skill
bog-agents skills info my-skill
bog-agents skills delete my-skill

In the TUI:

/plugin install <path-or-url>
/plugin info <name>
/plugin enable <name>
/plugin disable <name>

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Named agents

Run separate agents with separate memory, prompts, and history. Same install, different hats.

bog-agents -a researcher
bog-agents -a reviewer
bog-agents list                           # All agents
bog-agents reset --agent researcher       # Back to default prompt

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Remote sandboxes

When the work's too rough for the local machine, or you want it to run somewhere else while you get on with yours.

bog-agents --sandbox modal                # Modal serverless
bog-agents --sandbox daytona              # Daytona cloud
bog-agents --sandbox runloop              # Runloop
bog-agents --sandbox-id existing-id       # Hop back on an existing sandbox

Inside the TUI, /remote queues tracked tasks:

/remote config
/remote submit --label scout --branch-prefix fix "investigate the failing tests"
/remote status <id>
/remote stop <id>

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MCP (Model Context Protocol)

External tools, loaded on demand. The CLI auto-finds .mcp.json in your project, or you can point at one.

bog-agents --mcp-config ./my-mcp-servers.json
bog-agents --no-mcp                       # Off
bog-agents --trust-project-mcp            # Skip the approval prompt

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Server modes

Put the agent behind an HTTP API when another tool wants to drive.

bog-agents --serve                                    # localhost:8420
bog-agents --serve --serve-host 0.0.0.0 --serve-port 9000

Or run as an Agent Client Protocol server, for Zed:

bog-agents --acp

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Model configuration

Detection order

No -M flag? The CLI looks for credentials in this order and picks the first it finds:

1. [models].default in ~/.bog-agents/config.toml
2. [models].recent (last /model switch)
3. ANTHROPIC_API_KEY
4. OPENAI_API_KEY
5. AWS Bedrock (~/.aws/credentials, AWS_ACCESS_KEY_ID, AWS_PROFILE)
6. GOOGLE_API_KEY
7. GOOGLE_CLOUD_PROJECT (Vertex AI)
8. NVIDIA_API_KEY
9. Ollama (if the ollama binary is on PATH)
10. Setup wizard (if nothing found)

Setting a default

bog-agents --default-model anthropic:claude-sonnet-4-6
bog-agents --default-model                    # Show current
bog-agents --clear-default-model              # Remove

Config file

Advanced knobs live in ~/.bog-agents/config.toml:

[models]
default = "anthropic:claude-sonnet-4-6"

[providers.anthropic]
temperature = 0.7
max_tokens = 8192

[providers.openai]
api_base = "https://my-proxy.example.com/v1"

Runtime overrides

bog-agents -M gpt-4o --model-params '{"temperature": 0.2, "max_tokens": 4096}'
bog-agents -M claude-sonnet-4-6 --profile-override '{"max_input_tokens": 100000}'

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Providers

Use provider:model format. Any LangChain-compatible chat model works.

Provider	Extra	Example
Anthropic	anthropic	anthropic:claude-sonnet-4-6
OpenAI	(included)	openai:gpt-4o
AWS Bedrock	bedrock	bedrock_converse:anthropic.claude-sonnet-4-6
Google AI	google-genai	google_genai:gemini-2.5-pro
Vertex AI	vertexai	google_vertexai:gemini-2.5-pro
Ollama	ollama	ollama:llama3
Groq	groq	groq:llama-3.3-70b
DeepSeek	deepseek	deepseek:deepseek-chat
Fireworks	fireworks	fireworks:llama-v3p3-70b
Mistral	mistralai	mistralai:mistral-large
NVIDIA	nvidia	nvidia:nemotron-70b
OpenRouter	openrouter	openrouter:meta-llama/llama-3
Perplexity	perplexity	perplexity:sonar-pro
xAI	xai	xai:grok-2
Cohere	cohere	cohere:command-r-plus
Together	(via litellm)	litellm:together/llama-3-70b
HuggingFace	huggingface	huggingface:meta-llama/Llama-3
Azure OpenAI	(via openai)	azure_openai:gpt-4o

Local Ollama: which model to use

Ollama's chat API mimics OpenAI's tools-API JSON schema. Models trained against that exact schema engage tools cleanly; models trained against other formats (Mistral's [TOOL_CALLS]{}, Hermes' <tool_call>{}</tool_call>, Qwen's chat-template tool call) emit calls in the message text and Ollama's adapter doesn't translate them. The CLI ships a parser middleware that recovers most text-shaped tool calls automatically when you select an ollama: model, but recovery is best-effort.

• Recommended: ollama:gpt-oss:20b — OpenAI tools-API native, works end-to-end with no recovery needed. Fits in 16GB of VRAM.
• Recovers via parser: ollama:mistral-nemo:12b, ollama:hermes3:8b, some ollama:qwen2.5-coder runs.
• Doesn't work: ollama:deepseek-coder-v2:16b (Ollama's manifest doesn't expose the tools capability — see ollama/ollama#3303 if you want to nudge upstream), ollama:starcoder2, ollama:codellama.

Run bog-agents --doctor to see whether your configured default Ollama model is on the known-good list.

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Recipes for CI and scripting

# Code review in CI
git diff main...HEAD | bog-agents -p 'Review this diff for bugs and style issues'

# Commit message from staged changes
bog-agents -p 'Write a conventional commit message for the staged changes' \
  --shell-allow-list git

# Automated refactor
bog-agents -n 'Rename getUserData to fetch_user_data across the codebase' \
  --shell-allow-list recommended

# Docstring pass
bog-agents -n 'Generate docstrings for all public functions in src/' \
  --shell-allow-list recommended

# Security audit, JSON out
bog-agents -n 'Audit this repo for security vulnerabilities' \
  --shell-allow-list recommended --json

# Issue bot: fix and open a PR
bog-agents -n 'Fix issue #123' --pr --shell-allow-list all

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Environment variables

Variable	Purpose
ANTHROPIC_API_KEY	Anthropic
OPENAI_API_KEY	OpenAI
AWS_ACCESS_KEY_ID / AWS_PROFILE	AWS Bedrock
GOOGLE_API_KEY	Google AI
GOOGLE_CLOUD_PROJECT	Vertex AI
NVIDIA_API_KEY	NVIDIA
TAVILY_API_KEY	Tavily web search
BOG_AGENTS_SHELL_ALLOW_LIST	Default shell allow-list
BOG_AGENTS_LANGSMITH_PROJECT	LangSmith tracing project

Keys can also sit in a project-level .env or a user-level ~/.bog-agents/.env.

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CLI reference

bog-agents [OPTIONS] [COMMAND]

Commands:
  list                          List agents
  reset                         Reset an agent's prompt
  skills                        Manage skills (list/create/info/delete)
  threads                       Manage threads (list/delete)

Core:
  -M, --model MODEL             Model to use
  -a, --agent NAME              Agent name (default: agent)
  -r, --resume [ID]             Resume a thread
  -m, --message TEXT            Auto-submit prompt on start
  --auto-approve                Auto-approve tool calls
  --doctor                      Run diagnostics
  -v, --version                 Show versions
  -h, --help                    Show help

Non-Interactive:
  -n, --non-interactive MSG     Run task and exit
  -p, --print TEXT              Clean output mode (-n + -q)
  -q, --quiet                   Suppress UI chrome
  --no-stream                   Buffer response
  --json                        JSON output
  --shell-allow-list CMDS       Shell access control
  --pr                          Create PR from output
  --pr-base BRANCH              PR base branch
  --pr-draft                    Draft PR

Model:
  --model-params JSON           Extra model kwargs
  --profile-override JSON       Override profile fields
  --default-model [MODEL]       Set/show default model
  --clear-default-model         Clear default

Sandbox:
  --sandbox TYPE                Sandbox provider
  --sandbox-id ID               Reuse existing sandbox
  --sandbox-setup PATH          Setup script

MCP:
  --mcp-config PATH             MCP config file
  --no-mcp                      Disable MCP
  --trust-project-mcp           Trust project MCP

Server:
  --serve                       HTTP API mode
  --serve-host HOST             API host
  --serve-port PORT             API port
  --acp                         ACP server mode

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Requirements

• Python 3.11+
• At least one LLM provider (key or local model)

Contributing

See CONTRIBUTING.md.

License

MIT.

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The trail's marked. Saddle up.