term-llm

term-llm

A Swiss Army knife for your terminal—AI-powered commands, answers, and images at your fingertips.

Features

• Command suggestions: Natural language → executable shell commands
• Ask questions: Get answers with optional web search
• File editing: Edit code with AI assistance (supports line ranges)
• File context: Include files, clipboard, stdin, or line ranges as context (-f)
• Image generation: Create and edit images (Gemini, OpenAI, Flux)
• MCP servers: Extend with external tools via Model Context Protocol
• Multiple providers: Anthropic, OpenAI, OpenRouter, Gemini, Zen (free tier), Claude CLI, Ollama, LM Studio
• Local LLMs: Run with Ollama, LM Studio, or any OpenAI-compatible server
• Credential reuse: Works with Codex, gemini-cli credentials

$ term-llm exec "find all go files modified today"

> find . -name "*.go" -mtime 0   Uses find with name pattern
  fd -e go --changed-within 1d   Uses fd (faster alternative)
  find . -name "*.go" -newermt "today"   Alternative find syntax
  something else...

Installation

One-liner (recommended)

curl -fsSL https://raw.githubusercontent.com/samsaffron/term-llm/main/install.sh | sh

Or with options:

curl -fsSL https://raw.githubusercontent.com/samsaffron/term-llm/main/install.sh | sh -s -- --version v0.1.0 --install-dir ~/bin

Go install

go install github.com/samsaffron/term-llm@latest

Build from source

git clone https://github.com/samsaffron/term-llm
cd term-llm
go build

Setup

On first run, term-llm will prompt you to choose a provider (Anthropic, OpenAI, OpenRouter, Gemini, Zen, Ollama, or LM Studio).

Option 1: Use existing CLI credentials (recommended)

If you have Codex or gemini-cli installed and logged in, term-llm can use those credentials:

# In ~/.config/term-llm/config.yaml
providers:
  openai:
    credentials: codex       # uses Codex credentials

  gemini:
    credentials: gemini-cli  # uses gemini-cli OAuth credentials

Option 2: Use API key

Set your API key as an environment variable:

# For Anthropic
export ANTHROPIC_API_KEY=your-key

# For OpenAI
export OPENAI_API_KEY=your-key

# For OpenRouter
export OPENROUTER_API_KEY=your-key

# For Gemini
export GEMINI_API_KEY=your-key

Option 3: Use OpenRouter

OpenRouter provides a unified OpenAI-compatible API across many models. term-llm sends attribution headers by default.

# In ~/.config/term-llm/config.yaml
default_provider: openrouter

providers:
  openrouter:
    model: x-ai/grok-code-fast-1
    app_url: https://github.com/samsaffron/term-llm
    app_title: term-llm

Option 4: Use OpenCode Zen (free tier available)

OpenCode Zen provides free access to GLM 4.7 and other models. No API key required for free tier, or set ZEN_API_KEY for paid models:

# In ~/.config/term-llm/config.yaml
default_provider: zen

providers:
  zen:
    model: glm-4.7-free  # default model (free)
    # api_key: optional - leave empty for free tier, or set for paid models

Or use the --provider flag:

term-llm exec --provider zen "list files"
term-llm ask --provider zen "explain git rebase"

Model Discovery

List available models from any supported provider:

term-llm models --provider anthropic  # List Anthropic models
term-llm models --provider openrouter # List OpenRouter models
term-llm models --provider ollama     # List local Ollama models
term-llm models --provider lmstudio   # List local LM Studio models
term-llm models --json                # Output as JSON

Option 5: Use local LLMs (Ollama, LM Studio)

Run models locally with Ollama or LM Studio:

# List available models from your local server
term-llm models --provider ollama
term-llm models --provider lmstudio

# Configure in ~/.config/term-llm/config.yaml

default_provider: ollama

providers:
  ollama:
    type: openai_compatible
    base_url: http://localhost:11434/v1
    model: llama3.2:latest

  lmstudio:
    type: openai_compatible
    base_url: http://localhost:1234/v1
    model: deepseek-coder-v2

For other OpenAI-compatible servers (vLLM, text-generation-inference, etc.):

providers:
  my-server:
    type: openai_compatible
    base_url: http://your-server:8080/v1
    model: mixtral-8x7b
    models:  # optional: list models for shell autocomplete
      - mixtral-8x7b
      - llama-3-70b

The models list enables tab completion for --provider my-server:<TAB>. The configured model is always included in completions.

Option 6: Use Claude Code (claude-bin)

If you have Claude Code installed and logged in, you can use the claude-bin provider to run completions via the Claude Agent SDK. This requires no API key - it uses Claude Code's existing authentication.

# Use directly via --provider flag (no config needed)
term-llm ask --provider claude-bin "explain this code"
term-llm ask --provider claude-bin:haiku "quick question"  # use haiku model
term-llm exec --provider claude-bin "list files"           # command suggestions
term-llm ask --provider claude-bin -s "latest news"        # with web search

# Or configure as default

# In ~/.config/term-llm/config.yaml
default_provider: claude-bin

providers:
  claude-bin:
    model: sonnet  # opus, sonnet, or haiku

Features:

• No API key required - uses Claude Code's OAuth authentication
• Full tool support via MCP (exec, search, edit all work)
• Model selection: opus, sonnet (default), haiku
• Works immediately if Claude Code is installed and logged in

OpenAI-compatible providers support two URL options:

• base_url: Base URL (e.g., https://api.cerebras.ai/v1) - /chat/completions is appended automatically
• url: Full URL (e.g., https://api.cerebras.ai/v1/chat/completions) - used as-is without appending

Use url when your endpoint doesn't follow the standard /chat/completions path, or to paste URLs directly from API documentation.

Usage

term-llm exec "your request here"

Use arrow keys to select a command, Enter to execute, or press h for detailed help on the highlighted command. Select "something else..." to refine your request.

Flags

Flag	Short	Description
--provider		Override provider, optionally with model (e.g., openai:gpt-4o)
--file	-f	File(s) to include as context (supports globs, line ranges, 'clipboard')
--auto-pick	-a	Auto-execute the best suggestion without prompting
--max N	-n N	Limit to N options in the selection UI
--search	-s	Enable web search (configurable: Exa, Brave, Google, DuckDuckGo) and page reading
--native-search		Use provider's native search (override config)
--no-native-search		Force external search tools instead of native
--print-only	-p	Print the command instead of executing it
--debug	-d	Show provider debug information
--debug-raw		Emit raw debug logs with timestamps (tool calls/results, raw requests)

Examples

term-llm exec "list files by size"              # interactive selection
term-llm exec "compress folder" --auto-pick     # auto-execute best
term-llm exec "find large files" -n 3           # show max 3 options
term-llm exec "install latest node" -s          # with web search
term-llm exec "disk usage" -p                   # print only
term-llm exec --provider zen "git status"       # use specific provider
term-llm exec --provider openai:gpt-4o "list"   # provider with specific model
term-llm exec --debug-raw "list files"          # raw debug logs with timestamps
term-llm exec --provider ollama:llama3.2 "list" # use local Ollama model
term-llm exec --provider lmstudio:deepseek "list"  # use LM Studio model
term-llm ask --provider openai:gpt-5.2-xhigh "complex question"  # max reasoning
term-llm exec --provider openai:gpt-5.2-low "quick task"         # faster/cheaper

# With file context
term-llm exec -f error.log "find the cause"     # analyze a file
term-llm exec -f "*.go" "run tests for these"   # glob pattern
git diff | term-llm exec "commit message"       # pipe stdin

# Ask a question
term-llm ask "What is the difference between TCP and UDP?"
term-llm ask "latest node.js version" -s        # with web search
term-llm ask --provider zen "explain docker"    # use specific provider
term-llm ask -f code.go "explain this code"     # with file context
term-llm ask -f code.go:50-100 "explain this function"  # specific lines
term-llm ask -f clipboard "what is this?"       # from clipboard
cat README.md | term-llm ask "summarize this"   # pipe stdin
term-llm ask --debug-raw "latest zig release"   # raw debug logs with timestamps

# Edit files
term-llm edit "add error handling" -f main.go
term-llm edit "refactor loop" -f utils.go:20-40  # only lines 20-40
term-llm edit "add tests" -f "*.go" --dry-run    # preview changes
term-llm edit "use the API" -f main.go -c api/client.go  # with context

# Generate images
term-llm image "a sunset over mountains"
term-llm image "logo design" --provider flux    # use specific provider
term-llm image "make it purple" -i photo.png    # edit existing image

Debugging

Use --debug to print provider-level diagnostics (requests, model info, etc.). Use --debug-raw for a timestamped, raw view of tool calls, tool results, and reconstructed requests. Raw debug is most useful for troubleshooting tool calling and search.

Image Generation

Generate and edit images using AI models from Gemini, OpenAI, or Flux (Black Forest Labs).

term-llm image "a robot cat on a rainbow"

By default, images are:

• Saved to ~/Pictures/term-llm/ with timestamped filenames
• Displayed in terminal via icat (if available)
• Copied to clipboard (actual image data, pasteable in apps)

Image Flags

Flag	Short	Description
--input	-i	Input image to edit
--provider		Override provider (gemini, openai, flux)
--output	-o	Custom output path
--no-display		Skip terminal display
--no-clipboard		Skip clipboard copy
--no-save		Don't save to default location
--debug	-d	Show debug information

Image Examples

# Generate
term-llm image "cyberpunk cityscape at night"
term-llm image "minimalist logo" --provider flux
term-llm image "watercolor painting" -o ./art.png

# Edit existing image
term-llm image "add a hat" -i photo.png
term-llm image "make it look vintage" -i input.png --provider gemini
term-llm image "add sparkles" -i clipboard       # edit from clipboard

# Options
term-llm image "portrait" --no-clipboard        # don't copy to clipboard
term-llm image "landscape" --no-display         # don't show in terminal

Image Providers

Provider	Model	Environment Variable	Config Key
Gemini (default)	gemini-2.5-flash-image	GEMINI_API_KEY	image.gemini.api_key
OpenAI	gpt-image-1	OPENAI_API_KEY	image.openai.api_key
Flux	flux-2-pro / flux-kontext-pro	BFL_API_KEY	image.flux.api_key

Image providers use their own credentials, separate from text providers. This allows using different API keys or accounts for text vs image generation.

File Editing

Edit files using natural language instructions:

term-llm edit "add error handling" --file main.go
term-llm edit "refactor to use interfaces" --file "*.go"
term-llm edit "fix the bug" --file utils.go:45-60     # only lines 45-60
term-llm edit "use the API" -f main.go -c api/client.go  # with context files

Edit Flags

Flag	Short	Description
--file	-f	File(s) to edit (required, supports globs)
--context	-c	Read-only reference file(s) (supports globs, 'clipboard')
--dry-run		Preview changes without applying
--provider		Override provider (e.g., openai:gpt-5.2-codex)
--per-edit		Prompt for each edit separately
--debug	-d	Show debug information

Context Files

Use --context/-c to include reference files that inform the edit but won't be modified:

term-llm edit "refactor to use the client" -f handler.go -c api/client.go -c types.go

Context files are shown to the AI as read-only references. This is useful when your edit depends on types, interfaces, or patterns defined elsewhere.

You can also pipe stdin as context, which is handy for git diffs:

git diff | term-llm edit "apply these changes" -f main.go
git show HEAD~1 | term-llm edit "undo this change" -f handler.go

Line Range Syntax

Both edit and ask support line range syntax to focus on specific parts of a file:

# Edit specific lines
term-llm edit "fix this" --file main.go:11-22    # lines 11 to 22
term-llm edit "fix this" --file main.go:11-      # line 11 to end
term-llm edit "fix this" --file main.go:-22      # start to line 22

# Ask about specific lines
term-llm ask -f main.go:50-100 "explain this function"

Diff Format

term-llm supports two edit strategies:

Format	Description	Best For
replace	Multiple parallel find/replace tool calls	Most models (default)
udiff	Single unified diff with elision support	Codex models, large refactors

The udiff format uses unified diff syntax with -... elision to efficiently replace large code blocks without listing every line:

--- file.go
+++ file.go
@@ func BigFunction @@
-func BigFunction() error {
-...
-}
+func BigFunction() error {
+    return newImpl()
+}

Configure in ~/.config/term-llm/config.yaml:

edit:
  diff_format: auto  # auto, udiff, or replace

• auto (default): Uses udiff for Codex models, replace for others
• udiff: Always use unified diff format
• replace: Always use multiple find/replace calls

MCP Servers

MCP (Model Context Protocol) lets you extend term-llm with external tools—browser automation, database access, API integrations, and more.

# Add from registry
term-llm mcp add playwright              # search and install
term-llm mcp add @anthropic/mcp-server-fetch

# Add from URL (HTTP transport)
term-llm mcp add https://developers.openai.com/mcp

# Use with any command
term-llm exec --mcp playwright "take a screenshot of google.com"
term-llm ask --mcp github "list my open PRs"
term-llm chat --mcp playwright,filesystem

MCP Commands

Command	Description
mcp add <name-or-url>	Add server from registry or URL
mcp list	List configured servers
mcp test <name>	Test server connection
mcp remove <name>	Remove a server
mcp browse [query]	Browse/search the MCP registry
mcp path	Print config file path

Adding Servers

From the registry (stdio transport):

term-llm mcp add playwright           # search by name
term-llm mcp add @playwright/mcp      # exact package
term-llm mcp browse                   # interactive browser

From a URL (HTTP transport):

term-llm mcp add https://developers.openai.com/mcp
term-llm mcp add https://mcp.example.com/api

Using MCP Tools

The --mcp flag works with all commands (ask, exec, edit, chat):

# Single server
term-llm ask --mcp fetch "summarize https://example.com"
term-llm exec --mcp playwright "take a screenshot of google.com"
term-llm edit --mcp github -f main.go "update based on latest API"

# Multiple servers (comma-separated)
term-llm chat --mcp playwright,filesystem,github

# In chat, toggle servers with Ctrl+M

Configuration

MCP servers are stored in ~/.config/term-llm/mcp.json:

{
  "servers": {
    "playwright": {
      "command": "npx",
      "args": ["-y", "@playwright/mcp"]
    },
    "openai-docs": {
      "type": "http",
      "url": "https://developers.openai.com/mcp"
    },
    "github": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-github"],
      "env": {
        "GITHUB_PERSONAL_ACCESS_TOKEN": "ghp_xxx"
      }
    },
    "authenticated-api": {
      "type": "http",
      "url": "https://api.example.com/mcp",
      "headers": {
        "Authorization": "Bearer your-token"
      }
    }
  }
}

Transport Types

Type	Config	Description
stdio	command + args	Runs as subprocess (npm/pypi packages)
http	url	Connects to remote HTTP endpoint

HTTP transport uses Streamable HTTP (MCP spec 2025-03-26).

Shell Integration (Recommended)

Commands run by term-llm don't appear in your shell history. To fix this, add a shell function that uses --print-only mode.

Zsh

Add to ~/.zshrc:

tl() {
  local cmd=$(term-llm exec --print-only "$@")
  if [[ -n "$cmd" ]]; then
    print -s "$cmd"  # add to history
    eval "$cmd"
  fi
}

Bash

Add to ~/.bashrc:

tl() {
  local cmd=$(term-llm exec --print-only "$@")
  if [[ -n "$cmd" ]]; then
    history -s "$cmd"  # add to history
    eval "$cmd"
  fi
}

Then use tl instead of term-llm:

tl "find large files"
tl "install latest docker" -s      # with web search
tl "compress this folder" -a       # auto-pick best

Configuration

term-llm config        # Show current config
term-llm config edit   # Edit config file
term-llm config path   # Print config file path

Version & Updates

term-llm automatically checks for updates once per day and notifies you when a new version is available.

term-llm version       # Show version info
term-llm upgrade       # Upgrade to latest version
term-llm upgrade --version v0.2.0  # Install specific version

To disable update checks, set TERM_LLM_SKIP_UPDATE_CHECK=1.

Config is stored at ~/.config/term-llm/config.yaml:

default_provider: anthropic

providers:
  # Built-in providers - type is inferred from the key name
  anthropic:
    model: claude-sonnet-4-5

  openai:
    model: gpt-5.2
    credentials: codex  # or "api_key" (default)

  openrouter:
    model: x-ai/grok-code-fast-1
    app_url: https://github.com/samsaffron/term-llm
    app_title: term-llm

  gemini:
    model: gemini-3-flash-preview
    credentials: gemini-cli  # or "api_key" (default)

  zen:
    model: glm-4.7-free
    # api_key is optional - leave empty for free tier

  # Local LLM providers (require explicit type)
  # Run 'term-llm models --provider ollama' to list available models
  # ollama:
  #   type: openai_compatible
  #   base_url: http://localhost:11434/v1
  #   model: llama3.2:latest

  # Custom OpenAI-compatible endpoints
  # cerebras:
  #   type: openai_compatible
  #   base_url: https://api.cerebras.ai/v1  # /chat/completions appended automatically
  #   # url: https://api.cerebras.ai/v1/chat/completions  # alternative: full URL, used as-is
  #   model: llama-4-scout-17b
  #   api_key: ${CEREBRAS_API_KEY}
  #   models:  # optional: enable autocomplete for --provider cerebras:<TAB>
  #     - llama-4-scout-17b-16e-instruct
  #     - llama-4-maverick-17b-128e-instruct
  #     - qwen-3-32b

exec:
  suggestions: 3  # number of command suggestions
  # provider: openai    # override provider for exec only
  # model: gpt-4o       # override model for exec only
  instructions: |
    I use Arch Linux with zsh.
    I prefer ripgrep over grep, fd over find.

ask:
  # provider: anthropic
  # model: claude-opus-4  # use a smarter model for questions
  instructions: |
    Be concise. I'm an experienced developer.

edit:
  # provider: openai
  # model: gpt-5.2-codex  # Codex models are optimized for code edits
  diff_format: auto  # auto, udiff, or replace

image:
  provider: gemini  # gemini, openai, or flux
  output_dir: ~/Pictures/term-llm

  gemini:
    api_key: ${GEMINI_API_KEY}
    # model: gemini-2.5-flash-image

  openai:
    api_key: ${OPENAI_API_KEY}
    # model: gpt-image-1

  flux:
    api_key: ${BFL_API_KEY}
    # model: flux-2-pro

search:
  provider: duckduckgo  # exa, brave, google, or duckduckgo (default)

  # exa:
  #   api_key: ${EXA_API_KEY}

  # brave:
  #   api_key: ${BRAVE_API_KEY}

  # google:
  #   api_key: ${GOOGLE_SEARCH_API_KEY}
  #   cx: ${GOOGLE_SEARCH_CX}

Per-Command Provider/Model

Each command (exec, ask, edit) can have its own provider and model, overriding the global default:

default_provider: anthropic  # global default

providers:
  anthropic:
    model: claude-sonnet-4-5
  openai:
    model: gpt-5.2
  zen:
    model: glm-4.7-free

exec:
  provider: zen       # exec uses Zen (free)
  model: glm-4.7-free

ask:
  model: claude-opus-4  # ask uses global provider with a smarter model

edit:
  provider: openai
  model: gpt-4o       # edit uses OpenAI

Precedence (highest to lowest):

1. CLI flag: --provider openai:gpt-4o
2. Per-command config: exec.provider / exec.model
3. Global config: default_provider + providers.<name>.model

Reasoning Effort (OpenAI)

For OpenAI models, you can control reasoning effort by appending -low, -medium, -high, or -xhigh to the model name:

term-llm ask --provider openai:gpt-5.2-xhigh "complex question"  # max reasoning
term-llm exec --provider openai:gpt-5.2-low "quick task"         # faster/cheaper

Or in config:

providers:
  openai:
    model: gpt-5.2-high  # effort parsed from suffix

Effort	Description
low	Faster, cheaper, less thorough
medium	Balanced (default if not specified)
high	More thorough reasoning
xhigh	Maximum reasoning (only on gpt-5.2)

Extended Thinking (Anthropic)

For Anthropic models, you can enable extended thinking by appending -thinking to the model name:

term-llm ask --provider anthropic:claude-sonnet-4-5-thinking "complex question"

Or in config:

providers:
  anthropic:
    model: claude-sonnet-4-5-thinking  # enables 10k token thinking budget

Extended thinking allows Claude to reason through complex problems before responding. The thinking process uses ~10,000 tokens and is not shown in the output.

Web Search

When using -s/--search, some providers (Anthropic, OpenAI, Gemini) have native web search built-in. Others use external tools (configurable search provider + Jina Reader).

You can force external search even for providers with native support—useful for consistency, debugging, or when native search doesn't work well for your use case.

CLI flags:

term-llm ask "latest news" -s --no-native-search  # Force external search tools
term-llm ask "latest news" -s --native-search     # Force native (override config)

Global config (applies to all providers):

search:
  force_external: true  # Never use native search, always use external tools

Per-provider config:

providers:
  gemini:
    model: gemini-2.5-flash
    use_native_search: false  # Always use external search for this provider

  anthropic:
    model: claude-sonnet-4-5
    # use_native_search: true  # Default: use native if available

Priority (highest to lowest):

1. CLI flag: --native-search or --no-native-search
2. Global config: search.force_external: true
3. Provider config: use_native_search: false
4. Default: use native search if provider supports it

Search Providers

When using external search (non-native), you can choose from multiple search providers:

Provider	Environment Variable	Description
DuckDuckGo (default)	—	Free, no API key required
Exa	EXA_API_KEY	AI-native semantic search
Brave	BRAVE_API_KEY	Independent index, privacy-focused
Google	GOOGLE_SEARCH_API_KEY + GOOGLE_SEARCH_CX	Google Custom Search

Configure in ~/.config/term-llm/config.yaml:

search:
  provider: exa  # exa, brave, google, or duckduckgo (default)

  exa:
    api_key: ${EXA_API_KEY}

  brave:
    api_key: ${BRAVE_API_KEY}

  google:
    api_key: ${GOOGLE_SEARCH_API_KEY}
    cx: ${GOOGLE_SEARCH_CX}  # Custom Search Engine ID

Run term-llm config to see which search providers have credentials configured.

Credentials

Each provider supports a credentials field:

Provider	Value	Description
All	api_key	Use environment variable (default)
OpenAI	codex	Use Codex CLI credentials
Gemini	gemini-cli	Use gemini-cli OAuth credentials
Zen	api_key	Optional: empty for free tier, or set ZEN_API_KEY for paid models

Codex (credentials: codex):

• Reads from ~/.codex/auth.json

gemini-cli (credentials: gemini-cli):

• Reads OAuth credentials from ~/.gemini/oauth_creds.json
• Uses Google Code Assist API (same backend as gemini-cli)

Dynamic Configuration

For advanced setups, term-llm supports dynamic resolution of API keys and URLs using special prefixes. These are resolved lazily—only when actually making an API call, not when loading config.

1Password Integration (op://)

Retrieve API keys from 1Password using secret references:

providers:
  my-provider:
    type: openai_compatible
    base_url: https://api.example.com/v1
    api_key: "op://Private/My API Key/credential"

For multiple 1Password accounts, use the ?account= query parameter:

providers:
  work-llm:
    type: openai_compatible
    base_url: https://llm.company.com/v1
    api_key: "op://Engineering/LLM Service/api_key?account=company.1password.com"

This requires the 1Password CLI (op) to be installed and signed in.

DNS SRV Records (srv://)

Discover server endpoints dynamically via DNS SRV records:

providers:
  internal-llm:
    type: openai_compatible
    url: "srv://_llm._tcp.internal.company.com/v1/chat/completions"
    api_key: ${LLM_API_KEY}

The SRV record is resolved to https://host:port/path. This is useful for:

• Load-balanced services with multiple backends
• Internal services with dynamic IPs
• Kubernetes services exposed via external-dns

Shell Commands ($())

Execute arbitrary shell commands to get values:

providers:
  vault-backed:
    type: openai_compatible
    base_url: https://api.example.com/v1
    api_key: "$(vault kv get -field=api_key secret/llm)"

  aws-secrets:
    type: openai_compatible
    base_url: https://api.example.com/v1
    api_key: "$(aws secretsmanager get-secret-value --secret-id llm-key --query SecretString --output text)"

Combined Example

Using SRV discovery with 1Password credentials:

providers:
  production-llm:
    type: openai_compatible
    model: "Qwen/Qwen3-30B-A3B"
    url: "srv://_vllm._tcp.ml.company.com/v1/chat/completions"
    api_key: "op://Infrastructure/vLLM Cluster/credential?account=company.1password.com"

When you run term-llm config, these show as [set via 1password] or [set via command] without actually resolving the values (no 1Password prompt until you make an API call).

Diagnostics

Enable diagnostic logging to capture detailed information when edits fail and retry. This is useful for debugging and tuning prompts:

diagnostics:
  enabled: true
  # dir: /custom/path  # optional, defaults to ~/.local/share/term-llm/diagnostics/

When an edit fails and retries, two files are written:

• edit-retry-{timestamp}.json - Structured data for programmatic analysis
• edit-retry-{timestamp}.md - Human-readable with syntax-highlighted code blocks

Each diagnostic captures:

• Provider and model used
• Full system and user prompts
• LLM's partial response before failure
• Failed search pattern or diff
• Current file content
• Error reason

Shell Completions

Generate and install shell completions:

term-llm config completion zsh --install   # Install for zsh
term-llm config completion bash --install  # Install for bash
term-llm config completion fish --install  # Install for fish

License

MIT