Built-in Plugins

Indagis ships a small set of plugins bundled with the repository. They live under <repo>/plugins/<name>/ and load automatically alongside user-installed plugins in ~/.indagis/plugins/. They use the same plugin surface as third-party plugins — hooks, tools, slash commands — just maintained in-tree.

See the Plugins page for the general plugin system, and Build a Indagis Plugin to write your own.

How discovery works

The PluginManager scans four sources, in order:

1. Bundled — <repo>/plugins/<name>/ (what this page documents)
2. User — ~/.indagis/plugins/<name>/
3. Project — ./.indagis/plugins/<name>/ (requires INDAGIS_ENABLE_PROJECT_PLUGINS=1)
4. Pip entry points — indagis_agent.plugins

On name collision, later sources win — a user plugin named disk-cleanup would replace the bundled one.

plugins/memory/ and plugins/context_engine/ are deliberately excluded from bundled scanning. Those directories use their own discovery paths because memory providers and context engines are single-select providers configured through indagis memory setup / context.engine in config.

Bundled plugins are opt-in

Bundled plugins ship disabled. Discovery finds them (they appear in indagis plugins list and the interactive indagis plugins UI), but none load until you explicitly enable them:

indagis plugins enable disk-cleanup

Or via ~/.indagis/config.yaml:

plugins:
  enabled:
    - disk-cleanup

This is the same mechanism user-installed plugins use. Bundled plugins are never auto-enabled — not on fresh install, not for existing users upgrading to a newer Indagis. You always opt in explicitly.

To turn a bundled plugin off again:

indagis plugins disable disk-cleanup
# or: remove it from plugins.enabled in config.yaml

Currently shipped

The repo ships these bundled plugins under plugins/. All are opt-in — enable them via indagis plugins enable <name>.

Plugin	Kind	Purpose
disk-cleanup	hooks + slash command	Auto-track ephemeral files and clean them on session end
security-guidance	hooks	Pattern-match dangerous code on write_file/patch and append a security warning (or block) — 25 rules (Apache-2.0 fork of Anthropic's claude-plugins-official patterns)
observability/langfuse	hooks	Trace turns / LLM calls / tools to Langfuse
teams_pipeline	standalone	Microsoft Teams meeting pipeline — Graph-backed, transcript-first meeting summaries
spotify	backend (7 tools)	Native Spotify playback, queue, search, playlists, albums, library
google_meet	standalone	Join Meet calls, live-caption transcription, optional realtime duplex audio
image_gen/openai	image backend	OpenAI gpt-image-2 image generation backend (alternative to FAL)
image_gen/openai-codex	image backend	OpenAI image generation via Codex OAuth
image_gen/xai	image backend	xAI grok-2-image backend
indagis-achievements	dashboard tab	Steam-style collectible badges generated from your real Indagis session history
kanban/dashboard	dashboard tab	Kanban board UI for the multi-agent dispatcher — tasks, comments, fan-out, board switching. See Kanban Multi-Agent.

Memory providers (plugins/memory/*) and context engines (plugins/context_engine/*) are listed separately on Memory Providers — they're managed through indagis memory and indagis plugins respectively. The full per-plugin detail for the two long-running hooks-based plugins follows.

disk-cleanup

Auto-tracks and removes ephemeral files created during sessions — test scripts, temp outputs, cron logs, stale chrome profiles — without requiring the agent to remember to call a tool.

How it works:

Hook	Behaviour
post_tool_call	When write_file / terminal / patch creates a file matching test_*, tmp_*, or *.test.* inside INDAGIS_HOME or /tmp/indagis-*, track it silently as test / temp / cron-output.
on_session_end	If any test files were auto-tracked during the turn, run the safe quick cleanup and log a one-line summary. Stays silent otherwise.

Deletion rules:

Category	Threshold	Confirmation
test	every session end	Never
temp	>7 days since tracked	Never
cron-output	>14 days since tracked	Never
empty dirs under INDAGIS_HOME	always	Never
research	>30 days, beyond 10 newest	Always (deep only)
chrome-profile	>14 days since tracked	Always (deep only)
files >500 MB	never auto	Always (deep only)

Slash command — /disk-cleanup available in both CLI and gateway sessions:

/disk-cleanup status                     # breakdown + top-10 largest
/disk-cleanup dry-run                    # preview without deleting
/disk-cleanup quick                      # run safe cleanup now
/disk-cleanup deep                       # quick + list items needing confirmation
/disk-cleanup track <path> <category>    # manual tracking
/disk-cleanup forget <path>              # stop tracking (does not delete)

State — everything lives at $INDAGIS_HOME/disk-cleanup/:

File	Contents
tracked.json	Tracked paths with category, size, and timestamp
tracked.json.bak	Atomic-write backup of the above
cleanup.log	Append-only audit trail of every track / skip / reject / delete

Safety — cleanup only ever touches paths under INDAGIS_HOME or /tmp/indagis-*. Windows mounts (/mnt/c/...) are rejected. Well-known top-level state dirs (logs/, memories/, sessions/, cron/, cache/, skills/, plugins/, disk-cleanup/ itself) are never removed even when empty — a fresh install does not get gutted on first session end.

Enabling: indagis plugins enable disk-cleanup (or check the box in indagis plugins).

Disabling again: indagis plugins disable disk-cleanup.

security-guidance

Fast pattern-matched security warnings on file writes. When the agent's write_file / patch / skill_manage calls carry content matching a known-dangerous code pattern — pickle.load, yaml.load without SafeLoader, eval(, os.system, subprocess(..., shell=True), JS child_process.exec, React dangerouslySetInnerHTML, raw .innerHTML = / .outerHTML = / document.write, Node crypto.createCipher, AES ECB mode, TLS verification disabled, XXE-prone xml.etree / minidom parsers, <script src="//..." > without SRI, torch.load without weights_only=True, GitHub Actions ${{ github.event.* }} injection — the plugin appends a ⚠️ Security guidance block to the tool's result.

The file is still written. The model reads the warning in the next turn's tool message and can either fix the code or document why the construct is safe in this context. Pattern matching has a non-trivial false-positive rate, which is why warn (not block) is the default.

Coverage: 25 rules total, covering unsafe deserialization, command injection, XSS sinks, crypto footguns, XXE, supply-chain (SRI), and CI/CD workflow injection. The pattern data is a verbatim Apache-2.0 fork of Anthropic's claude-plugins-official — see the plugin's LICENSE and NOTICE files for attribution.

Modes:

Env var	Effect
(unset)	warn mode (default) — file is written, warning appended to result
SECURITY_GUIDANCE_BLOCK=1	block mode — write refused, warning returned as the block reason
SECURITY_GUIDANCE_DISABLE=1	kill switch — plugin loads but does nothing

Enabling: indagis plugins enable security-guidance (or check the box in indagis plugins).

Disabling again: indagis plugins disable security-guidance.

What it does not do (yet): the upstream Anthropic plugin has two more layers — an LLM diff review on each agent turn that touched files, and an agentic commit-time review that traces data flow across files. Neither is ported. The agent can already run those reviews on demand via delegate_task.

observability/langfuse

Traces Indagis turns, LLM calls, and tool invocations to Langfuse — an open-source LLM observability platform. One span per turn, one generation per API call, one tool observation per tool call. Usage totals, per-type token counts, and cost estimates come out of Indagis' canonical agent.usage_pricing numbers, so the Langfuse dashboard sees the same breakdown (input / output / cache_read_input_tokens / cache_creation_input_tokens / reasoning_tokens) that appears in indagis logs.

The plugin is fail-open: no SDK installed, no credentials, or a transient Langfuse error — all turn into a silent no-op in the hook. The agent loop is never impacted.

Setup (interactive — recommended):

indagis tools          # → Langfuse Observability → Cloud or Self-Hosted

The wizard collects your keys, pip installs the langfuse SDK, and adds observability/langfuse to plugins.enabled for you. Restart Indagis and the next turn ships a trace.

Setup (manual):

pip install langfuse
indagis plugins enable observability/langfuse

Then put the credentials in ~/.indagis/.env:

INDAGIS_LANGFUSE_PUBLIC_KEY=pk-lf-...
INDAGIS_LANGFUSE_SECRET_KEY=sk-lf-...
INDAGIS_LANGFUSE_BASE_URL=https://cloud.langfuse.com   # or your self-hosted URL

How it works:

Hook	Behaviour
pre_api_request / pre_llm_call	Open (or reuse) a per-turn root span "Indagis turn". Start a generation child observation for this API call with serialized recent messages as input.
post_api_request / post_llm_call	Close the generation, attach usage_details, cost_details, finish_reason, assistant output + tool calls. If no tool calls and non-empty content, close the turn.
pre_tool_call	Start a tool child observation with sanitized args.
post_tool_call	Close the tool observation with sanitized result. read_file payloads get summarized (head + tail + omitted-line count) so a huge file read stays under INDAGIS_LANGFUSE_MAX_CHARS.

Session grouping keys off the Indagis session ID (or task ID for sub-agents) via langfuse.propagate_attributes, so everything in a single indagis chat session lives under one Langfuse session.

Verify:

indagis plugins list                 # observability/langfuse should show "enabled"
indagis chat -q "hello"              # check the Langfuse UI for a "Indagis turn" trace

Optional tuning (in .env):

Variable	Default	Purpose
INDAGIS_LANGFUSE_ENV	—	Environment tag on traces (production, staging, …)
INDAGIS_LANGFUSE_RELEASE	—	Release/version tag
INDAGIS_LANGFUSE_SAMPLE_RATE	1.0	Sampling rate passed to the SDK (0.0–1.0)
INDAGIS_LANGFUSE_MAX_CHARS	12000	Per-field truncation for message content / tool args / tool results
INDAGIS_LANGFUSE_DEBUG	false	Verbose plugin logging to agent.log

Indagis-prefixed and standard SDK env vars (LANGFUSE_PUBLIC_KEY, LANGFUSE_SECRET_KEY, LANGFUSE_BASE_URL) are both accepted — Indagis-prefixed wins when both are set.

Performance: the Langfuse client is cached after the first hook call. If credentials or SDK are missing, that decision is also cached — subsequent hooks fast-return without re-checking env vars or reloading config.

Disabling: indagis plugins disable observability/langfuse. The plugin module is still discovered, but no module code runs until you re-enable.

NeMo Relay native integration (migration note)

NeMo Relay is no longer a bundled Indagis plugin. Do not run indagis plugins enable observability/nemo_relay; Indagis core now owns the Relay session, turn, LLM, and tool lifecycles.

To opt into Relay middleware or exporters, create a standard Relay plugins.toml, then set INDAGIS_NEMO_RELAY_PLUGINS_TOML to that file before starting Indagis. The policy is process-wide for every profile hosted by that Indagis process. See the NeMo Relay observability configuration for ATOF, ATIF, and OpenTelemetry options.

The old INDAGIS_NEMO_RELAY_ATOF_* and INDAGIS_NEMO_RELAY_ATIF_* settings no longer activate exporters. indagis doctor reports these stale settings when no replacement plugins.toml is selected.

Session-span segmentation (continuous sessions)

Relay exports a span when its scope closes. A continuous gateway session can keep its session span open for days even though each turn span exports normally. Optional segmentation rotates only the session scope at a turn boundary:

gateway:
  telemetry:
    session_segments:
      on_compaction: false  # rotate after context compaction
      max_turns: 0          # 0 = unlimited; N = turns per segment

Key	Default	Behavior
on_compaction	false	Rotate after compaction completes, at the next turn boundary.
max_turns	0	Rotate after every N completed turns; 0 disables the cap.

Both defaults preserve one session scope for the full session. Rotated spans retain the same session_id and add indagis.session.segment plus indagis.session.segment_reason (compaction or max_turns).

google_meet

Lets the agent join, transcribe, and participate in Google Meet calls — take notes on a meeting, summarize the back-and-forth after, follow up on specific points, and (optionally) speak replies back into the call via TTS.

What it adds:

• A headless virtual participant that joins a Meet URL using browser automation
• Live transcription of the meeting audio via the configured STT provider
• A meet_join / meet_status / meet_transcript / meet_leave / meet_say toolset the agent invokes to join calls, poll the live transcript, and act on what it heard
• Post-meeting artifacts (transcript, status) saved under ~/.indagis/workspace/meetings/<meeting_id>/

Setup:

indagis plugins enable google_meet
indagis meet setup   # preflight: playwright, chromium, auth file
indagis meet auth    # opens a browser to sign into Google and saves session state —
                    # needs a Google account with Meet access. Host approval may be
                    # required if the meeting enforces "only invited participants can join".

Usage from chat:

"Join meet.google.com/abc-defg-hij and take notes. After the call, send me a summary with action items."

The agent kicks off the meeting join, streams the transcription back into its context as the call proceeds, and produces a structured summary when the meeting ends (or when you tell it to stop).

When to use it: recurring standups where you want a bot to transcribe + summarize for async attendees; deposition-style interviews where you want structured notes; any case where you'd otherwise need Fireflies / Otter / Grain. When you'd rather not have an AI listening in — don't enable it.

Disabling: indagis plugins disable google_meet. Any saved transcripts stay in ~/.indagis/workspace/meetings/ until you remove them.

indagis-achievements

Adds a Steam-style achievements tab to the dashboard — 60+ collectible, tiered badges generated from your real Indagis session history. Tool-chain feats, debugging patterns, vibe-coding streaks, skill/memory usage, model/provider variety, lifestyle quirks (weekend and night sessions). Originally authored by @PCinkusz as an external plugin; brought in-tree so it stays in lockstep with Indagis feature changes.

How it works:

• Scans your entire ~/.indagis/state.db session history on the dashboard backend
• Per-session stats are cached by (started_at, last_active) fingerprint, so only new or changed sessions re-analyze on subsequent scans
• First-ever scan runs in a background thread — the dashboard never blocks waiting for it, even on databases with thousands of sessions
• Unlock state is persisted to $INDAGIS_HOME/plugins/indagis-achievements/state.json

Tier progression: Copper → Silver → Gold → Diamond → Olympian. Each card exposes a "What counts" section listing the exact metric being tracked.

Achievement states:

State	Meaning
Unlocked	At least one tier achieved
Discovered	Known achievement, progress visible, not yet earned
Secret	Hidden until Indagis detects the first related signal in your history

API — routes mount under /api/plugins/indagis-achievements/:

Endpoint	Purpose
GET /achievements	Full catalog with per-badge unlock state (returns a pending placeholder while the first cold scan is running)
GET /scan-status	State of the background scanner: idle / running / failed, last duration, run count
GET /recent-unlocks	Twenty most recently unlocked badges, newest first
GET /sessions/{id}/badges	Badges earned primarily in one specific session
POST /rescan	Manual synchronous rescan (blocks; use when the user clicks the rescan button)
POST /reset-state	Clear unlock history and cached snapshot

State files — live under $INDAGIS_HOME/plugins/indagis-achievements/:

File	Contents
state.json	Unlock history: which badges you've earned and when. Stable across Indagis updates.
scan_snapshot.json	Last completed scan payload (served immediately on dashboard load)
scan_checkpoint.json	Per-session stats cache keyed by fingerprint (makes warm rescans fast)

Performance notes:

• Cold scan on ~8,000 sessions takes a few minutes. It runs in a background thread on first dashboard request; the UI sees a pending placeholder and polls /scan-status.
• Incremental results during a cold scan — the scanner publishes a partial snapshot every ~250 sessions so each dashboard refresh shows more badges unlocked as the scan progresses. No minute-long stare at zeros.
• Warm rescan reuses per-session stats for every session whose started_at + last_active fingerprint matches the checkpoint — completes in seconds even on large histories.
• The in-memory snapshot TTL is 120s; stale requests serve the old snapshot immediately and kick a background refresh. You never wait on a spinner just because TTL expired.

Enabling: Nothing to enable — indagis-achievements is a dashboard-only plugin (no lifecycle hooks, no model-visible tools). It auto-registers as a tab in indagis dashboard on first launch. The plugins.enabled config only gates lifecycle/tool plugins; dashboard plugins are discovered purely via their dashboard/manifest.json.

Opting out: Delete or rename plugins/indagis-achievements/dashboard/manifest.json, or override it with a user plugin of the same name in ~/.indagis/plugins/indagis-achievements/ that ships no dashboard. The plugin's state files under $INDAGIS_HOME/plugins/indagis-achievements/ survive — reinstalling preserves your unlock history.

Adding a bundled plugin

Bundled plugins are written exactly like any other Indagis plugin — see Build a Indagis Plugin. The only differences are:

• Directory lives at <repo>/plugins/<name>/ instead of ~/.indagis/plugins/<name>/
• Manifest source is reported as bundled in indagis plugins list
• User plugins with the same name override the bundled version

A plugin is a good candidate for bundling when:

• It has no optional dependencies (or they're already pip install .[all] deps)
• The behaviour benefits most users and is opt-out rather than opt-in
• The logic ties into lifecycle hooks that the agent would otherwise have to remember to invoke
• It complements a core capability without expanding the model-visible tool surface

Counter-examples — things that should stay as user-installable plugins, not bundled: third-party integrations with API keys, niche workflows, large dependency trees, anything that would meaningfully change agent behaviour by default.

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