The automation engine built for AI agents. Intelligent AI workflow orchestration with long-term memory, 100+ integrations, and unified credits.
CLI for Agentled — manage workflows, apps, and knowledge from the command line. Zero context-window cost for AI agents.
2026-11-08
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What is Agentled?
Agentled is the automation engine built for AI agents.
It gives Claude, Codex, Cursor, Windsurf, and any MCP-compatible client direct access
to intelligent workflow orchestration, long-term memory, and 100+ integrations.
Three things make it different:
🧠 Long-Term Memory — A built-in Knowledge Graph stores insights across
workflow executions. Your agents get smarter over time — they remember past
research, lead scores, content performance, and business context.
⚡ Unified Credits — One API key, one credit system, 100+ services.
No need to sign up for LinkedIn, email, scraping, AI models, or video
generation separately. Connect once, use everything.
🎯 Intelligent Orchestration — AI reasons at every step. Workflows
aren't just "if this then that" — they understand context, make decisions,
and adapt to results.
See it in action
$ agentled create "Outbound to fintech CTOs in Europe"
Loading workspace context from Knowledge Graph...
✦ ICP loaded ✦ 3 prior campaigns ✦ 847 contacts in KG
Creating campaign with 3 workflows...
━━ Workflow 1: Prospect Research linkedin · hunter · clearbit
✓ LinkedIn: CTO + fintech + EU → 189 profiles
✓ Enriched via Hunter + Clearbit → 156 matched
✓ ICP scoring → 43 high-intent leads
━━ Workflow 2: Signal Detection web-scraper · crunchbase
✓ Job postings → 12 hiring devops
✓ Crunchbase → 8 recently funded
✓ Cross-match: hiring + funded → 5 hot leads
━━ Workflow 3: Outreach email · linkedin · kg
✓ Personalized emails from context
✓ LinkedIn requests with custom notes
✓ 43 leads saved to Knowledge Graph
Campaign saved. Scheduled: every 48h
Credits used: 720
→ https://www.agentled.app/your-team/fintech-cto-outbound
One prompt. Three workflows. LinkedIn enrichment, email finding, AI scoring, multi-channel outreach — all orchestrated, all stored in the Knowledge Graph for the next run.
Quick Start
claude mcp add --transport stdio --scope user agentled \
-e AGENTLED_API_KEY=wsk_... \
-- npx -y @agentled/mcp-server
--scope user registers the server in your user MCP config so it loads in every project (not only the repo where you ran the command). Use a distinct server name (e.g. agentled_my_workspace) if you add multiple workspaces. For team-shared config in git, use --scope project and .mcp.json instead (Claude Code MCP scopes).
Claude Code plugin (one-step install)
Prefer the plugin if you want the MCP server and the Agentled skill installed together. In Claude Code:
Then set AGENTLED_API_KEY=wsk_... in the shell that launches Codex and start a
new task. The plugin bundles the Agentled skills, starts the MCP server through
npx -y @agentled/mcp-server, and loads its lifecycle hooks from the standard
hooks/hooks.json path.
The plugin bundles the agentled skill (workflow-authoring guidance, namespaced agentled:agentled) and auto-starts the MCP server via npx -y @agentled/mcp-server. The same plugin directory carries both Claude Code and Codex manifests — one bundle, both hosts.
For Codex, the hook pack acts as in-session guidance around the CLI/MCP loop:
session start explains the Agentled/Codex business-loop split, prompt/tool hooks
add turn-level guidance when client needs, priorities, failures, or product gaps
appear, and stop hooks nudge implementation handoffs to include readiness,
validation, side effects, and next decision. Hooks do not store feedback, call
Agentled APIs, run automations, spend credits, or perform customer/workspace
writes. In Codex, run /hooks after installing or changing the plugin so the
local hook definitions are reviewed and trusted before they run.
Use Codex automations for outside-workspace FDE cadence such as Outlook/client
email follow-up, vendor replies, repo/build checks, and weekly operator reviews.
Use Agentled routines for Agentled workspace/runtime checks such as workflow
health, routine health, execution review, workspace summaries, and managed-agent
operations. Use submit_feedback_to_agentled or agentled feedback submit when
the user explicitly wants product feedback captured.
Pick one install path, not both. If you previously ran claude mcp add agentled ... or --setup-skills, remove those before (or instead of) installing the plugin — otherwise you get two identical MCP server processes and the skill registered twice. Cleanup: claude mcp remove agentled and delete .claude/skills/agentled/ (or ~/.claude/skills/agentled/). --setup-skills now detects an installed plugin and refuses to double-register unless you pass --force.
To develop the plugin locally:
claude --plugin-dir ./plugins/agentled # load from source
claude plugin validate ./plugins/agentled # check manifest + structure
plugins/agentled/skills/ is a generated mirror of skills/ (synced by publish.sh) — edit skills/agentled/SKILL.md, never the mirror.
Local development
Use the local built entrypoint when you want to test unpublished changes against a
local app. npx -y @agentled/mcp-server always uses the latest published npm package.
cd agentled-mcp-server
npm run build
claude mcp add --transport stdio agentled_local \
--env AGENTLED_API_KEY=wsk_... \
--env AGENTLED_URL=http://localhost:8080 \
-- node /absolute/path/to/agentsled-front/agentled-mcp-server/dist/index.js
No need to sign up for LinkedIn APIs, email services, web scrapers, video generators, or AI models separately. Agentled handles all integrations through a single credit system.
Capability
Credits
Without Agentled
LinkedIn company enrichment
50
LinkedIn API ($99/mo+)
Email finding & verification
5
Hunter.io ($49/mo)
AI analysis (Claude/GPT/Gemini)
10-30
Multiple API keys + billing
Web scraping
3-10
Apify account ($49/mo+)
Image generation
30
DALL-E/Midjourney subscription
Video generation (8s scene)
300
RunwayML ($15/mo+)
Text-to-speech
60
ElevenLabs ($22/mo+)
Knowledge Graph storage
1-2
Custom infrastructure
CRM sync (Affinity, HubSpot)
5-10
CRM API + middleware
Workflows That Learn
Other automation tools start from zero every run. Agentled's Knowledge Graph remembers across executions — what worked, what didn't, what humans corrected. Scoring workflows can use compact row-level scoring_profile summaries and bounded scoring-memory retrieval so every run compounds on the last without dumping raw history into prompts.
Run 1: Investor scoring → 62% accuracy (cold start)
Run 5: → 78% (learning from IC feedback)
Run 12: → 89% (compound learning from outcomes, zero manual tuning)
Intelligent Orchestration
Unlike trigger-action tools, Agentled workflows have AI reasoning at every step. Multi-model support (Claude, GPT-4, Gemini, Mistral, DeepSeek, Moonshot), adaptive execution, and human-in-the-loop approval gates when needed.
Agent Teams
Agent Teams let you run multiple AI specialists in a single workflow step. Pick a preset and describe what you need — the team handles coordination, delegation, and synthesis.
"Add an Agent Team step that researches the company and produces an investment memo"
Six built-in presets cover the most common patterns:
Preset
What it does
research-and-summarize
Specialists gather information, one synthesizes a summary
analyze-and-recommend
Multiple analysts evaluate options, produce a ranked recommendation
generate-then-review
A generator drafts content, reviewers critique and refine
compare-options
Specialists argue for competing options, coordinator arbitrates
investigate-in-parallel
Independent specialists explore different angles simultaneously
review-and-improve
Reviewers find issues, an editor applies improvements
When creating Agent Team steps via MCP, include preset metadata so the step opens correctly in the builder:
Existing steps created with raw orchestratorConfig and no metadata continue to work — they open in advanced mode in the builder without errors.
Analytics vs ROI semantics
When describing workflow outcomes, keep these terms separate:
pipeline.analyticsConfig = business metrics (execution outcome stats shown in Business Metrics cards/charts).
pipeline.metadata.roi = ROI assumptions/rollups (time saved and cost-value estimates).
If you update one without the other, name exactly what changed (e.g. "business metrics configured" vs "ROI assumptions configured").
CLI parity guard
The repository includes an automated parity guard so MCP tool additions do not silently drift from the CLI surface.
Test: __tests__/cli/cli-mcp-parity.test.ts
Docs: docs/CLI_MCP_PARITY.md
Run it with:
yarn test:node -- cli-mcp-parity.test.ts
What Can You Build?
Lead Enrichment & Sales Automation
"Find fintech CTOs in Europe, enrich via LinkedIn + Hunter, score by ICP fit,
draft personalized outreach, save everything to the Knowledge Graph"
Content & Media Production
"Scrape trending topics in our niche, generate 5 LinkedIn posts with AI,
create thumbnail images, schedule publishing for the week"
Company Research & Intelligence
"Research this company from its URL — team, funding, market position, competitors.
Generate an investment memo. Store in KG for future reference."
VC Investor Matching (real case study)
"Match this startup against our 2,000+ investor database. Score by sector focus,
stage preference, check size, and portfolio synergy. Compare with last round's outcomes."
3,000+ profiles processed. IC-ready reports. Prediction vs outcome learning — accuracy went from 62% to 89% over 12 runs with zero manual tuning.
Built-in Skills And Integrations
Media Production: Video generation, image generation, text-to-speech, auto-captions, media assembly
AI Intelligence: Multi-model AI (Claude, GPT-4, Gemini, Mistral, DeepSeek, Moonshot, xAI), Knowledge Graph, feedback loops, scoring & analytics
Data & Integration: LinkedIn (search, enrich, post), email (send, personalize), web scraping, social publishing, CRM sync, document analysis, OCR
Available Tools
Use-case operating guides
When work starts from an existing workflow ID, call get_workflow first and
inspect useCaseContext. Then call get_use_case with the returned key or
workflow graph ID, and read any operatingGuides before answering
workflow-specific questions, running backfills, or editing live config. Missing
guide warnings mean the operating README is not attached yet and the context is
incomplete.
When work starts from a user's business goal rather than an existing workflow,
use How to Build Use Cases From User Goals.
It covers the external-agent lifecycle from bounded chat result to KG text/list
state, WorkspaceUseCase, workflows, approval queues, and home summary updates.
Workflows
Tool
Description
list_workflows
List all workflows in the workspace
get_workflow
Get full workflow definition by ID, including useCaseContext when linked
get_workflow_credits
Get ledger-derived, period-labelled workflow credit usage; opt in to cost drivers with includeCostDrivers
create_workflow
Create a new workflow from pipeline JSON
update_workflow
Update an existing workflow (top-level scalars; for context/metadata prefer update_workflow_context)
update_workflow_context
Workflow-level analog of update_step — three explicit verbs (updates / replace / unset) on context.* and metadata.* paths, returns diff + warnings
add_step
Add a step with automatic positioning and next-pointer rewiring
update_step
Low-level deep-merge updates into a single step by ID
replace_step_dictionary
Merge keys into a dictionary path (fieldUpdates, responseStructure, fieldMapping) without dropping siblings
replace_step_path
Set one nested path while preserving siblings (e.g. renderer.config.layout)
unset_step_path
Delete one step path
append_step_array_item
Append one item to a step array (tools, integrations, …)
remove_step_array_item
Remove one array item by index or match
remove_step
Remove a step with automatic next-pointer rewiring
delete_workflow
Permanently delete a workflow
validate_workflow
Validate pipeline structure, returns errors per step
publish_workflow
Change workflow status (draft, live, paused, archived)
export_workflow
Export a workflow as portable JSON
import_workflow
Import a workflow from exported JSON
Public Form Links
Public form links are the external intake surface for workflows with
context.executionInputConfig fields. Use them when people outside the
workspace need to submit a workflow form without signing in: inbound lead
forms, pitch deck submissions, referral forms, support intake, assessment
questionnaires, or any workflow whose first step is a manual/input trigger.
Do not use a public form link for internal child workflows. Child workflows
should use context.executionInputConfig.internal: true and be called from
another workflow with agentled.call-workflow.
Tool
Description
list_public_form_links
List existing public form links for a workflow
create_public_form_link
Create and enable a public form link
update_public_form_link
Enable/disable a link or update limits, expiry, auto-share, and thank-you copy. To revoke external access, set enabled: false.
Deletion is intentionally not exposed via the external API or MCP. To
revoke a public form link, call update_public_form_link with
enabled: false. Permanent deletion requires an authenticated workspace
member acting through the UI — destructive ops on the form-link surface are
not granted to the public API key.
Typical agent flow:
1. get_workflow({ workflowId })
2. Confirm context.executionInputConfig exists and is not internal.
3. list_public_form_links({ workflowId })
4. If none exists, create_public_form_link({ workflowId, enabled: true })
5. Return the publicUrl to the user.
The public URL is /en/forms/{formLinkId}. On submit, Agentled validates the
form link, starts the workflow with the submitted input, records a
PublicFormSubmission, and increments submissionCount. Optional settings:
enabled: disable without deleting the link.
expiresAt: ISO datetime expiry.
submissionLimit: maximum accepted submissions.
autoShare: when true, the public form status page can show generated
results after completion. Use this only when the workflow output is safe for
the submitter to see.
shareExpiresInDays: expiry for auto-shared result links.
successMessage: custom thank-you message after submission.
Internal-only Workflows
Mark a workflow as a child / sub-workflow that is only run via agentled.call-workflow from an orchestrator by setting context.executionInputConfig.internal: true. The UI then hides the Run button and replaces the manual run form with an info banner. Inputs are still validated and passed by orchestrators via executionInputData exactly as before — this is a UI guard, not a runtime restriction.
Use it for any workflow whose goal/description starts with "Internal sub-workflow", that ends in a return step, or that you only intend to invoke from another workflow.
Flip the flag via update_workflow_context — fetch first, merge locally, replace at the parent level (the merge-order trap from update_step applies here too — see docs/MCP_STEP_EDITING.md):
Keep raw update_step for complex multi-path edits. It accepts three explicit operations on the same call; at least one must be non-empty.
updates — partial step patch, deep-merged ONE LEVEL deep. Top-level scalars are replaced; nested objects (pipelineStepPrompt, stepInputData, etc.) get their direct keys merged with the stored value's keys. Keys nested two levels deep are overwritten as a unit, not merged.
replace: string[] — dot-paths whose values from updates are assigned wholesale, skipping the deep-merge. Use this for dictionary-shaped fields where keys are user data (not config) — patching one inner key with updates alone silently wipes the others.
unset: string[] — dot-paths to delete. Each path must currently exist on the step (validated against the original).
Read before editing dictionary fields. Prefer replace_step_dictionary. With raw update_step, call get_step({ workflowId, stepId }) (~1KB), modify locally, send the full new object back via replace[].
Diff in the response. Every update_step / surgical-tool call returns diff: { addedPaths, changedPaths, removedPaths } and warnings[]. If the merge silently removed ≥6 fields without an explicit unset, a warning fires.
Three explicit verbs (updates / replace / unset) on workflow-relative paths. Compatibility: { contextKey, value } still accepted for wholesale per-key replacement.
Workflow-level, not step-level.update_step cannot reach context.* and vice versa.
metadata
update_workflow_context
Same three verbs on metadata.* paths
Workflow-level. Metadata bypasses the draft snapshot — even on live workflows it writes direct to the Pipeline row, immediately.
Executive summaries for workflow groups. When a user asks to save a summary for a workflow, workflow group, cluster, or home card, write it to metadata.executiveSummary with update_workflow_context. Do not store it as Knowledge text unless the user explicitly asks for a reusable note. For a group, write exactly once to the owner pipeline: prefer metadata.workflowGraph.role === "orchestrator", otherwise use the lowest metadata.workflowGraph.order pipeline. Keep the body to 1-2 short sentences, include concrete counts/rates and the reporting period when available, and set author to the active workspace agent, not the external tool/coding agent.
{
"workflowId": "2e1cdd60-2fcf-441a-856f-583ae76b38a5",
"updates": {
"metadata": {
"executiveSummary": {
"body": "Startup Outreach sent 46 founder emails for the reporting period, with 28 opens and 9 clicks: a 60.9% open rate, 19.6% click rate, and 32.1% click-to-open rate.",
"bullets": ["Clicks: 6 UTM Pitch Night, 2 plain Pitch Night, 1 calendar."],
"generatedAt": "2026-06-03T00:00:00.000Z",
"author": "AngelHive Assistant"
}
}
}
}
Type changes.step.type is technically mutable but stale type-specific fields (pipelineStepPrompt, app, tools, orchestratorConfig) persist unless you unset them. For clean conversions, prefer remove_step + add_step.
Live workflows. Edits are routed to a draft snapshot. Response includes editingDraft: true. Inspect via get_draft, ship via promote_draft, throw away via discard_draft. For high-stakes edits, create_snapshot first as a manual checkpoint.
Draft staleness. When a draft exists, every update_step and get_step response includes a draft summary with exists, draftCreatedAt, liveUpdatedAt, stale, modifiedStepIds, and modifiedFields. If draft.stale === true, the live workflow advanced after the draft was created — promoting will land the draft's older values for fields you didn't touch. update_step also emits a staleness warning. Recovery: discard_draft and re-apply.
⚠ discard_draft only reverts pending context (and step) changes — NOT metadata. Metadata writes via update_workflow_context bypass the draft and apply immediately to the live Pipeline row. If you need a single rollback point covering metadata too, create_snapshot before the edit. See docs/MCP_STEP_EDITING.md for the full atomicity contract.
Never send a full steps[] array via update_workflow. Use surgical path tools, update_step, add_step, remove_step instead.
For the deep reference (StepMergeError codes, dot-path validation rules, full diff semantics) see docs/MCP_STEP_EDITING.md.
Drafts & Snapshots
Tool
Description
get_draft
Get the current draft version of a workflow
promote_draft
Promote a draft to the live version
discard_draft
Discard the current draft
create_snapshot
Create a manual config snapshot
delete_snapshot
Delete a specific config snapshot
list_snapshots
List version snapshots for a workflow
get_snapshot_content
Read a snapshot's full config (steps, context, etc.) without restoring it
restore_snapshot
Restore a workflow to a previous snapshot
Executions
Tool
Description
start_workflow
Start a workflow execution with input. Returns executionInputId always and executionId only when the async execution row is already available. Pass useMocks: false to force a real (credit-consuming) run that ignores per-step mock data; defaults to honoring the workflow's configured mocks.
list_executions
List executions for a workflow (paginated via nextToken), including pipelineExecutionInputId for matching a start result that only returned executionInputId.
get_execution
Get execution details with step results. Requires the real executionId, not executionInputId; if needed, call list_executions and match pipelineExecutionInputId.
list_timelines
List step execution records (timelines) for an execution (paginated via nextToken)
get_timeline
Get a single timeline by ID with full step output
stop_execution
Stop a running execution
retry_execution
Retry a failed step — auto-detects the most recent failure if no timeline ID provided
rerun
Rerun or retry any step by timelineId — works for failed AND succeeded steps, disambiguates loop iterations
Run deep links use /<locale>/<workspace>/<workflowPathname>/runs?runId=<executionId>&step=<stepId>.
The step query param is optional only for run-level handoffs. When a response
points to a specific approval card, output, failure, or current step, include
&step=<stepId> and do not ask the human to open the step manually after a
run-only URL. When present, the app expands that workflow step and scrolls to
it. stepId is the workflow step id, not the timeline id. Inside the app
chat/navigation tool surface, navigateToExecutionPage accepts the same
stepId.
Knowledge row deep links use /<locale>/<workspace>/knowledge-and-data/<listKey>?rowId=<rowId>.
The app opens the Knowledge & Data list page and opens the row editor sheet for
that row. The backwards-compatible route
/<locale>/<workspace>/knowledge-and-data/<listKey>/row/<rowId> redirects to the
canonical query-param URL.
Knowledge text deep links use /<locale>/<workspace>/knowledge-and-data/<key>.
The backwards-compatible route
/<locale>/<workspace>/knowledge-and-data/text/<key> redirects to the canonical
item URL.
Apps & Testing
Tool
Description
list_apps
List available apps and integrations
list_connections
List which app connections are configured
verify_app_connection
Verify native OAuth health without executing an app action
get_app_actions
Get action schemas for an app
test_app_action
Execute one app action in isolation without creating a workflow
test_ai_action
Test an AI prompt without creating a workflow
test_code_action
Test JavaScript code in the same sandboxed VM as production
get_step_schema
Get allowed PipelineStep fields grouped by category
AI step types: aiAction vs aiActionWithTools
Pick the right type — validate_workflow will reject the wrong one:
You need…
Use
Reason over inputs already present in the prompt variables
aiAction (single LLM call, no tool loop)
Live web search, workspace memory recall/write, knowledge-graph lookup
aiActionWithTools with the matching builtinType
The AI to decide at runtime what inputs to pass to an app action
aiActionWithTools with an appActionConfig tool
aiActionWithTools requires at least one tool — placed under step.toolsorstep.agent.tools (both are merged at runtime). If you omit tools from both locations, validate_workflow returns a blocker AI_STEP_TOOLS_REQUIRED. If the prompt says "search the web" / "recall memory" / "knowledge graph" without the matching tool attached, you get a warning AI_STEP_TOOL_PROMPT_MISMATCH: web-search prompts need web_search; memory prompts need workspace_memory; KG lookup prompts need kg_search or kg_traverse. fetch_website_content fetches a known URL and kg_write writes KG data, so neither satisfies those lookup/search prompts.
Agentled enables provider prompt caching where supported. Caching reuses prompt processing for an identical prefix; it does not cache or replay the AI response. To benefit in bulk workflows, especially scoring/matching loops, write prompts with the stable material first:
[stable role and task]
[stable rubric / scoring dimensions]
[stable output JSON schema]
[stable examples or decision rules]
Runtime inputs:
{{currentItem}}
{{steps.previous.output}}
{{input.field}}
Do not start high-volume prompts with INPUTS, {{currentItem}}, {{steps.*}}, {{input.*}}, {{execution.id}}, {{now}}, or {{today}}. Keep changing payloads, dates, execution IDs, and per-item records at the end so OpenAI/Anthropic can cache the shared prefix.
// aiActionWithTools example
{
"id": "research",
"type": "aiActionWithTools",
"name": "Research Company",
"tools": [
{ "type": "builtin", "builtinType": "web_search", "name": "Web Search" }
],
"pipelineStepPrompt": {
"template": "Search the web for the founder of {{input.company}} and return their name.",
"responseStructure": { "firstName": "string", "lastName": "string" }
},
"creditCost": 10,
"next": { "stepId": "find-email" }
}
Knowledge & Data
Tool
Description
get_workspace
Get workspace info, company settings, active team members, pending invitations, and knowledge-list schemas
get_workspace_company_profile
Get the editable workspace company profile and company knowledge text
update_workspace_company_profile
Update top-level company profile fields like name, URLs, logo, industry, size, and additional information
update_workspace_executive_summary
Write the workspace-wide executive summary on the Workspace Assistant card
list_pinned_outputs
List output pages pinned to the workspace home/sidebar
set_output_page_pin
Pin or unpin a workflow output page on the workspace home/sidebar
inspect_home_recent_tab
Inspect the Home tabs, the default tab, the display-only Home Recent tab config, and the current workspace revision
configure_home_recent_tab
Configure or reset bounded recent-run sources, optional navigation CTAs, and which Home tab opens by default
list_workspace_views
List saved workspace view manifests and source/action guidance
create_workspace_view
Create a saved operating-surface manifest over KG, workflows, approvals, agents, routines, actions, output pages, external APIs, or custom sources
get_workspace_view
Get one saved workspace view by id or key
update_workspace_view
Update a saved workspace view manifest without mutating source data or executing actions
archive_workspace_view
Archive a workspace view without deleting linked data
list_knowledge_lists
List knowledge lists in the workspace
get_knowledge_rows
Get rows from a knowledge list (paginated via nextToken, max 200)
get_knowledge_rows_by_ids
Fetch specific rows by ID (max 200) — use after query_kg_edges
get_knowledge_text
Get text content from a knowledge entry
create_knowledge_list
Create a new knowledge list with a typed schema (idempotent on key collision)
update_knowledge_list_schema
Add or remove fields on an existing list schema
delete_knowledge_list
Permanently delete a list and all its rows
upsert_knowledge_rows
Insert or update rows in a list (max 500/call, per-row error reporting)
delete_knowledge_rows
Delete rows by ID
upsert_knowledge_text
Create or update a text knowledge entry
delete_knowledge_text
Delete a text knowledge entry by key
query_kg_edges
Query knowledge graph edges
get_scoring_history
Get scoring history for an entity
Credits and Cost Drivers
Credit reporting tools are opt-in for cost-driver detail so existing balance checks stay compact.
Every credit total is ledger-derived and returned with a period object containing label, display, start, and end; always show that period label next to totals.
For a human/operator UI check, open the workspace credit usage page:
GET /api/external/workspace/credits?period=rolling-30-days&include=costDrivers&limit=5
GET /api/external/workflows/{workflowId}/credits?period=current-month&include=costDrivers
Billing-period reporting is separate from calendar-month reporting and should not be implied unless a future API adds an explicit billing-period label.
Branding (Whitelabel)
Tool
Description
get_branding
Get the workspace's whitelabel branding config (displayName, logo, colors, favicon, badge)
First-class workspace AI Agents with identity, instructions, tools, config files, and assigned workflows. All agents are conversational by default. For scheduled/autonomous work, attach routines via create_routine to create an AI Agent with routines. SOUL.md and TOOLS.md live in configFiles; reflection context (JOURNAL.md, OBJECTIVES.md, PEOPLE.md) lives as linked AgentFiles and is auto-seeded for active AI Agents using reflection. Agents decide what durable signal belongs in those files; AgentLed only provides scoped storage and scheduled Reflection. An agent created entirely via MCP renders identically to one built in the Agent Wizard.
Tool
Description
list_agents
List agents in the workspace (filter by status: active, paused, draft)
get_agent
Get full agent config — instructions, files, workflows, attached routines
create_agent
Create an agent. Accepts agentType presets (personal-assistant, competitive-researcher, social-media-marketer, customer-support, content-marketer, lead-qualifier, deal-sourcer, custom), enabledApps, appPermissions, assignedWorkflowIds, linkedFileIds, configFiles (SOUL.md/TOOLS.md), avatar_icon_name, avatar_color, chatModel, activate: true or status: "active". Unknown fields are rejected; daily credit caps and model tier belong on routines (create_routine)
update_agent
Surgical update via updates / replace / unset (fields: name, slug, description, instructions, goals, chatModel, status, enabledApps, enabledActions, appPermissions, skillIds, assignedWorkflowIds, linkedFileIds, configFiles, avatar). status: "active" runs the activation check and returns its errors; updates.slug renames the agent email slug, moves the AgentEntity id to {slug}@{workspace}, and rebinds routines/file links/channel sessions/chat sessions where available
activate_agent
Activate an agent (draft/paused → active). Attached routines begin running on schedule
pause_agent
Pause an active agent. Attached routines stop until resumed
manage_agent_workflows
Add/remove/set the workflows assigned to an agent without rewriting the full config
delete_agent
Permanently delete an agent and all its files
chat_with_agent
Send a message to a specific agent. Multi-turn via session_id
Slug convention: slug is the short role ID used in URLs and email addresses. Keep Agent in the display name when useful, but do not append -agent to the slug just because the display name includes it; for example, Deal Sourcing Agent should use deal-sourcing@{workspace}.agentled.ai, not deal-sourcing-agent@{workspace}.agentled.ai.
Built-in app result cache
Separate from prompt caching: many app actions reuse prior results via workspace KnowledgeRow defaults. See the Agentled skill section Built-in app result cache (skills/agentled/SKILL.md). MCP rerun tools default forceWithoutCache: true.
Agent Files
Tool
Description
list_agent_files
List files attached to an agent (knowledge, context, reference docs)
get_agent_file
Get the content of a specific agent file
upload_agent_file
Upload a file (max 400KB text/markdown) to an agent
update_agent_file
Update a file already attached to an agent; use this after get_agent_file for JOURNAL.md, OBJECTIVES.md, and PEOPLE.md edits
delete_agent_file
Delete a file from an agent
Reflection files follow the same durable markdown pattern used by OpenClaw and Hermes memory surfaces: keep JOURNAL.md as a concise dated log, OBJECTIVES.md as active/completed goals, and PEOPLE.md as stable relationship context. Read first, update only when there is durable signal, and send full replacement content. See docs/AGENT_REFLECTION_FILES.md for the UI, MCP, and CLI editing contract.
Routines
Routines are scheduled prompts attached to an agent — the agent evaluates the prompt on a set interval and can trigger workflows or send notifications.
Example — add a daily deal-sourcer routine to an existing agent:
# Step 1: create the agent
create_agent({
name: "Daily Deal Sourcer",
agentType: "deal-sourcer",
enabledApps: ["agentled", "kg", "web-scraping"],
appPermissions: {
kg: { access: "write", writeApprovalRequired: true },
"web-scraping": { access: "read" }
},
assignedWorkflowIds: ["<opportunity-scoring-workflow-id>"],
activate: true
})
# Step 2: attach a routine
create_routine({
agent_id: "<agent-id>",
name: "Daily Sourcing Run",
prompt: "Find 5 new SaaS startups that match our deal criteria and trigger the scoring workflow for each.",
interval: "daily"
})
Read access is implicit and never requires approval. The internal agentled app is selected as an app when needed but is not configurable in appPermissions.
Tool
Description
list_routines
List all routines for an agent
create_routine
Create a routine (name, prompt, interval)
update_routine
Update routine fields; recalculates nextRunAt if interval changes
pause_routine
Pause a routine
resume_routine
Resume a paused routine
trigger_routine
Run a routine immediately without changing its schedule
Direct low-level monitor-runtime MCP tools are deprecated and no longer
registered. Use create_agent / update_agent plus routines for autonomous
work.
Channels (Email, Slack, WhatsApp, Signal)
Channels route inbound messages into the agent chat runtime. Each channel has a defaultAgentId that decides which agent handles the conversation. Replies are sent back through the originating channel.
Tool
Description
list_channels
List configured channels with their defaultAgentId, enabled state, and non-secret config (secrets redacted)
set_channel_default_agent
Assign the agent that handles a channel's inbound conversations
Secret credentials (Slack bot tokens, signing secrets, WhatsApp access tokens, Signal webhook secrets) are NEVER readable or writable via the external API. Connect those via Settings → Channels in the UI — OAuth flows store them encrypted at rest.
Conversational Agent
Tool
Description
chat
Send a message to the AgentLed AI agent. Build workflows through natural language — no JSON required. Supports multi-turn conversations via session_id.
Chat Tool — Usage & Examples
The chat tool is a conversational AI agent that can reason, plan, and build workflows through dialogue. Think of it as the difference between gh api (raw) and gh copilot (intelligent).
Parameters:
Parameter
Type
Required
Description
message
string
Yes
The message to send to the AI agent
session_id
string
No
Session ID from a previous response, for multi-turn conversations
Response format:
{
"response": "The agent's reply — may include workflow suggestions, explanations, or confirmation of actions taken",
"sessionId": "mcp-chat-ws123-1711929600000"
}
Multi-turn conversation:
# Turn 1: Describe what you want
chat("Build me a workflow that enriches LinkedIn companies and scores them by ICP fit")
# → Agent responds with a plan and creates a draft workflow
# → Response includes sessionId: "mcp-chat-ws123-1711..."
# Turn 2: Iterate on the design
chat("Add an email finding step using the company domain", session_id: "mcp-chat-ws123-1711...")
# → Agent modifies the workflow, maintaining conversation context
# Turn 3: Finalize
chat("Looks good, publish it as live", session_id: "mcp-chat-ws123-1711...")
# → Agent publishes the workflow
When to use chat vs structured tools:
Use chat when...
Use structured tools when...
You have a high-level goal
You know the exact pipeline JSON
You want AI recommendations
You need precise control
You're exploring available skills or integrations
You're automating a known pattern
You want to iterate through dialogue
You're doing bulk operations
Notes:
5-minute timeout — break complex requests into smaller steps if needed
Responses are non-streaming (collected then returned) for MCP client compatibility
The agent has full access to planning tools, workflow builder, and workspace context
Error responses include actionable messages (e.g., timeout → "try breaking it into smaller steps")
Intent Router
Tool
Description
do
Natural language intent router — describe what you want and it auto-selects and executes the right tool
Help & Feedback — Talk to the Agentled Team
For AI agents — pick the right channel:
What you hit
Where it goes
Workflow not behaving as expected, app/integration question, platform/billing question, missing feature, anything about how Agentled works
Call submit_feedback_to_agentled (below). Routes straight to the team.
Bug in the CLI itself (agentled command crashes, writes wrong data to .agentled/, flag doesn't work) — a reproducible CLI code defect
submit_feedback_to_agentled — the team will reroute if needed.
Do not search npm metadata, scrape GitHub, or shell out to gh to "find" a bug tracker — the routes above are the only ones. The bugs field in the package points at the human contact page (https://www.agentled.ai/en/contact-us) on purpose.
Ask a question, file a bug, request a feature, or escalate an issue. Save the returned feedbackId for follow-up; userEmail chooses the optional email reply address.
get_feedback
Read status and the latest Agentled response by feedbackId within the authenticated workspace. A null response means no team reply yet.
Coming from n8n?
Import existing n8n workflows and make them AI-native:
Tool
Description
preview_n8n_import
Preview an n8n workflow import (dry run)
import_n8n_workflow
Import an n8n workflow into Agentled
Looking Up Entity-Scoped Data
When you need all records related to a specific entity, use the two-tool chain instead of paginating get_knowledge_rows:
Example 1 — all deals scored by an investor:
1. query_kg_edges({ entityName: "Investor Name", relationshipType: "SCORED" })
→ returns edges with targetNodeIds
2. get_knowledge_rows_by_ids({ rowIds: <targetNodeIds from step 1> })
→ returns full row data for each matched deal
Example 2 — all leads sourced from a campaign:
1. query_kg_edges({ entityName: "Campaign Name", relationshipType: "SOURCED" })
→ returns edges with targetNodeIds
2. get_knowledge_rows_by_ids({ rowIds: <targetNodeIds from step 1> })
→ returns full contact/lead rows
Why this matters:get_knowledge_rows is limited to 200 rows per call. At 3k rows that means 15 round trips; at 10k it means 50. The KG-edge path is O(edges for that entity) — independent of total list size — so it stays fast regardless of how large the list grows.
Node ID convention:source_node_id and target_node_id values from query_kg_edges are knowledge row IDs. Rows outside the authenticated workspace are silently excluded.
For Agencies: White-Label Ready
Build workflows once, deploy to multiple clients under your own brand. Configure branding directly from the MCP server:
"Set my workspace branding: displayName 'Acme AI', primaryColor '#6366f1', tagline 'Powered by Acme'"
Use get_branding and update_branding to manage displayName, logo, colors, favicon, tagline, and badge visibility. Client portal appearance updates instantly.
Persistent Memory — Examples
Memories let workflows learn across executions. Store what worked, recall it next time.
Store a fact after enrichment
"Store a memory: key 'icp_criteria', value { industry: 'fintech', minEmployees: 50, region: 'EU' },
category 'preference', scope 'workspace'"
Recall before scoring
"Recall memory 'icp_criteria' at workspace scope — use it to score this batch of leads"
Search for past outcomes
"Search memories for 'conversion rate' in the 'outcome' category"
Track a running metric
"Store memory: key 'total_leads_processed', value 43, merge 'increment', scope 'workspace'"
Each subsequent call with merge: 'increment' adds to the existing value — no read-modify-write needed.
Outreach PCPL
For email or outbound workflows, track PCPL as a business metric:
PCPL = prospects contacted / positive replies
Use analyticsConfig for contacted prospects, positive replies, and PCPL. Literal PCPL should use a ratio metric with ratioMode: "raw"; positive reply rate should use the default percentage ratio.
Routines — Examples
Routines are scheduled prompts attached to agents. Use them for autonomous work
such as daily checks, weekly digests, and workflow follow-up.
Create an agent with a daily sourcing routine
"Create a deal sourcing agent, then add a daily routine that checks the
incoming-leads knowledge list and starts the lead-enrichment workflow for
qualified new rows. Limit the routine to 10 workflow starts per day."
Tool sequence:
create_agent({
name: "Daily Deal Sourcer",
agentType: "deal-sourcer",
enabledApps: ["agentled", "kg"],
assignedWorkflowIds: ["wf_abc123"],
activate: true
})
create_routine({
agent_id: "<agent-id-or-slug>",
name: "Daily New Lead Review",
prompt: "Review incoming-leads, identify qualified new rows, and start the lead-enrichment workflow for each. Do not start more than 10 workflow runs in one day.",
interval: "daily",
max_steps_per_run: 20,
max_credits_per_day: 50
})
Create a weekly workflow health routine
"Add a weekly routine to the operations agent that reviews workflow execution
history, flags abnormal failures, and notifies me only when action is needed."
create_routine({
agent_id: "operations",
name: "Weekly Workflow Health Review",
prompt: "Review recent workflow execution history. If failures or stalls require action, summarize the affected workflows, likely impact, and recommended next step. Otherwise record that no action is needed.",
interval: "weekly-monday"
})