Put an AI agent to work operating your Dify apps
Connect Dify in one click and delegate the operational layer: an autonomous agent that watches your LLM apps, triages failed runs, keeps knowledge bases fresh, and files evaluation summaries. You build in Dify; the agent runs the shop floor around it.
7-day trial · Cancel anytime
Fleece AI connects to Dify through managed OAuth and lets autonomous agents operate the apps you build there — reading usage and run history, triaging failed runs, checking knowledge-base freshness, and summarizing evaluations. Fleece does not build your LLM apps; it coordinates around them, combining Dify with 3,000+ other apps — Slack, Notion, GitHub, Google Sheets — under the approval rules you set. Dify builds; Fleece operates.
At a glance
| Category | AI |
|---|---|
| Availability | Business plan and up — included in the 7-day trial |
| Connects to | 3,000+ apps via managed OAuth |
| Setup | Connect your tools — no code required |
| Autonomy | Suggest-only to fully autonomous, with approval gates |
| Pricing | 7-day trial (€1 card check, credited back), then paid plans |
By Loïc Jané · Updated June 27, 2026
What a Fleece agent does with Dify
Dify is where you build LLM applications — agentic workflows, RAG pipelines over your knowledge bases, tuned prompts and datasets, all exposed as API endpoints your product calls. Building the app is one job; keeping it healthy in production is another. Someone has to watch usage and cost, catch runs that fail or degrade, notice when a knowledge base has gone stale, and turn evaluation results into something the team can read. That operational layer is exactly where a Fleece agent lives — not building your Dify apps, but running the shop floor around them.
Under the hood, the connection runs through managed OAuth — you authorize Dify once, and Fleece handles tokens, scopes, and refresh securely, with no API keys to paste. The agent can read app usage and run history, spot failed or degraded runs, inspect the freshness of RAG knowledge bases, pull evaluation results, and watch your API endpoints. Event triggers let it react the moment a run fails or an endpoint's error rate climbs, and scheduled flows let it post the digests and reviews that used to be someone's manual chore.
This is deliberately complementary, not competing. A workflow you build in Dify answers a request; a Fleece agent makes sure that workflow keeps answering — noticing when it degrades, flagging the RAG sources that drifted, filing the eval summary, escalating the incident, and coordinating the people and tools around it. And because Fleece agents work as a hierarchy — a lead agent delegating to specialized child agents — one Dify-facing agent can hand knowledge work to a docs agent, incident work to an ops agent, and reporting to a Slack agent, then bring the outcome back. You keep building in Dify; you delegate the operations.
What the agent can do around Dify
App usage digests
Monitors your Dify LLM apps and agentic workflows and posts readable digests — request volume, latency, cost trends, and the intents showing up most.
Failed-run triage
Catches runs that fail or degrade, summarizes the likely cause from the logs, and routes each one to the owner who can actually fix it.
Knowledge-base freshness
Checks RAG knowledge bases for documents that have gone stale or missing against the canonical source, and flags exactly what needs updating.
Evaluation summaries
Pulls evaluation results across prompts and datasets, turns raw scores into a readable what-regressed-what-improved summary, and files it for the team.
Endpoint monitoring
Watches your Dify API endpoints and raises an alert the moment one degrades or error rates climb — before a user has to report it.
Approval gates
Anything that touches a live app, endpoint, or dataset pauses for your one-click sign-off; routine digests and read-only checks run on their own.
Automations teams run around Dify
These are concrete setups you can describe to a Fleece agent in plain language. Each one operates around your Dify apps and combines them with other connected tools.
AI ops: keep your Dify apps healthy without watching a dashboard
The agent watches your Dify LLM apps and agentic workflows, posts a daily usage digest to Slack — volume, latency, cost — and when a run fails or an endpoint's error rate climbs, it summarizes the likely cause from the logs and routes it to the owner. Recurring failures get a GitHub issue opened with the context attached, so the pattern is tracked instead of forgotten.
Knowledge: RAG bases that stay fresh
For each RAG knowledge base, the agent checks which source documents are stale or missing against the canonical versions in Notion, flags the gaps, and coordinates the update — opening a task in Notion for the owner and re-checking once it is done. Retrieval quality stops quietly decaying because someone forgot a doc changed.
Quality: evaluation summaries filed for the team
After each evaluation run, the agent pulls the scores, turns the raw numbers into a plain summary of what regressed and what improved across prompts and datasets, and appends it to a Google Sheets log plus a Notion page. The team gets a running quality record without anyone hand-copying results out of Dify.
Reliability: incident escalation when an endpoint degrades
When a Dify API endpoint starts erroring or latency spikes, the agent escalates within seconds — a Slack alert to the on-call channel naming the failing app and the symptom, plus a GitHub issue pre-filled with context — instead of waiting for a user complaint. It watches, it doesn't just wait for the next scheduled check.
How to connect Dify to Fleece AI
Create your Fleece account
Sign up and start the 7-day trial. You land in a workspace where agents, flows, and integrations live together.
Connect Dify via managed OAuth
Pick Dify from the integrations catalog and authorize it in one click. Fleece manages tokens and scopes for you; you can revoke access at any time from Dify or from Fleece.
Describe the operational job in plain language
Create an agent and tell it what to watch — "post a daily usage digest to Slack, triage failed runs to the owner, and flag stale RAG docs in Notion". No flowchart building required.
Set autonomy and approval gates
Choose what the agent may do on its own and what waits for your sign-off. Anything touching a live app or dataset pauses for one-click approval.
Run it on triggers or a schedule
Let the agent react to Dify run and endpoint events in real time, or schedule recurring flows — daily usage digests, weekly eval summaries — that run without you.
Dify operations get powerful across your stack
Operating an LLM app well means crossing app boundaries. Pair Dify with Slack to route run failures and post usage digests where the team lives, with Notion to keep RAG knowledge bases and evaluation records current, with GitHub to file incidents as issues linked to the failing endpoint, or with Google Sheets to track quality and cost over time — all through one agent, under one set of approval rules. Fleece connects to 3,000+ apps. And it complements Dify rather than replacing it: you build the LLM apps in Dify, and the agent operates and coordinates everything around them.
Frequently asked questions
Go deeper on the blog
Put your Dify apps on autopilot
Connect Dify in one click, describe the operational job in plain language, and let an autonomous agent watch, triage, and coordinate around your LLM apps. 7-day trial, cancel anytime.
Powered by Fleece AI · autonomous agents for 3,000+ apps