FlowRunner for developers

Ship agents and LLM apps on a platform that gives you every model provider, every vector store, and the tool library your agent needs, with a human gate on the moves that publish, write to production, or cost real money.

Start free See the stack

The gap between a demo and production

Your prototype works. The agent retrieves, reasons, and answers. Then you try to ship it and the real work appears: routing across providers so one outage does not take you down, keeping an eval and a guardrail between the model and the user, catching the low-confidence extraction before it becomes a wrong fact in a customer record. The model was the easy part. The orchestration around it is where the weeks go.

You already know your stack. OpenAI, Anthropic Claude, Gemini, a vector store, an embeddings model, an OCR service. The problem is the plumbing between them, and the fact that the consequential steps, publishing generated content, letting a tool call write to a production system, kicking off a batch job that burns tokens, deleting a namespace, are exactly the steps you cannot hand to an autonomous loop that does not know when to stop.

The demo works; the production plumbing takes the weeks.
One provider outage takes your whole app down.
A low-confidence extraction gets written as if it were true.
Generated content ships with no one reviewing it first.
An agent tool call writes to production with nothing watching.

What you can build

Automations

Each of these runs as a flow. The agent does the retrieval, the routing, and the routine steps on its own. It stops and asks a human at the one point where publishing, a production write, a real bill, or a wrong fact is expensive or irreversible. Every tool named here is a FlowRunner connector, built and verified against the provider's official API.

Grounded RAG over your own sources

Answers come cited and grounded. The reindex of your knowledge base waits for a human.
1. Trigger
  A question hits your app, or new source documents land in the pipeline.
2. Agent
  The agent chunks and embeds the source with Cohere or OpenAI embeddings, upserts the vectors into Pinecone, retrieves the top matches for the query, and passes the grounded context to Anthropic Claude for an answer that cites what it used.
3. Result
  Users get answers grounded in your own sources with citations, and the index that everything depends on never gets rebuilt unsupervised.
Run 4400 in progress
1. Triggerfired
2. Agent done
3. Human checkpointwaiting
4. Resultpending
Paused. A person was notified in Slack and by email, with the full context attached.
Human checkpoint
Read-only retrieval and answering run on their own. Rebuilding the index or overwriting a production namespace is destructive and hard to undo, so the agent stages the reindex and waits for an engineer to confirm before it replaces the live vectors.
Document extraction into structured data

Clean documents post themselves. The low-confidence read waits for a person.
1. Trigger
  An invoice, form, or contract arrives by email, upload, or into a watched bucket.
2. Agent
  The agent runs the document through AWS Textract for layout and Mindee for typed fields, reconciles the two, and maps the result to your schema with a confidence score on every field.
3. Result
  Clean documents flow straight into structured data, and the ambiguous read gets a human eye before it becomes a wrong number someone trusts downstream.
Run 4437 in progress
1. Triggerfired
2. Agent done
3. Human checkpointwaiting
4. Resultpending
Paused. A person was notified in Slack and by email, with the full context attached.
Human checkpoint
When every field clears the confidence bar you set, it writes the record and moves on. When a total, an account number, or a date comes back low-confidence, it stops and routes the page plus the extracted values to a human, rather than writing a guess into the system of record as if it were fact.
Multi-provider routing and fallback

Requests route to the right model. The expensive batch run waits for a go-ahead.
1. Trigger
  A request enters your app, or a scheduled batch of work is ready to process.
2. Agent
  The agent routes each request by task: cheap and fast to Groq, long-context reasoning to Anthropic Claude, and it fails over through OpenRouter the moment a provider errors or rate-limits, so a single outage never takes the app down.
3. Result
  Traffic always reaches a working model at the right price and latency, and the runs that cost real money have a named human behind the start button.
Run 4474 in progress
1. Triggerfired
2. Agent done
3. Human checkpointwaiting
4. Resultpending
Paused. A person was notified in Slack and by email, with the full context attached.
Human checkpoint
Live traffic routes and fails over automatically. A large batch reprocess, the kind that burns real tokens across thousands of items, stops and shows an engineer the estimated scope and cost before it kicks off, so no run surprises you on the bill.
Content and media generation with a publish gate

Drafts and assets generate on demand. Nothing ships to the public without a sign-off.
1. Trigger
  A content request comes in, or a scheduled generation job fires.
2. Agent
  The agent drafts the copy with OpenAI, generates the accompanying visual with AI Image Generator, produces a voiceover with ElevenLabs, and assembles the finished asset ready to review.
3. Result
  Content and media get produced at speed, and a person always owns the moment anything generated reaches the public.
Run 4511 in progress
1. Triggerfired
2. Agent done
3. Human checkpointwaiting
4. Resultpending
Paused. A person was notified in Slack and by email, with the full context attached.
Human checkpoint
Generation runs on its own. Publishing to an audience is public and hard to unsay, so the agent stops and hands the full package to a human, who approves, edits, or rejects before a single asset goes out under your brand.
Agent memory with an eval and guardrail step

The agent remembers and gets checked. The write that changes memory or fails a check waits.
1. Trigger
  An agent finishes a turn, or a response is ready to return to the user.
2. Agent
  The agent stores and recalls long-term context in Zep, screens each candidate output through Perspective for toxicity, and runs an OpenAI eval pass to score the response against your rubric before anything is returned.
3. Result
  The agent carries context across sessions and every output passes a guardrail, with a person owning the writes to memory and the outputs that fail the bar.
Run 4548 in progress
1. Triggerfired
2. Agent done
3. Human checkpointwaiting
4. Resultpending
Paused. A person was notified in Slack and by email, with the full context attached.
Human checkpoint
Responses that clear the eval and the guardrail go back to the user. When a response fails a check, or when the agent wants to commit a durable fact to long-term memory that will shape every future answer, it stops and routes to a human rather than trusting itself unsupervised.

The pattern that makes it safe to ship

AI apps fail in production when an autonomous loop does something it should not: publishes a hallucination, writes a low-confidence guess as truth, deletes an index, or burns a fortune in tokens on a runaway batch. FlowRunner's answer is the digital andon cord: the agent runs the line and pulls it the instant a step carries real consequence. It retrieves, routes, extracts, and generates on its own. Publishing to the public, writing to a system of record, starting a high-cost run, committing to long-term memory, or dropping a vector namespace always stops and routes to a person through the channel they already watch, with everything the agent produced attached. Read-only work happens immediately; the consequential write waits for a human. That is the difference between a demo and an agent you trust in production.

The digital andon cord

What your team gets

Outcomes

Skip the orchestration plumbing

Routing, fallback, retrieval, and eval steps are flow components you compose, not glue code you write and maintain between every provider and store.

No single point of failure

Route and fail over across providers, so one vendor outage or rate limit never takes your whole app down with it.

Wrong facts get caught first

A low-confidence extraction or a failed eval stops for a human instead of flowing into your system of record as if it were true.

Nothing ships unreviewed

Generated content and media always pass a human before they reach the public, so the model never publishes under your brand alone.

No runaway costs

High-cost batch runs stop and show the scope before they start, so a loop never burns tokens at scale without a person behind it.

Every provider and store, one platform

The model providers, embeddings services, and vector stores you already use, each verified against its official API, so there is no tool your agent cannot reach.

Built for developers' requirements

Controls

The controls you would demand of anything running your models against real data and real users are native to the platform, not add-ons.

Bring your own keys

Your model providers, your keys. Inference runs on credentials you control, you keep your provider rate limits and pricing, and the platform never sits between you and your vendor bill.

Complete audit trail

Every model call, tool call, retrieval, and human decision recorded with the actor and timestamp, so you can trace exactly what your agent did and why.

RBAC and SSO/SAML

Role-based access to the platform itself, with SSO and SAML so your identity provider stays the source of truth for who can touch which flows and keys.

Self-hosted option

Deploy inside your own infrastructure so prompts, embeddings, retrieved context, and customer data never leave your environment.

It sounds super interesting. I'm thinking about so many scenarios.

Operations Manager, CPG brand

Every model provider and the tools around them

The stack

Full-surface coverage of the providers you build on, from OpenAI, Anthropic Claude, Gemini, and AWS Bedrock to the vector stores that ground them: Pinecone, Weaviate, Qdrant, Chroma, and PGVector. Alongside them sit the tools your agent actually needs: OCR, vision, translation, transcription, embeddings, and speech. Every connector is built and verified against the provider's official API, so an agent calls it the way the provider actually allows.

The tools the automations above run on.

AI & LLMs 154 integrations Vector Stores & AI Infra 10 integrations

Every connector verified against each vendor's official API.

Ship agents you can trust in production

Free plan, no card required. Every integration here is built and verified against the vendor's official API, with a human in the loop where it counts.

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