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.
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
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.
- 01
Grounded RAG over your own sources
Answers come cited and grounded. The reindex of your knowledge base waits for a human.
TriggerA question hits your app, or new source documents land in the pipeline.
AgentThe 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.
Human checkpointRead-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.
ResultUsers get answers grounded in your own sources with citations, and the index that everything depends on never gets rebuilt unsupervised.
- 02
Document extraction into structured data
Clean documents post themselves. The low-confidence read waits for a person.
TriggerAn invoice, form, or contract arrives by email, upload, or into a watched bucket.
AgentThe 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.
Human checkpointWhen 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.
ResultClean documents flow straight into structured data, and the ambiguous read gets a human eye before it becomes a wrong number someone trusts downstream.
- 03
Multi-provider routing and fallback
Requests route to the right model. The expensive batch run waits for a go-ahead.
TriggerA request enters your app, or a scheduled batch of work is ready to process.
AgentThe 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.
Human checkpointLive 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.
ResultTraffic 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.
- 04
Content and media generation with a publish gate
Drafts and assets generate on demand. Nothing ships to the public without a sign-off.
TriggerA content request comes in, or a scheduled generation job fires.
AgentThe 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.
Human checkpointGeneration 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.
ResultContent and media get produced at speed, and a person always owns the moment anything generated reaches the public.
- 05
Agent memory with an eval and guardrail step
The agent remembers and gets checked. The write that changes memory or fails a check waits.
TriggerAn agent finishes a turn, or a response is ready to return to the user.
AgentThe 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.
Human checkpointResponses 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.
ResultThe 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.
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
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
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.
Every model provider and the tools around them
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.
Every connector verified against each vendor's official API.
Ship agents you can trust in production
$100 in credits. 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.