AI Agents — built in, not bolted on

A drag-and-drop AI Agent component runs the full agent loop — model → tool selection → execution → observation → next step — with streaming and structured output.

Build Workflows with Ease using Copilot

Build AI agents and workflows by talking to ByteChef. The Copilot generates workflows from a sentence, drops in configured agent steps, explains failed runs and suggests fixes.

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Quick Start

Docker Compose (Fastest Setup)

Requirement: Docker Desktop

This is the fastest way to start ByteChef. Download the docker-compose.yml file from the repository:

curl -O https://raw.githubusercontent.com/bytechefhq/bytechef/master/docker-compose.yml
docker compose -f docker-compose.yml up

Both PostgreSQL database and ByteChef containers will start automatically.

Open http://localhost:8080/login → Create Account → sign in.

Docker (Manual Setup)

If Docker Compose isn't supported in your environment, follow these steps:

1. Create Docker Network

docker network create -d bridge bytechef_network

2. Start PostgreSQL Container

docker run --name postgres -d -p 5432:5432 \
    --env POSTGRES_USER=postgres \
    --env POSTGRES_PASSWORD=postgres \
    --hostname postgres \
    --network bytechef_network \
    -v /opt/postgre/data:/var/lib/postgresql/data \
    postgres:15-alpine

3. Start ByteChef Container

ByteChef generates the key that encrypts stored connection credentials on first start. Mounting ~/.bytechef keeps that key on the host, so it survives recreating the container:

docker run --name bytechef -it -p 8080:8080 \
    --env BYTECHEF_DATASOURCE_URL=jdbc:postgresql://postgres:5432/bytechef \
    --env BYTECHEF_DATASOURCE_USERNAME=postgres \
    --env BYTECHEF_DATASOURCE_PASSWORD=postgres \
    --env BYTECHEF_SECURITY_REMEMBER_ME_KEY=e48612ba1fd46fa7089fe9f5085d8d164b53ffb2 \
    -v ~/.bytechef:/root/.bytechef \
    --network bytechef_network \
    docker.bytechef.io/bytechef/bytechef:latest

Note: Use -d flag instead of -it to run in detached mode.

Open http://localhost:8080/login → Create Account → sign in.

Build your first agent in 60 seconds

1. New Project → New Workflow,
2. Add a trigger
3. Add the AI Agent component
4. Pick a model, attach tools from 250+ connectors, optionally add a knowledge base and guardrails
5. Fill the necessary credentials
6. Configure each component's parameters in the properties panel
7. Test your workflow
8. Deploy

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Workflow Automation

• Visual editor with JSON underneath, Git-friendly
• Flow controls — condition · branch · loop · each · map · parallel · fork-join · subflow · on-error · terminate · waitForApproval
• Triggers — static & dynamic webhooks · polling · hybrid · app-event listeners · callable, plus schedule and form components
• Polyglot code — JavaScript · Python · Ruby on GraalVM
• Durable execution on the Atlas runtime, Postgres-backed, queue-mode for horizontal scale (memory · Redis · RabbitMQ · Kafka · JMS · AMQP · SQS)
• Workflows-as-APIs — workflows can be an authenticated HTTP endpoint
• Git-native — push from the UI, environments backed by branches

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The Unification

• Agents inside workflows — an agent is a step; downstream branches react to its decisions
• Workflows as agent tools — a "refund order" workflow with retries and approvals becomes one tool
• Sub-agents — coordinator agents call specialist agents
• Human-in-the-loop — pause on approval, route to Slack/email, resume on response
• One audit log — agent decisions, tool calls, workflow runs, human approvals, all in one trail

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250+ connectors

CRM · marketing · communication · e-commerce · cloud storage · databases · AI/ML · helpdesk · finance. Every connector is also an agent tool, also an MCP tool. Browse the full catalog.

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Open core — Apache 2.0 + EE

Capability	CE (Apache 2.0)	EE
Visual editor, AI agents, workflows, 250+ connectors	✅	✅
Polyglot code (JS/Python/Ruby)	✅	✅
Knowledge bases, vector stores, guardrails, MCP server	✅	✅
Agent skills, agent evaluations	🚧 in development	🚧 in development
Self-host (Docker / Kubernetes / Helm)	✅	✅
Workflows-as-APIs	—	✅
Git-native	—	✅
Microservices deployment	—	🚧 in development
AI Copilot	—	✅
SSO / SAML / OIDC, SCIM, advanced RBAC	—	🚧 in development
Connection scope sharing (Workspace / Project / Organization)	—	🚧 in development
Multi-environment promotion, audit log with correlation IDs	—	✅
AI Gateway - model routing, quotas, cost controls	—	🚧 in development
Embedded iPaaS - ship integrations and AI agents inside your SaaS product	—	✅

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FAQ

How is this different from n8n, Zapier or Make?

Those are automation tools where AI is a node you call and get an answer back. In ByteChef an agent is a step that owns a loop — it selects tools, executes them, observes the result and decides what to do next — and any workflow can be published as an MCP tool for agents to call. You get deterministic branching, retries and approvals in the same graph as the non-deterministic part, under one audit trail.

How is this different from LangChain, LangGraph or CrewAI?

Those are libraries you build an application around: you own deployment, persistence, retries, credential storage and the UI. ByteChef is the running system — durable execution, a visual editor, managed connections, and 250+ connectors that are already agent tools. You can still drop into code where it earns its place; it just isn't the only way in.

Which LLM providers ship out of the box?

Twelve direct providers — OpenAI, Anthropic, Azure OpenAI, Amazon Bedrock, Google Gemini, Mistral, Groq, DeepSeek, Nvidia, Perplexity, Stability and Ollama — plus three aggregator components (OpenRouter, LiteLLM, NanoGPT) if you would rather route through a gateway.

How does an agent get its tools?

Every connector is already a tool, and workflows you expose through the MCP server become tools too. To let the model supply a value at runtime, put the expression =fromAi('order_id', 'STRING', {'description': 'The order to refund'}) in the field instead of a literal — that property then becomes part of the tool schema the model sees. It is the same properties panel you would otherwise type into; there is no separate tool definition to write.

What is available for memory and RAG?

Eight chat-memory backends (built-in, JDBC, Redis, MongoDB, Cassandra, Neo4j, vector-store-backed, in-memory) and fourteen vector stores (pgvector, Pinecone, Qdrant, Weaviate, Milvus, Couchbase, MongoDB Atlas, Neo4j, Redis, Typesense, MariaDB, Oracle, S3, and the built-in knowledge base). Ingestion and chunking are native, and two RAG patterns ship as components: rag-modular and rag-questionanswer.

What guardrails are there?

Twelve, attached to an agent the same way tools and memory are: PII, LLM-based PII, jailbreak, NSFW, topical alignment, keywords, secret keys, URLs, text sanitization, custom regex, custom rules, and a violation aggregator that decides what happens when several fire at once.

Does ByteChef work with MCP?

In both directions. It consumes external MCP servers as a tool source, so remote MCP tools show up alongside connectors in an agent's tool list. It also exposes your own workflows as an MCP server over an API-key-authenticated endpoint, so Claude Desktop, Cursor or Windsurf can call them.

Do I need the Enterprise Edition?

Only for the rows marked EE in the table above. Everything outside /ee/ is Apache 2.0 — free to self-host and use commercially, including modified. Code under /ee/ is covered by the ByteChef Enterprise License and is not; see License.

Where do I get help?

• Docs — docs.bytechef.io
• Discord — discord.gg/VKvNxHjpYx
• Issues — GitHub Issues, with templates for bugs, features and connector requests
• Roadmap — project board
• Email — support@bytechef.io

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Contributing

If you would like to contribute to the software, read the contributing guide to get started.

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License

This project is licenced under Apache 2.0 for the core (everything outside /ee/) and the ByteChef Enterprise License for code under /ee/ (microservices, embedded, AI Copilot, SSO/SCIM, advanced RBAC)

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Contributors

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Credits

ByteChef started as a fork of Piper.