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Build AI Agents, Visually. 🤖 https://t.co/zB8Y9d5Z1H

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Welcome to the game OpenAI btw, here's an open source version that support all models

Welcome to the game OpenAI btw, here's an open source version that support all models

397,369 Aufrufe • vor 8 Monaten

Introducing Flowise 3.0 For the first time, anyone can build AI agents from just an idea. Simply describe what you want to create, and a draft will be generated for you.

Introducing Flowise 3.0 For the first time, anyone can build AI agents from just an idea. Simply describe what you want to create, and a draft will be generated for you.

96,517 Aufrufe • vor 1 Jahr

Running multi agents on open source models has always been a hit or miss until... Llama 3.1 on Groq Inc with 405b and 70b

Running multi agents on open source models has always been a hit or miss until... Llama 3.1 on Groq Inc with 405b and 70b

48,208 Aufrufe • vor 1 Jahr

This is the biggest update we've had in a while. Flowise v2.0 and Flowise Cloud With v2.0, we've introduced Sequential Agentic Workflow. The new agentic workflow allows you to: ⛓️Chain agents together 🔁Loopback mechanisms 🙋Human-in-the-Loop 🔶Conditional branches Different from existing chatflow which relies LLM to act on its own, now you have greater control over the flow. Huge shoutout to team for the exceptional LangGraph framework, which made all of this possible! We're also excited to announce the closed beta release of Flowise Cloud! In addition to all existing features, cloud version also includes Evals and Logging. Join the waitlist here: Here's 7 examples to help you get started with agentic workflow:

This is the biggest update we've had in a while. Flowise v2.0 and Flowise Cloud With v2.0, we've introduced Sequential Agentic Workflow. The new agentic workflow allows you to: ⛓️Chain agents together 🔁Loopback mechanisms 🙋Human-in-the-Loop 🔶Conditional branches Different from existing chatflow which relies LLM to act on its own, now you have greater control over the flow. Huge shoutout to team for the exceptional LangGraph framework, which made all of this possible! We're also excited to announce the closed beta release of Flowise Cloud! In addition to all existing features, cloud version also includes Evals and Logging. Join the waitlist here: Here's 7 examples to help you get started with agentic workflow:

46,701 Aufrufe • vor 1 Jahr

GPT-4o combines with function calling is insane. Here's an example. 1. User submits image of bugs/issues 2. GPT-4o to analyze it 3. Function calling to search for possible fixes Cheaper. Faster. What other use cases can you think of?

GPT-4o combines with function calling is insane. Here's an example. 1. User submits image of bugs/issues 2. GPT-4o to analyze it 3. Function calling to search for possible fixes Cheaper. Faster. What other use cases can you think of?

48,729 Aufrufe • vor 2 Jahren

Today, we're supporting MCP in Flowise! There has been a lot of discussion about MCP lately. From our perspective, MCP is nothing fancy - just tools with third-party APIs or functions, wrapped under a unified protocol that can be consumed by client LLM applications. In fact, we've had something similar for a long time with Langchain Tools. However, MCP offers two key benefits: - Its open protocol isn’t limited to specific frameworks - Client applications (like us, Cursor , @windsurf , etc) can offload the burden of maintaining and updating these tools to the official MCP creators These MCP servers (a.k.a. tools) are provided to the LLM, allowing it to decide when and how to use them. This is sometimes referred to as an "agentic" approach, as opposed to a "deterministic" approach like Zapier, where each step must be explicitly mapped out. In real-world scenarios, you might find yourself needing both approaches depending on the use case. It's still early days, and it will take time for the community to establish standards. We're excited to see where this goes!

Today, we're supporting MCP in Flowise! There has been a lot of discussion about MCP lately. From our perspective, MCP is nothing fancy - just tools with third-party APIs or functions, wrapped under a unified protocol that can be consumed by client LLM applications. In fact, we've had something similar for a long time with Langchain Tools. However, MCP offers two key benefits: - Its open protocol isn’t limited to specific frameworks - Client applications (like us, Cursor , @windsurf , etc) can offload the burden of maintaining and updating these tools to the official MCP creators These MCP servers (a.k.a. tools) are provided to the LLM, allowing it to decide when and how to use them. This is sometimes referred to as an "agentic" approach, as opposed to a "deterministic" approach like Zapier, where each step must be explicitly mapped out. In real-world scenarios, you might find yourself needing both approaches depending on the use case. It's still early days, and it will take time for the community to establish standards. We're excited to see where this goes!

27,360 Aufrufe • vor 1 Jahr

Flowise v1.8.3 release 🧠 New models - Claude Sonnet 3.5 - Azure gpt-4o - Voyage-2 embeddings and reranker models 💬 Chat Embed - Agent reasoning steps - Tooltip display - Notification sound 🔥Firecrawl Web Scraper 🔭LangWatch 📚Multi Query Retriever

Flowise v1.8.3 release 🧠 New models - Claude Sonnet 3.5 - Azure gpt-4o - Voyage-2 embeddings and reranker models 💬 Chat Embed - Agent reasoning steps - Tooltip display - Notification sound 🔥Firecrawl Web Scraper 🔭LangWatch 📚Multi Query Retriever

36,090 Aufrufe • vor 1 Jahr

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