Charles YaoandClaude Opus 5
a82e4ca97d
Code review finding: handle_review's re_fix branch and handle_reverted both
emitted PlanCard("Build plan", "v1") before falling through to
build.initial_plan's unconditional resource discovery, which falls through
again to confirm_resources -- which emits PlanCard("Build plan", "v1") a
SECOND time, now with resources bound. Before this task those two cards were
tagged v1 then v2, so they were visibly different plans; now both say v1,
so the loop-back shows the same plan card twice in one pass. Task 2 already
removed this exact duplicate from the straight-through path
(handle_goal_analysis); it should not survive on the loop-back and not on
the straight-through path.
Fix: handle_review's re_fix branch and handle_reverted still draft
fc.plan_items/fc.plan_version_tag (needed by discovery and the eventual
confirm_resources card) but no longer emit a PlanCard or claim cards=["plan"]
on their AssistantTurnItem (now cards=[], matching the existing
handlers_fix.py precedent for a turn with no card). There is now exactly ONE
plan card per pass, tagged v1, on every path: straight-through,
continue_adjusting, and retry_after_revert.
Updated the two direct-handler unit tests that asserted the now-removed
card, and extended test_review_continue_adjusting_then_reapprove_is_
idempotent (an end-to-end Runner-driven test) to assert re_fix adds zero new
plan cards and the subsequent confirm_resources adds exactly one, tagged v1.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Dify Cloud · Self-hosting · Documentation · Dify edition overview
Dify is an open-source LLM app development platform. Its intuitive interface combines AI workflow, RAG pipeline, agent capabilities, model management, observability features (including Opik, Langfuse, and Arize Phoenix) and more, letting you quickly go from prototype to production. Here's a list of the core features:
Quick start
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- CPU >= 2 Core
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The easiest way to start the Dify server is through Docker Compose. Before running Dify with the following commands, make sure that Docker and Docker Compose v2.24.0 or later are installed on your machine:
cd dify
cd docker
cp .env.example .env
docker compose up -d
After running, you can access the Dify dashboard in your browser at http://localhost/install and start the initialization process.
Seeking help
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Key features
1. Workflow: Build and test powerful AI workflows on a visual canvas, leveraging all the following features and beyond.
2. Comprehensive model support: Seamless integration with hundreds of proprietary / open-source LLMs from dozens of inference providers and self-hosted solutions, covering GPT, Mistral, Llama3, and any OpenAI API-compatible models. A full list of supported model providers can be found here.
3. Prompt IDE: Intuitive interface for crafting prompts, comparing model performance, and adding additional features such as text-to-speech to a chat-based app.
4. RAG Pipeline: Extensive RAG capabilities that cover everything from document ingestion to retrieval, with out-of-box support for text extraction from PDFs, PPTs, and other common document formats.
5. Agent: Autonomous agents with a sandbox of their own: they run commands, install software, and handle files to get open-ended tasks done. Describe the agent you want and it builds itself. Give it skills, connect tools from Dify Marketplace, MCP servers, or your own APIs, and put it to work as a chat app or as a step in any workflow.
6. LLMOps: Monitor and analyze application logs and performance over time. You could continuously improve prompts, datasets, and models based on production data and annotations.
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Cloud
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