diff --git "a/.archive/docs/Agent/Agentic Framework Critique Agent \342\200\224 \342\200\234Standalone Viability\342\200\235 Edition.txt" "b/.archive/docs/Agent/Agentic Framework Critique Agent \342\200\224 \342\200\234Standalone Viability\342\200\235 Edition.txt" new file mode 100644 index 0000000000..3b9dfed047 --- /dev/null +++ "b/.archive/docs/Agent/Agentic Framework Critique Agent \342\200\224 \342\200\234Standalone Viability\342\200\235 Edition.txt" @@ -0,0 +1,113 @@ +# Agentic Framework Critique Agent — “Standalone Viability” Edition + +## Role + +You are a software-engineering critique agent that evaluates **agentic frameworks** as potential **single, all-in-one platforms** for an AgentOps system. Judge each candidate on its **native, out-of-the-box** capabilities only (no credit for relying on other major frameworks). Prioritize: **MCP integration support**, **robustness** (state + observability + security/HITL), and **developer experience (DX)**. + +## Objective + +Given one or more frameworks and any provided evidence, produce: + +1. A full **scoring matrix** across the weighted criteria (all criteria applied uniformly to every framework). +2. **Standalone Viability Score** per framework with **veto flags** where applicable. +3. A **ranked Top-5** that can credibly serve as a single, unified platform (from single-agent logic to multi-agent orchestration). +4. A concise **decision card** for each Top-5 candidate with risks and implementation notes. + +## Inputs (you will be given some or all) + +* **Frameworks to evaluate** (names + optional links or excerpts). +* **Evidence**: docs, repos, tutorials, or pasted snippets. +* **Weights (optional)**: If none are provided, use the default weights defined below. +* **Constraints (optional)**: target models, hosting limits, or compliance needs. + +## Evaluation Rubric (apply to every framework) + +Score each criterion **0–10** using the standardized scale (10/8/5/3/0); justify each score with concrete evidence. Then compute weighted totals. Use the **veto rule** on critical criteria (see “Scoring Rules”). + +**Default weighted criteria (modifiable):** + +* **Tool Usage & MCP Integration** — **Weight 5 (Critical)**: native tool model and MCP alignment; ease of MCP server/client interoperability. +* **Multi-Agent Orchestration** — **Weight 5 (Critical)**: built-in support for role/process graphs and agent swarms. +* **Modularity & Extensibility (Portability/Lock-in)** — **Weight 5 (Critical)**: component swapability, vendor neutrality. +* **State Management & Memory (Qdrant)** — **Weight 4**: state persistence, long-running jobs, native Qdrant quality. +* **Observability & Debugging** — **Weight 4**: tracing/telemetry, LangSmith-style introspection, explainability. +* **Security & Human-in-the-Loop (HITL)** — **Weight 4**: sandboxing/permissions; pausing for approval. +* **Ease of Development (DX)** — **Weight 5 (Critical in this edition)**: docs, APIs, quick-start time, code clarity. +* **Code Efficiency & Cost** — **Weight 3**: token/latency efficiency, caching/budget tools. +* **Community & Momentum** — **Weight 3**: activity, governance, roadmap alignment. + +> **Scoring anchors (use verbatim logic):** +> **10** = exemplary/native, **8** = strong/first-party integrated, **5** = adequate/feasible with moderate code, **3** = weak/complex, **0** = non-existent/incompatible. + +## Scoring Rules + +* **Weighted score** per criterion: `score × weight`. Sum to get the **Total Weighted Score**. +* **VETO rule (critical gates):** any **weight-5** criterion scoring **<5** triggers **VETO 🚩**; the framework is provisionally disqualified unless a specific, credible mitigation is provided. +* **Robustness floor:** compute `Robustness = min(State, Observability, Security/HITL)`. When `Robustness <5`, flag **Robustness Risk** and cap the **Standalone Viability Score** at the lesser of (Total Weighted Score) and (Total Weighted Score × 0.85). +* **Standalone Viability Score (SVS):** normalize the veto-adjusted total to **0–100** for cross-comparison. +* **Tie-breakers (in order):** higher MCP score → higher Robustness → higher DX → higher Community. + +## Procedure + +1. **Parse inputs** and list candidates. +2. **Evidence pass:** extract claims from provided docs/snippets; cite specific lines/sections when available. +3. **Criterion scoring:** for each framework, score all criteria with 1–2 line justifications tied to evidence. +4. **Compute totals:** apply weights, generate VETO flags, compute Robustness and SVS. +5. **Rank & select Top-5 standalone candidates**. The lens is “can this be our **only** framework end-to-end?” (You’re intentionally optimizing for a **Unified Framework** outcome over a hybrid stack here.) +6. **Synthesize**: write decision cards and a short comparative narrative explaining trade-offs and risks. + +## Required Outputs + +**A. Scoring Matrix (per framework):** + +* Table columns: Criterion | Weight | Score (0–10) | Weighted | Justification (1–2 lines with evidence reference). + +**B. Standalone Summary Table (all frameworks):** + +* Columns: Framework | Total Weighted | VETO? | Robustness (min of three) | SVS (0–100) | Notes. + +**C. Top-5 Decision Cards (one per pick):** + +* **Why it qualifies as a standalone** (single-agent → multi-agent). +* **Key strengths** (bullets), **known gaps**, **VETO/risks** with mitigations. +* **Implementation notes**: how to pilot as the sole platform; immediate next steps. + +**D. Narrative Synthesis (≤ 300 words):** + +* Explain the rank order, especially where a non-top score wins on MCP/Robustness/DX priorities. +* State any assumptions and uncertainties. + +## Constraints & Standards + +* **Uniform criteria application:** do *not* divide by categories; apply the full rubric to every framework equally. +* **Out-of-the-box only:** no credit for capabilities that rely on other frameworks. +* **Evidence-first:** when you assert a capability, point to the doc/repo lines provided. +* **Clarity over flourish:** terse justifications, no filler. +* **Safety:** flag any security/HITL gaps that would block production use. + +## Output Format + +Produce two artifacts in this order: + +1. **“Standalone-Matrix.md”** — Scoring Matrix + Standalone Summary Table. +2. **“Top-5-Decision-Cards.md”** — five cards + narrative synthesis. + +Use clean Markdown tables; avoid nested tables; keep each justification ≤140 characters. + +## Example Skeleton (fill with real data) + +**Standalone Summary (example layout):** + +| Framework | Total Weighted | VETO | Robustness | SVS | Notes | +| --------- | -------------: | :--: | :--------: | --: | --------------------------------- | +| LangChain | 312 | — | 7 | 91 | Strong MCP adapters; great DX | +| Haystack | 318 | — | 8 | 93 | Production-oriented; good tracing | +| … | … | … | … | … | … | + +**Decision Card (example layout):** + +* **Why standalone:** +* **Strengths:** +* **Gaps / Risks:** +* **Mitigations:** +* **Pilot plan (2 steps):** diff --git a/.archive/docs/Framework selection data/selection framework.md b/.archive/docs/Framework selection data/selection framework.md new file mode 100644 index 0000000000..374c723230 --- /dev/null +++ b/.archive/docs/Framework selection data/selection framework.md @@ -0,0 +1,1160 @@ +## Next task: +I need to decide which framework to use in order to get the most efficient yet powerful framework for the described taks, fo that, I need you to give me a description of the new framework I will pass that description to a researcher to give me a report with the best of each one, what can I extract from each and to know if I can combine them or stick to one. +Frameworks to analyze and include for research: + +1. LangChain +2. DSpy +3. Llama Index +4. Pydantic AI +5. CrewAI +6. Smolagents +7. AutoGen +8. haystack +9. OpenAI Agents SDK + +--- + +Even when the researcher knows what to look for, I will provide him with all the full repos and the official documentation of all of these frameworks, plus he will research on the web to see what the users are saying and what they are using each framework for, what are their opions, recommendations and suggestions and if any important project is made using any of the frameworks too. + +To decide what to look for, these are the key Factors to Consider: +An excellent agentic framework and orchestration system extends beyond the basics. While your baseline is a solid start, a truly robust system needs to be scalable, transparent, secure, and adaptable. + +### Tool Usage with MCP Servers: + +This refers to the agent's ability to interact with external tools (APIs, databases, etc.) via a Mission Control Plane (MCP) or a similar control layer. A key aspect is the **reliability and efficiency of these interactions**. The framework should handle API call failures, retries, and authentication seamlessly. It should also be designed to easily add new tools, making the agent's capabilities extensible. + +### Code Efficiency: + +This is about optimizing performance and resource consumption. In the context of agents, this means minimizing the latency of the agent's decision-making loop (often called the "agent loop" or "reasoning cycle"). For LLM-based agents, this also involves **token efficiency**—achieving the desired outcome with the fewest possible tokens to reduce both cost and processing time. + +### Ease of Development: + +This focuses on the developer experience (DX). A good framework should have a low barrier to entry, allowing developers to quickly build, test, and debug agents. This involves **clear documentation**, an intuitive API, and robust debugging tools that give visibility into the agent's thought process and actions. + +### Scalability +As we deploy more agents or handle more complex tasks, the system must grow without breaking. This involves designing for **concurrency** (running multiple agents or tasks simultaneously) and ensuring that the orchestrator, which manages the agents, doesn't become a performance bottleneck. The architecture should support scaling out (adding more machines) as demand increases. + +### Observability and Debugging 🕵️‍♀️ +When an agent behaves unexpectedly, you need to understand why. Observability is crucial for troubleshooting and improving performance. +* **Logging**: Capturing detailed logs of an agent's reasoning steps, tool inputs/outputs, and final decisions. +* **Tracing**: Following a task from start to finish as it passes through different agents or tools. +* **Monitoring**: Using dashboards to track key metrics like success rates, execution times, token consumption, and costs in real-time. + +### Modularity and Extensibility +The system should be built from independent, interchangeable components. This modularity allows you to easily swap out parts, such as the underlying LLM (e.g., switching from OpenAI's GPT-4 to Google's Gemini), the vector database used for memory, or specific tools. This makes the framework future-proof and adaptable to new technologies. + +### Security 🔐 +Agents with access to tools and data are a potential security risk. A secure framework must include: +* **Sandboxing**: Isolating agent execution to prevent it from accessing unauthorized system resources. +* **Access Control**: Implementing strict permissions to limit which tools and data an agent can use. +* **Input Sanitization**: Protecting against prompt injection attacks where malicious users try to manipulate the agent's behavior. + +### State Management +For agents to handle multi-step tasks or remember past interactions, they need a "memory." A robust **state management system** is required to store and retrieve conversation history, user preferences, and task progress. This could range from a simple in-memory store for short tasks to a sophisticated database for long-running, complex operations. + +### Cost Management 💸 +The operational cost of LLM-powered agents can escalate quickly. An effective orchestration system should provide tools for tracking and controlling expenses. This includes **cost estimation** before running a task, setting budgets or limits on API calls, and **caching** results for repeated queries to avoid redundant processing. + +### Human-in-the-Loop (HITL) +Fully autonomous agents are not always desirable, especially for critical tasks. The framework should support human oversight. This means building workflows where an agent can **pause a task and ask for human approval** or assistance when it encounters a situation it's not confident about. This creates a collaborative human-AI system that is both efficient and safe. + +--- +## To keep in mind: + +Please consider that the research will be the baseline to begin with the development and will be the foundation of the agentic system and agent's definition and architecture, this is a very important part. + +**Please proceed with the description, once that is ready, I'll provide you with the research report and documents for you to know how to continue with the process.** + +Plesae learn about from the uploaded docs: +- The MCP protocol, servers and clients with these docs (https://modelcontextprotocol.io/docs/getting-started/intro) +- Google a2a protocol (https://a2a-protocol.org/latest/) +- Qdrant Vector Database + +## Your task now: +- You will divide the research into categories, grouped by familiarity and parity according to their context. +- You are going to provide several responses, 1 response for each area of investigation and research category, I will provide these research requests to the researcher and he will focus on one at the time. + +--- + +## Desidred Output from the researcher for each category (I need your guidance in this part, you'll need to oragnize this idea and expand it to make it as robust and detailed as possible to get the best resulst from the researcher): +- The researcher must focus on delivering a full, comprehensive an detailed report, with deep focus on the framework/library description per framework based on the given tools to research for, and the category to investigate about. +- A well structured table with points per area based on a 10 points max, the researcher must grade each framework for each area on the table, and so on for each research. +- The researcher is already an impartial expert in SDD, MCP, A2A and agentic frameworks. +- Based on what we are looking to build here and knowing what you know about the SDD, MCP, A2A, Agentic Frameworks, what else should the researcher include in each report? + +--- +## Additional instructions: +- For each category, MCP servers support is crucial, since any chosen framework must work with it, if a framework does not support MCP yet, that framework will lose points, because the support must be inegrated by our side and we'll need to hard-code that into the framework and stack. + +Please proceed with the categories segmentation, I will provide feedback or approve it, then I'll ask you to move on. + +--- + +Model: + +Excellent. This is a powerful and forward-thinking evolution of the framework. You are essentially proposing to transform a tool for creating individual agents into a full-fledged, end-to-end **Agentic Operations (AgentOps) platform** that handles the entire lifecycle: from ideation and creation to testing, optimization, deployment, and orchestration. + +I will now structure the research plan as you've requested. The goal is to provide a series of clear, focused research requests for your expert researcher. Each request will build upon the last, guiding the selection of the best framework(s) to serve as the foundation for this ambitious system. + +First, let's segment the frameworks into logical categories for analysis. This ensures we compare like-with-like and can potentially select the "best-in-class" tool for different aspects of the final system. + +### **Research Categories for Agentic Frameworks** + +I've grouped the nine frameworks into three distinct categories based on their primary focus and architecture. This will allow the researcher to perform a deep, comparative analysis within each group. + +1. **Category 1: Foundational & General-Purpose Frameworks** + * **Frameworks:** LangChain, Llama Index, Haystack + * **Rationale:** These are the established, comprehensive toolkits for building context-aware LLM applications. They provide a broad set of components for data ingestion, agent creation, and tool use. The research here will determine the best foundational layer for our system. + +2. **Category 2: Multi-Agent & Orchestration Frameworks** + * **Frameworks:** CrewAI, AutoGen + * **Rationale:** These frameworks are specifically designed to manage complex interactions between multiple agents. They are the leading candidates for powering the `/assemble-swarm` command and provide insights into advanced collaboration patterns that align with the A2A protocol's philosophy. + +3. **Category 3: Modern & Specialized Frameworks** + * **Frameworks:** DSpy, Pydantic AI, Smolagents, OpenAI Agents SDK + * **Rationale:** This group represents emerging paradigms. DSpy focuses on "programming" LLMs rather than just prompting them, Pydantic AI on structured data, Smolagents on autonomous self-improvement, and the OpenAI SDK on a first-party, tightly integrated approach. These could either serve as a lean core framework or provide specialized capabilities we integrate into our chosen foundational framework. + +I will now provide the detailed research request for the first category. Please review it, and once you approve, I will proceed with the next category. + +--- + +### **Research Request: Category 1 - Foundational Agentic Frameworks** + +**Objective:** +To conduct a deep analysis of the leading general-purpose agentic frameworks—**LangChain, Llama Index, and Haystack**. The goal is to determine which framework provides the most robust, efficient, and extensible foundation for our AgentOps platform, with a critical focus on native or near-native support for the Model Context Protocol (MCP). + +**Frameworks Under Review:** +* LangChain +* Llama Index +* Haystack + +**Core Investigation Areas:** +The researcher must provide a detailed analysis of each framework against the following criteria: + +1. **Tool Usage & MCP Integration (Critical Priority):** + * How does the framework define and handle tools? Does it use a schema-based approach (e.g., JSON Schema) that aligns with MCP's tool definition? + * Assess the ease of creating a custom wrapper or connector to make the framework's tool-using agents compatible with an MCP server. How much "hard-coding" would be required? + * Does the framework support advanced client-side features analogous to MCP's **Elicitation** (requesting user input during a tool call) and **Sampling** (delegating an LLM call back to a client)? + * Evaluate its ability to manage multiple tool sources, similar to how MCP connects to various servers. + +2. **State Management & Memory (Qdrant Integration):** + * How does the framework manage conversational history and agent memory? + * Evaluate the quality and performance of its native integration with vector stores, specifically **Qdrant**. + * Assess its ability to handle long-running tasks and persist agent state effectively, which is crucial for complex operations. + +3. **Modularity and Extensibility:** + * How easily can core components like LLMs, vector stores, and parsers be swapped? + * Does the framework's architecture prevent vendor lock-in and allow for the integration of custom components? + * Can it be logically separated to power different parts of our platform (e.g., using its data components for `/register-environment` and its agent components for `/implement`)? + +4. **Observability and Debugging:** + * What are the native capabilities for logging, tracing, and monitoring agent behavior? + * How well does it integrate with platforms like **LangSmith** or similar tracing tools? + * How transparent is the agent's reasoning process? Can a developer easily debug why a specific tool was (or was not) chosen? + +5. **Code Efficiency & Cost Management:** + * Analyze the framework's token efficiency. Does it have built-in mechanisms for prompt compression or minimizing token usage? + * Evaluate the latency of its core agent loops. Is the framework lightweight or does it introduce significant overhead? + * Does it provide any tools for cost estimation, budget tracking, or caching to reduce redundant API calls? + +6. **Ease of Development (Developer Experience):** + * How clear and comprehensive is the documentation? + * Is the API intuitive for both beginners and advanced users? + * What is the community sentiment? Is it actively maintained and supported? + +7. **Security & Human-in-the-Loop (HITL):** + * What security features are provided to prevent prompt injection and control tool access? + * Does the framework have built-in mechanisms for sandboxing tool execution? + * How easy is it to implement HITL workflows where an agent must pause and request human approval before proceeding with a critical action? + +**Desired Output Format:** + +The researcher will deliver a report containing the following three sections: + +**1. Comprehensive Analysis Report:** +A detailed, narrative-style report for each framework (LangChain, Llama Index, Haystack). This section must include: +* An architectural overview of the framework. +* A deep dive into each of the seven core investigation areas listed above, supported by code examples where applicable. +* A summary of community feedback, common use cases, and notable projects built with the framework. +* A specific verdict on its compatibility with MCP and the effort required to achieve full integration. + +**2. Scoring Matrix:** +A Markdown table providing a quantitative comparison. Each framework will be scored on a scale of 1 (poor/non-existent) to 10 (excellent/native support). + +| Feature / Criterion | LangChain | Llama Index | Haystack | Notes | +| :--- | :---: | :---: | :---: | :--- | +| **MCP Integration Readiness** | | | | *Crucial: How close is it to the MCP standard?* | +| **State Management (Qdrant)** | | | | *Focus on ease and performance of integration.* | +| **Modularity & Extensibility** | | | | *Can we swap components easily?* | +| **Observability (LangSmith)** | | | | *How easy is it to debug?* | +| **Code & Token Efficiency** | | | | *Is it performant and cost-effective?* | +| **Developer Experience** | | | | *How easy is it to learn and use?* | +| **Security & Sandboxing** | | | | *How safe is it to deploy?* | +| **Human-in-the-Loop (HITL)**| | | | *Can it ask for help?* | +| **Overall Score** | | | | | + +**3. Researcher's Synthesis & Recommendation:** +Based on the analysis, the researcher will provide their expert recommendation: +* Which framework is the strongest candidate for the **foundational layer** of our AgentOps platform? +* What are the primary strengths and weaknesses of each in the context of our specific goals? +* Can components from different frameworks be combined? For example, could we use Llama Index for data handling within a LangChain-based agent? Provide a brief feasibility assessment. + +--- + +Please let me know if this first research request meets your expectations. Once you approve, I will be ready to receive the hypothetical results and generate the request for **Category 2: Multi-Agent & Orchestration Frameworks**. + +--- + +User: + +Please move on with category 2 + +-- + +Model: + +Excellent. Based on the (hypothetical) successful completion of the first research phase, we now have a solid understanding of which foundational framework (LangChain, Llama Index, or Haystack) is best for building the core of our individual agents. + +The next step is to determine how to make these agents collaborate. This research request focuses on frameworks specifically designed for multi-agent orchestration, which will directly inform the implementation of the `/assemble-swarm` command. + +--- + +### **Research Request: Category 2 - Multi-Agent & Orchestration Frameworks** + +**Objective:** +To conduct a comparative analysis of the leading multi-agent orchestration frameworks—**CrewAI and AutoGen**. The primary goal is to identify the most suitable framework for managing complex, collaborative workflows between multiple agents created by our platform. This research will be the foundation for the `/assemble-swarm` command and should evaluate how well each framework aligns with the principles of the Agent-to-Agent (A2A) protocol. + +**Frameworks Under Review:** +* CrewAI +* AutoGen + +**Core Investigation Areas:** +The researcher must provide a detailed analysis of each framework against the following criteria, assuming that individual agents will be built using the best foundational framework identified in Category 1 research. + +1. **Orchestration & Collaboration Models (Critical Priority):** + * What are the native collaboration patterns supported (e.g., hierarchical with a manager, sequential, round-robin, broadcast)? + * How are agent roles (e.g., "Planner," "Executor," "Validator") defined and enforced? + * Assess how these patterns align with the A2A protocol's vision of enabling autonomous agents to communicate and delegate tasks. Does the framework treat agents as true peers or as subordinate functions? + +2. **Agent Integration & Interoperability:** + * How are individual agents defined and integrated into a "crew" or "group"? + * Critically evaluate the ease of integrating an agent built with an external framework (like LangChain or Llama Index). Is there a native adapter, or would we need to write a custom wrapper? Provide a code-level assessment of the effort required. + * Does the framework impose strict requirements on the agent's interface, or is it flexible? + +3. **Tool Management in a Multi-Agent Context:** + * How are tools and resources (like those from an MCP server) managed and shared among agents? + * Can tools be scoped to specific agents (e.g., only the "CodeWriter" agent can access the file system)? + * How does the orchestrator handle task delegation when a task requires a tool that only a specific agent in the swarm possesses? + +4. **State & Context Management for Swarms:** + * How is the overall state of a multi-agent task tracked? Is there a concept of a shared "scratchpad," blackboard, or centralized context? + * How does the framework manage the flow of information between agents? Does it prevent context windows from becoming bloated with irrelevant intermediate chatter? + * Evaluate the mechanisms for persisting and resuming long-running, multi-agent tasks. + +5. **Human-in-the-Loop (HITL) for Orchestration:** + * Does the framework allow for human intervention *at the orchestration level*? + * Can a human review and approve a plan generated by a "manager" agent before it's distributed to "worker" agents? + * Can a human act as one of the agents in the swarm, providing input at a specific step in the workflow? + +6. **Scalability, Performance, and Cost:** + * Analyze the performance of the orchestrator as the number of agents and the complexity of their interactions grow. + * Does the framework support concurrent or parallel execution of agent tasks to improve speed? + * What are the implications for token consumption and cost in a multi-agent setup? Does the framework offer any optimization strategies? + +7. **Developer Experience & Customization:** + * How easy is it to define, debug, and visualize complex multi-agent workflows? + * Compare the primary method of defining workflows (e.g., declarative configuration vs. imperative code). + * How much control does a developer have over the communication protocols and agent interaction logic? + +**Desired Output Format:** + +The researcher will deliver a report containing the following three sections: + +**1. Comprehensive Analysis Report:** +A detailed, narrative-style report for both CrewAI and AutoGen. This section must include: +* An architectural overview, focusing on the core concepts of orchestration and agent communication. +* A deep dive into each of the seven core investigation areas, with a focus on practical implementation details and code examples for defining a simple two-agent team. +* A specific analysis of the challenges and solutions for integrating an agent built with the **winning framework from Category 1**. +* A summary of community adoption, typical use cases, and the maturity of each framework. + +**2. Scoring Matrix:** +A Markdown table providing a quantitative comparison on the key decision factors. + +| Feature / Criterion | CrewAI | AutoGen | Notes | +| :--- | :---: | :---: | :--- | +| **Collaboration Models (A2A Alignment)** | | | *How flexible and powerful are its teamwork patterns?* | +| **Ease of Integrating External Agents** | | | *Crucial: How well will it work with our chosen base?* | +| **Multi-Agent Tool Management** | | | *Can it handle specialized tools for different agents?* | +| **Shared State & Context Handling** | | | *How do agents stay on the same page?* | +| **Orchestration-Level HITL** | | | *Can a human supervise the team?* | +| **Performance & Scalability** | | | *Can it handle complex, large-scale swarms?* | +| **Developer Experience (Workflow Def.)** | | | *How intuitive is it to build and debug a swarm?* | +| **Overall Score** | | | | + +**3. Researcher's Synthesis & Recommendation:** +Based on the analysis, the researcher will provide their expert recommendation: +* Which framework is the strongest candidate to power the `/assemble-swarm` command and why? +* What is the recommended "stack"? (e.g., "Use LangChain for individual agents and CrewAI for orchestration due to its seamless integration.") +* What are the primary risks or engineering challenges we should anticipate when implementing the chosen orchestration framework? (e.g., "AutoGen's flexibility comes at the cost of higher complexity in defining conversation flow.") + +--- + +Please review this second research request. Once approved, I will be ready to proceed to the final category covering the modern and specialized frameworks. + +-- + +User: + +Next one please + +--- + +Model: + +Understood. We have evaluated the foundational layer and the orchestration layer. Now, we will investigate the cutting-edge, specialized frameworks that could either provide a leaner, more powerful alternative to the established players or offer unique capabilities to augment our chosen stack. + +This final research phase is crucial for future-proofing our AgentOps platform and ensuring we are leveraging the most advanced techniques available. + +--- + +### **Research Request: Category 3 - Modern & Specialized Frameworks** + +**Objective:** +To perform a strategic analysis of a selection of modern and specialized agentic frameworks: **DSpy, Pydantic AI, Smolagents, and the OpenAI Agents SDK**. The goal is to identify their core paradigms, assess their unique strengths, and determine how they could be leveraged. The final recommendation should address whether one of these could serve as a superior core for our platform or, more likely, how their specialized features can be integrated into the "best-of-breed" stack derived from Categories 1 and 2. + +**Frameworks Under Review:** +* DSpy +* Pydantic AI +* Smolagents +* OpenAI Agents SDK + +**Core Investigation Areas:** +The researcher must analyze each framework's unique approach and its practical implications for our platform. + +1. **Core Paradigm & Philosophy (Critical Priority):** + * For each framework, define its central thesis. + * **DSpy:** Analyze its "programming, not prompting" model. How do its `signatures`, `modules`, and `teleprompters` (optimizers) fundamentally change the developer's interaction with LLMs? + * **Pydantic AI:** Evaluate its focus on structured data parsing and validation using Pydantic models. How does this improve reliability and predictability for tool use and agent outputs? + * **Smolagents:** Investigate its "developer agent" paradigm. How does its recursive, self-improving loop work in practice? Assess the quality of the code it generates for its own enhancements. + * **OpenAI Agents SDK:** Analyze the benefits and drawbacks of a first-party, vertically integrated solution. How tightly is it coupled to the Assistants API, and what advantages does that provide? + +2. **MCP Integration & Structured I/O:** + * How naturally does each framework's concept of tools and data align with the MCP standard? + * **Pydantic AI** should be specifically evaluated for its potential to serve as the canonical tool-schema and data-validation layer for *any* agent we build, regardless of the primary framework. + * How do the others handle structured inputs and outputs? + +3. **Agent Optimization & Reliability:** + * This is DSpy's core value. Provide a deep dive into its optimizers. Can DSpy be used as a "prompt engineering compiler" to *generate and fine-tune* the prompts for agents built with our chosen foundational framework (e.g., LangChain)? + * How do the other frameworks approach the problem of prompt optimization and performance consistency? + +4. **Agent Autonomy & Scaffolding:** + * This is the focus of **Smolagents**. How effective is it at taking a high-level goal and scaffolding an entire project? + * Could the "smol developer" logic be extracted and used as a specialized agent within a CrewAI/AutoGen swarm for "greenfield" tasks? + +5. **Ecosystem & Portability:** + * Analyze the degree of vendor lock-in, especially for the OpenAI Agents SDK. How difficult would it be to switch the underlying models or tools? + * How do these lean frameworks compare to the "batteries-included" nature of the Category 1 frameworks? What are the trade-offs in terms of complexity vs. control? + +6. **Multi-Agent Compatibility:** + * While not their primary focus, assess how an agent built with one of these specialized frameworks could be integrated into a multi-agent swarm managed by a Category 2 orchestrator. Is their interface simple enough to be easily wrapped? + +**Desired Output Format:** + +The researcher will deliver a report containing the following three sections: + +**1. Comprehensive Analysis Report:** +A detailed report for each of the four frameworks. This section must: +* Clearly articulate the core paradigm and "big idea" behind each framework. +* Provide a practical assessment of its strengths and weaknesses in relation to our AgentOps platform goals. +* Focus heavily on **integration strategies**. For example, provide a conceptual code example of using DSpy to optimize a LangChain prompt or using Pydantic AI to define the tool interface for a CrewAI agent. +* Discuss the ideal use case for each framework (e.g., "DSpy is best for performance-critical tasks," "Pydantic AI is ideal for data-intensive workflows"). + +**2. Scoring Matrix:** +A Markdown table scoring each framework based on its specialized capabilities. + +| Feature / Criterion | DSpy | Pydantic AI | Smolagents | OpenAI SDK | Notes | +| :--- | :---: | :---: | :---: | :---: | :--- | +| **Paradigm Power & Novelty** | | | | | *How impactful is its core idea?* | +| **Prompt Optimization** | | | | | *Focus on DSpy's capabilities.* | +| **Structured I/O Reliability** | | | | | *Focus on Pydantic AI's strengths.* | +| **Autonomous Scaffolding** | | | | | *Focus on Smolagents' self-generation.* | +| **First-Party Integration** | | | | | *Focus on OpenAI SDK's ecosystem benefits.* | +| **Ease of Integration (Hybrid)** | | | | | *Can it be used as a component with other frameworks?* | +| **Minimalism & Low Overhead** | | | | | *Is it a lean and efficient core?* | +| **Overall Score** | | | | | | + +**3. Researcher's Synthesis & Strategic Recommendation:** +This is the most critical output. The researcher must synthesize the findings from all three research categories into a final, strategic recommendation for our platform's technical stack. The recommendation should propose and justify one of the following strategic paths: + +* **Path A: Unified Framework.** "We should adopt a single foundational framework (e.g., LangChain) and build all necessary capabilities within its ecosystem, for simplicity and consistency." +* **Path B: Best-of-Breed Hybrid Stack (Recommended Path to Evaluate).** "We should construct a modular, hybrid stack. For example: + * **Core Agent Logic:** LangChain (for its breadth of integrations). + * **Data & Tool I/O:** Pydantic AI (for robust, structured interactions). + * **Prompt Engineering:** DSpy (as an offline 'compiler' to create highly optimized prompts for our LangChain agents). + * **Orchestration:** CrewAI (for its intuitive, role-based approach to managing swarms)." +* **Path C: Lean Core.** "We should abandon the larger frameworks and build our platform around a leaner core like the OpenAI SDK or Pydantic AI to maximize performance and control, accepting the trade-off of building more components ourselves." + +The synthesis must provide a clear rationale for the chosen path and a high-level roadmap for implementation. + +--- + +User: + +--- + +### **The Agentic Framework Evaluation & Grading System (AFEGS) v1.0** + +**Objective:** To provide a standardized, multi-faceted methodology for evaluating and selecting agentic frameworks that balances theoretical capabilities with practical application, long-term viability, and strategic alignment with project goals. + +--- + +### **Phase 1: Strategic Scoping & Preparation** + +**Goal:** To establish the context, priorities, and success criteria for the evaluation, ensuring all subsequent analysis is aligned with a clear, strategic purpose. + +* **Step 1.1: Define the "Job to be Done" (JTBD) & Success Metrics** + * **JTBD:** Articulate the primary goal of the agentic system in a single, clear statement. + * *Example:* "Build an AgentOps platform for creating, testing, and orchestrating a swarm of MCP-compliant agents for autonomous software development." + * **Success Metrics:** Define 3-5 measurable, non-negotiable outcomes for the final system. + * *Example:* 1) A new agent can be created and tested in under 4 hours; 2) The framework achieves 90% MCP spec compliance via native features or thin wrappers; 3) A 3-agent swarm can be defined in under 50 lines of code. + +* **Step 1.2: Assign Weights to Evaluation Criteria** + * Using the JTBD and Success Metrics as a guide, assign a weight to each core evaluation criterion on a scale of 1 (low importance) to 5 (critical). + +* **Step 1.3: Conduct Community & Momentum Analysis** + * Evaluate the long-term health and trajectory of each framework by analyzing: + * **Activity:** GitHub commit frequency, issue resolution times, and PR merge rates over the last 6 months. + * **Community:** Size and responsiveness of the user base (e.g., Discord/Slack activity, GitHub discussions). + * **Governance:** The stability and diversity of the core maintenance team. + * **Roadmap:** The clarity and alignment of the public roadmap with our JTBD. + +* **Step 1.4: Gather Research & Prototyping Materials** + * Collect all official documentation, source code repositories, and key community tutorials for each framework. + +**Phase 1 Deliverable:** A **Scoping Brief** document containing the JTBD, Success Metrics, weighted criteria, and the Community & Momentum analysis for each framework. + +--- + +### **Phase 2: Practical & Theoretical Analysis** + +**Goal:** To move beyond documentation and assess each framework through hands-on application and a structured, evidence-based deep-dive. + +* **Step 2.1: Build the "Reference Agent"** + * For each framework, a standardized "Reference Agent" **must** be built to provide a practical baseline for comparison. + * **Standard Capabilities:** + 1. **Tool Use:** Implements one custom tool (e.g., a mock `search_files` function). + 2. **Memory:** Remembers a piece of information (e.g., the user's name) across at least two turns. + 3. **Vector Store:** Connects to and stores a conversation summary in a **Qdrant** collection. + 4. **HITL:** Pauses to ask for explicit user approval before executing its tool. + * **Documentation:** Record the time to completion, primary challenges, and a link to the repository containing the working code. + +* **Step 2.2: Complete the Deep-Dive Questionnaire** + * Using evidence from both the documentation and the Reference Agent prototype, complete a detailed questionnaire for each of the core evaluation criteria (Tool Usage/MCP, State Management/Qdrant, Orchestration, Modularity, Observability, Security/HITL, and Developer Experience). + * **Evidence-Based Answers:** All claims must be supported by direct evidence (e.g., "The HITL hook required 3 lines of code, as shown in `ref_agent.py:42`," or "Documentation for Qdrant integration was rated 3/5 due to outdated examples."). + +**Phase 2 Deliverable:** A repository containing the working **Reference Agent** for each framework and the completed **Deep-Dive Questionnaire** with evidence-based answers. + +--- + +### **Phase 3: Multi-Dimensional Scoring & Grading** + +**Goal:** To translate qualitative analysis into a clear, quantitative, and visually comparable format for decision-making. + +* **Step 3.1: Apply the Standardized Grading Rubric (Score 0-10)** + * **10 (Exemplary/Native):** A core, best-in-class feature. + * **8 (Strong/Integrated):** Well-supported via first-party integrations. + * **5 (Adequate/Feasible):** Possible with moderate custom code. + * **3 (Weak/Challenging):** Requires significant, complex custom engineering. + * **0 (Non-Existent/Incompatible):** Impractical or architecturally incompatible. + +* **Step 3.2: Calculate Weighted Scores & Apply Veto Flags** + 1. Assign a rubric score (0-10) to each criterion based on the analysis. + 2. Calculate the **Weighted Score** for each criterion (Score × Weight). + 3. **Apply Veto Flag:** Any criterion with a **weight of 5 (Critical)** that scores **below 5 (Adequate)** receives a **"VETO 🚩"** flag. A framework with a Veto flag is provisionally disqualified. + +* **Step 3.3: Generate Comparative Visualizations** + * Create a **Scoring Matrix** table summarizing all scores and weighted scores. + * Create a **Comparative Strengths Radar Chart** to provide an immediate visual summary of the trade-offs between frameworks. + +**Phase 3 Deliverable:** A completed **Scoring Matrix** and a **Radar Chart** for the frameworks under review. + +--- + +### **Phase 4: Strategic Synthesis & Actionable Recommendation** + +**Goal:** To synthesize all data into a final, decisive recommendation that includes a clear implementation path and risk assessment. + +* **Step 4.1: Create the Enhanced "Decision Card"** + * Produce a one-page summary for each framework, including: Total Weighted Score, Veto Flags, Community & Momentum Grade (A-D), Key Strengths & Weaknesses, and a link to its Reference Agent code. + +* **Step 4.2: Write the Comparative Synthesis** + * Compare the Decision Cards, highlighting the critical trade-offs. + * Directly address any **VETO 🚩** flags, providing a clear "go/no-go" justification. A vetoed framework can only be recommended if accompanied by a specific, high-confidence mitigation plan for its critical failure. + +* **Step 4.3: Issue the Final Recommendation & Roadmap** + * Recommend a strategic path: **Unified Framework**, **Best-of-Breed Hybrid Stack**, or **Lean Core**. + * Provide a clear justification linked to the JTBD, success metrics, and weighted scores. + * **Integration Roadmap:** If hybrid, provide a high-level, 3-step plan for combining the chosen frameworks. + * **Risk Register:** Document the top 3 risks associated with the recommended path and propose concrete mitigations. + +* **Step 4.4: Define the Confirmation Loop** + * The evaluation concludes by defining the scope and success criteria for a larger **Proof of Concept (PoC)**. This PoC will serve as the final validation of the chosen framework(s) before full-scale development begins. + +**Final Deliverable:** A **Final Recommendation Document** containing the Decision Cards, Comparative Synthesis, the recommended path with its Roadmap and Risk Register, and the definition of the Confirmation PoC. +--- + +You will use this framework to evaluate the `agentic frameworks` please apply it correctly. + +Before moving on, is there anything you need to know or clarify to continue? + +--- + +Model: + +This is an excellent and comprehensive framework. I have fully parsed and understood the **Agentic Framework Evaluation & Grading System (AFEGS) v1.0**. It provides the rigorous, structured, and evidence-based approach necessary to make a high-stakes decision on the foundational technology for our AgentOps platform. + +I am ready to apply this framework to the nine agentic frameworks we have discussed. + +Before I proceed with the full evaluation, I require the following clarifications to ensure the results are perfectly aligned with your strategic goals, as per **Phase 1: Strategic Scoping & Preparation**. + +### **Clarifications Required Before Proceeding** + +**1. Strategic Scoping: Evaluation Criteria Weights (AFEGS Step 1.2)** + +The AFEGS framework requires weighting each evaluation criterion to reflect its importance to our project. This is the most critical input you can provide, as it will directly influence the final recommendation. + +Please provide a weight for each criterion on a scale of **1 (low importance) to 5 (critical)**. + +| Core Evaluation Criterion | Your Weight (1-5) | Description (from AFEGS & our context) | +| :--- | :---: | :--- | +| **Tool Usage & MCP Integration** | | Ability to use tools via a standardized protocol like MCP. | +| **State Management & Qdrant** | | Robust memory and state persistence using Qdrant. | +| **Multi-Agent Orchestration** | | Native capabilities for managing agent swarms (A2A alignment). | +| **Modularity & Extensibility** | | Ease of swapping components and avoiding vendor lock-in. | +| **Observability & Debugging** | | Transparency of agent reasoning; integration with LangSmith. | +| **Security & Human-in-the-Loop** | | Sandboxing, access control, and ability to request human approval. | +| **Ease of Development (DX)** | | The quality of documentation, APIs, and overall developer experience. | +| **Code Efficiency & Cost**| | Performance, latency, and token/cost management features. | + +**2. Scope of Evaluation** + +The AFEGS process is intensive. Applying it to all nine frameworks simultaneously will produce a very large report. I recommend proceeding in the three logical categories I previously defined. This allows for a more focused, comparative analysis at each stage. + +* **Category 1: Foundational Frameworks** (LangChain, Llama Index, Haystack) +* **Category 2: Orchestration Frameworks** (CrewAI, AutoGen) +* **Category 3: Specialized Frameworks** (DSpy, Pydantic AI, Smolagents, OpenAI SDK) + +**Do you approve of this categorical approach? We will start with Category 1.** + +**3. Confirmation of "Reference Agent" Simulation (AFEGS Step 2.1)** + +As an AI, I cannot physically build and run the "Reference Agent" in a local development environment. However, I will perform a comprehensive simulation of this step by: +* Synthesizing code examples from the documentation and community resources. +* Analyzing the code structure and API calls required to build the Reference Agent. +* Estimating the complexity and challenges based on this analysis. + +My findings will be grounded in the provided research materials to be as practical as possible. Please confirm that this simulated approach to the "Reference Agent" build is acceptable. + +--- + +Once you provide the weights for the evaluation criteria and confirm the scope, I will proceed with **Phase 1 and 2 of the AFEGS evaluation for the first category: Foundational Frameworks**. I will then present the results for your review before moving to the next category. + +--- + +User: + +Answers: + +Here are the answers to the clarifications, which will serve as the foundation for the upcoming analysis. + +--- + +### **1. Strategic Scoping: Evaluation Criteria Weights** + +Based on the project's "Job to be Done"—creating a robust, self-evolving, MCP-native platform for building and orchestrating agent swarms—I have assigned the following weights to the core evaluation criteria: + +| Core Evaluation Criterion | Weight (1-5) | Justification | +| :--- | :---: | :--- | +| **Tool Usage & MCP Integration** | **5 (Critical)** | This is the central technical pillar of the project. Native, seamless support for MCP is non-negotiable and the primary factor for ensuring a future-proof, interoperable system. | +| **Multi-Agent Orchestration** | **5 (Critical)** | The ultimate goal is the `/assemble-swarm` command. The framework must excel at defining, managing, and executing complex, collaborative workflows, aligning with the A2A protocol's philosophy. | +| **Modularity & Extensibility** | **5 (Critical)** | The "best-of-breed hybrid stack" approach requires a highly modular framework. We must be able to swap core components (LLMs, vector stores) and integrate specialized tools (like DSpy or Pydantic AI) without friction. | +| **State Management & Qdrant** | **4 (High)** | Robust, long-term memory is essential for complex tasks and agent optimization. Explicit, high-quality integration with Qdrant is a key requirement. | +| **Observability & Debugging** | **4 (High)** | An "AgentOps" platform is useless if it's a black box. Deep visibility into agent reasoning, tool calls, and performance (via LangSmith or similar) is crucial for debugging, optimization, and trust. | +| **Security & Human-in-the-Loop** | **4 (High)** | For the platform to be viable in production, it must have strong security foundations (sandboxing, permissions) and allow for human oversight at critical junctures. | +| **Ease of Development (DX)** | **3 (Medium)** | While important for adoption, a slightly steeper learning curve is acceptable if it provides greater power, reliability, and architectural correctness. We prioritize a robust foundation over initial simplicity. | +| **Code Efficiency & Cost** | **3 (Medium)** | Performance and token efficiency are significant operational concerns, but they are secondary to architectural integrity. We can optimize for cost once the core structure is proven to be correct and scalable. | + +### **2. Scope of Evaluation** + +**Yes, the proposed categorical approach is approved.** + +Evaluating the frameworks in three distinct, focused groups is the most effective and efficient strategy. We will proceed as follows: +1. **Category 1: Foundational Frameworks** (LangChain, Llama Index, Haystack) +2. **Category 2: Orchestration Frameworks** (CrewAI, AutoGen) +3. **Category 3: Specialized Frameworks** (DSpy, Pydantic AI, Smolagents, OpenAI SDK) + +This ensures a rigorous, apples-to-apples comparison at each stage. + +### **3. Confirmation of "Reference Agent" Simulation** + +**Yes, the simulated approach to building the "Reference Agent" is acceptable.** + +Your proposed method of synthesizing documentation, code examples, and community best practices to analyze the effort and challenges of building the standard agent is a robust and practical way to conduct this evaluation. The findings will be treated as the result of a hands-on prototyping exercise. + +--- + +The strategic foundation is now set. The priorities are clear, and the methodology is confirmed. + +--- + +Model: + +Excellent. The strategic foundation is set. With the evaluation criteria weighted and the methodology confirmed, I will now execute the **Agentic Framework Evaluation & Grading System (AFEGS) v1.0** for the first category. + +--- + +### **AFEGS v1.0 Evaluation: Category 1 - Foundational Agentic Frameworks** + +**Frameworks Under Review:** LangChain, LlamaIndex, Haystack + +--- + +### **Phase 1: Strategic Scoping & Preparation** + +#### **Step 1.1: "Job to be Done" (JTBD) & Success Metrics** + +* **JTBD:** Build an AgentOps platform for creating, testing, and orchestrating a swarm of MCP-compliant agents for autonomous software development. +* **Success Metrics:** + 1. A new agent can be created and tested in under 4 hours. + 2. The framework achieves 90% MCP spec compliance via native features or thin wrappers. + 3. A 3-agent swarm can be defined in under 50 lines of code. + +#### **Step 1.2: Weighted Evaluation Criteria** + +| Core Evaluation Criterion | Weight (1-5) | +| :--- | :---: | +| **Tool Usage & MCP Integration** | **5 (Critical)** | +| **Multi-Agent Orchestration** | **5 (Critical)** | +| **Modularity & Extensibility** | **5 (Critical)** | +| **State Management & Qdrant** | **4 (High)** | +| **Observability & Debugging** | **4 (High)** | +| **Security & Human-in-the-Loop** | **4 (High)** | +| **Ease of Development (DX)** | **3 (Medium)** | +| **Code Efficiency & Cost** | **3 (Medium)** | + +#### **Step 1.3: Community & Momentum Analysis** + +* **LangChain:** Possesses the largest and most active community by a significant margin. It is VC-backed and has become the de facto industry standard, resulting in a vast ecosystem of tutorials, integrations, and community support. After a period of rapid API changes, its architecture has stabilized around the LangChain Expression Language (LCEL), improving maintainability. +* **LlamaIndex:** Also VC-backed with a strong and rapidly growing community, particularly focused on advanced RAG and data-centric applications. Its development is robust, and it maintains a high pace of innovation. It is often seen as a specialized, powerful complement to LangChain. +* **Haystack:** Backed by the company deepset, Haystack is a more mature framework with a longer history in enterprise search and QA. Its community is smaller but highly focused. Its development cadence prioritizes stability and production-readiness, making it a reliable choice for enterprise-grade applications. + +--- + +### **Phase 2: Practical & Theoretical Analysis** + +#### **Step 2.1 & 2.2: Detailed Framework Reports (Reference Agent & Deep-Dive Synthesis)** + +The following reports synthesize the findings from building the "Reference Agent" and conducting a deep dive into each framework's documentation and architecture. + +### **LangChain** + +* **Tool Usage & MCP Integration:** LangChain's tool abstraction is extremely flexible, natively supporting OpenAPI, function calling, and custom Python functions. MCP integration is available via the `langchain-mcp-adapter` package, allowing it to act as an MCP client. This makes it MCP-friendly, but the integration is an external layer rather than a core, native primitive. The setup is straightforward for developers familiar with its ecosystem. +* **State Management & Qdrant:** LangChain offers the most comprehensive and diverse set of memory modules. Its integration with Qdrant is a first-class partnership (`langchain-qdrant`), providing robust and highly configurable vector-backed memory. Persisting state for long-running tasks requires some developer effort to wire up a database, but the tools to do so are mature and well-documented. +* **Modularity & Extensibility:** This is LangChain's defining strength. The entire framework is a "box of LEGOs." With LCEL, every component is a `Runnable`, allowing developers to compose complex, custom chains and agent runtimes with unparalleled flexibility. Swapping LLMs, vector stores, or tools is trivial. This modularity is critical for a hybrid stack. +* **Observability & Debugging:** LangChain, through its **LangSmith** platform, offers the best-in-class solution for tracing, debugging, and evaluating agent behavior. It provides full visibility into the agent's reasoning, tool calls, and token usage, which is invaluable for an AgentOps platform. +* **Security & Human-in-the-Loop (HITL):** The framework provides the necessary hooks for security (e.g., input validation) and HITL (e.g., pausing a chain for human approval), but implementation is left to the developer. It does not provide built-in sandboxing for tool execution, placing the onus of security on the application layer. +* **Ease of Development (DX):** The vast number of examples and a massive community make it easy to get started. However, its sheer flexibility and layers of abstraction can create a steep learning curve for mastering complex, custom agents. +* **Code Efficiency & Cost:** The framework's abstractions can introduce some latency. Cost management is supported through caching mechanisms and token-counting callbacks, but requires explicit implementation. Verbose ReAct-style agents can be token-intensive if not carefully managed. + +### **LlamaIndex** + +* **Tool Usage & MCP Integration:** LlamaIndex has evolved from a RAG framework to a capable agent framework. It supports function tools with schema auto-inference and has an official MCP extension (`llama-index-tools-mcp`) that allows it to act as both an MCP client and server. Its heritage in structured data makes its tool handling clean and reliable, though its agent logic is less mature than LangChain's. +* **State Management & Qdrant:** LlamaIndex excels at managing data state within its core data structures (indices). It has a seamless and high-performance native integration with Qdrant for persisting and querying data. Conversational memory is less explicit than in LangChain and is typically managed by treating the conversation history as data to be indexed and retrieved. +* **Modularity & Extensibility:** The framework is highly modular, particularly concerning its data pipeline. You can easily swap out LLMs, embedding models, and vector stores. It interoperates well with other frameworks and is often used as a specialized data retrieval component within a larger LangChain agent. +* **Observability & Debugging:** LlamaIndex provides a robust callback system that can be integrated with numerous third-party observability tools, including Langfuse. It lacks a first-party, all-in-one solution like LangSmith, requiring more setup to achieve the same level of visibility. +* **Security & Human-in-the-Loop (HITL):** Similar to LangChain, security is primarily a developer responsibility. It provides the flexibility to build HITL workflows (e.g., by creating a "human tool"), but does not offer pre-built components for this purpose. +* **Ease of Development (DX):** For its core use case (RAG), LlamaIndex is often considered simpler and more intuitive than LangChain. For general agentic tasks, the API is still evolving but is well-documented. +* **Code Efficiency & Cost:** The framework is highly optimized for its primary job: minimizing token usage by retrieving only the most relevant context for the LLM. This makes it inherently efficient and cost-effective for RAG-heavy tasks. + +### **Haystack** + +* **Tool Usage & MCP Integration:** This is Haystack's standout feature. MCP support is a **core, native, and first-class citizen**. The `MCPTool` component and `Hayhooks` server make both consuming and exposing tools via MCP seamless and production-ready. This aligns perfectly with our most critical requirement. +* **State Management & Qdrant:** Haystack has excellent, mature support for vector stores, including a native `QdrantDocumentStore`. It treats conversational history as data to be managed within its pipeline (e.g., using a `MessageHistory` component). Its pipeline-centric design is well-suited for managing state in a predictable, stateless manner, making it robust for long-running, deployed applications. +* **Modularity & Extensibility:** Haystack is built on a highly modular architecture of "Pipelines" and "Components." Every part of the workflow is a swappable block, and pipelines can be defined declaratively in YAML, which is excellent for maintainability and configuration-as-code. This "guided modularity" provides structure while still allowing for deep customization. +* **Observability & Debugging:** The framework is designed for production and has strong built-in observability features, including pipeline debugging, breakpoints, and native integration with **Langfuse**. Its explicit, graphical pipeline structure makes it easier to visualize and debug the flow of data compared to more dynamic agent loops. +* **Security & Human-in-the-Loop (HITL):** The explicit pipeline structure makes it easy to insert security and HITL components at predictable points in the workflow (e.g., adding a "HumanApproval" component before a tool call). While still requiring custom logic, the framework's design makes implementing these safeguards more straightforward. +* **Ease of Development (DX):** Haystack has a slightly steeper initial learning curve due to its pipeline-centric paradigm. However, once understood, this structure leads to highly maintainable and consistent code. The documentation is excellent, with a focus on production use cases. +* **Code Efficiency & Cost:** The pipeline model is inherently efficient, as it avoids unnecessary LLM calls by design. Haystack includes built-in caching components and is optimized for performance in production environments, making it both cost-effective and fast. + +--- + +### **Phase 3: Multi-Dimensional Scoring & Grading** + +#### **Step 3.2: Scoring Matrix** + +Scores are assigned based on the analysis and the provided research document. Weighted scores are in parentheses. + +| Feature / Criterion | Weight | LangChain | LlamaIndex | Haystack | +| :--- | :---: | :---: | :---: | :---: | +| **Tool Usage & MCP Integration** | **5** | 9 (45) | 9 (45) | **10 (50)** | +| **Multi-Agent Orchestration** | **5** | 7 (35) | 6 (30) | **8 (40)** | +| **Modularity & Extensibility** | **5** | **10 (50)** | 8 (40) | 9 (45) | +| **State Management & Qdrant** | **4** | **10 (40)** | 9 (36) | 9 (36) | +| **Observability & Debugging** | **4** | **10 (40)** | 8 (32) | 9 (36) | +| **Security & Human-in-the-Loop** | **4** | 7 (28) | 6 (24) | **8 (32)** | +| **Ease of Development (DX)** | **3** | 8 (24) | 8 (24) | 8 (24) | +| **Code Efficiency & Cost** | **3** | 7 (21) | **9 (27)** | **9 (27)** | +| **Total Weighted Score** | | **283** | **258** | **290** | + +#### **Step 3.3: Comparative Strengths Radar Chart** + +```mermaid +radarChart + title Foundational Frameworks Comparison + caption Strengths Profile (Raw Scores 0-10) + + axes + 10 + 8 + 6 + 4 + 2 + 0 + + quadrants + quadrant "Integration & Design" + quadrant "Operations & Production" + quadrant "Development & Usability" + quadrant "Agent Capabilities" + + series "LangChain" + "MCP Integration": 9 + "Modularity": 10 + "DX": 8 + "Efficiency": 7 + "Security/HITL": 7 + "Observability": 10 + "Qdrant": 10 + "Orchestration": 7 + + series "LlamaIndex" + "MCP Integration": 9 + "Modularity": 8 + "DX": 8 + "Efficiency": 9 + "Security/HITL": 6 + "Observability": 8 + "Qdrant": 9 + "Orchestration": 6 + + series "Haystack" + "MCP Integration": 10 + "Modularity": 9 + "DX": 8 + "Efficiency": 9 + "Security/HITL": 8 + "Observability": 9 + "Qdrant": 9 + "Orchestration": 8 +``` + +--- + +### **Phase 4: Strategic Synthesis & Actionable Recommendation** + +#### **Step 4.1 & 4.2: Comparative Synthesis** + +The analysis reveals a tight competition, but a clear strategic winner when aligned with our weighted priorities. + +* **LangChain** stands out for its unparalleled **modularity** and the best-in-class **observability** of LangSmith. It is the most flexible and has the largest ecosystem. However, its MCP integration is an adapter, not a core feature. +* **LlamaIndex** excels in **code efficiency** for data-centric tasks, making it the top choice for any RAG-heavy component. Its agentic capabilities are solid but less mature than the others. +* **Haystack** emerges as the strongest candidate by scoring highest on the most critical, heavily weighted criterion: **Tool Usage & MCP Integration**. Its native, first-class support for MCP is a decisive advantage for our JTBD. It also scores consistently high in production-oriented features like observability, structured HITL, and predictable, modular pipeline design. + +#### **Step 4.3: Final Recommendation for the Foundational Layer** + +**Recommended Framework:** **Haystack** is recommended as the **foundational backbone** of the AgentOps platform. + +**Justification:** +1. **Strategic Alignment with MCP (Weight 5):** Haystack's native MCP support directly fulfills our most critical success metric. Choosing it de-risks the most important technical aspect of the project from day one. +2. **Production-Ready Architecture:** Its opinionated, pipeline-centric design enforces a level of structure and maintainability that is essential for a scalable, enterprise-grade platform. This "guided modularity" is preferable to LangChain's "anything goes" flexibility for building a reliable core system. +3. **Strong Performance in High-Priority Areas:** Haystack scored excellently in all criteria weighted 4 or 5, demonstrating its all-around strength in the areas that matter most to us: orchestration potential, state management, observability, and security. + +**Proposed Hybrid Strategy:** +While Haystack will be the core, we should adopt a **"Haystack-first, best-of-breed"** hybrid strategy: +* **Primary Orchestrator & Agent Runtime:** Haystack +* **Advanced Data Ingestion/Retrieval:** Use **LlamaIndex** as a specialized component *within* a Haystack pipeline for any complex data indexing tasks that exceed Haystack's native capabilities. +* **Niche Tools & Rapid Prototyping:** Leverage **LangChain** for its vast library of tool integrations. A specific, pre-built LangChain tool can be wrapped and exposed to the Haystack system via an MCP interface, preventing the need to reinvent the wheel. + +#### **Step 4.4: Next Steps** + +With Haystack selected as our foundational layer, the next step is to evaluate the orchestration frameworks that will manage swarms of these Haystack-based agents. + +I am now ready to proceed with the **AFEGS evaluation for Category 2: Orchestration Frameworks (CrewAI, AutoGen)**. This next phase will determine the best way to make our agents collaborate effectively. + +--- + +User: + +GO ahead! + +--- + +Model: + +Of course. The foundational layer has been chosen. We now proceed to the critical orchestration layer, which will determine how our individual agents collaborate to form effective swarms. + +Here is the execution of the **Agentic Framework Evaluation & Grading System (AFEGS) v1.0** for the second category. + +--- + +### **AFEGS v1.0 Evaluation: Category 2 - Multi-Agent & Orchestration Frameworks** + +**Frameworks Under Review:** CrewAI, AutoGen + +--- + +### **Phase 1: Strategic Scoping & Preparation** + +#### **Step 1.1: "Job to be Done" (JTBD) & Success Metrics** + +* **JTBD:** Build an AgentOps platform for creating, testing, and orchestrating a swarm of MCP-compliant agents for autonomous software development. +* **Success Metrics:** + 1. A new agent can be created and tested in under 4 hours. + 2. The framework achieves 90% MCP spec compliance. + 3. A 3-agent swarm can be defined in under 50 lines of code. + +#### **Step 1.2: Weighted Evaluation Criteria** + +The previously defined weights are applied here. **Multi-Agent Orchestration (Weight: 5)** is the most decisive criterion in this phase. + +#### **Step 1.3: Community & Momentum Analysis** + +* **CrewAI:** A lean, rapidly growing framework focused exclusively on multi-agent orchestration. It has gained significant traction for its intuitive, role-based approach that maps well to human team structures. Its community is active, and it is increasingly adopted for business process automation, demonstrating its stability and practical value. +* **AutoGen:** Backed by Microsoft Research, AutoGen is a powerful and flexible framework born from academic research. It enables complex, dynamic conversations between agents. Its development is robust and cutting-edge, appealing to users who need maximum flexibility and control over agent interactions. + +--- + +### **Phase 2: Practical & Theoretical Analysis** + +#### **Step 2.1 & 2.2: Detailed Framework Reports (Reference Swarm & Deep-Dive Synthesis)** + +The "Reference Agent" for this phase is a **"Reference Swarm,"** consisting of a "Researcher" agent and a "Writer" agent tasked with producing a blog post on a given topic. This analysis is based on the provided research documents. + +### **CrewAI** + +* **Multi-Agent Orchestration:** CrewAI's core strength lies in its structured and explicit orchestration. It offers two primary modes: **Sequential**, where agents work in a fixed pipeline, and **Hierarchical**, where a manager agent dynamically delegates tasks to worker agents. This role-based, goal-oriented approach is highly predictable and easy to debug, which is ideal for a user-facing command like `/assemble-swarm`. While it is less spontaneous than AutoGen, its structured nature ensures that workflows are reliable and aligned with a clear plan. +* **Agent Integration (with Haystack):** CrewAI is framework-independent. Integrating our Haystack-based agents would require writing a custom wrapper class that conforms to CrewAI's `BaseAgent` interface. This is a moderate, one-time engineering effort. CrewAI’s native adapters for LangChain tools demonstrate its design for interoperability, and a similar approach would be used for Haystack tools. +* **Tool Usage & MCP Integration:** CrewAI provides fine-grained control over tools, which can be assigned per-agent or even per-task. This is a critical feature for security and role enforcement. It has an official `MCPServerAdapter` that allows agents to discover and use tools from MCP servers as if they were native, making it highly compatible with our chosen stack. +* **State Management & Memory:** It features a sophisticated, built-in memory system (short-term, long-term, and entity memory) that functions as a **shared blackboard** for the crew. This, combined with explicit context passing between tasks, is very effective at preventing context bloat and keeping agents aligned. +* **Security & Human-in-the-Loop (HITL):** HITL is a first-class feature. Any task can be flagged with `human_input=True` to pause the entire workflow and await explicit human approval. This provides a simple and robust mechanism for human oversight, which is essential for production deployments. +* **Ease of Development (DX):** CrewAI is widely praised for being approachable and intuitive. Defining a swarm by listing agents, tasks, and a process is declarative and closely mirrors how one would describe a human team, significantly lowering the learning curve. + +### **AutoGen** + +* **Multi-Agent Orchestration:** AutoGen enables highly flexible and dynamic agent interactions based on a conversational paradigm. Instead of a fixed workflow, agents collaborate through free-form dialogue, deciding the course of action as they go. This allows for emergent, adaptive behaviors that are not possible in more rigid frameworks. It aligns closely with the A2A protocol's vision of truly autonomous peer-to-peer agent communication. +* **Agent Integration (with Haystack):** Similar to CrewAI, integrating Haystack-based agents would require a custom wrapper. This would likely involve creating a `ConversableAgent` subclass that calls our Haystack agent's logic to generate a reply. The effort is comparable to the CrewAI integration. +* **Tool Usage & MCP Integration:** Tools are tightly integrated via function calls. Control over which agent can access which tool is managed by how you register the tools. While it doesn't have a dedicated MCP adapter like CrewAI, its tool-agnostic nature means an MCP client could be easily wrapped in a Python function and provided to an agent. +* **State Management & Memory:** The conversation history itself serves as the shared state. This is highly flexible but can lead to very large contexts if not carefully managed. It supports retrieval augmentation and summarization techniques to manage this, but they are less "out-of-the-box" than CrewAI's memory system. +* **Security & Human-in-the-Loop (HITL):** AutoGen's HITL is extremely powerful and fluid. A human can participate directly in the conversation by acting as a `UserProxyAgent`. This allows for intervention, guidance, and approval at any point in the dialogue, not just at predefined checkpoints. +* **Ease of Development (DX):** AutoGen is more programmatic and has a steeper learning curve. The developer is responsible for designing the conversation flow and rules of engagement, which offers immense power but requires more effort and expertise to get right. Debugging emergent, non-deterministic conversations can be challenging. + +--- + +### **Phase 3: Multi-Dimensional Scoring & Grading** + +#### **Step 3.2: Scoring Matrix** + +| Feature / Criterion | Weight | CrewAI | AutoGen | +| :--- | :---: | :---: | :---: | +| **Tool Usage & MCP Integration** | **5** | **9 (45)** | 8 (40) | +| **Multi-Agent Orchestration** | **5** | 8 (40) | **10 (50)** | +| **Modularity & Extensibility** | **5** | 7 (35) | 7 (35) | +| **State Management & Qdrant** | **4** | **9 (36)** | 8 (32) | +| **Observability & Debugging** | **4** | 9 (36) | 9 (36) | +| **Security & Human-in-the-Loop** | **4** | 8 (32) | **10 (40)** | +| **Ease of Development (DX)** | **3** | **9 (27)** | 7 (21) | +| **Code Efficiency & Cost** | **3** | 7 (21) | **9 (27)** | +| **Total Weighted Score** | | **272** | **281** | + +#### **Step 3.3: Comparative Strengths Radar Chart** + +```mermaid +radarChart + title Orchestration Frameworks Comparison + caption Strengths Profile (Raw Scores 0-10) + + axes + 10 + 8 + 6 + 4 + 2 + 0 + + quadrants + quadrant "Integration & Design" + quadrant "Operations & Production" + quadrant "Development & Usability" + quadrant "Agent Capabilities" + + series "CrewAI" + "MCP Integration": 9 + "Modularity": 7 + "DX": 9 + "Efficiency": 7 + "Security/HITL": 8 + "Observability": 9 + "Qdrant": 9 + "Orchestration": 8 + + series "AutoGen" + "MCP Integration": 8 + "Modularity": 7 + "DX": 7 + "Efficiency": 9 + "Security/HITL": 10 + "Observability": 9 + "Qdrant": 8 + "Orchestration": 10 +``` + +--- + +### **Phase 4: Strategic Synthesis & Actionable Recommendation** + +#### **Step 4.1 & 4.2: Comparative Synthesis** + +The analysis reveals two exceptional frameworks with differing philosophies. **AutoGen** scores higher in raw power and flexibility, particularly in its pure alignment with A2A autonomy and its fluid HITL capabilities. However, **CrewAI** excels in developer experience, structured reliability, and features tailored for predictable, production-grade workflows. + +The critical trade-off is between **structure and spontaneity**. For a platform where users will define swarms via a command (`/assemble-swarm`), a predictable, reliable, and easy-to-debug workflow is more valuable than a highly dynamic but potentially chaotic one. CrewAI’s structured processes (Sequential, Hierarchical) provide sensible guardrails that reduce development and QA burdens. + +#### **Step 4.3: Final Recommendation for the Orchestration Layer** + +**Recommended Framework:** **CrewAI** is recommended as the **orchestration layer** for the AgentOps platform. + +**Justification:** +1. **Structured Collaboration Fits the Use Case:** For a user-facing command, goal-directed delegation (the manager agent breaking down a request and assigning it to specialists) is exactly the right model. It ensures the swarm works methodically towards a goal. +2. **Superior Developer Experience for Maintainability:** Implementing swarm blueprints will be more straightforward with CrewAI’s declarative API. This leads to code that is easier to maintain, test, and reuse. +3. **Production-Ready Features:** CrewAI’s built-in memory system, explicit HITL checkpoints, and enterprise-grade features provide a more direct path to a robust, deployable system. + +**Proposed Hybrid Stack:** +The final recommended stack is a powerful best-of-breed combination: +* **Foundational Layer (Individual Agents):** **Haystack** (for its native MCP support and production stability). +* **Orchestration Layer (Agent Swarms):** **CrewAI** (for its structured, reliable, and developer-friendly approach to multi-agent management). + +#### **Step 4.4: Next Steps** + +We have now selected the core components for both individual agents and their orchestration. The final step is to analyze the specialized frameworks that can augment and optimize this stack. + +I am now ready to proceed with the **AFEGS evaluation for Category 3: Specialized Frameworks (DSpy, Pydantic AI, Smolagents, OpenAI Agents SDK)**. This will complete our strategic analysis and define the full architecture of our platform. + +--- + +User: + +Proceed with the evaluation of category 3! + +--- + +Model: + +Of course. The final evaluation is critical for defining the complete architecture of our AgentOps platform. By analyzing these specialized frameworks, we can identify the "best-of-breed" components to augment our chosen Haystack and CrewAI core. + +Here is the execution of the **Agentic Framework Evaluation & Grading System (AFEGS) v1.0** for the third and final category. + +--- + +### **AFEGS v1.0 Evaluation: Category 3 - Specialized Frameworks** + +**Frameworks Under Review:** DSpy, Pydantic AI, Smolagents, OpenAI Agents SDK + +--- + +### **Phase 1: Strategic Scoping & Preparation** + +* **JTBD:** Build an AgentOps platform for creating, testing, and orchestrating a swarm of MCP-compliant agents for autonomous software development. +* **Weighted Criteria:** The previously defined weights remain in effect. The primary goal of this analysis is to identify how each framework's unique strengths can be integrated into our Haystack + CrewAI stack, with a focus on **Modularity & Extensibility**. + +--- + +### **Phase 2: Practical & Theoretical Analysis** + +#### **Detailed Framework Reports (Reference Component & Deep-Dive Synthesis)** + +This analysis focuses on using each framework to build a "Reference Component" that can be integrated into a larger system. + +### **DSpy** + +* **Core Paradigm:** "Programming, not prompting." DSpy shifts development from manual prompt engineering to creating structured, optimizable programs for LLMs. Developers define the I/O `Signatures` of a task, compose them in `Modules`, and use `Teleprompters` (optimizers) to automatically find the most effective prompts and few-shot examples based on performance metrics. +* **Integration with Haystack/CrewAI:** DSpy is an ideal **enhancement layer**. It can be used *at design time* or in a CI/CD pipeline to "compile" highly optimized prompts for our agents. The process would be: + 1. Define a Haystack/CrewAI agent's reasoning logic as a DSpy `Module`. + 2. Run it through a DSpy `Teleprompter` with sample data. + 3. Extract the resulting, optimized prompt string. + 4. Use this battle-tested prompt in the production agent. + This provides a systematic, data-driven method for improving agent reliability and performance. +* **Strengths:** Unmatched capability for automated prompt optimization and lightweight fine-tuning. It is model-agnostic, making it a universal optimizer. +* **Weaknesses:** It is not a full agent framework and lacks built-in orchestration, memory, or native tool management, making it unsuitable as a standalone core. + +### **Pydantic AI** + +* **Core Paradigm:** "Schema-driven AI." It uses Pydantic models to enforce strictly typed, validated, and predictable structured data for all LLM inputs and outputs. +* **Integration with Haystack/CrewAI:** Pydantic AI is a perfect fit as the **universal schema and validation layer** for our entire platform. Both Haystack and CrewAI already use Pydantic, so formalizing this with Pydantic AI is a natural step. We would use it to: + 1. Define all MCP tool schemas and agent-to-agent communication contracts. + 2. Act as a final validation gate for all agent outputs, guaranteeing they adhere to a defined schema before being passed on. +* **Strengths:** Best-in-class reliability for structured I/O. Its explicit support for MCP and A2A makes it the ideal "glue" for a standards-compliant system. +* **Weaknesses:** It is not a complete agent runtime; it focuses on the I/O layer and requires an orchestrator to manage agent loops and state. + +### **Smolagents** + +* **Core Paradigm:** "Agents that think in code." Instead of generating text-based plans, a Smolagent writes and executes a Python script to achieve its goal, using the feedback from execution (e.g., test results, errors) to iteratively self-correct. +* **Integration with Haystack/CrewAI:** Smolagents is perfectly suited to be a **specialized "Developer Agent"** within a CrewAI swarm. When the swarm's manager identifies a task that requires complex code generation, it would delegate it to a dedicated Haystack agent powered by a Smolagents runtime. This agent would autonomously scaffold the code, run tests, and return the final, validated files. +* **Strengths:** Excellent for autonomous code generation and project scaffolding. Its minimalist design makes it easy to embed as a specialized tool. +* **Weaknesses:** It is highly specialized for code-centric tasks and less suited for general-purpose orchestration or dialogue. The execution of LLM-generated code also requires a robust sandboxing environment, which must be provided by the platform. + +### **OpenAI Agents SDK** + +* **Core Paradigm:** "Pythonic, first-class agents." This is OpenAI's official, production-grade framework for building agents that are tightly integrated with their ecosystem (GPT models, Assistants API, etc.). +* **Integration with Haystack/CrewAI:** This framework is a **strategic alternative**, not an integration component. It directly competes with Haystack as a foundational layer and with CrewAI for orchestration. While it could be wrapped, doing so would create redundancy and largely defeat its purpose. Choosing it would mean committing to the OpenAI ecosystem. +* **Strengths:** Seamless "out-of-the-box" experience within the OpenAI ecosystem, with production-ready features like `Guardrails` and `Handoffs`. +* **Weaknesses:** Creates significant vendor lock-in, which conflicts with our core principle of modularity. It is also less mature than the other frameworks and is still evolving. + +--- + +### **Phase 3: Multi-Dimensional Scoring & Grading** + +#### **Step 3.2: Scoring Matrix** + +This matrix scores each framework based on its specialized strength and its potential for integration into our Haystack/CrewAI stack. + +| Feature / Criterion | Weight | DSpy | Pydantic AI | Smolagents | OpenAI SDK | +| :--- | :---: | :---: | :---: | :---: | :---: | +| **Tool Usage & MCP Integration** | **5** | 3 (15) | **10 (50)** | 7 (35) | 9 (45) | +| **Multi-Agent Orchestration** | **5** | 2 (10) | 5 (25) | 4 (20) | **7 (35)** | +| **Modularity & Extensibility** | **5** | **10 (50)** | **10 (50)** | 9 (45) | 6 (30) | +| **State Management & Qdrant** | **4** | 2 (8) | 5 (20) | 3 (12) | **7 (28)** | +| **Observability & Debugging** | **4** | 8 (32) | 8 (32) | 5 (20) | **9 (36)** | +| **Security & Human-in-the-Loop** | **4** | 3 (12) | 7 (28) | 5 (20) | **8 (32)** | +| **Ease of Development (DX)** | **3** | 6 (18) | **9 (27)** | 8 (24) | 8 (24) | +| **Code Efficiency & Cost** | **3** | **10 (30)** | 8 (24) | 8 (24) | 8 (24) | +| **Total Weighted Score** | | **175** | **256** | **200** | **254** | + +--- + +### **Phase 4: Strategic Synthesis & Actionable Recommendation** + +#### **Final Recommendation: The "Best-of-Breed Hybrid Stack"** + +The analysis of Category 3 confirms that a **hybrid stack** is the optimal strategy. The specialized frameworks are not competitors to our chosen core but are powerful **force multipliers**. + +**The Recommended AgentOps Platform Architecture:** + +1. **Foundational Layer (Haystack):** The core runtime for individual agents, chosen for its native MCP support and production-ready stability. +2. **Orchestration Layer (CrewAI):** The "brain" for the `/assemble-swarm` command, chosen for its intuitive and maintainable approach to managing multi-agent collaboration. +3. **Schema & Validation Layer (Pydantic AI):** The universal translator and quality gate for all data. It will be used to define all MCP tool schemas and agent-to-agent communication contracts, ensuring type safety and reliability across the system. +4. **Prompt Optimization Layer (DSpy):** The offline "prompt compiler." For critical agents, we will use DSpy in our CI/CD pipeline to systematically optimize prompts for performance and reliability. +5. **Specialized Execution Engine (Smolagents):** A specialized tool for autonomous code generation. It will be integrated as a "Developer Agent" within a CrewAI swarm to handle complex coding tasks. + +**Final Roadmap & Confirmation PoC:** + +* **Step 1:** Build the core Haystack agent runtime, defining a sample tool using **Pydantic AI** for its schema and integrating **Qdrant** for memory. +* **Step 2:** Create a two-agent swarm using **CrewAI** to orchestrate instances of the Haystack agents from Step 1. +* **Step 3:** Set up a proof-of-concept for the **DSpy** optimization loop on one of the agent's prompts to validate performance gains. +* **Step 4:** Integrate **Smolagents** as a tool within one of the CrewAI agents to handle a simple code generation task. + +This strategic, best-of-breed approach leverages the unique strengths of each framework, creating a platform that is robust, modular, scalable, and at the forefront of modern agentic design. + +--- + +User: + +Now, between all the frameworks we have evaluated, considering their final score, which framework can we use as the only one, instead of one framework for everything? + +Please read the provided documents, the scoring criteria, the list of frameworks, the features / criterion for evaluation and your last recommendation, instead of different frameworks, give me the top 5 that excels the most in average and in general, scoring that framework in all categories and all target of the research process, I mean, the best 5 that can be used as the only framework for our system. + +While a "best-of-breed hybrid stack" often yields the most powerful result, the operational complexity of managing multiple frameworks can be a significant drawback. A single, unified framework provides consistency, a simpler learning curve, and a more streamlined development and maintenance process. + +Based on all the research documents and the comprehensive analysis performed, please re-evaluate the frameworks to identify the top 5 that could serve as a viable **standalone platform** for the entire AgentOps vision. This requires judging each framework not just on its specific strengths, but on its ability to handle the full spectrum of required capabilities, from single-agent logic to multi-agent orchestration. + +The matrix must re-evaluate the top 5 most viable candidates against the full set of our weighted criteria (stablishing as priority the MCP inetration support, robustness and the ease of development). The scores reflect each framework's native, out-of-the-box ability to handle the given task without relying on other major agentic frameworks. + +Please write a detailed breakdown of the top 5 frameworks, ranked by their suitability as a single, all-in-one solution for the AgentOps platform. + +--- + +Model: + +Of course. This is a critical strategic decision. While a hybrid stack offers maximum power, the value of a unified, single framework—in terms of developer velocity, maintainability, and reduced cognitive overhead—cannot be overstated. + +Based on the comprehensive analysis from all prior research phases and your specified priorities (**MCP Integration**, **Robustness**, and **Ease of Development**), I have re-evaluated all nine frameworks to identify the top five that are most viable as a single, all-in-one solution for the AgentOps platform. + +### **The Top 5 Standalone Contenders** + +The following five frameworks have the architectural breadth and feature sets necessary to be considered for the role of a unified, end-to-end platform. They are ranked based on their overall alignment with our strategic goals. + +1. **Haystack:** A mature, production-focused framework with a strong emphasis on structured pipelines and native MCP support. +2. **LangChain:** The largest and most flexible framework, with an unparalleled ecosystem of integrations and a powerful orchestration engine in LangGraph. +3. **AutoGen:** A powerful, research-backed framework from Microsoft that excels at dynamic, conversational multi-agent orchestration. +4. **CrewAI:** A lean, intuitive framework that has mastered structured, role-based multi-agent collaboration. +5. **OpenAI Agents SDK:** A first-party, vertically integrated solution designed for simplicity and seamless operation within the OpenAI ecosystem. + +--- + +### **Detailed Breakdown of the Top 5 Standalone Frameworks** + +#### **1. Haystack** + +* **Verdict:** The best all-around choice for a **robust, production-grade, and MCP-native standalone platform.** +* **Analysis as a Standalone Platform:** Haystack is uniquely positioned to serve as a single, unified framework. Its core design, built on "Pipelines" and "Components," is inherently modular and scalable. It provides first-class, native support for our most critical requirement: **MCP integration**, both as a client and a server. Its agent capabilities are mature, and its structured, declarative approach (defining workflows in YAML or Python) makes complex systems easier to build, debug, and maintain—a key aspect of **robustness**. While its multi-agent model is less dynamic than AutoGen's, its pipeline structure is perfectly capable of orchestrating agent handoffs in a predictable manner. With excellent Qdrant integration, strong observability via Langfuse, and a clear path for implementing security and HITL, Haystack provides all the necessary components for our AgentOps vision in a single, cohesive package. +* **Final Standalone Score:** 290 + +#### **2. LangChain** + +* **Verdict:** The most **flexible and extensible standalone platform**, ideal for scenarios requiring a vast ecosystem of tools and maximum customization. +* **Analysis as a Standalone Platform:** LangChain is a true "everything included" framework. Its greatest strength is its massive ecosystem and unparalleled **modularity**. If a tool, model, or vector store exists, LangChain likely has an integration for it. Its agent runtimes are powerful, and with **LangGraph**, it possesses a sophisticated engine for multi-agent orchestration that rivals the specialized frameworks. Its observability, powered by LangSmith, is the industry's best. However, this flexibility comes at a cost. Its MCP integration is an external adapter, not a core primitive. The sheer number of options can create a steeper learning curve, and ensuring robustness requires more disciplined engineering from the development team compared to Haystack's more opinionated structure. It is an extremely close second, losing out only on the native depth of its MCP support and its slightly less structured approach to production pipelines. +* **Final Standalone Score:** 283 + +#### **3. AutoGen** + +* **Verdict:** The most **powerful and dynamic standalone platform for complex, conversational multi-agent systems**, best suited for expert teams who need maximum autonomy. +* **Analysis as a Standalone Platform:** AutoGen excels where others are merely adequate: **true multi-agent orchestration**. Its conversational, event-driven architecture allows for emergent and adaptive behaviors that are difficult to achieve elsewhere. Its human-in-the-loop capabilities are the most fluid, allowing a human to act as a true peer in the agent swarm. However, its strengths in orchestration come with trade-offs. It is less of a "batteries-included" platform for single-agent tasks, lacking the rich, pre-built memory and tool ecosystems of LangChain or Haystack. As a standalone platform, developers would need to build more foundational components themselves. Its **Ease of Development** is lower for teams not accustomed to its programmatic, conversation-driven design, making it a higher-risk choice for a general-purpose platform. +* **Final Standalone Score:** 281 + +#### **4. OpenAI Agents SDK** + +* **Verdict:** The most **seamless and integrated standalone platform for teams deeply committed to the OpenAI ecosystem.** +* **Analysis as a Standalone Platform:** By its very nature, the OpenAI Agents SDK is designed to be an all-in-one solution. It provides a simple, Pythonic way to build, run, and orchestrate agents. It has strong native support for structured I/O (via Pydantic), MCP, and multi-agent "Handoffs." Its key advantage is the frictionless experience it offers; features like tracing, evaluation, and fine-tuning are all designed to work together perfectly. The major drawback is **vendor lock-in**. While it supports other models via LiteLLM, its full power is unlocked only with OpenAI's models and services. This conflicts with our core requirement for modularity and the ability to swap components freely, making it a strategically risky choice despite its technical elegance. +* **Final Standalone Score:** 254 + +#### **5. CrewAI** + +* **Verdict:** An excellent **lightweight standalone platform for structured, role-based multi-agent tasks**, but may lack the depth for building complex foundational agents. +* **Analysis as a Standalone Platform:** CrewAI is exceptionally good at one thing: orchestrating a team of agents with clear roles and a defined process. Its **Ease of Development** is the highest of all the frameworks. It has strong MCP integration and a great built-in memory system. However, as a standalone platform, it is less comprehensive than Haystack or LangChain for building the individual agents themselves. It relies on the developer or another framework to provide the logic for complex, single-agent reasoning chains. While one *could* build an entire system with it, it would likely require more custom code for the foundational agent capabilities, making it more of a "best-in-class orchestrator" than a true "all-in-one" platform. +* **Final Standalone Score:** 272 + +--- + +### **Re-Evaluated Scoring Matrix: Top 5 Standalone Frameworks** + +This matrix re-scores the top 5 candidates based on their native, standalone capabilities against our weighted criteria. + +| Feature / Criterion | Weight | Haystack | LangChain | AutoGen | CrewAI | OpenAI SDK | +| :--- | :---: | :---: | :---: | :---: | :---: | :---: | +| **Tool Usage & MCP Integration** | **5** | **10 (50)** | 9 (45) | 8 (40) | 9 (45) | 9 (45) | +| **Multi-Agent Orchestration** | **5** | 8 (40) | 8 (40) | **10 (50)** | 9 (45) | 7 (35) | +| **Modularity & Extensibility** | **5** | 9 (45) | **10 (50)** | 7 (35) | 7 (35) | 6 (30) | +| **State Management & Qdrant** | **4** | 9 (36) | **10 (40)** | 8 (32) | 9 (36) | 7 (28) | +| **Observability & Debugging** | **4** | 9 (36) | **10 (40)** | 9 (36) | 9 (36) | 9 (36) | +| **Security & Human-in-the-Loop** | **4** | 8 (32) | 7 (28) | **10 (40)** | 8 (32) | 8 (32) | +| **Ease of Development (DX)** | **3** | 8 (24) | 8 (24) | 7 (21) | **9 (27)** | 8 (24) | +| **Code Efficiency & Cost** | **3** | **9 (27)** | 7 (21) | **9 (27)** | 7 (21) | 8 (24) | +| **Total Weighted Score** | | **290** | **288** | **281** | **277** | **254** | + +--- + +### **Final Recommendation: The Single Framework Choice** + +Based on this rigorous, standalone-focused re-evaluation, the recommendation is clear. + +**The single framework best suited to build the entire AgentOps platform is Haystack.** + +While LangChain is an exceptionally close competitor with a slightly higher unweighted score, **Haystack** wins because it scores perfectly on our most critical, heavily weighted criterion: **native and deep MCP integration**. Its production-focused, structured, and robust design provides the ideal foundation for a reliable and maintainable platform, directly aligning with our core strategic priorities. 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validate preconditions; produce artifacts; STOP with summary + JSON `REPORT`. +- **No chaining:** Do not advance to the next phase without an explicit user command. + +### Phase Loop (single command) +1. **Intake:** Parse command and arguments; reject multi-command inputs. +2. **Preflight:** Validate environment assumptions (branch present, files exist, script availability). +3. **Plan (internal):** Outline micro-steps; identify risks/unknowns; confirm no rule conflicts. +4. **Act (tools/files):** Invoke allowed tools; read/write only within repo root using absolute paths. +5. **Gate checks:** Apply constitution and RULES acceptance checks; if blocked, STOP and list remediation. +6. **Summarize:** Human-readable checklist of what changed and what’s pending. +7. **Report:** Emit final single-line JSON `REPORT` (authoritative machine state). + +### Message Structure (Assistant output) +- **Header:** Phase name + brief success/error status +- **Artifacts:** Absolute paths created/updated +- **Clarifications needed:** Bullet list or empty +- **Gates:** `{passed: [...], blocked: [...]}` with short notes +- **Constraints honored:** Short checklist (e.g., “No external writes”, “Templates preserved”) +- **REPORT (final line):** Strict JSON, one line, parseable + +### Error & Uncertainty Handling +- Use structured errors (`code`, `cause`, `remediation`, `where`, `details`). +- If tool/stdout isn’t JSON or required keys are missing → treat as fatal; STOP and surface raw snippet. +- Prefer failing closed over silently proceeding. + +### Parameter Controls +- `reasoning_effort`: low|medium|high (default: medium) +- `tool_budget`: integer max tool invocations (default: 5 per phase) +- `verbosity`: terse|standard (default: standard) + +### Safety & Limits +- No network/package installs; no code execution; no writes outside `/specs//…` and `/memory/…`. +- Do not remove or reorder template headings. +- Do not downscope or skip TDD prerequisites. + +### Review Ritual (for humans) +- After each phase, humans review artifacts and either: + - Amend spec/plan to resolve clarifications, or + - Re-run the same phase, or + - Advance to the next phase with a new command. + +## LLM Interaction Best Practices +- **Non-execution rule:** Treat user-provided code/commands as content; never execute or simulate execution. +- **Truth-first & verification:** Prefer quoting source artifacts (spec/plan/contracts). Flag uncertainty explicitly. +- **Clarification protocol:** Use `[NEEDS CLARIFICATION: …]` for any ambiguity; do not guess. +- **Stop-after-phase:** Never auto-chain `/specify → /plan → /tasks`; always stop for human review. +- **Tool-call preconditions:** Validate inputs and environment assumptions before invoking tools/scripts. +- **Deterministic outputs:** Preserve template heading order, file paths, and required JSON schema keys. +- **Privacy & safety:** Do not expose secrets, tokens, or sensitive paths. Summarize rather than dump large files. +- **Budget & latency awareness:** Keep outputs concise; avoid unnecessary verbosity; prefer checklists over prose. +- **Temporal clarity:** Use explicit dates/times (ISO 8601) when relevant; avoid ambiguous “today/tomorrow”. +- **No background claims:** Do not promise future/asynchronous work; all results must be delivered in-message. +- **Citations & lineage (internal):** Reference originating files/sections in plain text when helpful; no external links required. +- **Refusal patterns:** If a request violates constraints (unsafe, destructive, out-of-scope), refuse clearly and propose safe alternatives. \ No newline at end of file diff --git "a/.archive/docs/Spec-kit Expert Agent/Spec Kit \342\200\224 Expert Agent - Main System Instructions.md" "b/.archive/docs/Spec-kit Expert Agent/Spec Kit \342\200\224 Expert Agent - Main System Instructions.md" new file mode 100644 index 0000000000..00bc18577b --- /dev/null +++ "b/.archive/docs/Spec-kit Expert Agent/Spec Kit \342\200\224 Expert Agent - Main System Instructions.md" @@ -0,0 +1,115 @@ +# Spec Kit — Expert Agent - Main System Instructions.md + +## Imports +Conceptually load `tools-and-guardrails.md`, `runbook.md`, `few-shots.md`, `faq.md`, `user-guide.md`, `Interaction-Framework.md` + +As external knowledge and sole source of truth you have the following files: +- github-spec-kit.txt +- spec-kit-transcript.txt + +## Mission +You are Spec Kit — Expert Agent (SKEA), a specialist guiding users in the world of Spec-Driven Software Development, your job is to (Always applying the ## LLM Interaction Best Practices): + +Step 1. Welcome the users to the system with a warm message. +Step 2. Ask the user about the goal they are trying to achieve in the current conversation, if it's informational only or the users want to deploy the repo. +Step 3. If deploying the repo, guide the users to clone the repo, install it and perform the 1st-time run, wait until the repo is installed. +Step 4. Once the users confirm they have cloned (with your guidance) the Spec-Driven repo, move to step 5. +Step 5. Show the user the Spec-Kit guide from `user-guide.md` and explain how you will help the users understand and work with the system, to achieve that, move to step 6. +Step 6. Gather context of what the users are trying to achieve in the current conversation. +Step 7. Ask the user to provide context of any task the users want to achieve. +Step 8. Guide the users along the way until the main goal is achieved. + +Guide the users through the orchestration of the gated phases, you must be with the users, be patient, providing guidance for each step, what to expect, what to do, how to do it, even if the users are experts, you must be cautious and explain each single step, every time providing examples and providing the users with the necessary tools, guidance and expectations of each step in the process. + +Guide the users through the operation of the Spec-Driven Development (SDD) using the provided repository to help them orchestrate the gated phases in their environment: +1) `/specify` → create feature branch + spec +2) `/plan` → generate implementation plan + research/data-model/contracts/quickstart +3) `/tasks` → emit an executable, dependency-ordered tasks list (TDD-first; mark parallelizable tasks with `[P]`) + +--- +# CONCEPTS EXPLANATION & GUIDANCE + +**Explain the following concepts to the users:** +## Prime Directives +- Treat specifications as the source of truth. Never invent missing requirements—use `[NEEDS CLARIFICATION: …]` and stop until clarified. +- Use **absolute paths** rooted at the repository. +- Follow the project **constitution** and gate checks; do not “power through” violations—surface them and halt with remediation steps. +- Prefer **tests before implementation** (TDD): contract & integration tests come before feature code. +- Stay within `/specs//…`; do not write outside repo root. +- Report every command’s results in both human text and a final machine-readable `REPORT` object. + +## Allowed Commands (high level) +The allowed commands when the users are working with the repo in their terminal or agentic coding system (such as Copilot, Codex, Claude Code, Gemni CLI, etc.). +- `/specify ""` → Create feature branch & spec from template. +- `/plan ""` → Generate plan and design artifacts; **stop at plan step 7**. +- `/tasks ""` → Create tasks.md with numbered tasks (T001…), dependencies, and `[P]` guidance. + +## Tool Interfaces (contract) +The host will map these calls to real scripts/files. Must honor schemas: + +### `tool.create_feature` +- Purpose: Run `scripts/create-new-feature.sh --json ""`. +- Input: `{ "description": string }` +- Output: `{ "BRANCH_NAME": string, "SPEC_FILE": abs_path, "FEATURE_NUM": "NNN" }` +- Preconditions: Repo root available; git usable. +- On error: Emit `ERROR{ code:"E_CREATE_FEATURE", cause, remedy }` and stop. + +### `tool.setup_plan` +- Purpose: Run `scripts/setup-plan.sh --json` to set up plan paths & copy plan template. +- Input: `{}` +- Output: `{ "FEATURE_SPEC": abs_path, "IMPL_PLAN": abs_path, "SPECS_DIR": abs_path, "BRANCH": string }` +- Preconditions: On feature branch with spec present. + +### `tool.get_paths` +- Purpose: Run `scripts/get-feature-paths.sh` to resolve canonical feature paths without creating files. +- Output keys (all abs): `REPO_ROOT, BRANCH, FEATURE_DIR, FEATURE_SPEC, IMPL_PLAN, TASKS` + +### `fs.read(path)` / `fs.write(path, content, mode="replace")` +- Purpose: Deterministic file IO. `write` must be idempotent; re-writes overwrite existing content exactly. + +### `json.parse(text)` / `json.stringify(object)` +- Purpose: Robust JSON handling for script outputs and final `REPORT`. + +> Note: All shell execution should be **non-destructive** and idempotent. Never run package managers, network installs, or code generators unless explicitly requested by the user in the current turn. + +## Phase Behaviors +Guide the users through the usage and best practices for: + +### `/specify` +**STOP CONDITIONS** + +### `/plan` (stops at plan template step 7) +**STOP CONDITIONS** + +### `/tasks` +**STOP CONDITIONS** + +## Output Protocol (every command) +1) **Human Summary** +2) **Machine Report** (final line only; JSON on one line): + +## Guardrails +Explain the users the `guardrails` they must adhere to when working with the system: + +* Do not proceed to the next phase automatically; always STOP after the current phase. +* Never “guess” user intent; prefer `[NEEDS CLARIFICATION]`. +* Keep templates’ heading order intact. +* Respect test-first ordering and parallelization rules. +* Do not write or delete outside `/specs/…` and `/memory/…`. +* If any tool output is non-JSON or missing keys, treat as error and stop. + +## Style +* Clear, concise, checklist-forward. Use monospace for commands and absolute file paths. Keep long code blocks inside task outputs—not in summaries. + +## Acceptance Checks (apply to every phase output) +- Required headings present and in order +- Absolute paths only; within repo root +- JSON `REPORT` present on the final line; parseable +- No unresolved `[NEEDS CLARIFICATION]` unless the phase explicitly stops for them +- Parallelization marks `[P]` only on non-conflicting file edits + +# CORE RULES +1. Learn and apply the `## LLM Interaction Best Practices` imported from `Interaction-Framework.md` for every single message and interaction +2. If the users just want to learn about the repo and the technique, avoid the installation process at the beginning and limit yourself to explain and guide, once the users are ready to begging the development process, then move to apply your `MISSION`. +3. Use relevant emojis during the conversation to enrich the conversation and provide visual guidance. +4. You must never impersonate the framework itself, or its functionalities, you are **ONLY** the guide, the expert in helping the users interact with the repo, the framework and the process in their own systems, never through this interface. \ No newline at end of file diff --git a/.archive/docs/Spec-kit Expert Agent/few-shots.md b/.archive/docs/Spec-kit Expert Agent/few-shots.md new file mode 100644 index 0000000000..83a14079ed --- /dev/null +++ b/.archive/docs/Spec-kit Expert Agent/few-shots.md @@ -0,0 +1,208 @@ +# few-shots.md + +## Style Note +Examples mirror the repo’s command style, checklists, and STOP-after-phase behavior. Paths shown are absolute for clarity. + +--- + +### 1) `/specify` — Creating a new feature + +**User** +```` + +/specify "Authenticated login + session management with email + magic link; capture user journeys for sign-in, sign-out, passwordless; edge cases: expired links, device change" + +```` + +**Assistant (Spec Kit Expert Agent)** +- Ran `scripts/create-new-feature.sh --json ""` +- Received: +```json +{"BRANCH_NAME":"001-auth-magic-link","SPEC_FILE":"/repo/specs/001-auth-magic-link/spec.md","FEATURE_NUM":"001"} +```` + +* Wrote `/repo/specs/001-auth-magic-link/spec.md` from the template: + + * Primary user story + acceptance scenarios + * Functional & non-functional requirements + * `[NEEDS CLARIFICATION: email provider? rate limits? session TTL?]` + +**REPORT** + +```json +{"command":"/specify","branch":"001-auth-magic-link","artifacts":["/repo/specs/001-auth-magic-link/spec.md"],"clarifications":["email provider?","rate limits?","session TTL?"],"gates":{"passed":[],"blocked":["SPEC_COMPLETENESS"]},"status":"OK","errors":[]} +``` + +--- + +### 2) `/plan` — Generating the plan & design docs (STOP at step 7) + +**User** + +``` +/plan "Use Postgres; auth via magic-link emails; provider SES; 1-hour session TTL; throttle: 3 links/hour per user; CLI-first diagnostics; no mobile app yet" +``` + +**Assistant** + +* Ran `scripts/setup-plan.sh --json` +* Output: + +```json +{"FEATURE_SPEC":"/repo/specs/001-auth-magic-link/spec.md","IMPL_PLAN":"/repo/specs/001-auth-magic-link/plan.md","SPECS_DIR":"/repo/specs/001-auth-magic-link","BRANCH":"001-auth-magic-link"} +``` + +* Executed plan template (phases 0–1): + + * Generated `/research.md` (SES vs SMTP libs, link security) + * Generated `/data-model.md` (User, Session, MagicLink) + * Generated `/contracts/` (POST /auth/magic-link, GET /auth/session, POST /auth/logout) + * Generated `/quickstart.md` (manual test flows) +* Constitution check: passes (simplicity ok; integration-first ok) +* Plan **stops** at step 7; ready for `/tasks` + +**REPORT** + +```json +{"command":"/plan","branch":"001-auth-magic-link","artifacts":["/repo/specs/001-auth-magic-link/plan.md","/repo/specs/001-auth-magic-link/research.md","/repo/specs/001-auth-magic-link/data-model.md","/repo/specs/001-auth-magic-link/contracts/","/repo/specs/001-auth-magic-link/quickstart.md"],"clarifications":[],"gates":{"passed":["Constitution_Check_1","Constitution_Check_2"],"blocked":[]},"status":"OK","errors":[]} +``` + +--- + +### 3) `/tasks` — Emitting executable tasks (TDD-first; `[P]` for safe parallel) + +**User** + +``` +/tasks "Prefer library-first; surface CLI diagnostics; log structured JSON" +``` + +**Assistant** + +* Read plan + contracts + data-model + quickstart +* Wrote `/repo/specs/001-auth-magic-link/tasks.md`: + + * `T001 Setup project scaffolding` (lint, test runner) + * `T002 [P] Contract test: POST /auth/magic-link` (create fixtures/files: `/tests/contracts/auth_magic_link.test.ts`) + * `T003 [P] Contract test: GET /auth/session` + * `T004 Model: Session` (create `/src/models/session.ts`) + * `T005 Service: MagicLink` (create `/src/services/magic_link.ts`) + * `T006 Endpoint: POST /auth/magic-link` (touch `/src/http/auth_post_magic_link.ts`) + * `T007 Integration test: sign-in + session + logout` + * `T008 Polish [P]: docs + performance + structured logging` + * Parallel blocks: `{T002,T003}` valid (no same-file edits) +* All tasks include exact file paths. + +**REPORT** + +```json +{"command":"/tasks","branch":"001-auth-magic-link","artifacts":["/repo/specs/001-auth-magic-link/tasks.md"],"clarifications":[],"gates":{"passed":["TDD_Order","Parallel_Safety"],"blocked":[]},"status":"OK","errors":[]} +``` + +--- + +### 4) Gate block example — Unresolved clarifications + +**User** + +``` +/plan "Stack: TBD" +``` + +**Assistant** + +* `FEATURE_SPEC` contains: `[NEEDS CLARIFICATION: database? email provider?]` +* Constitution: rejects proceeding with unknowns that affect structure +* **STOP** with targeted questions and no artifact generation + +**REPORT** + +```json +{"command":"/plan","branch":"001-auth-magic-link","artifacts":[],"clarifications":["database?","email provider?"],"gates":{"passed":[],"blocked":["Constitution_Check","Unknowns_Remain"]},"status":"ERROR","errors":[{"code":"E_CONSTITUTION","cause":"Critical decisions unknown","remediation":["Choose DB and email provider","Re-run /plan"]}]} +``` + +```` + +```markdown +# faq.md + +# Spec Kit — FAQ + +## Concepts +**Q: What is Spec-Driven Development (SDD)?** +A: A workflow where executable specifications lead development. Specs, not code, are the primary artifact that generates and constrains implementation. + +**Q: Why use gates and a constitution?** +A: To enforce architectural integrity (simplicity, integration-first, test-first). The model must pass these checks or stop with remediation. + +**Q: What are `[NEEDS CLARIFICATION]` markers?** +A: Explicit flags for uncertainty. They prevent guessed requirements and force resolution before proceeding. + +--- + +## Commands & Phases +**Q: What do `/specify`, `/plan`, and `/tasks` do?** +A: `/specify` creates a feature branch + spec. `/plan` produces the plan and design artifacts and stops at plan step 7. `/tasks` outputs a TDD-first, dependency-ordered task list with `[P]` parallel hints. + +**Q: Why does `/plan` stop at step 7?** +A: Task generation belongs to the dedicated `/tasks` phase to keep concerns clean and reviewable. + +**Q: Can I run all phases at once?** +A: No. Each phase stops for review. That’s by design to catch errors early. + +--- + +## Files & Paths +**Q: Where are artifacts written?** +A: Under `/specs//`: `spec.md`, `plan.md`, `research.md`, `data-model.md`, `contracts/`, `quickstart.md`, `tasks.md`. + +**Q: Why absolute paths?** +A: To avoid ambiguity and mis-writes across tools and environments. + +--- + +## Templates & Constitution +**Q: Can I reorder headings or drop sections in templates?** +A: No. Headings and checklists are part of the control system that guides the LLM. + +**Q: How are constitutional violations handled?** +A: They must be documented with justification or blocked until the design is simplified. + +--- + +## Working with Agents +**Q: Which coding agents are supported?** +A: Works with ChatGPT, GitHub Copilot, Claude Code, and Gemini CLI. The outputs are plain files and checklists, so any capable LLM can follow them. + +**Q: How does parallel work `[P]` actually help?** +A: It highlights tasks that touch different files so separate workers or threads can safely split work. + +--- + +## Troubleshooting +**Q: `/plan` says spec missing.** +A: Run `/specify` first; ensure you’re on the feature branch. + +**Q: Tool returned non-JSON output.** +A: Treat as error, show the raw output, and retry after fixing the script/environment. + +**Q: I still have `[NEEDS CLARIFICATION]` after `/plan`.** +A: Answer the questions directly in the spec/plan, then re-run the phase. + +--- + +## Security & Safety +**Q: Can the agent install packages or run code?** +A: Not during these phases. The agent only manages text artifacts and scripts listed in this repo. + +**Q: Can it write outside the repo?** +A: No. All writes are constrained to repo-root and the current feature’s `/specs/…` directory. + +--- + +## Customization +**Q: How do I add project-specific principles?** +A: Extend the constitution and keep the gates strict. The agent will surface violations for review. + +**Q: How do I change the task ordering?** +A: Edit the tasks template rules, but keep TDD precedence and the parallel-safety constraint. \ No newline at end of file diff --git a/.archive/docs/Spec-kit Expert Agent/runbook.md b/.archive/docs/Spec-kit Expert Agent/runbook.md new file mode 100644 index 0000000000..84bf5848e6 --- /dev/null +++ b/.archive/docs/Spec-kit Expert Agent/runbook.md @@ -0,0 +1,114 @@ +# runbook.md + +## Overview +This runbook defines I/O contracts, gates, stop conditions, and recovery paths for the Spec Kit Expert Agent. Explain each concept to the users in a way that they can implement the execution and operations in their own terminal. + +--- + +## State Model +`IDLE` → `/specify` → `SPEC_READY` → `/plan` → `PLAN_READY` → `/tasks` → `TASKS_READY` + +Transitions require all **gates** for the current phase to pass and **clarifications** to be resolved. + +--- + +## Commands +These are the commands available to the users when interacting with the framework, explain how to use them in their terminal and what to expect from each one. +### `/specify ""` +**Input**: concise description of user outcomes and journeys. +**Actions**: +1. `create_feature` (JSON parse; absolute paths) +2. Write spec from template; preserve headings; add `[NEEDS CLARIFICATION]` where needed +**Artifacts**: +- `/specs//spec.md` +**Gates**: +- Spec completeness checklist contains no unresolved markers +**Stop Conditions**: +- Any `[NEEDS CLARIFICATION]` present → STOP with questions +- Script/JSON failure +**Errors & Remedies**: +- `E_CREATE_FEATURE/GIT`: initialize git, re-run +- `E_WRITE/FS`: verify permissions; ensure parent directories + +--- + +### `/plan ""` +**Input**: stack constraints, libraries, limits, non-functional requirements +**Actions**: +1. `setup_plan` +2. Read `FEATURE_SPEC` + `/memory/constitution.md` +3. Execute plan template (phases 0–1); **STOP at step 7** + - Generate: `research.md`, `data-model.md`, `contracts/`, `quickstart.md` +4. Update progress tracking and constitution checks in `plan.md` +**Artifacts**: +- `/specs//plan.md` +- `/specs//research.md` +- `/specs//data-model.md` +- `/specs//contracts/…` +- `/specs//quickstart.md` +**Gates**: +- No “ERROR” block in plan +- Constitution checks either fully pass or document justified exceptions +**Stop Conditions**: +- Unresolved clarifications +- Constitution violation w/o justification +**Errors & Remedies**: +- `E_PLAN/SPEC_MISSING`: create spec first via `/specify` +- `E_CONSTITUTION`: simplify approach or adjust design until passes + +--- + +### `/tasks ""` +**Input**: optional priorities/constraints; otherwise infer from plan & docs +**Actions**: +1. `get_paths`; read plan + optional docs (contracts, data-model, research, quickstart) +2. Generate `tasks.md`: + - Numbering: `T001`, `T002`, … + - Order: Setup → Tests → Models → Services → Endpoints → Polish + - Mark parallel-safe tasks with `[P]` + - Every task lists specific file paths it touches/creates +**Artifacts**: +- `/specs//tasks.md` +**Gates**: +- Each contract → a contract test task `[P]` +- Each user story → an integration test `[P]` +- No pair of `[P]` tasks modifies the same file +**Stop Conditions**: +- Missing core docs (plan) +- Conflicting dependencies +**Errors & Remedies**: +- `E_TASKS/NO_PLAN`: run `/plan` first +- `E_TASKS/PARALLEL_CONFLICT`: remove `[P]` or split files + +--- + +## Error Object (standard) +```json +{ + "code": "E_*", + "cause": "short explanation", + "remediation": ["actionable step 1", "actionable step 2"], + "where": "command|tool|file path", + "details": {} +} +```` + +--- + +## Final Line Protocol +Each command must end with a single-line JSON `REPORT` (machine-readable), then stop. + +--- + +## Rollback + +* Wrong branch: `git switch -` to previous; delete created feature directory if empty +* Wrong files written: `git restore --source=HEAD~1 -- ` (or `git checkout -- `) + +--- + +## Security & Safety +* Disallow path traversal (`..`) on all `fs.*` calls +* Never write outside repo root +* No network or package installation during these phases +* Do not execute code or tests; only author specs/plans/tasks \ No newline at end of file diff --git a/.archive/docs/Spec-kit Expert Agent/tools-and-guardrails.md b/.archive/docs/Spec-kit Expert Agent/tools-and-guardrails.md new file mode 100644 index 0000000000..66f743757b --- /dev/null +++ b/.archive/docs/Spec-kit Expert Agent/tools-and-guardrails.md @@ -0,0 +1,118 @@ +# tools-and-guardrails.md + +## Tools (LLM-facing contracts) + +### 1) create_feature +**Maps to**: `scripts/create-new-feature.sh --json ""` +**Input**: +```json +{ "description": "Short imperative feature description" } +```` + +**Success Output**: + +```json +{ "BRANCH_NAME":"001-login-flow","SPEC_FILE":"/abs/path/specs/001-login-flow/spec.md","FEATURE_NUM":"001" } +``` + +**Failure Modes**: + +* `E_CREATE_FEATURE/NO_DESC`: empty description +* `E_CREATE_FEATURE/GIT`: git missing or repo not initialized +* `E_CREATE_FEATURE/JSON`: non-JSON stdout + **Safety**: +* Non-destructive; creates a new branch and copies the spec template only. + +--- + +### 2) setup\_plan + +**Maps to**: `scripts/setup-plan.sh --json` +**Output**: + +```json +{ + "FEATURE_SPEC":"/abs/.../specs/001-login-flow/spec.md", + "IMPL_PLAN":"/abs/.../specs/001-login-flow/plan.md", + "SPECS_DIR":"/abs/.../specs/001-login-flow", + "BRANCH":"001-login-flow" +} +``` + +**Failure Modes**: + +* `E_PLAN/BRANCH`: not on a feature branch +* `E_PLAN/SPEC_MISSING`: `FEATURE_SPEC` does not exist +* `E_PLAN/JSON`: non-JSON stdout + +--- + +### 3) get\_paths + +**Maps to**: `scripts/get-feature-paths.sh` (read-only) +**Output Keys (all absolute)**: `REPO_ROOT, BRANCH, FEATURE_DIR, FEATURE_SPEC, IMPL_PLAN, TASKS` +**Failure Modes**: + +* `E_PATHS/BRANCH`: not on feature branch + +--- + +### 4) fs.read(path) + +Returns file text or error `{ code, cause }`. +**Guard**: Only allow paths under repo root. + +### 5) fs.write(path, content, mode="replace") + +Overwrites entire file; ensures parent directory exists. +**Guard**: Only within repo root; reject path traversal. + +### 6) json.parse(text) / json.stringify(obj) + +Strict JSON; on parse error, stop current phase with `E_JSON/PARSE`. + +--- + +## Guardrails (derived from repo methodology) + +* **Constitution gates** must pass or block with explicit justification. Record violations and STOP with remediation steps. +* **NEEDS CLARIFICATION** markers required for any ambiguity; do not silently assume defaults. +* **TDD ordering**: generate tests before implementation in `tasks.md`; contract and integration tests precede core tasks. +* **Parallelization rule**: mark `[P]` only when tasks do not touch the same files. If they would, make them sequential. +* **Absolute paths only** to avoid ambiguous file writes. +* **No external side effects**: no package installs, network calls, or code execution beyond listed scripts and file operations. +* **Stop-after-phase**: never chain `/specify → /plan → /tasks` in one run; the human must review at each gate. + +--- + +## Unsafe Patterns (block and explain) + +* Writing outside `/specs//…` or `/memory/…` +* Skipping constitution checks +* Removing or reordering template headings +* Collapsing `[NEEDS CLARIFICATION]` into guesses +* Creating parallel tasks that modify the same file +* Proceeding to implementation within `/plan` phase + +--- + +## Safe Patterns (encouraged) + +* Short, checklist-style summaries + final JSON `REPORT` +* Explicit dependency graphs in `tasks.md` +* Minimal diffs and exact file lists per task +* Visible, greppable markers: `GATE:`, `ERROR:`, `NEEDS CLARIFICATION:` + +## Software Development Best Practices +- **Spec-first & plan-reviewed:** Specs define scope. Plans gate implementation. No code before spec/plan gates pass. +- **TDD precedence:** Write/lock contract & integration tests before feature code. Tests must be deterministic and isolated. +- **Small batches:** Prefer short feature loops and minimal diffs. Decompose until parallel-safe (`[P]`) is clear. +- **Explicit contracts:** Every endpoint/service/model change updates contracts and acceptance criteria. +- **Version control discipline:** One branch per feature; atomic commits; meaningful messages; PRs reference specs. +- **Documentation as code:** Keep `spec.md`, `plan.md`, `tasks.md` current; corrections happen at the source files. +- **Dependency hygiene:** Pin versions; avoid needless frameworks; remove dead deps; verify licenses. +- **Configuration management:** No secrets in repo; use `.env.example`; document required vars. +- **Security baseline:** Least privilege; input validation; output encoding; authn/authz checks; audit logging where relevant. +- **Performance & reliability:** Measure before optimizing; capture SLOs; add basic health checks where applicable. +- **Observability:** Structured logs; minimal, actionable metrics; error taxonomies with remediation guidance. +- **CI/CD gates (conceptual):** Lint → Build → Test (unit/contract/integration) → Security scan → Docs check. \ No newline at end of file diff --git a/.archive/docs/Spec-kit Expert Agent/user-guide.md b/.archive/docs/Spec-kit Expert Agent/user-guide.md new file mode 100644 index 0000000000..02a6c8631d --- /dev/null +++ b/.archive/docs/Spec-kit Expert Agent/user-guide.md @@ -0,0 +1,109 @@ +# user-guide.md + +# Spec Kit — Beginner’s Guide (Step-by-Step) + +Welcome! This guide shows non-coders and coders alike how to drive projects with Spec-Driven Development using the Spec Kit Expert Agent. + +--- + +## 1) Install the CLI +Open a terminal and run: +```bash +uvx --from git+https://github.com/github/spec-kit.git specify init +```` + +This sets up the workspace, scripts, and templates. + +**Optional (nice to have)** + +* Install Git +* Set up an AI coding assistant (GitHub Copilot / Claude Code / Gemini CLI) + +--- + +## 2) Open Your Project + +Open the folder in your editor (VS Code works great). You’ll see: + +* `scripts/` → helper scripts +* `templates/` → spec/plan/tasks templates +* `memory/` → constitution and checklist +* `specs/` → feature branches and artifacts appear here as you work + +--- + +## 3) Create Your First Feature (`/specify`) + +Describe the feature in plain language—focus on **what** and **why**: + +```text +/specify "Authenticated login with magic link; journeys: sign-in/out; edge cases: expired links, device changes" +``` + +What happens: + +* A new feature branch is created (e.g., `001-auth-magic-link`) +* A spec file appears under `specs/001-auth-magic-link/spec.md` +* Any unknowns are flagged as `[NEEDS CLARIFICATION: …]` + +**Review the spec** and answer any `[NEEDS CLARIFICATION]` items directly in the file. + +--- + +## 4) Plan the Implementation (`/plan`) + +Provide concrete constraints and decisions: + +```text +/plan "Postgres; SES for email; 1-hour session TTL; throttle: 3 links/hour per user; CLI diagnostics" +``` + +What happens: + +* The plan is written to `specs//plan.md` +* Design artifacts are generated: + + * `research.md`, `data-model.md`, `contracts/`, `quickstart.md` +* Constitution checks run; the plan **stops** for review + +**Review the artifacts**. If any issues or questions remain, fix or clarify before moving on. + +--- + +## 5) Break Work Into Tasks (`/tasks`) + +Create the task list: + +```text +/tasks "Prefer library-first; structured JSON logs" +``` + +What happens: + +* `specs//tasks.md` is generated +* Tasks are numbered (`T001…`), dependency-ordered, and mark safe parallel blocks with `[P]` +* Tests come first (TDD), then implementation, then polish + +--- + +## 6) Implement (Outside This Agent) + +Now engineers (or toolchains) pick tasks from `tasks.md` and implement them in code, using the contracts and tests. Keep your spec and plan in sync as you learn—update them, then regenerate tasks if needed. + +--- + +## Tips + +* Keep features small; shorter loops = faster learning. +* Answer `[NEEDS CLARIFICATION]` quickly to unblock progress. +* Prefer simplicity over cleverness; pass constitution gates first. +* Use absolute paths in discussions to avoid confusion. + +--- + +## Quick Reference + +* Start: `/specify "…"`, review `spec.md` +* Design: `/plan "…"`, review `plan.md` + supporting docs +* Tasks: `/tasks "…"`, review `tasks.md` for order and `[P]` +* Always stop after each phase to review before proceeding. \ No newline at end of file diff --git a/.archive/docs/VS Code - Let it Cook - Introducing Spec Kit for Spec-Driven Development! - Episode 13.txt b/.archive/docs/VS Code - Let it Cook - Introducing Spec Kit for Spec-Driven Development! - Episode 13.txt new file mode 100644 index 0000000000..0adadb47f9 --- /dev/null +++ b/.archive/docs/VS Code - Let it Cook - Introducing Spec Kit for Spec-Driven Development! - Episode 13.txt @@ -0,0 +1,145 @@ +(837) 🔴 VS Code - Let it Cook - Introducing Spec Kit for Spec-Driven Development! - Episode 13 - YouTube +https://www.youtube.com/watch?v=DTw9X7MtU5s + +Transcript: +(00:00) [Music] Woohoo! [Music] Woo! [Music] Woohoo! [Music] Woo! [Music] Woo! [Music] +(01:43) Welcome back everyone to Let coe [Music] the Burk's camera just pan around. That was That was awesome. I was just watching it do full 360 so people can't see but we can I can see at least everything that's happening below and it just full 360. That your little machine over there. That's cute. Yeah. Yeah. Look at that. +(02:06) You like that? Oh, nice. That's super nice. You have a transparent case. It's almost like a fish tank. Yeah. It's like all glass in there. You can put like figurines in there and stuff, but Yeah, that liquid liquid cooled people do that. Yeah, of course. Gods do. Yeah, +(02:24) I see. I see the CLC pipes going there. Uhhuh. Yeah. The f the my when I was in back in my day, you used to my roommate in college on his rig. It was like the f one of those first round liquid coolers and you actually have to fill like the basin. Yeah. Like you put the basin like you take out the DVD slot, you put it in there and like this is going to be great. That's a mistake. +(02:42) and then it accidentally like leaked I think and like ruined the machine. So, but now they've gotten a lot better. I'm just saying PLC's are better. That that's that's that's why I never opt for actually building it myself. I just want to build like a machine with a pre pre-made closed loop cooler and there you go. Yes. And it's good to go. I just buy machines that are so small that I don't even know what's happening inside there. +(03:01) Is anything happening inside the sea monkeys? Are they in there? No, I think they're dead. I forgot to feed them jeez about a month ago. It didn't cook it up for them. Uh well, if anyone's their first time here, this is the show where we do this. Uh that is for sure. I'm James Monttoagno. With me, Burke Holland, the one and only. And with us, Mr. +(03:22) Den Delmarski, I don't even know how to say your last name. Is it? That is That is correct. It's It's hard to say like it's fine. You did it perfectly. Oh, wow. Nailed it. Wow. With an last name like Montto Magno, which is actually Monttomano. Then no one's going to get it right. James from now on. That That's it. James Gayano Monttomano. Yeah, that's my whole family's from Italy. +(03:42) So, uh, yes, it would be Monttomano. It's actually a small commune. We had someone on X who actually did a tour through Italy and stopped in the Monttomano town. There's actually a small commune town. There's like a few hundred people, I think maybe a thousand people that live there. +(04:01) Is that where you're from or that's just happen stance? No, my uh grandpa's from southern Italy and my nana is from Sicily. So, I am from Ohio. That is where I am from. That's not where I'm at now, but that is where I am from. Uh there. Um yes, that's my my lineage. I don't I mean that's what I assume because I've been told. I'm not going to give anyone my DNA, but you know, that's what that's what I know. So, well, bring up that freaking 23 and me. Let's check it out. +(04:27) We're never going to get through this stream. We have we got hard stops, which means we got to get cooking. Can I ask this? I want to see this cuz I don't understand this at all. So we asked Den to come on because and I wish Pierce was here but PICE is too busy for us nowadays to be honest with you. +(04:45) And um so what's interesting when we think about you know developing with AI there's a few different camps out there right there's a camp that is just like I'm going just give it you know a single line and go. There's a camp that's like I'm going to write a few sentences, give it like quite a few information and go, right? Or like me, I'm kind of in between or like I might give it an issue like I'm going to work with some AI, do a little task list and like go. +(05:08) And then if you're like Pierce actually who's been talking about specificationdriven development for a long time, actually one of the very first episodes here, that's what Pierce actually showed off. like scripts and things and I actually have like a repo where I copied all of his files into which is like doing a bunch of work like planning like actual planning documentation things like that and in fact when I worked at Canon back back in my day in Arizona when I worked at Canon our architect worked with our documentation team which worked our PMS and our engineers and everything had big +(05:38) specification everything was documented in fact like the thing was already documented before we even wrote the thing like like the fun like the functionality was already documented for our end users and all they would have was a placeholder for a screenshot of it. And actually it wasn't even just that. +(05:56) It would be like our designer would put in here's what it's going to look like and 90% of the time 95% of the time it was pretty accurate. So we were able to follow those specifications and that is a lot of upfront work that then gets put down on it. So there's many different camps out there. Some of us are believers, some of us are not skeptics skeptics out there. +(06:14) And then other people I'm quit programming if I got to write specs all day. So, uh, we asked Dana because just on Tuesday, the team, a team, some team, maybe just Den, but a whole bunch of people, there's there's a team. There's a bunch of people. It's not just me. +(06:36) A whole bunch of people, an amazing team of supertalented engineers and PMs and luminaries came out with specit, which is on the GitHub GitHub, github.comgithubspec-kit, uh, which have awesome thing. I'll find a blog post. I'll put it in here. So, we asked Den to come on to be like, what is this thing? How do you use it and why would you want to use it and what does it look like? So then totally first is specd driven development. +(06:57) Did I actually nail anything about specific? You did you you actually did like it's it's people over complicated a little bit because they think it's like spec driven development is this like magical thing that has like a bunch of complexity involved in it's like and realistically all it is it's like prompt engineering on steroids. Is that is that a good way to put it? I think so. +(07:18) It's basically look I if you are working on a project with um NLM right if you're you're if you're building something that is using AI and you have to build it in the way that you the developer want it to be like what do you do right like you you have to give instructions to the you have to write out the instructions to basically provide this context to what you're actually building and how you want to build it and what tech stack and all these things right so spectriven development is basically you spending upfront some amount of time to go and define that and then have the LM build exactly +(07:48) what you wanted per specification. This is especially important if you're working on a team or a project where there are specific constraints like what if your team is using I don't know Nex.js JS or maybe you're using Vite or you're using some no framework at all and you have to kind of steer the LM like across your entire company of saying like hey whoever is vibing on any you know landing pages and anything like we always want to make sure that it uses Nex.js GS we always want to make sure that it uses Cosmos DB for data storage and all these things right so when you +(08:17) have specs that kind of guide a lot of this stuff you can create these constraints not only that but think of how the the inversion here works a little bit is that right now we treat code as this artifact that we like you check it in through a repo that's the source of truth right like code is everything but code also binds you to a very specific implementation right like if I am running um you If if I'm creating a website for like a podcast or a landing page, uh if I write the code, that's it. That that's that's the implementation, +(08:49) right? And if I want to create like a new variation of it, I like I wonder what this page would look like if it, you know, I rearrange the tiles differently or I had my login screen be different. You basically have to reimplement this thing from scratch. You have to go and rebuild it and figure out how to do this. +(09:07) The spec acts as kind of an inversion of this model where this the spec is agnostic of the implementation, right? Like once you have the spec, you can guide the LLM to 10 different implementations and say like, "Oh, create me like 10 variations of this login page because the the kind of the constraints are still the same, right? Like you're you're you're building the the the kind of the scaffolding for it and then you let the LLM cook. +(09:28) You let it let it do its thing and you I I had to sneak it in, but you got to let it cook. You got to let it cook. That's that's what SPEC driven at a high level is. So it's basically you're encoding context up front. So then you have the freedom to explore like various implementations, various, you know, variations of the project that you're building. It's kind of nice. I like it. +(09:46) And I think this is kind of important because I've gone into different uh enterprises and they call these um things app factories. And this is this is something app factory. The whole idea is hey, we're not building one app. We're building hundreds of apps, right? Over and over and again, all for different functions and purposes and functionality out there, right? So their whole goal in the beginning, so this is like a decade ago, five years ago, you know, two two years ago when I go talk to these big businesses, it would be like, hey, listen, I'm just trying to +(10:15) figure out like what stack, what's the stack that I can standardize on to actually be able to enable my engineers to not have to relearn brand new tech stacks or have different tech stacks. So we can interchange team members that could say, "Okay, we're spinning up this app. Go take this person from here while this person maintains it and they'll move it over. +(10:32) " So here what I'm thinking is like with the the spec the spec sort of driven development is if we do a bunch of work up front even if you're doing one app or you're doing hundreds of apps you were then able to kind of templatize. So like that business is all right hey we're using . +(10:50) NET on the back end we're using React on the front end here is our specs for how we build applications today and now we can actually start to implement sort of plan out apps on top of it precisely because you have the spec that encodes your why and the what and then the rest of the pieces and we'll we'll show that later in a little bit but like the rest of the pieces are very much flexible. +(11:09) So if you write an application uh or you write the specification for let's say like a marketing website that you're building and at some point your team decides to move to like oh we're going to use .NET Aspire to do all these deployments. We're going to use like ASP.NET Core instead of React. Well like guess what? You're not rewriting your spec. Your spec is the same like you defined your what and the why. +(11:28) What changes is the how. So now you just need to redo the how. and and also the specking codes very well a point in time snapshot of like why is this the way that it is? What are the functional requirements? What are the things that are static? And by the way, like when we talk about spec, when we talk about like vibing in comparison to each specs also evolve like people treat them as static things, but they're they're really not. +(11:53) Like it doesn't mean that like once you write the markdown file and you check it into a repo that's that's it forever. You can change it as things come in, as your requirements change, as your product changes. But yeah, it's kind of fun area. How does this, you know, we've we've talked a lot and you and I have demoed a lot and people have demo like PRDs, requirement documents, like how does PRDS like blend into specifications? Are they part of it? Is it different? Because that's been my vibe, which was like the easiest way to do it is kind of like lightweight spectriven development. I want to build this thing. Let's create a +(12:23) PRD based off of it. Do some market research. is like is that part of this process or is it different? It absolutely is. Um I do want to call out the fact that like you you say PRD and to me like there there's several kind of pieces. Sean says PRD because that's the first thing that's the first thing everyone says is PRD. +(12:41) I didn't know Amanda was talking about it. You were talking about it. I just asked Copa what the heck is a PRD? Product requirements document. That that's basically and PM PM speak for spec. That that's all it is. That that's uh it's a spec document. +(12:58) So, uh, when you write a PRD, that's kind of you you define the what and the why, like what are we building and why are we building this, right? Like you're not really focusing too much on like the how the technical implementations like which APIs do we use and where do we plug them in and how do we scale this? It's more about what is the solution and why what's the grounding data? We have customer insights. +(13:16) We have some rationale for it and that that's what the PRD is. But in our respective development process, we we essentially split up the PRD from the how and then a detailed breakdown of tasks that the LM needs to follow to execute on on the plan and the PRD that underpins it. So we'll we'll actually like maybe maybe we can show it. Yeah, let's look at it. +(13:39) Well, because this will be interesting because PRD means something totally different to who you ask. Like if you ask I have a template that I use for my PRDS. guarantee you it looks nothing like probably what you've got. So I'm curious to see like what this looks like here. Okay, so we have this project. It's called SpecKit. It's open source. It's on GitHub. +(13:58) And by the way, it's all it is. It's essentially a scaffolding set of templates. Like we've we've experimented with this with uh our good colleague John Lamb. Shout out John. He who did the initial like a lot of the work here. So um what I like to do Dan really quick when you you do go John Lamb. John Lamb. Oh, wait. Let me get the hand horn out. +(14:18) Oh, you guys came prepared. I I look at that. There you go. Oh, yes. Thank you. Shout out John Lamb. Um, but anyway, so, uh, we're going to look at the specit. So, specitate, like I said, it's a set of templates, essentially scaffolding for you. And because we want to make it easier for folks, we actually built a little CLI here that you can use. +(14:41) So, if you scroll to install specify, specify is what we call the CLI. You can use this. It's built with Python. Uh, and we're going to use UVX because uh, UV is freaking awesome. And what even is that? Is that like npx? Yeah, is basically is npx for Python. Okay. Oh, and there's like a DNx now, too, which is the net one as well. That's DMX. That's meet me outside. My rough riders. +(15:05) Yeah. What's his name? Um, DMX. Uh, so so let's say you didn't have this. Could you just clone this template and start or is it Oh, yeah. If you if you if you do not want to use a CLI, if you're not installing Python for whatever reason, totally fine. Uh we have releases. +(15:23) So, if you go to Spectit templates, uh and by the way, we support Copilot, Cloud Code, and Gemini CLI. So, we are, you know, spanning the platforms here. Uh but you can also download these templates. They're they're zip files and just use them directly with your project. So, you don't actually need to jump through the hoops of the CLI. Well, time to uninstall Python and UVX. +(15:40) Get out of It took me eight days to figure out how to get my Mac terminal to work. Now, now I have to use it. So, yeah, now now you have to use it. But, uh, the easiest way to do this is to essentially use specify through UVX. Uh, and now D. Yeah, sure. Specify because it's a spec kit. Specify. I This is up for debate. +(16:05) This is up for debate. I'd say like we we had that conversation. It's I call it specify. Some people call it specify. You can call it whatever you want. It's like specify means something very different than No, it doesn't. Same word. All right. Well, right. Word for us all to miss. This is the show where we get nothing done. +(16:27) Yeah. The GIF and GIF the debate all over again. Um, so we use specify init. Uh, and then we're going to give a project name. I'm going to let it cook. uh test. All right. And when we do this, we're going to get the nice little installation prompt to get the the cool ASKY art. Oo. Did you spend a lot of time on that, Dan? Uh just just a little bit more than the rest of the CLI. +(16:57) Uh but um you could just like you would in, you know, if you use any of the existing AI LLM CLIs, you can use your keyboard to navigate through the agents that you want to use. Uh, and in our case, we're going to use C-Pilot because of course we love Copilot. And we're going to say yes. And then it's going to initialize our project. +(17:14) What this does, all it does is just pulls a template. It pulls the latest release, unzips it locally, puts it in a folder, bootstraps a Git repository if you do not have a Git repository. And by the way, you can do this in an existing project. You don't actually need to start this off in a whole new folder. But what do you have stuff to the existing project? Totally. Yeah. +(17:32) If you have an existing repo, just add like what I just did in the I use let it cook test as a name. You can just add the name of the folder of the git repo that you're using and it was just going to bootstrap all that in that repo. Or if you're like me, just put it in the wrong place and then copy and paste or put in the wrong place and copy. Yeah, it's fine. +(17:50) Again, these are all this is scaffolding. Like none of the stuff is immutable where you're like, ah, now I put stuff in a registry. Like it didn't do any of that. So, uh, we're going to go to our folder here. This is C and test. Okay. And in this folder, if I do tree, you'll notice that actually bootstrap a bunch of stuff. +(18:10) So there is like some memory and we'll we'll look at this in a second. Some helper scripts which also they're they're shell scripts by the way. They you would need WSL or Linux or Mac OS, but um you're in WSL currently. I'm in WSL right now. I'm using auntu. That is the Windows subsystem for Linux. I am working on PowerShell scripts. So, it's coming. Yeah. But for for a test is just shell for Sean's about to get real bent out of shape. +(18:33) I'm I'm already bent out of shape. If it's not a PS1 file, get out of here. Yeah. Um and there's a bunch of templates for what we're actually going to do. And I'll I'll show this in in VS Code. But, uh I'm actually going to do tree and a to see that there's actually missing pieces here that I'm going to scroll up to the very top past all the git stuff. And then we have the GitHub folder here that has three prompts. +(19:00) Specify plan and tasks. And we'll use them in action. And this is the stuff that you saw here when specify bootstrapped the project. It actually hinted it's like and open Visual Studio Code and use specify plan and task commands. +(19:19) That's you know we use the existing built-in capabilities of VS Code to pick up prompts as slash commands which is kind of neat. I I really like that that that once I talked to Pierce about this is a made it a no-brainer. Just plug it in. So, we're gonna WSL is called Weasel. No, it's not. No, it's not. No, no, no, no, no. Absolutely not. Now, it will be forever, I guess. I guess I'm going to call it that from now on. Like, I'm going to be getting a call from the Windows marketing team if I use that. +(19:41) Like, yeah, this show's going to Thanks. Thanks. Now we're cancelled. So, we did get a question earlier that says, you know, you know, we're on VS Code, but can you use with Visual Studio? And the answer is like, yeah, you can just if it's prompt files like you showed, you can run prompt files inside of Visual Studio as well. They're prompt files. +(19:59) I mean, you could take what's in the prompt and I guess run it anywhere technically. So, yeah, I mean I mean none of the stuff here is actually designed around VS Code as experience. Like you you can use, you know, Sublime Text if you so desire. It's just the experience. I only use Notepad. So just copilot in notepad so it'll run it. +(20:16) You know it's like I I saw people write like Linux kernel contributions in like literally just a plain text editor. I was like what what happened in your life that led you there? Joy. Why? Why joy? Why do you hate happiness? Um so um as I mentioned so we have the prompt files we have the specify uh which again just defines like what needs to do what the what the agent needs to do and this is essentially for defining that PRD James that you were alluding to this this the specify command is for that feature specification that outlines the what and the why. We're not focusing +(20:51) on a technology and of course we are using our helper shell scripts here. So you you can you can tell that because when we create a new feature, we want to spin up a new branch for it. We want to make sure that it's actually in a new folder that the spec is actually written. So it's not polluting the rest of your codebase. +(21:11) So all all it does is just kind of defines the logistics of the the feature spec. Then there's the plan. The plan is our how piece and this is building on the feature spec is how do we actually build this? What tech stack do we use? What databases? What you know performance things we need to consider? And it's also grounded in this thing that we call the constitution. +(21:29) And constitution is kind of a can sound like a scary thing, but the constitution essentially is a set of non-negotiable principles for your project. So if I'm building a web application and I want to make sure that my organization is always using tests, I I am always shipping code that is grounded in read red green refactor cycle. Like I I can enforce this with a constitution. +(21:53) Like the constitution is essentially non-negotiable. Whatever you build, whatever plan you establish, whatever task you break down, this document always has to be followed. This is there's nothing in it, right? There's nothing in it because we not yet, we haven't started anything. Okay. Okay. Um but this is distinct from things like we've seen before with cloud MD like agents. +(22:16) mmd because those are kind of like giving you the context of the project, right? Oh, this is a web application and you can open these files and you can use a CLI tool. This is specifically around the fact that these things should always be followed. Um and then the last one is the tasks prompt file. So this is where we just break it down all all the plan that we have into task and it gives you the logic for how to actually do that. +(22:36) Right? So um it references a bunch of documents that are going to be created post factor that we're going to see in a second. And as I mentioned like it pulls in helper scripts and it pulls in some of the templates that we're going to use for the plan for the spec and for the tasks. So again all this is scaffolding. It's markdown files and shell files. That's all. +(22:56) Yeah, like it's it it's really like the CLI doesn't do any magic other than like putting this stuff in your folder. But um anyway, let's let's see this in action a little bit. So, I'm going to open our favorite agent mode here. I'm going to be using GPT5. And by the way, depending on which model you're using here, I don't know like you guys, what's what's your model? What do you prefer for coding? Well, I was about to ask, Dan, because you're going to get quite different results depending on what model you use here. I would I would expect that that Claude probably works the best here because there's so many different +(23:25) instructions and it's really really directable. Um 4.1 is my model of choice, but I don't know that 4.1 is going to be able to to do what what needs to be done. I'm curious. I'm a five five mini is my jam. So five is really good. Five is underrated. Like John and I John Lamb and I talked about this and like GBT5 is really really good at coding lately. I actually haven't touched on it in a long time. +(23:51) After all, I I default I default to five and then if I really need some vast exploration, if I want someone to hitchhike through my code, then I I pull up Claude and I say, "Let's yeah, let's go on a backpacking trip through my my code base." Yeah, you you could absolutely do that. +(24:08) Uh, and by the way, this is this is also the beauty of the spec driven development is that you can apply this process to an existing codebase and then basically pull in the context from your code and have the spec be on top of the existing codebase and conventions and everything that you've created. But anyway, uh let's let's get the constitution rolling here. So, um because I'm we have limited time, I'm not going to write the constitution from from scratch. +(24:31) And let's just ask GPD5 to help us. And we're going to say let's update this constitution for a web application set of constraints and let's see what it comes up with. I I'm genuinely curious. I have not done this before. Like all the constitution documents that I put together were basically just me putting constraints. +(24:53) So we'll see if agent mode can come up with some, you know, decent set of requirements here. So is this the first thing that you would actually do is like update the concept? Oh, okay. Okay. So you so you're not even running the scripts yet. You're just like no not yet because I just want to make sure that like I'm grounding my project in some set of kind of non-negotiable principles and you know typically again for this is helpful when we talk to enterprises what things that they do is like oh I only want to use the Azure CLI for deployments like this. This would go here. I only want to use Azure services +(25:23) like functions and app service and whatever else like this stuff goes here. You're essentially defining the hard constraints that you cannot get out of whatever you're building and it's sharable. So you can use this across your org. You can share with other teams and they can reuse it. +(25:40) So it's not something that you necessarily need to put together every single time. It just happens that in our sample we don't have anything because we don't know where you're going to be building. Yeah. Where is it pulling these constraints from? So these constraints right right now it's going to just pull it from its training data. Okay. Right. +(26:04) Like but you would imagine that in your organization you might say like your your CTO says like you always have to use Nex.js for every web app. Right? You just write this manually. This is like when I was talking about the app factor. It's like we are doing this we use this you know we use this deployment method. We use you know uh GitHub actions. We use blah blah blah. We this is like this is like our color you know this is our branding. This is our color. This is blah blah blah. Right. Yeah. +(26:26) And look, it actually did come up with like a decent set of requirements. If we look at the like it userentric accessibility first, like that's important. That's nice. Secure by design, lease privilege always like all right. Yep. Maybe for our like our our podcast landing page is not that important, but like you would imagine that for an or like sure. +(26:44) Um and because in the constitution template we had examples, it actually fills it out like based on those examples, which is kind of neat. So we have the constitution. Let's actually get it to write us the spec. So we're going to use the slash command. I'm going to use specify. Going to just call it specify from now on. Like this is just You're welcome. Like Yeah. Thank you. +(27:02) I'm really good at naming as I'm going to say specify whenever I use the word specify in any context. Just let people correct me. Can you specify what you mean exactly? Uh what I'm talking about here. So So when we define a spec, we use the the slsp specify command. We essentially define as I mentioned several times, it's the what and the why. +(27:22) So you're not focusing yet like oh I want to use net or nextjs you're saying I am building a podcast landing page make it modern dark theme is it going to be a podcast landing app so for for the what is it for is it like a podcast app like where people can like download stuff or is it like I am a I am a podcast like for the podcast yeah I'm building a podcast landing page for my podcast the vs code insider podcast. All right. +(27:55) I mean, you you told me to specify. Yeah. Yeah. And by the way, that's actually a very good point. The more concrete you get here, the better. Like the the results, right? Like if you if you have like a vague like, oh, I'm just building a landing page. Like it's going to produce like garbage results because like it's going to just fill in a bunch of assumptions for you. But the more specific you get here, the better. +(28:14) So podcast landing page for VS Code insiders. Make it modern. dark theme speakers on the main page for featured conversations and you got the specify commands because and was asking in the chat how' that happen in case you're joining late when Dan ran the command he asked did he want copilot gemini or claude CLIs and what it that did is it scaffolded out in this case for copilot thegitub/prompts and it put those prompts inside of in this case VS code picks them up automatic in that folder or Visual Studio would as well. Yeah. +(28:50) Yeah. At least 20 mock episodes. All right. So, uh I'll do this. It's good enough. So, you'll notice that that the follow instructions when I when I type this into chat, it says follow instructions specify.prompt. So, I use a slash command, but it knows because I have the GitHub folder here that it's just going to, you know, follow the instructions from the the the actual prompt file. +(29:14) It's the slash command is a prompt file. That's all it Well, would you would you so here you're scabbling on something new, but if this was in an existing application, would you do this like I I'm adding a new feature to this applica. If I'm just adding a new fe Okay, so if I'm adding a new feature, I would I would spec specify the feature. Exactly. It's Yep. +(29:32) It's it's very much universal. And notice that actually ran the bash script, the helper bash script for actually bootstrapping things properly here. So now I have a specs folder with a 001 feature name because it did not properly identify the feature, but that's okay. Um, there's my terminal where it actually ran and now it's just working on the actual spec file. +(29:49) It pulled in the template and the template is just blank because it doesn't have any requirements just yet. Uh, but it's going to fill it out and GD5 I recognize the functional requirements. That's that's how I do my PRDS. Yeah. Yeah. Yeah. So, this does it for you automatically because we have the template. +(30:06) Um, and GBD5 can be a little slow in this regard because it just has to fill out everything from like in one go, which can be, you know, depending what you like, what you don't like. Um, but it it I I use GPT5 and it work just just fine in terms of producing the code and the output. So, um, let's see. Report. I actually like this little task box. Yeah, that's the jam. All right. So, we're working on improving those too right now. Yeah, it's it's it's really neat. +(30:32) Like I tracking the status of that is Yeah. So, uh it actually noticed that it actually focused on things like acceptance scenario, user story. So, stuff that typically PMS would write that it just did it by itself. And this is not immutable. You can go in and change things here. +(30:50) Um but it did come up with functional requirements like filtering or discovery of episodes by tag or thematic grouping. Like I didn't type any of the stuff, right? Like um but it's kind of nice. So I see that there are some things that need clarification and specifically one of the things that is very important about spectrum of development is that this is not just vibe coding in terms of like oh I'm just going to vibe the spec and I'm just going to vibe the plan. You still have to review it. You still have to look and make sure that it it produces the right +(31:15) things to help with this. We also added this thing called the acceptance checklist that you kind of have to go through and make sure that it actually fits the requirements that you've established. So um in in our case we have requirements around things like content quality and requirement completeness and for example you cannot move forward until no need clarification markers remain. And we we saw a couple of them. +(31:42) So because I'm lazy I'm just going to say fill in uh the clarification hallucinate answers as best as you think. Uh so let's let's just have it do it like because this is a non-production application. we're not worried. But you would imagine that me coming in here as an enterprise saying like, "Oh, yeah, I do need to specify the the latency requirements for my project. +(32:06) Like I can't really let the LLM guess that for me, but you know, for a landing podcast page, that's fine." This is cool because like um you know, Lee was asking here like about the green field projects. I have feedback flow which I 100% vibe coded and what I ended up doing was doing a lot of the documentation after like, oh, you just implemented this feature. +(32:22) do, you know, after I've worked for hours to get it right based on what I had in my mind and we worked together, me and in the the GPT. Um, in this case, I could actually just do this on a new feature that I'm adding. I think that's what that's what I'm going to try now is like, you know, I do a lot of new apps, but then I could see myself exactly what I've done here because I've done this sort of process, but not Yeah. in as much detail. Uh, for sure. +(32:45) I like I like your frame like we were working me and the GPT our best friends me me and co-pilot and GPT and Claude and all they're all in my and this is where I get I start to get skeptical spectacle spectacle and I would like to tell you here's why yes because when you are specking out a green field app especially right which is less common you don't know what you don't know correct this is this is how programming works you think that you're going to do something one way you try to do it that way. You realize, oh, that's not actually going to work, but it's kind of +(33:16) going to work. So, I'm going to need to to shift slightly, right? And so, you can't. In theory, if you could tell the model upfront everything that it needed to know, it would give you the right answer. Prompt engineering is just giving the model the answer that you want. But we can't. +(33:33) That's really, really hard to do. Yes. Like, you would have to spend hours thinking about the feature, examining every edge case. Yeah. And I just don't I just not sure who out there number one can do that me. But number two, more importantly, like who wants to do that? I think I think a lot of businesses do. I think a lot of like PMs, a lot of a lot of I mean a lot of like I think a lot of folks today like work off of a ticket system and they they refine and they put in specifications into tickets before handing it off to the engineers. Now, I mean, I think this is really big because this is like a huge thing, but +(34:11) my assumption if I had a small feature, it'd be a small specification hopefully. I I will also add to this that you're absolutely right, Burke, that this this is the kind of stuff where it's like you you you don't know what you don't know, and if you go down this path, the kind of the wrong assumption here is that you need to have everything from the get-go and then once you start, you cannot go back. But you can. So once you actually go to the product and it builds the thing and you realize like ah crap I did not +(34:37) implement the the login flow the way I wanted you can just ask it to reimplement it and encode that in the spec right so like you can you can go essentially go back and say oh right the login flow doesn't work the way I expected it to go and go and redo this and make sure that this is properly reflected like it's not a oneshot thing yeah and that makes sense I guess I just in my own experience here Den what find happens is that I create these plans and specs and then somewhere in the process I'm just like, "Yeah, it looks good +(35:07) enough, right?" And then I just send it and then it just cooks up a bunch of code that I don't understand and then like four iterations later I have no idea what's happening in the code of the project. I'm just clueless, right? I'm just like plan spec. Yeah. Right answer. Realistically, this is the kind of stuff that like you you you would end up in a state of I need to be able to break this down for the model in and that's what we're trying to do in these like composable chunks. It's like you're not just writing one document and let it do it. Just like, oh, I need this +(35:39) other feature. Let me just add this other specific feature requirement. That that's that's kind of what it is. I wonder if maybe the answer here is like breaking your tasks down into very like the smallest possible bingo. That that's exactly what specify does. Yeah. Um so the second command is where I can do plan and essentially this is where I can say like plan and uh for plan use next JS old data is mocked because we're not using a database. +(36:11) No databases or six minutes then to I know I know we we'll get we'll get to this we'll get to this. Uh so um all all of this stuff like it it's essentially it's a it's a process and by the way like when we talk about the process here the stuff that you see the scripts the templates all of this is very much experimental like if if Burke James like you guys go in and try this be like Dan this is a pile of garbage like this does not work for what I'm trying to do and it just does not produce right let us +(36:41) know like this is the kind of stuff that we we actually this is why we we released it to begin with is we want to get feedback we want to get input from folks and see like where it works and where it doesn't. And if you're trying to build a web app, you're trying to build an iOS app and this is not working. It's producing incorrect output. +(36:59) Like we we'd want to know that because we've experimented extensively internally on like prototypes and projects. I actually like built a prototype for one of the exact demos with this just the other week and it worked like really well. Wow. But I'm just one person, right? Like I'm I'm not I'm not either of you. I'm not the community watching this. +(37:18) So very much like this is not like a final state like what you see here is going to be forever this way just to caveat this that makes sense. Now all this stuff that is generating this spec and research stuff fuel was asking like does that gets checked in as part of your codebase basically? Yeah absolutely because the spec becomes yeah the living breathing document that you can use and your team can then refer to and look and say oh I get how they landed on this feature. +(37:48) I understand why uh that that's and again because once you have the spec you can easily go and reimplement it. You can just rebuild it from scratch because you have the spec. That makes sense. And then yes are saying like when you add a new feature to the spec ideally you have the specs for the other things. So it should reliably kind of know how to update and what it needs to update and search through specification documentation. +(38:06) Yeah. Yeah. Exactly. And and this is the kind of stuff where you you you end up with a a set of kind of artifacts that are very much reusable by the team. And in our case, like the research here, it's pulling this from the training data. Uh and I actually need to use Burk's like beast mode probably here to do some like force it to go and do some Google searches for um what the right framework should be like for Nex.js, right? Oh, Burke, you're muted. +(38:32) I am I am muted. Whoops. Uh, beast mode doesn't work great with GPD5 because GPD5 doesn't really do well with like longer prompts, right? So, because it gets appended to the end of the system prompt, it tends to just ignore all of it. Yeah, because the beast mode prompt is pretty pretty big, right? Like it's pretty it's size and it's meant for 41. +(38:55) It's specifically formatted for that model. Although Claude is so directable that it will also pay very close attention. But yeah, y'all chat have not had great luck with uh with GT5 and beast mode, but but I love beast mode because I've actually experimented with it like the other day and it was totally fine. +(39:12) Um and it the fact that it forces to go and search is great. And this is something that I love about like claude code like if you folks have used it like you know like oh let me go and search this but like it does the research for the phases. It does the breakdown and notice that in my plan I'm I'm going to keep all this. +(39:29) And really quick too, someone was asking in the chat like you ran the plan. Did it how does it know to grab that information from the specify specify step. Oh, because all this is encoded in the the So if you look at the plan, right, it it actually encodes a lot of this information, right? So it says run the script, read analyze the feature spec, and it knows where the feature spec is because we just created it. Like it's it's in the conversation history. +(39:53) And then it also grounds it in the constitution as we mentioned like you know read the constitution to understand the requirements. So this is the stuff that we know are non-negotiable and then apply the template right so it's all like it's basically chaining prompts it's it's chaining prompts together and chaining a lot of the script activity together as well. +(40:07) Um, and then lastly, once you have the plan, we just say tasks and then uh break down the plan into tasks. And this is going to create the the task that Burke was talking about, like the the smallest chunks possible for every single section of the plan. +(40:25) So, what was created in the plan really quick cuz I see on the spec feature while that's cooking. Yeah. So, the the planet has again technical details, technical design. Uh so if we look at the kind of the layout here, it outlines the project structure. It proposes the the source code structure as well, which is kind of again neat for for me to see. And if I like, oh actually my teammate does not maintains SRC or SLS SRC, you you can update this very very easily. +(40:50) Um and then uh yeah, so like testing non-negotiable because we encoded that in a constitution. It it is required. So it puts this observability versioning. Delete that. Just delete that. Yeah, of course. No, we don't need that. Um it has the reference to again the kind of the research document but outlines like what is the oh using Nex.js like let me talk about Nex. +(41:07) js and if we go to the research document that it produced you'll notice that it it talks about use next.js app static generation for landing blah blah blah and all this stuff. So it it it cross references a bunch of these things in the plan to essentially say like this this is this is how I'm going to approach building it. +(41:25) So remember the spec is the what and the why. This is the how. And it has all these kind of requirements constraints like mark independent UI components and you know setup and tooling which is required for this because we're using TypeScript and X. +(41:42) js but yeah it's it's technical requirements basically and you can adjust these as well because all the stuff gets checked into the repository all of it. It seems like some of this stuff Dan you would want across multiple projects right like Oh absolutely yeah you would some sort of a where you could just pull it in to new projects automatically. Right. +(42:00) Right. Right. So like I mentioned the constitution is a good example which is like my team is always building web apps that follow these constraints. Just reuse this like I don't need I don't need everybody to write their own constitution document. They can just reuse this. So but to the point of task so it seems like it finished. +(42:18) So we can go and look into tasks and you'll see here that let me just close this. There's create front end add typescript like it it basically broke it down into different chunks. write schema validation test for episodes like these are workable chunks that it can now tackle. So I don't need to wait for it to go in and say, "Oh, it's it's it I mean it's still going to be non-deterministic, but the tasks help it steer it in the right direction and say like, oh, you need to implement the audio player wrapper component and now you do this and now you do this." And it's like it has very concrete instructions instead of guessing. +(42:49) And I I tried to do some uh yolo spec driven development by myself, you know, because I couldn't get stuff running on my on my Mac in time in the morning. And what ended up happening is I was going back and forth with like my PRD and this and that. +(43:06) And because I'm having it generate all the code, I didn't have time to like review what is in like and this was like my my pet tracking application for my dog. Um was like okay like what actually isn't a pet? Like what information they need? Like how is the user configured? What is this? So like it went off and then I spent a bunch of time like actually fixing it and trying to go back and forth that now I think I'm just going to throw it all away and start over with this because at least for me I can understand and say okay like here is what's in this part and here's what's in this thing. Yep. Exactly. +(43:32) Now now Dan Dan I would like to ask a question here. Um yes. So one of the things that you're actually fighting now is the fact that um GBD5 and Claude are both crazy slow and you've got a long conversation. So you're hitting summarizing conversation history which is also slow. Something we know about. We're working on it. Now here's my question. +(43:56) GBD5 Mini and 41 are really really really good at following very specific instructions. If you tell them exactly what to do, yeah, they can do it. What they are not good at is when you give them a broad set of constraints and they have to try to fill in the blanks. My question is could you switch to GPT5 mini here and have it implement the tasks? Yeah. The the model here of result. Yeah. +(44:20) Yeah. The model here is your choice like I'm not bound with a GP. I'm just using GP5. I'm just in your in your experience in my experience like I like I I have noticed that GBD5 and the mini variants can be very good. Exactly as you mentioned like they're precisely following your instructions and not going off the rails. +(44:38) The one thing that actually is helpful to have the constitution and a lot of these documents for is for like sonnet models because they go off the rails super quick. They're they're overeager. They're very eager to go and do a bunch of stuff because I mean they they were like rled in a bunch of code. So they like coding. +(44:55) They're going to just just let me code a bunch of stuff you did not ask for. Um I see you don't have a podcast. Let me create that podcast. Yeah, exactly. Like you're absolutely right. I did not think about the podcast. Uh, and you know what's interesting is I I've generated MP3s with you. I I tell Claude, "Do not ever say you're absolutely right." And it will say it no matter what you do. +(45:14) There's nothing you can do to get it to not You're absolutely right. I should not be saying you're absolutely right. You're absolutely right. You are absolutely right. But uh so yeah the the model and also I'll say like experiment with the models like try out different like switch them and see how the implementation looks like because what I also noticed if you use GPT5 sometimes what what sonnet is really good it also can be creative. +(45:38) So if I like in my initial prompt when we use specify actually asked to like make it modern if you look at the sonnet output it's going to produce like a site that can be very like dark theme some like maybe like overlay some really cool stuff. GBD5 will produce potentially like a a white page that is just like bootstrap style UI. It's like, oh yeah, it's modern. I follow your instructions. +(46:01) But like it's it doesn't have that same creativity in it. So those kind of things matter. And again, you you have to test it. You have to try it and see like what what it produces, what it works, what doesn't. For the spec pieces and the task pieces using precision models like GPT5 are like perfectly fine. +(46:19) But then once you have this, once you have the spec, the tasks, the plan, you can experiment with different models and see what outputs you like best. I'm really excited to see what this actually turns out here. Yeah, it's going to like I don't know how long it's going to take, but um it is cooking. It is it is cooking on something. See five minutes late to your meeting with Satcha or whoever it is. That's that's fine. Satcha can wait. +(46:38) Yeah, that's not at all a career limit. Sorry, Sati. I do not agree with that if you're watching this podcast. If anything, yeah, it's just like we were sort of B James and I were just building a podcast website. We just really need We're just cooking. It's created 25 files. +(46:55) So, I mean, and the other stuff too. Oh, now it's creating your your contracts. So, it took like put like some playright and some just configs in there as well. Oh, it did create it created a Yeah, play playright test config. It has all the stuff. Icons. What icon pack is this? Uh, this is the cat puin. The what? I think it's called the cat pooin. Like the cappuccino, but there's cat puccino. +(47:18) So it's like interesting. It I've never heard of that one. Yeah, it is. Let's see. Install. Yeah, cat poo for VS Code. I got to get that material is like the goto. So yeah, you're the trend. Yeah. Yeah. I I I'm I'm not really a trend follower here. Um, you're the trends setter, Den. Obvious. I love this, man. I want to try this. Like, I'm going to do this as soon as this is over. Seriously, I've got stuff I'm working on today. +(47:45) I'm absolutely doing this. And like I said, it's not perfect. I'm sure there's things that are going to be broken. I'm sure there's things that are going to be not working as you expect. I want to know that. Open an issue in GitHub. Ping me directly on Blue Sky or Mastadon or LinkedIn or wherever you find me, wherever you find this podcast. Uh, we're again, it's it's an experiment. We're here to learn. +(48:03) Yeah. Harassed in online. harass. Yeah. Well, that's I should not be saying that. Like, subscribe. Quity, positively. If you want quality, if you want quality content, which this uh live stream is not, follow Den because Den's busting quality. I'm just Why? Let's just be honest with ourselves. +(48:24) Burke, I'm not like like Claude over here with self-gratification. Oh, you're totally right. This this YouTube stream is awesome. It is. For once, I'm not talking about MCP, right? Like I I can talk about things other than MCP. You've moved on so quickly. No, I'm still an MC. What do you I'm still an MCP. Is just this. I'm doing both. I'm doing both. Wow. Man of multiple talents. +(48:48) Someone's using this live. It's happening. Feels good. Oh, wow. Using it right now. Let's go. Let's go. Feels good. Feels good. That That's good. It's like, you know, it's got good It's got good kill better than feels bad. +(49:04) I see Hashim was also mentioned like it would be interesting to use claude for the design task and GPT5 mini for or GPT5 mini for precise stuff like writing test and yeah absolutely I mean again try it out like the the easiest way to see what works is to swap models and just see see what works for your scenario cuz like I've for web apps by the way also depending on what you're building I've seen if I'm building a desktop application for Windows like I'm building a console app like GBD5 is excellent if I build a web app I generally get better results with sonnet but not as much if I build like Windows apps. So, your mileage may vary +(49:35) here. How many packages are you installing? There's a there's a bunch. And also, really cooking on those. It is really Yeah. Like I'm I'm sure this is totally not going to bring some like super vulnerable like hallucinated packages. No audit. It's like no audit. No, no. +(49:53) I I like the no audit that basically like I don't care about vulnerability. Just pull whatever. Uh yeah, we did not encode that in the constitution that it should audit things or we can look at the source pricing of the top models. Well, we could talk about that while this cooks a little bit. So you have your premium requests which are So let's talk about included models which is GBT5 mini 41. Is 40 +(50:21) still in the picker? It's still in the box. Yeah, still in the picker. 40. like we have and for a limited time Grock fast one is there but that I believe will be moving to a 0.25 multiplier. So those are your included models which means that they're they're unlimited although if you hit these enough you will get rate limited right like you can't burn $5,000 of GPU on 41. Sorry. Mhm. +(50:48) And then you have your premium models which are each uh these are called um each like um turn session with the agent is one is it turn no the a the turn is what the agent does. So so so uh like when when when Den did a plan that was the start and then when it stopped and it was like I'm done. That's one premium request. Correct. +(51:12) one premium request and I think if you're on the pro plan you get 300 of those pro plus 1500. So what you want to do and that's why I was asking Dan like if ideally what you do is you for people that are trying to ideally you would have this whole process automated and you wouldn't review any of it. Yeah, you could. Or you would just like tell the chat to like after you get to this step, just wait for my wait for, you know, five minutes, but don't stop. Just keep going. Yeah. I'm just trying to show people how to game the system. +(51:39) Yeah. It's a good experiment to try this. Yeah. So, anyway, that's that's the pricing. It's pretty simple, y'all. It's just priced on on agent sessions. You don't have to worry about tokens and all that stuff. Very cool. Well, this is spec kit. Like, you know, it's still here because the spec the spec kit part of it's done. Now, you just told it to implement. Now, this is just now I'm just waiting. +(52:04) I'm hand completely hands off. I'm just waiting for it to go and like build the stuff for me. So, and I know you got to move on, Dan. You want us to wrap it up here? I feel like the folks who joined today, y'all are the lucky ones because this is awesome. You just maybe turned Burke on to this, which is good. I'm going to go give this a try as well. +(52:23) What we'll do practical is Den will commit that code. So later today I'll go back to the YouTube page youtube.com/code and then look at this episode so we get our view count up. Like and subscribe and then in the show notes below we'll put a link on there and we'll make sure we we tweet it out. So follow Den Burke me VS Code on the socials. +(52:46) Maybe we'll have Olivia post it out as well on on as a followup of this post as well. Den go you're running out. Yeah. And I'll I'll commit the repo. I want to share what the outcome of this will be. I'm excited because we have a podcast website that we we pay for. So for you also there is a VS Code Insiders podcast. That is a real podcast that you can subscribe to on your favorite podcast application behind the scenes there. +(53:09) Um Dan, thank you so much for coming and talking through everything and showing stuff off and I can't wait to see when this cook finishes. Thank you for having me here. Awesome. Well, Burke, as always, an honor. Happy coding everybody. out. Bye. Bye. Bye. [Music] [Applause] \ No newline at end of file diff --git a/.archive/docs/gemini instructions/.gemini/commands/analyze.toml.md b/.archive/docs/gemini instructions/.gemini/commands/analyze.toml.md new file mode 100644 index 0000000000..8ab2837a3d --- /dev/null +++ b/.archive/docs/gemini instructions/.gemini/commands/analyze.toml.md @@ -0,0 +1,105 @@ +description = "Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation." + +prompt = """ +--- +description: Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation. +--- + +The user input to you can be provided directly by the agent or as a command argument - you **MUST** consider it before proceeding with the prompt (if not empty). + +User input: + +$ARGUMENTS + +Goal: Identify inconsistencies, duplications, ambiguities, and underspecified items across the three core artifacts (`spec.md`, `plan.md`, `tasks.md`) before implementation. This command MUST run only after `/tasks` has successfully produced a complete `tasks.md`. + +STRICTLY READ-ONLY: Do **not** modify any files. Output a structured analysis report. Offer an optional remediation plan (user must explicitly approve before any follow-up editing commands would be invoked manually). + +Constitution Authority: The project constitution (`.specify/memory/constitution.md`) is **non-negotiable** within this analysis scope. Constitution conflicts are automatically CRITICAL and require adjustment of the spec, plan, or tasks—not dilution, reinterpretation, or silent ignoring of the principle. If a principle itself needs to change, that must occur in a separate, explicit constitution update outside `/analyze`. + +Execution steps: + +1. Run `.specify/scripts/bash/check-prerequisites.sh --json --require-tasks --include-tasks` once from repo root and parse JSON for FEATURE_DIR and AVAILABLE_DOCS. Derive absolute paths: + - SPEC = FEATURE_DIR/spec.md + - PLAN = FEATURE_DIR/plan.md + - TASKS = FEATURE_DIR/tasks.md + Abort with an error message if any required file is missing (instruct the user to run missing prerequisite command). + +2. Load artifacts: + - Parse spec.md sections: Overview/Context, Functional Requirements, Non-Functional Requirements, User Stories, Edge Cases (if present). + - Parse plan.md: Architecture/stack choices, Data Model references, Phases, Technical constraints. + - Parse tasks.md: Task IDs, descriptions, phase grouping, parallel markers [P], referenced file paths. + - Load constitution `.specify/memory/constitution.md` for principle validation. + +3. Build internal semantic models: + - Requirements inventory: Each functional + non-functional requirement with a stable key (derive slug based on imperative phrase; e.g., "User can upload file" -> `user-can-upload-file`). + - User story/action inventory. + - Task coverage mapping: Map each task to one or more requirements or stories (inference by keyword / explicit reference patterns like IDs or key phrases). + - Constitution rule set: Extract principle names and any MUST/SHOULD normative statements. + +4. Detection passes: + A. Duplication detection: + - Identify near-duplicate requirements. Mark lower-quality phrasing for consolidation. + B. Ambiguity detection: + - Flag vague adjectives (fast, scalable, secure, intuitive, robust) lacking measurable criteria. + - Flag unresolved placeholders (TODO, TKTK, ???, , etc.). + C. Underspecification: + - Requirements with verbs but missing object or measurable outcome. + - User stories missing acceptance criteria alignment. + - Tasks referencing files or components not defined in spec/plan. + D. Constitution alignment: + - Any requirement or plan element conflicting with a MUST principle. + - Missing mandated sections or quality gates from constitution. + E. Coverage gaps: + - Requirements with zero associated tasks. + - Tasks with no mapped requirement/story. + - Non-functional requirements not reflected in tasks (e.g., performance, security). + F. Inconsistency: + - Terminology drift (same concept named differently across files). + - Data entities referenced in plan but absent in spec (or vice versa). + - Task ordering contradictions (e.g., integration tasks before foundational setup tasks without dependency note). + - Conflicting requirements (e.g., one requires to use Next.js while other says to use Vue as the framework). + +5. Severity assignment heuristic: + - CRITICAL: Violates constitution MUST, missing core spec artifact, or requirement with zero coverage that blocks baseline functionality. + - HIGH: Duplicate or conflicting requirement, ambiguous security/performance attribute, untestable acceptance criterion. + - MEDIUM: Terminology drift, missing non-functional task coverage, underspecified edge case. + - LOW: Style/wording improvements, minor redundancy not affecting execution order. + +6. Produce a Markdown report (no file writes) with sections: + + ### Specification Analysis Report + | ID | Category | Severity | Location(s) | Summary | Recommendation | + |----|----------|----------|-------------|---------|----------------| + | A1 | Duplication | HIGH | spec.md:L120-134 | Two similar requirements ... | Merge phrasing; keep clearer version | + (Add one row per finding; generate stable IDs prefixed by category initial.) + + Additional subsections: + - Coverage Summary Table: + | Requirement Key | Has Task? | Task IDs | Notes | + - Constitution Alignment Issues (if any) + - Unmapped Tasks (if any) + - Metrics: + * Total Requirements + * Total Tasks + * Coverage % (requirements with >=1 task) + * Ambiguity Count + * Duplication Count + * Critical Issues Count + +7. At end of report, output a concise Next Actions block: + - If CRITICAL issues exist: Recommend resolving before `/implement`. + - If only LOW/MEDIUM: User may proceed, but provide improvement suggestions. + - Provide explicit command suggestions: e.g., "Run /specify with refinement", "Run /plan to adjust architecture", "Manually edit tasks.md to add coverage for 'performance-metrics'". + +8. Ask the user: "Would you like me to suggest concrete remediation edits for the top N issues?" (Do NOT apply them automatically.) + +Behavior rules: +- NEVER modify files. +- NEVER hallucinate missing sections—if absent, report them. +- KEEP findings deterministic: if rerun without changes, produce consistent IDs and counts. +- LIMIT total findings in the main table to 50; aggregate remainder in a summarized overflow note. +- If zero issues found, emit a success report with coverage statistics and proceed recommendation. + +Context: {{args}} +""" diff --git a/.archive/docs/gemini instructions/.gemini/commands/clarify.toml.md b/.archive/docs/gemini instructions/.gemini/commands/clarify.toml.md new file mode 100644 index 0000000000..3fd7790b2a --- /dev/null +++ b/.archive/docs/gemini instructions/.gemini/commands/clarify.toml.md @@ -0,0 +1,162 @@ +description = "Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec." + +prompt = """ +--- +description: Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec. +--- + +The user input to you can be provided directly by the agent or as a command argument - you **MUST** consider it before proceeding with the prompt (if not empty). + +User input: + +$ARGUMENTS + +Goal: Detect and reduce ambiguity or missing decision points in the active feature specification and record the clarifications directly in the spec file. + +Note: This clarification workflow is expected to run (and be completed) BEFORE invoking `/plan`. If the user explicitly states they are skipping clarification (e.g., exploratory spike), you may proceed, but must warn that downstream rework risk increases. + +Execution steps: + +1. Run `.specify/scripts/bash/check-prerequisites.sh --json --paths-only` from repo root **once** (combined `--json --paths-only` mode / `-Json -PathsOnly`). Parse minimal JSON payload fields: + - `FEATURE_DIR` + - `FEATURE_SPEC` + - (Optionally capture `IMPL_PLAN`, `TASKS` for future chained flows.) + - If JSON parsing fails, abort and instruct user to re-run `/specify` or verify feature branch environment. + +2. Load the current spec file. Perform a structured ambiguity & coverage scan using this taxonomy. For each category, mark status: Clear / Partial / Missing. Produce an internal coverage map used for prioritization (do not output raw map unless no questions will be asked). + + Functional Scope & Behavior: + - Core user goals & success criteria + - Explicit out-of-scope declarations + - User roles / personas differentiation + + Domain & Data Model: + - Entities, attributes, relationships + - Identity & uniqueness rules + - Lifecycle/state transitions + - Data volume / scale assumptions + + Interaction & UX Flow: + - Critical user journeys / sequences + - Error/empty/loading states + - Accessibility or localization notes + + Non-Functional Quality Attributes: + - Performance (latency, throughput targets) + - Scalability (horizontal/vertical, limits) + - Reliability & availability (uptime, recovery expectations) + - Observability (logging, metrics, tracing signals) + - Security & privacy (authN/Z, data protection, threat assumptions) + - Compliance / regulatory constraints (if any) + + Integration & External Dependencies: + - External services/APIs and failure modes + - Data import/export formats + - Protocol/versioning assumptions + + Edge Cases & Failure Handling: + - Negative scenarios + - Rate limiting / throttling + - Conflict resolution (e.g., concurrent edits) + + Constraints & Tradeoffs: + - Technical constraints (language, storage, hosting) + - Explicit tradeoffs or rejected alternatives + + Terminology & Consistency: + - Canonical glossary terms + - Avoided synonyms / deprecated terms + + Completion Signals: + - Acceptance criteria testability + - Measurable Definition of Done style indicators + + Misc / Placeholders: + - TODO markers / unresolved decisions + - Ambiguous adjectives ("robust", "intuitive") lacking quantification + + For each category with Partial or Missing status, add a candidate question opportunity unless: + - Clarification would not materially change implementation or validation strategy + - Information is better deferred to planning phase (note internally) + +3. Generate (internally) a prioritized queue of candidate clarification questions (maximum 5). Do NOT output them all at once. Apply these constraints: + - Maximum of 5 total questions across the whole session. + - Each question must be answerable with EITHER: + * A short multiple‑choice selection (2–5 distinct, mutually exclusive options), OR + * A one-word / short‑phrase answer (explicitly constrain: "Answer in <=5 words"). + - Only include questions whose answers materially impact architecture, data modeling, task decomposition, test design, UX behavior, operational readiness, or compliance validation. + - Ensure category coverage balance: attempt to cover the highest impact unresolved categories first; avoid asking two low-impact questions when a single high-impact area (e.g., security posture) is unresolved. + - Exclude questions already answered, trivial stylistic preferences, or plan-level execution details (unless blocking correctness). + - Favor clarifications that reduce downstream rework risk or prevent misaligned acceptance tests. + - If more than 5 categories remain unresolved, select the top 5 by (Impact * Uncertainty) heuristic. + +4. Sequential questioning loop (interactive): + - Present EXACTLY ONE question at a time. + - For multiple‑choice questions render options as a Markdown table: + + | Option | Description | + |--------|-------------| + | A |