{"id":"9cab9877-a9d8-474b-99aa-c3f880e447d8","entityType":"agent","slug":"crewai-gabmwiha-crewai-product-planner-starter","name":"crewai-product-planner-starter","canonicalUrl":"https://www.xpersona.co/agent/crewai-gabmwiha-crewai-product-planner-starter","canonicalPath":"/agent/crewai-gabmwiha-crewai-product-planner-starter","generatedAt":"2026-10-10T06:45:42.789Z","source":"GITHUB_REPOS","claimStatus":"UNCLAIMED","verificationTier":"NONE","summary":{"evidence":{"source":"editorial-content","verified":true,"confidence":"high","updatedAt":"2026-10-09T18:04:03.335Z","emptyReason":null},"description":"A multi-role planner that turns a product name into a design plan, built with CrewAI. CrewAI Product Planner A multi-agent product planning assistant built with CrewAI on EdgeOne Makers — a PM, Tech Lead, and Reviewer collaborate to turn your product idea into a PRD and Tech Spec through interactive Q&A. **Framework:** CrewAI · **Category:** Quick Start · **Language:** Python $1 Overview CrewAI Product Planner simulates a product team: a Product Manager interviews you to gather requirements, then coll","descriptionLabel":"Technical summary","evidenceSummary":"Capability contract not published. No trust telemetry is available yet. Last updated 10/9/2026.","installCommand":null,"sourceUrl":"https://github.com/Gabmwiha/crewai-product-planner-starter","homepage":null,"primaryLinks":[{"label":"View Source","url":"https://github.com/Gabmwiha/crewai-product-planner-starter","kind":"source"}],"safetyScore":66,"overallRank":18.8,"popularityScore":0,"trustScore":null,"claimedByName":null,"isOwner":false,"seoDescription":"A multi-role planner that turns a product name into a design plan, built with CrewAI. CrewAI Product Planner A multi-agent product planning assistant built with"},"coverage":{"evidence":{"source":"public-profile","verified":false,"confidence":"medium","updatedAt":"2026-10-09T18:04:03.335Z","emptyReason":null},"protocols":[{"protocol":"OPENCLEW","label":"OpenClaw","status":"self-declared","notes":"Declared in the public agent profile."}],"capabilities":[{"label":"crewai","status":"self-declared"},{"label":"multi-agent","status":"self-declared"}],"verifiedCount":0,"selfDeclaredCount":3,"capabilityMatrix":{"rows":[{"key":"OPENCLEW","type":"protocol","support":"unknown","confidenceSource":"profile","notes":"Listed on profile"},{"key":"crewai","type":"capability","support":"supported","confidenceSource":"profile","notes":"Declared in agent profile metadata"},{"key":"multi-agent","type":"capability","support":"supported","confidenceSource":"profile","notes":"Declared in agent profile metadata"}],"flattenedTokens":"protocol:OPENCLEW|unknown|profile capability:crewai|supported|profile capability:multi-agent|supported|profile"}},"adoption":{"evidence":{"source":"no-adoption-signals","verified":false,"confidence":"low","updatedAt":"2026-10-09T18:04:03.335Z","emptyReason":"No source adoption metrics were available."},"stars":0,"forks":0,"downloads":null,"packageName":null,"latestVersion":null,"tractionLabel":null},"release":{"evidence":{"source":"agent-index","verified":false,"confidence":"medium","updatedAt":"2026-10-09T18:04:03.328Z","emptyReason":null},"lastUpdatedAt":"2026-10-09T18:04:03.335Z","lastCrawledAt":"2026-10-09T18:04:03.328Z","lastIndexedAt":null,"nextCrawlAt":"2026-10-16T18:04:03.328Z","lastVerifiedAt":null,"highlights":[]},"execution":{"evidence":{"source":"GITHUB REPOS","verified":false,"confidence":"low","updatedAt":null,"emptyReason":"No published capability contract is available yet."},"installCommand":null,"setupComplexity":"low","setupSteps":["Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.","Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data."],"contract":{"contractStatus":"missing","authModes":[],"requires":[],"forbidden":[],"supportsMcp":false,"supportsA2a":false,"supportsStreaming":false,"inputSchemaRef":null,"outputSchemaRef":null,"dataRegion":null,"contractUpdatedAt":null,"sourceUpdatedAt":null,"freshnessSeconds":null},"invocationGuide":{"preferredApi":{"snapshotUrl":"https://www.xpersona.co/api/v1/agents/crewai-gabmwiha-crewai-product-planner-starter/snapshot","contractUrl":"https://www.xpersona.co/api/v1/agents/crewai-gabmwiha-crewai-product-planner-starter/contract","trustUrl":"https://www.xpersona.co/api/v1/agents/crewai-gabmwiha-crewai-product-planner-starter/trust"},"curlExamples":["curl -s \"https://www.xpersona.co/api/v1/agents/crewai-gabmwiha-crewai-product-planner-starter/snapshot\"","curl -s \"https://www.xpersona.co/api/v1/agents/crewai-gabmwiha-crewai-product-planner-starter/contract\"","curl -s \"https://www.xpersona.co/api/v1/agents/crewai-gabmwiha-crewai-product-planner-starter/trust\""],"jsonRequestTemplate":{"query":"summarize this repo","constraints":{"maxLatencyMs":2000,"protocolPreference":["OPENCLEW"]}},"jsonResponseTemplate":{"ok":true,"result":{"summary":"...","confidence":0.9},"meta":{"source":"GITHUB_REPOS","generatedAt":"2026-10-10T06:45:42.789Z"}},"retryPolicy":{"maxAttempts":3,"backoffMs":[500,1500,3500],"retryableConditions":["HTTP_429","HTTP_503","NETWORK_TIMEOUT"]}},"endpoints":{"dossierUrl":"https://www.xpersona.co/api/v1/agents/crewai-gabmwiha-crewai-product-planner-starter/dossier","snapshotUrl":"https://www.xpersona.co/api/v1/agents/crewai-gabmwiha-crewai-product-planner-starter/snapshot","contractUrl":"https://www.xpersona.co/api/v1/agents/crewai-gabmwiha-crewai-product-planner-starter/contract","trustUrl":"https://www.xpersona.co/api/v1/agents/crewai-gabmwiha-crewai-product-planner-starter/trust"}},"reliability":{"evidence":{"source":"runtime-metrics","verified":false,"confidence":"low","updatedAt":null,"emptyReason":"No trust, reliability, or runtime telemetry is available."},"trust":{"status":"unavailable","handshakeStatus":"UNKNOWN","verificationFreshnessHours":null,"reputationScore":null,"p95LatencyMs":null,"successRate30d":null,"fallbackRate":null,"attempts30d":null,"trustUpdatedAt":null,"trustConfidence":"unknown","sourceUpdatedAt":null,"freshnessSeconds":null},"decisionGuardrails":{"doNotUseIf":["Contract metadata is missing or unavailable for deterministic execution."],"safeUseWhen":[],"riskFlags":["missing_or_unavailable_contract","trust_data_unavailable","schema_references_missing"],"operationalConfidence":"low"},"executionMetrics":{"observedLatencyMsP50":null,"observedLatencyMsP95":null,"estimatedCostUsd":null,"uptime30d":null,"rateLimitRpm":null,"rateLimitBurst":null,"lastVerifiedAt":null,"verificationSource":null},"runtimeMetrics":{"successRate":null,"avgLatencyMs":null,"avgCostUsd":null,"hallucinationRate":null,"retryRate":null,"disputeRate":null,"p50Latency":null,"p95Latency":null,"lastUpdated":null}},"benchmarks":{"evidence":{"source":"no-benchmark-data","verified":false,"confidence":"low","updatedAt":null,"emptyReason":"No benchmark suites or observed failure patterns are available."},"suites":[],"failurePatterns":[]},"artifacts":{"evidence":{"source":"GITHUB REPOS","verified":false,"confidence":"high","updatedAt":"2026-10-09T18:04:03.335Z","emptyReason":null},"readme":"# CrewAI Product Planner\n\n> A multi-agent product planning assistant built with CrewAI on EdgeOne Makers — a PM, Tech Lead, and Reviewer collaborate to turn your product idea into a PRD and Tech Spec through interactive Q&A.\n\n**Framework:** CrewAI · **Category:** Quick Start · **Language:** Python\n\n[![Deploy to EdgeOne Makers](https://cdnstatic.tencentcs.com/edgeone/pages/deploy.svg)](https://edgeone.ai/makers/new?template=crewai-product-planner-starter&from=within&fromAgent=1&agentLang=python)\n\n## Overview\n\nCrewAI Product Planner simulates a product team: a Product Manager interviews you to gather requirements, then collaborates with a Tech Lead to produce a PRD and Technical Specification. A Reviewer provides improvement suggestions at each stage. The entire process is conversational — you guide the direction through multiple-choice options or free-text input.\n\n- **Multi-agent orchestration** — three agents (PM, Tech Lead, Reviewer) with distinct roles working in sequence via CrewAI Flows\n- **Interactive Q&A** — the PM asks clarifying questions before drafting; you choose from options or type custom answers\n- **Iterative refinement** — after the initial draft, continue providing feedback until you're satisfied with the final documents\n- **Session persistence** — conversation state is recoverable across instances via external store sync\n- **Streaming output** — real-time SSE streaming of agent responses with per-agent attribution\n\n## Environment Variables\n\n| Variable | Required | Description |\n|----------|----------|-------------|\n| `AI_GATEWAY_API_KEY` | Yes | Model gateway API key. Use your **Makers Models API Key**, or any OpenAI-compatible provider key. |\n| `AI_GATEWAY_BASE_URL` | Yes | Gateway base URL. For Makers Models, use `https://ai-gateway.edgeone.link/v1`. |\n| `AI_GATEWAY_MODEL` | No | Model ID. Defaults to `@makers/deepseek-v4-flash` (a free built-in model). |\n\n> This template follows the **OpenAI-compatible** standard — you can point these variables at Makers Models or any other compatible gateway / provider.\n\n### How to get `AI_GATEWAY_API_KEY`\n\n1. Open the [Makers Console](https://edgeone.ai/makers/new?s_url=https://console.tencentcloud.com/edgeone/makers).\n2. Sign in and enable Makers.\n3. Go to **Makers → Models → API Key** and create a key.\n4. Copy it into `AI_GATEWAY_API_KEY` (set `AI_GATEWAY_BASE_URL` to `https://ai-gateway.edgeone.link/v1`).\n\nBuilt-in models (`@makers/deepseek-v4-flash`, `@makers/hy3-preview`, `@makers/minimax-m2.7`) are free and rate-limited — great for prototyping. For production, bind your own provider key (BYOK) in the console.\n\n## Local Development\n\n**Prerequisites:** Node.js, npm, Python 3.11+\n\n```bash\nnpm install\ncp .env.example .env\nedgeone makers dev\n```\n\n> The CLI automatically installs Python dependencies from `agents/requirements.txt`.\n\nOpen `http://localhost:8080/agent-metrics` for the local observability panel.\n\n## Project Structure\n\n```text\ncrewai-planner-python/\n├── agents/\n│   ├── stream.py              # /stream — main conversation endpoint (SSE)\n│   ├── _lib/\n│   │   ├── flow.py            # TurnFlow: CrewAI Flow with pause/resume\n│   │   ├── llm.py             # LLM singleton initialization\n│   │   ├── persistence.py     # In-memory + store-backed state persistence\n│   │   ├── feedback_provider.py # Async feedback provider (raises HumanFeedbackPending)\n│   │   └── logger.py          # Shared logger factory\n│   ├── _crews/\n│   │   ├── agents.yaml        # Agent role definitions (PM, TL, Reviewer)\n│   │   ├── discovery_crew/    # Requirements gathering crew\n│   │   ├── planning_crew/     # PRD + Tech Spec generation crew\n│   │   └── iteration_crew/    # Feedback iteration crew\n│   └── requirements.txt       # Python dependencies\n├── cloud-functions/\n│   ├── history.py             # /history — retrieve conversation messages\n│   └── delete.py              # /delete — delete conversation data\n├── src/                       # Frontend (React + Tailwind)\n├── edgeone.json               # Agent runtime configuration\n└── package.json\n```\n\n> Files prefixed with `_` are private modules — not exposed as public routes by EdgeOne.\n\n## How It Works\n\nThe agent runs as a **session-mode** runtime: requests sharing the same `conversation_id` are routed to the same instance.\n\n### Workflow\n\n1. **First turn** — user enters a product name. The PM agent asks a clarifying question and the Flow pauses (via `@human_feedback`).\n2. **Discovery phase** — the PM asks 2-3 rounds of questions (A/B/C options). After enough context is gathered (or 3 rounds), the Flow transitions to drafting.\n3. **Drafting phase** — the PM writes a PRD, the Tech Lead writes a Technical Specification, and the Reviewer suggests improvements. The Flow pauses for user feedback.\n4. **Iteration phase** — user provides feedback (or selects \"looks good\"). The PM and TL respond to each piece of feedback. This loop continues until the user confirms finalization.\n5. **Finalization** — PM and TL produce the final versions of both documents.\n\n### Key Mechanisms\n\n- **CrewAI Flow + `@human_feedback`**: each pause raises `HumanFeedbackPending`; the next HTTP request resumes via `resume_async(feedback)`.\n- **Streaming**: CrewAI's `FlowStreamingOutput` delivers token-by-token SSE events with agent attribution.\n- **Persistence**: in-memory dict stores Flow state; `sync_pending_to_store()` backs it up to the platform store so state is recoverable across instances.\n- **Session history**: `/history` (cloud function) reads stored messages; `/delete` clears conversation data.\n\n### Routes\n\n| Route | Method | Description |\n|-------|--------|-------------|\n| `/stream` | POST | Main conversation turn (SSE streaming) |\n| `/history` | POST | Retrieve conversation messages |\n| `/delete` | POST | Delete conversation and flow state |\n\nThe `conversation_id` is passed via the `makers-conversation-id` request header.\n\n## Resources\n\n- [Makers Agents Documentation](https://pages.edgeone.ai/document/agents)\n- [Quick Start: Agent Development](https://pages.edgeone.ai/document/agents-quick-start)\n- [Makers Models](https://pages.edgeone.ai/document/models)\n\n## License\n\nMIT\n","readmeExcerpt":"CrewAI Product Planner A multi-agent product planning assistant built with CrewAI on EdgeOne Makers — a PM, Tech Lead, and Reviewer collaborate to turn your product idea into a PRD and Tech Spec through interactive Q&A. **Framework:** CrewAI · **Category:** Quick Start · **Language:** Python $1 Overview CrewAI Product Planner simulates a product team: a Product Manager interviews you to gather requirements, then coll","codeSnippets":[],"executableExamples":[{"language":"bash","snippet":"npm install\ncp .env.example .env\nedgeone makers dev"},{"language":"text","snippet":"crewai-planner-python/\n├── agents/\n│   ├── stream.py              # /stream — main conversation endpoint (SSE)\n│   ├── _lib/\n│   │   ├── flow.py            # TurnFlow: CrewAI Flow with pause/resume\n│   │   ├── llm.py             # LLM singleton initialization\n│   │   ├── persistence.py     # In-memory + store-backed state persistence\n│   │   ├── feedback_provider.py # Async feedback provider (raises HumanFeedbackPending)\n│   │   └── logger.py          # Shared logger factory\n│   ├── _crews/\n│   │   ├── agents.yaml        # Agent role definitions (PM, TL, Reviewer)\n│   │   ├── discovery_crew/    # Requirements gathering crew\n│   │   ├── planning_crew/     # PRD + Tech Spec generation crew\n│   │   └── iteration_crew/    # Feedback iteration crew\n│   └── requirements.txt       # Python dependencies\n├── cloud-functions/\n│   ├── history.py             # /history — retrieve conversation messages\n│   └── delete.py              # /delete — delete conversation data\n├── src/                       # Frontend (React + Tailwind)\n├── edgeone.json               # Agent runtime configuration\n└── package.json"}],"parameters":null,"dependencies":[],"permissions":[],"extractedFiles":[],"languages":["python"],"docsSourceLabel":"GITHUB REPOS","editorialOverview":"A multi-role planner that turns a product name into a design plan, built with CrewAI. CrewAI Product Planner A multi-agent product planning assistant built with CrewAI on EdgeOne Makers — a PM, Tech Lead, and Reviewer collaborate to turn your product idea into a PRD and Tech Spec through interactive Q&A. **Framework:** CrewAI · **Category:** Quick Start · **Language:** Python $1 Overview CrewAI Product Planner simulates a product team: a Product Manager interviews you to gather requirements, then coll","editorialQuality":{"score":100,"threshold":65,"status":"ready","wordCount":393,"uniquenessScore":64,"reasons":[]}},"media":{"evidence":{"source":"no-media","verified":false,"confidence":"low","updatedAt":"2026-10-09T18:04:03.335Z","emptyReason":"No screenshots, media assets, or demo links are available."},"primaryImageUrl":null,"mediaAssetCount":0,"assets":[],"demoUrl":null},"ownerResources":{"evidence":{"source":"unclaimed","verified":false,"confidence":"low","updatedAt":"2026-10-09T18:04:03.335Z","emptyReason":"This page has not been claimed by the agent owner."},"hasCustomPage":false,"customPageUpdatedAt":null,"customLinks":[],"structuredLinks":{"docsUrl":null,"demoUrl":null,"supportUrl":null,"pricingUrl":null,"statusUrl":null},"customPage":null},"relatedAgents":{"evidence":{"source":"protocol-neighbors","verified":false,"confidence":"medium","updatedAt":"2026-10-10T06:45:42.789Z","emptyReason":null},"items":[{"id":"8ebccd8e-3863-4187-8355-c3f14e1f9edf","entityType":"agent","canonicalPath":"/agent/iofficeai-aionui","slug":"iofficeai-aionui","name":"AionUi","description":"Free, local, open-source 24/7 Cowork app and OpenClaw for Gemini CLI, Claude Code, Codex, OpenCode, Qwen Code, Goose CLI, Auggie, and more | 🌟 Star if you like it!","url":"https://github.com/iOfficeAI/AionUi","homepage":"https://www.aionui.com","source":"GITHUB_REPOS","protocols":["MCP","OPENCLAW"],"capabilities":[],"safetyScore":100,"overallRank":70,"updatedAt":"2026-10-09T19:11:12.944Z","createdAt":"2026-02-25T03:38:16.584Z","downloads":null},{"id":"b917f68a-ebff-438e-84f8-3f4b2494c0bc","entityType":"agent","canonicalPath":"/agent/activepieces-activepieces","slug":"activepieces-activepieces","name":"activepieces","description":"AI Agents & MCPs & AI Workflow Automation • (~400 MCP servers for AI agents) • AI Automation / AI Agent with MCPs • AI Workflows & AI Agents • MCPs for AI Agents","url":"https://github.com/activepieces/activepieces","homepage":"https://www.activepieces.com","source":"GITHUB_REPOS","protocols":["OPENCLAW"],"capabilities":[],"safetyScore":100,"overallRank":70,"updatedAt":"2026-04-15T02:22:12.426Z","createdAt":"2026-02-25T03:38:12.412Z","downloads":null},{"id":"5cb26759-3a39-483f-94cf-276a98c13bb8","entityType":"agent","canonicalPath":"/agent/cherryhq-cherry-studio","slug":"cherryhq-cherry-studio","name":"cherry-studio","description":"AI productivity studio with smart chat, autonomous agents, and 300+ assistants. Unified access to frontier LLMs","url":"https://github.com/CherryHQ/cherry-studio","homepage":"https://cherry-ai.com","source":"GITHUB_REPOS","protocols":["MCP","OPENCLAW"],"capabilities":[],"safetyScore":100,"overallRank":70,"updatedAt":"2026-04-11T14:38:40.986Z","createdAt":"2026-02-25T03:38:19.379Z","downloads":null},{"id":"6f6582d0-5d76-4f0f-b81d-86520247950b","entityType":"agent","canonicalPath":"/agent/copilotkit-copilotkit","slug":"copilotkit-copilotkit","name":"CopilotKit","description":"The Frontend for Agents & Generative UI. React + Angular","url":"https://github.com/CopilotKit/CopilotKit","homepage":"https://docs.copilotkit.ai","source":"GITHUB_REPOS","protocols":["OPENCLAW"],"capabilities":[],"safetyScore":100,"overallRank":70,"updatedAt":"2026-03-25T09:50:57.846Z","createdAt":"2026-02-25T03:39:14.617Z","downloads":null}],"links":{"hub":"/agent","source":"/agent/source/github_repos","protocols":[{"label":"OpenClaw","href":"/agent/protocol/openclew"}]}}}