{"id":"29d774fd-0b39-43fa-8327-dfafcb46cd8b","entityType":"agent","slug":"crewai-kksen18-collab-financial-researcher","name":"financial-researcher","canonicalUrl":"https://www.xpersona.co/agent/crewai-kksen18-collab-financial-researcher","canonicalPath":"/agent/crewai-kksen18-collab-financial-researcher","generatedAt":"2026-10-09T01:20:14.339Z","source":"GITHUB_OPENCLEW","claimStatus":"UNCLAIMED","verificationTier":"NONE","summary":{"evidence":{"source":"editorial-content","verified":true,"confidence":"high","updatedAt":"2026-05-18T06:45:32.179Z","emptyReason":null},"description":"Two AI agents walk into a terminal... one Googles, one writes. Out comes a full financial research report. Built with CrewAI. Financial Researcher 🔍📈 An AI-powered multi-agent system that researches any publicly traded company and delivers a polished financial analysis report — in minutes. Built with $1, it orchestrates two specialised AI agents that work in sequence: one scours the web for real-time data, the other synthesises those findings into a professional-grade Markdown report. --- Table of Contents - $1 - $1 - $1 - $1 - $1 - $1 -","descriptionLabel":"Technical summary","evidenceSummary":"Capability contract not published. No trust telemetry is available yet. Last updated 5/18/2026.","installCommand":"git clone https://github.com/kksen18-collab/financial-researcher.git","sourceUrl":"https://github.com/kksen18-collab/financial-researcher","homepage":null,"primaryLinks":[{"label":"View Source","url":"https://github.com/kksen18-collab/financial-researcher","kind":"source"}],"safetyScore":66,"overallRank":21.2,"popularityScore":0,"trustScore":null,"claimedByName":null,"isOwner":false,"seoDescription":"Two AI agents walk into a terminal... one Googles, one writes. Out comes a full financial research report. Built with CrewAI. Financial Researcher 🔍📈 An AI-po"},"coverage":{"evidence":{"source":"public-profile","verified":false,"confidence":"medium","updatedAt":"2026-05-18T06:45:32.179Z","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":"2026-05-18T06:45:32.179Z","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":"2026-05-18T06:45:32.178Z","emptyReason":null},"lastUpdatedAt":"2026-05-18T06:45:32.179Z","lastCrawledAt":"2026-05-18T06:45:32.178Z","lastIndexedAt":null,"nextCrawlAt":"2026-05-25T06:45:32.178Z","lastVerifiedAt":null,"highlights":[]},"execution":{"evidence":{"source":"GITHUB OPENCLEW","verified":false,"confidence":"low","updatedAt":null,"emptyReason":"No published capability contract is available yet."},"installCommand":"git clone https://github.com/kksen18-collab/financial-researcher.git","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-kksen18-collab-financial-researcher/snapshot","contractUrl":"https://www.xpersona.co/api/v1/agents/crewai-kksen18-collab-financial-researcher/contract","trustUrl":"https://www.xpersona.co/api/v1/agents/crewai-kksen18-collab-financial-researcher/trust"},"curlExamples":["curl -s \"https://www.xpersona.co/api/v1/agents/crewai-kksen18-collab-financial-researcher/snapshot\"","curl -s \"https://www.xpersona.co/api/v1/agents/crewai-kksen18-collab-financial-researcher/contract\"","curl -s \"https://www.xpersona.co/api/v1/agents/crewai-kksen18-collab-financial-researcher/trust\""],"jsonRequestTemplate":{"query":"summarize this repo","constraints":{"maxLatencyMs":2000,"protocolPreference":["OPENCLEW"]}},"jsonResponseTemplate":{"ok":true,"result":{"summary":"...","confidence":0.9},"meta":{"source":"GITHUB_OPENCLEW","generatedAt":"2026-10-09T01:20:14.339Z"}},"retryPolicy":{"maxAttempts":3,"backoffMs":[500,1500,3500],"retryableConditions":["HTTP_429","HTTP_503","NETWORK_TIMEOUT"]}},"endpoints":{"dossierUrl":"https://www.xpersona.co/api/v1/agents/crewai-kksen18-collab-financial-researcher/dossier","snapshotUrl":"https://www.xpersona.co/api/v1/agents/crewai-kksen18-collab-financial-researcher/snapshot","contractUrl":"https://www.xpersona.co/api/v1/agents/crewai-kksen18-collab-financial-researcher/contract","trustUrl":"https://www.xpersona.co/api/v1/agents/crewai-kksen18-collab-financial-researcher/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 OPENCLEW","verified":false,"confidence":"high","updatedAt":"2026-05-18T06:45:32.179Z","emptyReason":null},"readme":"# Financial Researcher 🔍📈\n\n> An AI-powered multi-agent system that researches any publicly traded company and delivers a polished financial analysis report — in minutes.\n\nBuilt with [CrewAI](https://crewai.com), it orchestrates two specialised AI agents that work in sequence: one scours the web for real-time data, the other synthesises those findings into a professional-grade Markdown report.\n\n---\n\n## Table of Contents\n\n- [Demo](#demo)\n- [How It Works](#how-it-works)\n- [CrewAI Concepts Explained](#crewai-concepts-explained)\n  - [Agent](#-agent)\n  - [Task](#-task)\n  - [Crew](#-crew)\n  - [Process — Sequential vs Hierarchical](#-process--sequential-vs-hierarchical)\n  - [Tools](#-tools)\n- [Project Structure](#project-structure)\n- [Agents & Tasks in This Project](#agents--tasks-in-this-project)\n- [Prerequisites](#prerequisites)\n- [Installation](#installation)\n- [Configuration](#configuration)\n- [Running the Crew](#running-the-crew)\n- [Sample Output](#sample-output)\n- [Contributing](#contributing)\n- [License](#license)\n\n---\n\n## Demo\n\n```\n$ crewai run\n\nEnter the company to research: Apple\n\n[Researcher Agent] Searching the web for Apple financial data...\n[Analyst Agent] Synthesising research into a comprehensive report...\n\n=== FINAL REPORT ===\n\n# Comprehensive Report on Apple Inc.\nAs of 2026-03-24 ...\n\nReport has been saved to output/report.md\n```\n\n---\n\n## How It Works\n\n```\n┌─────────────────────────────────────────────────────────────┐\n│                        USER INPUT                           │\n│                   \"Enter company name\"                      │\n└─────────────────────────┬───────────────────────────────────┘\n                          │\n                          ▼\n┌─────────────────────────────────────────────────────────────┐\n│                    CREW  (Sequential)                       │\n│                                                             │\n│  ┌──────────────────────────────────────────────────────┐   │\n│  │  STEP 1 — Research Task                              │   │\n│  │  Agent: Senior Financial Researcher                  │   │\n│  │  Tool:  SerperDevTool (Google Search)                │   │\n│  │  Output: Structured research document                │   │\n│  └──────────────────────────┬───────────────────────────┘   │\n│                             │  context passed downstream    │\n│  ┌──────────────────────────▼───────────────────────────┐   │\n│  │  STEP 2 — Analysis Task                              │   │\n│  │  Agent: Market Analyst & Report Writer               │   │\n│  │  Tool:  (none — works from research context)         │   │\n│  │  Output: output/report.md                            │   │\n│  └──────────────────────────────────────────────────────┘   │\n└─────────────────────────────────────────────────────────────┘\n```\n\n---\n\n## CrewAI Concepts Explained\n\n### 🤖 Agent\n\nAn **Agent** is an autonomous AI entity with a specific **role**, **goal**, and **backstory**. Think of it as hiring a specialist for a job — you describe who they are, what they're trying to achieve, and the context that shapes their expertise. Each agent is backed by an LLM and can optionally be equipped with tools to interact with the outside world.\n\n| Property    | Purpose |\n|-------------|---------|\n| `role`      | The agent's job title / persona (e.g., *Senior Financial Researcher*) |\n| `goal`      | What the agent is trying to accomplish |\n| `backstory` | Background context that shapes the agent's reasoning style |\n| `llm`       | The language model powering the agent |\n| `tools`     | External capabilities the agent can invoke (search, code execution, APIs…) |\n| `verbose`   | Prints the agent's chain-of-thought reasoning to the console |\n\n```yaml\n# config/agents.yaml\nresearcher:\n  role: Senior Financial Researcher for {company}\n  goal: Research the company, news and potential for {company}\n  backstory: You're a seasoned financial researcher...\n  llm: openai/gpt-4o-mini\n```\n\n---\n\n### 📋 Task\n\nA **Task** is a specific piece of work assigned to an agent. It defines *what* needs to be done, what a successful result looks like, and which agent is responsible. Tasks can receive the output of previous tasks as **context**, enabling agents to build on each other's work.\n\n| Property          | Purpose |\n|-------------------|---------|\n| `description`     | Detailed instructions for what to do |\n| `expected_output` | Describes what a complete, correct result looks like |\n| `agent`           | Which agent is assigned to this task |\n| `context`         | List of prior tasks whose output is fed into this task |\n| `output_file`     | (Optional) Persist the result to a file |\n\n```yaml\n# config/tasks.yaml\nanalysis_task:\n  description: Analyze the research findings and create a comprehensive report...\n  expected_output: A polished, professional report...\n  agent: analyst\n  context:\n    - research_task          # ← analyst reads the researcher's full output\n  output_file: output/report.md\n```\n\n---\n\n### 🚢 Crew\n\nA **Crew** is the team — it binds agents and tasks together under a shared **process** (execution strategy). The crew is responsible for scheduling tasks, routing context between them, and collecting the final result.\n\n```python\n@crew\ndef crew(self) -> Crew:\n    return Crew(\n        agents=self.agents,   # [researcher, analyst]\n        tasks=self.tasks,     # [research_task, analysis_task]\n        process=Process.sequential,\n        verbose=True,\n    )\n```\n\nKickoff the crew with dynamic inputs:\n\n```python\nResearchCrew().crew().kickoff(inputs={\"company\": \"Apple\", \"date\": \"2026-03-24\"})\n```\n\n---\n\n### ⚙️ Process — Sequential vs Hierarchical\n\nThe **Process** controls how tasks are executed relative to each other.\n\n#### `Process.sequential` *(used in this project)*\n\nTasks run **one after another**, in the order they are defined. The output of each task is automatically passed as context to the next. Simple, predictable, and great for linear pipelines.\n\n```\nTask 1 ──► Task 2 ──► Task 3 ──► Final Result\n```\n\n#### `Process.hierarchical`\n\nA designated **manager agent** (or manager LLM) dynamically **delegates** tasks to the most suitable agent, decides the order of execution, and reviews outputs. Best for complex, open-ended workflows where the path isn't known upfront.\n\n```\n              Manager Agent\n             /      |       \\\n        Task A   Task B   Task C   (assigned dynamically)\n             \\      |       /\n              Final Result\n```\n\n| Feature              | Sequential           | Hierarchical               |\n|----------------------|----------------------|----------------------------|\n| Execution order      | Fixed, top-to-bottom | Dynamic delegation         |\n| Manager agent needed | No                   | Yes                        |\n| Complexity           | Low                  | High                       |\n| Best for             | Linear pipelines     | Complex, adaptive workflows |\n\n---\n\n### 🛠️ Tools\n\n**Tools** extend an agent's capabilities beyond text generation. They allow agents to interact with the real world — search the web, read files, execute code, call APIs, query databases, and more.\n\n#### How tools work\n\n1. The agent decides it needs external information\n2. It calls the appropriate tool with the required arguments\n3. The tool returns results back to the agent\n4. The agent incorporates the results into its reasoning\n\n#### Tools used in this project\n\n| Tool            | Source         | Purpose |\n|-----------------|----------------|---------|\n| `SerperDevTool` | `crewai_tools` | Executes real-time Google searches — gives the researcher access to current news, stock data, and filings |\n\n#### Other popular CrewAI tools\n\n| Tool                      | Purpose |\n|---------------------------|---------|\n| `FileReadTool`            | Read local files |\n| `WebsiteSearchTool`       | Scrape and search a specific website |\n| `YoutubeVideoSearchTool`  | Search YouTube transcripts |\n| `CodeInterpreterTool`     | Execute Python code |\n| `ScrapeWebsiteTool`       | Extract full HTML content from a URL |\n\n---\n\n## Project Structure\n\n```\nfinancial_researcher/\n├── src/\n│   └── financial_researcher/\n│       ├── __init__.py\n│       ├── crew.py          # Agent, Task, and Crew definitions\n│       ├── main.py          # Entry point — prompts for company name, kicks off crew\n│       └── config/\n│           ├── agents.yaml  # Agent personas (role, goal, backstory, llm)\n│           └── tasks.yaml   # Task definitions (description, expected output, agent)\n├── output/\n│   └── report.md            # Generated report (auto-created after each run)\n├── pyproject.toml           # Project metadata and dependencies\n├── .env                     # Your secret API keys (never commit this!)\n├── .env.example             # Template — copy to .env and fill in your keys\n└── README.md\n```\n\n---\n\n## Agents & Tasks in This Project\n\n### Agents\n\n#### 1. Senior Financial Researcher\n- **Role:** Finds and organises raw information about the target company\n- **LLM:** `openai/gpt-4o-mini`\n- **Tools:** `SerperDevTool` — performs live Google searches\n- **Produces:** A structured research document covering company health, history, challenges, recent news, and future outlook\n\n#### 2. Market Analyst & Report Writer\n- **Role:** Transforms raw research into a polished, professional report\n- **LLM:** `openai/gpt-4o-mini`\n- **Tools:** None — works entirely from the researcher's context\n- **Produces:** A well-formatted Markdown report with executive summary, sections, and conclusion\n\n### Tasks\n\n| Task            | Agent      | Input                       | Output                |\n|-----------------|------------|-----------------------------|-----------------------|\n| `research_task` | Researcher | Company name + date         | Research document     |\n| `analysis_task` | Analyst    | Research document (context) | `output/report.md`    |\n\n---\n\n## Prerequisites\n\n- Python `>=3.10` and `<3.13`\n- An [OpenAI API key](https://platform.openai.com/api-keys)\n- A [Serper API key](https://serper.dev) — free tier includes 2,500 searches/month\n\n---\n\n## Installation\n\n```bash\n# 1. Clone the repository\ngit clone https://github.com/your-username/financial-researcher.git\ncd financial-researcher/financial_researcher\n\n# 2. Install uv (if not already installed)\npip install uv\n\n# 3. Install project dependencies\ncrewai install\n\n# 4. Copy the environment template and fill in your keys\ncopy .env.example .env    # Windows\ncp .env.example .env      # macOS / Linux\n```\n\n---\n\n## Configuration\n\nOpen `.env` and add your API keys:\n\n```env\nOPENAI_API_KEY=sk-...\nSERPER_API_KEY=...\n```\n\nTo swap the LLM model, edit `src/financial_researcher/config/agents.yaml`:\n\n```yaml\nresearcher:\n  llm: openai/gpt-4o-mini   # or: openai/gpt-4o, anthropic/claude-3-5-sonnet, etc.\n```\n\n---\n\n## Running the Crew\n\n```bash\n# Activate the virtual environment\n.venv\\Scripts\\activate          # Windows\nsource .venv/bin/activate       # macOS / Linux\n\n# Run\ncrewai run\n```\n\nYou will be prompted:\n\n```\nEnter the company to research: Tesla\n```\n\nThe crew runs (typically 1–3 minutes) and saves the full report to `output/report.md`.\n\n---\n\n## Sample Output\n\nThe `output/` directory contains a real example report generated for **Apple Inc.** as of March 24, 2026.\n\nSections include:\n- Executive Summary\n- Current Company Status and Health\n- Historical Performance\n- Challenges & Opportunities\n- Market Outlook\n- Conclusion\n\n---\n\n## Contributing\n\nPull requests are welcome! For major changes, please open an issue first to discuss what you would like to change.\n\n1. Fork the repo\n2. Create a feature branch (`git checkout -b feature/my-feature`)\n3. Commit your changes (`git commit -m 'Add my feature'`)\n4. Push to the branch (`git push origin feature/my-feature`)\n5. Open a Pull Request\n\n---\n\n## License\n\nThis project is licensed under the [MIT License](LICENSE).\n\n---\n\n> **Disclaimer:** Reports generated by this tool are for informational and educational purposes only. They should **not** be used as the basis for any financial or investment decisions.\n","readmeExcerpt":"Financial Researcher 🔍📈 An AI-powered multi-agent system that researches any publicly traded company and delivers a polished financial analysis report — in minutes. Built with $1, it orchestrates two specialised AI agents that work in sequence: one scours the web for real-time data, the other synthesises those findings into a professional-grade Markdown report. --- Table of Contents - $1 - $1 - $1 - $1 - $1 - $1 - ","codeSnippets":[],"executableExamples":[{"language":"text","snippet":"$ crewai run\n\nEnter the company to research: Apple\n\n[Researcher Agent] Searching the web for Apple financial data...\n[Analyst Agent] Synthesising research into a comprehensive report...\n\n=== FINAL REPORT ===\n\n# Comprehensive Report on Apple Inc.\nAs of 2026-03-24 ...\n\nReport has been saved to output/report.md"},{"language":"text","snippet":"┌─────────────────────────────────────────────────────────────┐\n│                        USER INPUT                           │\n│                   \"Enter company name\"                      │\n└─────────────────────────┬───────────────────────────────────┘\n                          │\n                          ▼\n┌─────────────────────────────────────────────────────────────┐\n│                    CREW  (Sequential)                       │\n│                                                             │\n│  ┌──────────────────────────────────────────────────────┐   │\n│  │  STEP 1 — Research Task                              │   │\n│  │  Agent: Senior Financial Researcher                  │   │\n│  │  Tool:  SerperDevTool (Google Search)                │   │\n│  │  Output: Structured research document                │   │\n│  └──────────────────────────┬───────────────────────────┘   │\n│                             │  context passed downstream    │\n│  ┌──────────────────────────▼───────────────────────────┐   │\n│  │  STEP 2 — Analysis Task                              │   │\n│  │  Agent: Market Analyst & Report Writer               │   │\n│  │  Tool:  (none — works from research context)         │   │\n│  │  Output: output/report.md                            │   │\n│  └──────────────────────────────────────────────────────┘   │\n└─────────────────────────────────────────────────────────────┘"},{"language":"yaml","snippet":"# config/agents.yaml\nresearcher:\n  role: Senior Financial Researcher for {company}\n  goal: Research the company, news and potential for {company}\n  backstory: You're a seasoned financial researcher...\n  llm: openai/gpt-4o-mini"},{"language":"yaml","snippet":"# config/tasks.yaml\nanalysis_task:\n  description: Analyze the research findings and create a comprehensive report...\n  expected_output: A polished, professional report...\n  agent: analyst\n  context:\n    - research_task          # ← analyst reads the researcher's full output\n  output_file: output/report.md"},{"language":"python","snippet":"@crew\ndef crew(self) -> Crew:\n    return Crew(\n        agents=self.agents,   # [researcher, analyst]\n        tasks=self.tasks,     # [research_task, analysis_task]\n        process=Process.sequential,\n        verbose=True,\n    )"},{"language":"python","snippet":"ResearchCrew().crew().kickoff(inputs={\"company\": \"Apple\", \"date\": \"2026-03-24\"})"}],"parameters":null,"dependencies":[],"permissions":[],"extractedFiles":[],"languages":["python"],"docsSourceLabel":"GITHUB OPENCLEW","editorialOverview":"Two AI agents walk into a terminal... one Googles, one writes. Out comes a full financial research report. Built with CrewAI. Financial Researcher 🔍📈 An AI-powered multi-agent system that researches any publicly traded company and delivers a polished financial analysis report — in minutes. Built with $1, it orchestrates two specialised AI agents that work in sequence: one scours the web for real-time data, the other synthesises those findings into a professional-grade Markdown report. --- Table of Contents - $1 - $1 - $1 - $1 - $1 - $1 -","editorialQuality":{"score":100,"threshold":65,"status":"ready","wordCount":377,"uniquenessScore":68,"reasons":[]}},"media":{"evidence":{"source":"no-media","verified":false,"confidence":"low","updatedAt":"2026-05-18T06:45:32.179Z","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":"2026-05-18T06:45:32.179Z","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":"2026-10-09T01:20:14.339Z","emptyReason":null},"items":[{"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":"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":"2026-04-10T18:48:31.762Z","createdAt":"2026-02-25T03:38:16.584Z","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_openclew","protocols":[{"label":"OpenClaw","href":"/agent/protocol/openclew"}]}}}