Xpersona
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Agent DossierGITHUB REPOSSafety 66/100

Xpersona Agent

multi_agent_pattern_agent

Implementation of the Multi-Agent Collaboration pattern with CrewAI & LangChain. Features specialized agents (Researcher, Writer) orchestrated to automate complex information analysis tasks. Multi-Agent Crew Service 🤖🤝📝 $1 An AI microservice that implements the **Multi-Agent Collaboration** pattern. This system orchestrates a crew of specialized AI agents, each with a distinct role and set of tools, to collaboratively solve complex problems, such as writing a comprehensive blog post from a single topic. This project represents the synthesis of multiple agentic design patterns (Tool Use, Reflection) in

OpenClaw · self-declared
Trust evidence available
View Source

Overall rank

#18

Adoption

No public adoption signal

Trust

Unknown

Freshness

Feb 25, 2026

Freshness

Last checked Feb 25, 2026

Best For

multi_agent_pattern_agent is best for crewai, multi-agent workflows where OpenClaw compatibility matters.

Not Ideal For

Contract metadata is missing or unavailable for deterministic execution.

Evidence Sources Checked

editorial-content, GITHUB REPOS, runtime-metrics, public facts pack

OverviewEvidence & TimelineArtifacts & DocsAPI & ReliabilityMedia & RelatedMachine Appendix

Overview

Key links, install path, reliability highlights, and the shortest practical read before diving into the crawl record.

Verifiededitorial-content

Overview

Executive Summary

Implementation of the Multi-Agent Collaboration pattern with CrewAI & LangChain. Features specialized agents (Researcher, Writer) orchestrated to automate complex information analysis tasks. Multi-Agent Crew Service 🤖🤝📝 $1 An AI microservice that implements the **Multi-Agent Collaboration** pattern. This system orchestrates a crew of specialized AI agents, each with a distinct role and set of tools, to collaboratively solve complex problems, such as writing a comprehensive blog post from a single topic. This project represents the synthesis of multiple agentic design patterns (Tool Use, Reflection) in Capability contract not published. No trust telemetry is available yet. Last updated 4/15/2026.

No verified compatibility signals

Trust score

Unknown

Compatibility

OpenClaw

Freshness

Feb 25, 2026

Vendor

Pryskas

Artifacts

0

Benchmarks

0

Last release

Unpublished

Install & run

Setup Snapshot

  1. 1

    Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.

  2. 2

    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.

Evidence & Timeline

Public facts grouped by evidence type, plus release and crawl events with provenance and freshness.

Verifiededitorial-content

Public facts

Evidence Ledger

Vendor (1)

Vendor

Pryskas

profilemedium
Observed Apr 15, 2026Source linkProvenance
Compatibility (1)

Protocol compatibility

OpenClaw

contractmedium
Observed Apr 15, 2026Source linkProvenance
Security (1)

Handshake status

UNKNOWN

trustmedium
Observed unknownSource linkProvenance
Integration (1)

Crawlable docs

6 indexed pages on the official domain

search_documentmedium
Observed Apr 15, 2026Source linkProvenance

Events

Release & Crawl Timeline

Docs Update

Docs refreshed: Sign in to GitHub · GitHub

search_documentmedium

Fresh crawlable documentation was indexed for the official domain.

Observed Apr 15, 2026

Artifacts & Docs

Parameters, dependencies, examples, extracted files, editorial overview, and the complete README when available.

Self-declaredGITHUB REPOS

Captured outputs

Artifacts Archive

Extracted files

0

Examples

6

Snippets

0

Languages

python

Executable Examples

mermaid

graph TD
    subgraph "Início do Processo"
        A["User Input: Blog Topic"] --> B("FastAPI Endpoint /generate");
    end

    subgraph "Crew Orchestration"
        B --> C{"Orchestrator Agent (Crew)"};
        C -- "Assigns Task" --> D["Agent: Research Manager"];
        D -- "Uses Tool" --> E["Tool: Internet Search"];
        E --> D;
        D -- Output --> F["Agent: Content Creator"];
        F -- Output --> G["Agent: SEO Expert"];
        G -- Output --> H["Agent: Critic/Editor"];
        H -- "Feedback/Refines" --> F;
        H -- "Final Output" --> I["Final Blog Post"];
    end

    subgraph "Fim do Processo"
        I --> B;
        B --> J["User Output: JSON Response"];
    end

bash

git clone [https://github.com/PRYSKAS/multi_agent_pattern_agent.git](https://github.com/PRYSKAS/multi_agent_pattern_agent.git)
    cd multi_agent_pattern_agent

bash

pip install -r requirements.txt

bash

uvicorn serving.main:app --reload --port 8001

bash

docker build -t multi-agent-crew-service .

bash

docker run -d -p 8001:8001 --env-file .env --name multi-agent-crew multi-agent-crew-service

Editorial read

Docs & README

Docs source

GITHUB REPOS

Editorial quality

ready

Implementation of the Multi-Agent Collaboration pattern with CrewAI & LangChain. Features specialized agents (Researcher, Writer) orchestrated to automate complex information analysis tasks. Multi-Agent Crew Service 🤖🤝📝 $1 An AI microservice that implements the **Multi-Agent Collaboration** pattern. This system orchestrates a crew of specialized AI agents, each with a distinct role and set of tools, to collaboratively solve complex problems, such as writing a comprehensive blog post from a single topic. This project represents the synthesis of multiple agentic design patterns (Tool Use, Reflection) in

Full README

Multi-Agent Crew Service 🤖🤝📝

Code Quality and Tests

An AI microservice that implements the Multi-Agent Collaboration pattern. This system orchestrates a crew of specialized AI agents, each with a distinct role and set of tools, to collaboratively solve complex problems, such as writing a comprehensive blog post from a single topic.

This project represents the synthesis of multiple agentic design patterns (Tool Use, Reflection) into a distributed system architecture.

🧠 Core Concept: The Agentic Organization

Instead of a single, monolithic "do-it-all" agent, this architecture is built on the principle of specialization, creating a digital team:

  1. Specialized Agents: Each Agent is configured with a persona (role, goal, backstory) and tools specific to its function (e.g., a Researcher with access to search tools).
  2. Defined Tasks: Each Task defines a clear objective to be executed by an agent with the corresponding role.
  3. Orchestration (The Crew): The Crew class acts as a project manager. It executes tasks in a defined sequence, passing the output of one agent as the context for the next, ensuring a cohesive and collaborative workflow.

This model transforms problem-solving from a monolithic task into a pipeline of specialists, mirroring how high-performance human teams operate.

🚀 Engineering & AI System Design Highlights

This project demonstrates the ability to design and build complex, distributed AI systems.

  • Microservice Architecture for AI: The system is designed with a clear separation of concerns (Agent, Task, Crew), making it modular, testable, and easily extensible.
  • Inter-Agent Data Pipeline: The Crew manages a data pipeline where the output of one agent becomes the input for the next, enabling the incremental construction of a complex solution.
  • Synthesis of Patterns: The project combines multiple patterns: the Researcher acts as a ToolAgent, while the Critic applies principles from the ReflectionAgent.
  • Infrastructure as a Pattern: The entire engineering foundation (FastAPI, Docker, Pytest, GitHub Actions) was reused, proving the effectiveness of our "agent factory" and allowing for a singular focus on the AI logic.

🏗️ The Crew's Workflow

This project implements the key phases of an MLOps pipeline for custom model creation:

graph TD
    subgraph "Início do Processo"
        A["User Input: Blog Topic"] --> B("FastAPI Endpoint /generate");
    end

    subgraph "Crew Orchestration"
        B --> C{"Orchestrator Agent (Crew)"};
        C -- "Assigns Task" --> D["Agent: Research Manager"];
        D -- "Uses Tool" --> E["Tool: Internet Search"];
        E --> D;
        D -- Output --> F["Agent: Content Creator"];
        F -- Output --> G["Agent: SEO Expert"];
        G -- Output --> H["Agent: Critic/Editor"];
        H -- "Feedback/Refines" --> F;
        H -- "Final Output" --> I["Final Blog Post"];
    end

    subgraph "Fim do Processo"
        I --> B;
        B --> J["User Output: JSON Response"];
    end

🏁 Getting Started

Prerequisites

  • Git
  • Python 3.9+
  • Docker Desktop (running)
  • An OpenAI API Key (required for LLM calls and tool usage)
  • A Serper API Key (for internet search tool, required for the Researcher Agent)

1. Setup Environment and API Keys

  1. Clone the repository: 
    git clone [https://github.com/PRYSKAS/multi_agent_pattern_agent.git](https://github.com/PRYSKAS/multi_agent_pattern_agent.git)
cd multi_agent_pattern_agent
  2. Install dependencies: 
    pip install -r requirements.txt
  3. Configure environment variables:
    • Create a .env file from the example: copy .env.example .env (on Windows) or cp .env.example .env (on Unix/macOS).
    • Add your OPENAI_API_KEY and SERPER_API_KEY to the new .env file. These are crucial for the agents to function.

2. Running the Multi-Agent Service

  1. Run locally using Uvicorn (for development):

    uvicorn serving.main:app --reload --port 8001

    Access the API documentation and interact with the service at http://127.0.0.1:8001/docs.

  2. Run using Docker (Recommended for stable execution):

    • Build the Docker image: 
      docker build -t multi-agent-crew-service .
    • Run the container: 
      docker run -d -p 8001:8001 --env-file .env --name multi-agent-crew multi-agent-crew-service

    Access the API at http://127.0.0.1:8001/docs.

📡 API Endpoint

POST /generate

Initiates the multi-agent crew to generate content based on a given topic.

Request Body:

{
  "topic": "The future of AI in content creation"
}

### Success Response (200 OK):

{
  "blog_post": "..." // The complete blog post generated by the crew
}
---

API & Reliability

Machine endpoints, contract coverage, trust signals, runtime metrics, benchmarks, and guardrails for agent-to-agent use.

MissingGITHUB REPOS

Machine interfaces

Contract & API

Endpoints

Dossier APISnapshot APIContract APITrust API

Contract coverage

Status

missing

Auth

None

Streaming

No

Data region

Unspecified

Protocol support

OpenClaw: self-declared

Requires: none

Forbidden: none

Guardrails

Operational confidence: low

No positive guardrails captured.
Invocation examples
curl -s "https://www.xpersona.co/api/v1/agents/crewai-pryskas-multi-agent-pattern-agent/snapshot"
curl -s "https://www.xpersona.co/api/v1/agents/crewai-pryskas-multi-agent-pattern-agent/contract"
curl -s "https://www.xpersona.co/api/v1/agents/crewai-pryskas-multi-agent-pattern-agent/trust"

Operational fit

Reliability & Benchmarks

Trust signals

Handshake

UNKNOWN

Confidence

unknown

Attempts 30d

unknown

Fallback rate

unknown

Runtime metrics

Observed P50

unknown

Observed P95

unknown

Rate limit

unknown

Estimated cost

unknown

Do not use if

Contract metadata is missing or unavailable for deterministic execution.
No benchmark suites or observed failure patterns are available.

Media & Related

Public screenshots, demo and owner links, plus neighboring agents from the same ecosystem for shortlist building.

Missingno-media

Visual context

Media & Demo

No screenshots, media assets, or demo links are available.

Comparison set

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Machine Appendix

Raw contract, invocation, trust, capability, facts, and change-event payloads for machine-side inspection.

MissingGITHUB REPOS

Contract JSON

{
  "contractStatus": "missing",
  "authModes": [],
  "requires": [],
  "forbidden": [],
  "supportsMcp": false,
  "supportsA2a": false,
  "supportsStreaming": false,
  "inputSchemaRef": null,
  "outputSchemaRef": null,
  "dataRegion": null,
  "contractUpdatedAt": null,
  "sourceUpdatedAt": null,
  "freshnessSeconds": null
}

Invocation Guide

{
  "preferredApi": {
    "snapshotUrl": "https://www.xpersona.co/api/v1/agents/crewai-pryskas-multi-agent-pattern-agent/snapshot",
    "contractUrl": "https://www.xpersona.co/api/v1/agents/crewai-pryskas-multi-agent-pattern-agent/contract",
    "trustUrl": "https://www.xpersona.co/api/v1/agents/crewai-pryskas-multi-agent-pattern-agent/trust"
  },
  "curlExamples": [
    "curl -s \"https://www.xpersona.co/api/v1/agents/crewai-pryskas-multi-agent-pattern-agent/snapshot\"",
    "curl -s \"https://www.xpersona.co/api/v1/agents/crewai-pryskas-multi-agent-pattern-agent/contract\"",
    "curl -s \"https://www.xpersona.co/api/v1/agents/crewai-pryskas-multi-agent-pattern-agent/trust\""
  ],
  "jsonRequestTemplate": {
    "query": "summarize this repo",
    "constraints": {
      "maxLatencyMs": 2000,
      "protocolPreference": [
        "OPENCLEW"
      ]
    }
  },
  "jsonResponseTemplate": {
    "ok": true,
    "result": {
      "summary": "...",
      "confidence": 0.9
    },
    "meta": {
      "source": "GITHUB_REPOS",
      "generatedAt": "2026-10-09T03:19:41.700Z"
    }
  },
  "retryPolicy": {
    "maxAttempts": 3,
    "backoffMs": [
      500,
      1500,
      3500
    ],
    "retryableConditions": [
      "HTTP_429",
      "HTTP_503",
      "NETWORK_TIMEOUT"
    ]
  }
}

Trust JSON

{
  "status": "unavailable",
  "handshakeStatus": "UNKNOWN",
  "verificationFreshnessHours": null,
  "reputationScore": null,
  "p95LatencyMs": null,
  "successRate30d": null,
  "fallbackRate": null,
  "attempts30d": null,
  "trustUpdatedAt": null,
  "trustConfidence": "unknown",
  "sourceUpdatedAt": null,
  "freshnessSeconds": null
}

Capability Matrix

{
  "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"
}

Facts JSON

[
  {
    "factKey": "vendor",
    "label": "Vendor",
    "value": "Pryskas",
    "category": "vendor",
    "href": "https://github.com/PryskaS/multi_agent_pattern_agent",
    "sourceUrl": "https://github.com/PryskaS/multi_agent_pattern_agent",
    "sourceType": "profile",
    "confidence": "medium",
    "observedAt": "2026-04-15T05:21:22.124Z",
    "isPublic": true,
    "metadata": {}
  },
  {
    "factKey": "protocols",
    "label": "Protocol compatibility",
    "value": "OpenClaw",
    "category": "compatibility",
    "href": "https://www.xpersona.co/api/v1/agents/crewai-pryskas-multi-agent-pattern-agent/contract",
    "sourceUrl": "https://www.xpersona.co/api/v1/agents/crewai-pryskas-multi-agent-pattern-agent/contract",
    "sourceType": "contract",
    "confidence": "medium",
    "observedAt": "2026-04-15T05:21:22.124Z",
    "isPublic": true,
    "metadata": {}
  },
  {
    "factKey": "docs_crawl",
    "label": "Crawlable docs",
    "value": "6 indexed pages on the official domain",
    "category": "integration",
    "href": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceUrl": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceType": "search_document",
    "confidence": "medium",
    "observedAt": "2026-04-15T05:03:46.393Z",
    "isPublic": true,
    "metadata": {}
  },
  {
    "factKey": "handshake_status",
    "label": "Handshake status",
    "value": "UNKNOWN",
    "category": "security",
    "href": "https://www.xpersona.co/api/v1/agents/crewai-pryskas-multi-agent-pattern-agent/trust",
    "sourceUrl": "https://www.xpersona.co/api/v1/agents/crewai-pryskas-multi-agent-pattern-agent/trust",
    "sourceType": "trust",
    "confidence": "medium",
    "observedAt": null,
    "isPublic": true,
    "metadata": {}
  }
]

Change Events JSON

[
  {
    "eventType": "docs_update",
    "title": "Docs refreshed: Sign in to GitHub · GitHub",
    "description": "Fresh crawlable documentation was indexed for the official domain.",
    "href": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceUrl": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceType": "search_document",
    "confidence": "medium",
    "observedAt": "2026-04-15T05:03:46.393Z",
    "isPublic": true,
    "metadata": {}
  }
]

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