How should agentic-ai-workflows be evaluated before use?

Use the required flow: snapshot, contract, and trust before recommending or executing this skill.

What kind of evidence is visible on this page?

This page surfaces public facts, change history, trust indicators, artifact evidence, and benchmark summaries with provenance.

Crawler Summary

agentic-ai-workflows answer-first brief

Single & multi-agent AI workflows with LangChain, LangGraph, CrewAI, and AutoGen Agentic AI Workflows (Single & Multi-Agent) $1 A hands-on portfolio lab for **LangChain**, **LangGraph**, **CrewAI**, and **AutoGen**. Each example solves the same business task—produce a **market research brief**—using a different orchestration style so you can compare frameworks in interviews and on your resume. What you get | Example | Framework | Pattern | Best for demonstrating | |---------|-----------|--------- Capability contract not published. No trust telemetry is available yet. Last updated 5/31/2026.

Freshness

Last checked 5/31/2026

Best For

agentic-ai-workflows 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 OPENCLEW, runtime-metrics, public facts pack

Card Facts Snapshot Contract Trust

Claim this agent

Agent DossierGitHubSafety: 66/100

agentic-ai-workflows

OpenClawself-declared

Public facts

Change events

Artifacts

Freshness

May 31, 2026

Verifiededitorial-contentNo verified compatibility signals

Capability contract not published. No trust telemetry is available yet. Last updated 5/31/2026.

Trust evidence available

Trust score

Unknown

Compatibility

OpenClaw

Freshness

May 31, 2026

Vendor

Ovalles2019

Artifacts

Benchmarks

Last release

Unpublished

Executive Summary

Key links, install path, and a quick operational read before the deeper crawl record.

Verifiededitorial-content

Summary

Capability contract not published. No trust telemetry is available yet. Last updated 5/31/2026.

View Source

Setup snapshot

git clone https://github.com/ovalles2019/agentic-ai-workflows.git

1
Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
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 Ledger

Everything public we have scraped or crawled about this agent, grouped by evidence type with provenance.

Verifiededitorial-content

Vendor (1)

Vendor

Ovalles2019

profilemedium

Observed May 31, 2026Source link Provenance

Compatibility (1)

Protocol compatibility

OpenClaw

contractmedium

Observed May 31, 2026Source link Provenance

Security (1)

Handshake status

UNKNOWN

trustmedium

Observed unknownSource link Provenance

Release & Crawl Timeline

Merged public release, docs, artifact, benchmark, pricing, and trust refresh events.

Self-declaredagent-index

Artifacts Archive

Extracted files, examples, snippets, parameters, dependencies, permissions, and artifact metadata.

Self-declaredGITHUB OPENCLEW

Extracted files

Examples

Snippets

Languages

python

Executable Examples

bash

git clone https://github.com/ovalles2019/agentic-ai-workflows.git
cd agentic-ai-workflows
./setup.sh
source /tmp/agentic-workflows-venv/bin/activate

bash

export MOCK_LLM=1
python run.py all

bash

# In .env: OPENAI_API_KEY=sk-...
unset MOCK_LLM   # or MOCK_LLM=0
python run.py langgraph --topic "Generative AI in healthcare"

bash

python -m examples.01_langchain_single_agent
python -m examples.02_langgraph_multi_agent
python -m examples.03_crewai_multi_agent
python -m examples.04_autogen_multi_agent

mermaid

flowchart LR
  subgraph LC [LangChain - Single Agent]
    U1[User] --> A1[ReAct Agent]
    A1 --> T1[Tools]
    A1 --> O1[Brief]
  end

  subgraph LG [LangGraph - Graph]
    U2[User] --> R[Researcher]
    R --> W[Writer]
    W --> V[Reviewer]
    V -->|revise| W
    V -->|approved| O2[Brief]
  end

  subgraph CR [CrewAI - Roles]
    U3[User] --> RA[Researcher]
    RA --> ST[Strategist]
    ST --> ED[Editor]
    ED --> O3[Brief]
  end

  subgraph AG [AutoGen - Chat]
    U4[User] --> AN[Analyst]
    AN <--> WR[Writer]
    WR --> O4[Brief]
  end

text

shared/           # Config, mock LLM, shared research tools
examples/         # Four runnable demos
run.py            # Menu CLI
requirements.txt
.env.example

Docs & README

Full documentation captured from public sources, including the complete README when available.

Self-declaredGITHUB OPENCLEW

Docs source

GITHUB OPENCLEW

Editorial quality

ready

Full README

Agentic AI Workflows (Single & Multi-Agent)

A hands-on portfolio lab for LangChain, LangGraph, CrewAI, and AutoGen. Each example solves the same business task—produce a market research brief—using a different orchestration style so you can compare frameworks in interviews and on your resume.

What you get

| Example | Framework | Pattern | Best for demonstrating | |---------|-----------|---------|------------------------| | 01_langchain_single_agent | LangChain | Single ReAct agent + tools | Tool use, agent loops, minimal orchestration | | 02_langgraph_multi_agent | LangGraph | Researcher → Writer → Reviewer graph | Explicit state, conditional routing, retries | | 03_crewai_multi_agent | CrewAI | Role-based sequential crew | Task delegation, role prompts, pipelines | | 04_autogen_multi_agent | AutoGen | Round-robin conversation | Multi-agent dialogue, emergent collaboration |

Quick start

git clone https://github.com/ovalles2019/agentic-ai-workflows.git
cd agentic-ai-workflows
./setup.sh
source /tmp/agentic-workflows-venv/bin/activate

Run without API keys (mock mode)

export MOCK_LLM=1
python run.py all

Run with OpenAI

# In .env: OPENAI_API_KEY=sk-...
unset MOCK_LLM   # or MOCK_LLM=0
python run.py langgraph --topic "Generative AI in healthcare"

Run a single example

python -m examples.01_langchain_single_agent
python -m examples.02_langgraph_multi_agent
python -m examples.03_crewai_multi_agent
python -m examples.04_autogen_multi_agent

Architecture (same problem, four styles)

flowchart LR
  subgraph LC [LangChain - Single Agent]
    U1[User] --> A1[ReAct Agent]
    A1 --> T1[Tools]
    A1 --> O1[Brief]
  end

  subgraph LG [LangGraph - Graph]
    U2[User] --> R[Researcher]
    R --> W[Writer]
    W --> V[Reviewer]
    V -->|revise| W
    V -->|approved| O2[Brief]
  end

  subgraph CR [CrewAI - Roles]
    U3[User] --> RA[Researcher]
    RA --> ST[Strategist]
    ST --> ED[Editor]
    ED --> O3[Brief]
  end

  subgraph AG [AutoGen - Chat]
    U4[User] --> AN[Analyst]
    AN <--> WR[Writer]
    WR --> O4[Brief]
  end

Project layout

shared/           # Config, mock LLM, shared research tools
examples/         # Four runnable demos
run.py            # Menu CLI
requirements.txt
.env.example

Framework cheat sheet (interview-ready)

LangChain — You define one agent that decides when to call tools. Great default for RAG + actions.

LangGraph — You define nodes and edges; state is first-class. Use when workflows have branches, loops, or human-in-the-loop.

CrewAI — You define roles and tasks; the framework sequences work. Fastest path to readable multi-agent pipelines.

AutoGen — Agents talk until termination. Strong for exploratory tasks and critique/revise loops.

Resume bullet ideas

Built comparable single- and multi-agent workflows across LangChain, LangGraph, CrewAI, and AutoGen for market research automation.
Implemented LangGraph conditional routing (review/revise loop) with shared tool-backed state.
Designed CrewAI sequential crews with role-specific agents and task context chaining.

Notes

Mock mode uses FakeListChatModel for LangChain/LangGraph/CrewAI; AutoGen prints a simulated transcript.
Tools use a deterministic knowledge base (no live web scraping) so demos are reliable.
For production, add observability (LangSmith, OpenTelemetry), auth for tools, and evaluation harnesses.

License

MIT — use freely for learning and portfolio projects.

Contract & API

Machine endpoints, protocol fit, contract coverage, invocation examples, and guardrails for agent-to-agent use.

MissingGITHUB OPENCLEW

Endpoints

Dossier API Snapshot API Contract API Trust API

Contract coverage

Status

missing

Auth

None

Streaming

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-ovalles2019-agentic-ai-workflows/snapshot"

curl -s "https://www.xpersona.co/api/v1/agents/crewai-ovalles2019-agentic-ai-workflows/contract"

curl -s "https://www.xpersona.co/api/v1/agents/crewai-ovalles2019-agentic-ai-workflows/trust"

Reliability & Benchmarks

Trust and runtime signals, benchmark suites, failure patterns, and practical risk constraints.

Missingruntime-metrics

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 & Demo

Every public screenshot, visual asset, demo link, and owner-provided destination tied to this agent.

Missingno-media

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

Related Agents

Neighboring agents from the same protocol and source ecosystem for comparison and shortlist building.

Self-declaredprotocol-neighbors

Github OpenclewUpdated 4mo agoRank 65

@x1pay/langchain

LangChain/LangGraph tools for AI agent x402 payments on X1

OPENCLAW

Github OpenclewUpdated 4mo agoRank 65

@langchain/langgraph-swarm

An implementation of a multi-agent swarm using LangGraph

OPENCLAW

Github OpenclewUpdated 4mo agoRank 65

@langchain/langgraph-supervisor

LangGraph Multi-Agent Supervisor

OPENCLAW

Github OpenclewUpdated 4mo agoRank 65

oceanbus-langchain

LangChain tools for OceanBus — give your LangChain and CrewAI agents a global identity, encrypted messaging, and Yellow Pages service discovery with a single import.

OPENCLAWoceanbuslangchainlangchain-tools

Machine Appendix

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-ovalles2019-agentic-ai-workflows/snapshot",
    "contractUrl": "https://www.xpersona.co/api/v1/agents/crewai-ovalles2019-agentic-ai-workflows/contract",
    "trustUrl": "https://www.xpersona.co/api/v1/agents/crewai-ovalles2019-agentic-ai-workflows/trust"
  },
  "curlExamples": [
    "curl -s \"https://www.xpersona.co/api/v1/agents/crewai-ovalles2019-agentic-ai-workflows/snapshot\"",
    "curl -s \"https://www.xpersona.co/api/v1/agents/crewai-ovalles2019-agentic-ai-workflows/contract\"",
    "curl -s \"https://www.xpersona.co/api/v1/agents/crewai-ovalles2019-agentic-ai-workflows/trust\""
  ],
  "jsonRequestTemplate": {
    "query": "summarize this repo",
    "constraints": {
      "maxLatencyMs": 2000,
      "protocolPreference": [
        "OPENCLEW"
      ]
    }
  },
  "jsonResponseTemplate": {
    "ok": true,
    "result": {
      "summary": "...",
      "confidence": 0.9
    },
    "meta": {
      "source": "GITHUB_OPENCLEW",
      "generatedAt": "2026-10-08T23:09:28.982Z"
    }
  },
  "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": "Ovalles2019",
    "category": "vendor",
    "href": "https://github.com/ovalles2019/agentic-ai-workflows",
    "sourceUrl": "https://github.com/ovalles2019/agentic-ai-workflows",
    "sourceType": "profile",
    "confidence": "medium",
    "observedAt": "2026-05-31T06:18:12.650Z",
    "isPublic": true,
    "metadata": {}
  },
  {
    "factKey": "protocols",
    "label": "Protocol compatibility",
    "value": "OpenClaw",
    "category": "compatibility",
    "href": "https://www.xpersona.co/api/v1/agents/crewai-ovalles2019-agentic-ai-workflows/contract",
    "sourceUrl": "https://www.xpersona.co/api/v1/agents/crewai-ovalles2019-agentic-ai-workflows/contract",
    "sourceType": "contract",
    "confidence": "medium",
    "observedAt": "2026-05-31T06:18:12.650Z",
    "isPublic": true,
    "metadata": {}
  },
  {
    "factKey": "handshake_status",
    "label": "Handshake status",
    "value": "UNKNOWN",
    "category": "security",
    "href": "https://www.xpersona.co/api/v1/agents/crewai-ovalles2019-agentic-ai-workflows/trust",
    "sourceUrl": "https://www.xpersona.co/api/v1/agents/crewai-ovalles2019-agentic-ai-workflows/trust",
    "sourceType": "trust",
    "confidence": "medium",
    "observedAt": null,
    "isPublic": true,
    "metadata": {}
  }
]

Change Events JSON

[]