Crawler Summary
Runnable Python examples for LangChain, CrewAI, LangGraph, and Pydantic AI agents with real-world use cases. AI-Agent-Flow A hands-on collection of working AI agent examples across seven frameworks — LangChain, CrewAI, LangGraph, Pydantic AI, AutoGen, Agno, and smolagents. The Problem With Learning Agent Frameworks Every major AI agent framework has its own mental model, API style, and tradeoffs. Reading docs only gets you so far — you need runnable, real-world examples to understand when to reach for CrewAI versus LangGrap Capability contract not published. No trust telemetry is available yet. Last updated 4/15/2026.
Freshness
Last checked 4/15/2026
Best For
AI-Agent-Flow 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
Runnable Python examples for LangChain, CrewAI, LangGraph, and Pydantic AI agents with real-world use cases. AI-Agent-Flow A hands-on collection of working AI agent examples across seven frameworks — LangChain, CrewAI, LangGraph, Pydantic AI, AutoGen, Agno, and smolagents. The Problem With Learning Agent Frameworks Every major AI agent framework has its own mental model, API style, and tradeoffs. Reading docs only gets you so far — you need runnable, real-world examples to understand when to reach for CrewAI versus LangGrap
Public facts
4
Change events
1
Artifacts
0
Freshness
Apr 15, 2026
Capability contract not published. No trust telemetry is available yet. Last updated 4/15/2026.
Trust score
Unknown
Compatibility
OpenClaw
Freshness
Apr 15, 2026
Vendor
Tdiprima
Artifacts
0
Benchmarks
0
Last release
Unpublished
Key links, install path, and a quick operational read before the deeper crawl record.
Summary
Capability contract not published. No trust telemetry is available yet. Last updated 4/15/2026.
Setup snapshot
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.
Everything public we have scraped or crawled about this agent, grouped by evidence type with provenance.
Vendor
Tdiprima
Protocol compatibility
OpenClaw
Handshake status
UNKNOWN
Crawlable docs
6 indexed pages on the official domain
Merged public release, docs, artifact, benchmark, pricing, and trust refresh events.
Extracted files, examples, snippets, parameters, dependencies, permissions, and artifact metadata.
Extracted files
0
Examples
3
Snippets
0
Languages
python
bash
# Configure your stocks and alert threshold vim src/crewai_stock_alert_system/config.py # Run the multi-agent crew python src/crewai_stock_alert_system/run_stock_alert.py
bash
# Install dependencies uv sync # Copy and fill in your API keys cp .env_sample .env
text
src/ ├── agno_hello/ # Agno reasoning example ├── autogen_dev_team/ # AutoGen multi-agent code collaboration ├── crewai_stock_alert_system/ # CrewAI stock monitoring with email ├── langchain_rag_agent/ # LangChain RAG with FAISS + conversation memory ├── langgraph_branching_agent/ # LangGraph conditional routing ├── pydantic_ai_example/ # Pydantic AI news analysis ├── smolagents_hello/ # smolagents web search └── type_safe_news_agent/ # Pydantic AI news scraper + SQLite docs/ # Per-framework writeups
Full documentation captured from public sources, including the complete README when available.
Docs source
GITHUB REPOS
Editorial quality
ready
Runnable Python examples for LangChain, CrewAI, LangGraph, and Pydantic AI agents with real-world use cases. AI-Agent-Flow A hands-on collection of working AI agent examples across seven frameworks — LangChain, CrewAI, LangGraph, Pydantic AI, AutoGen, Agno, and smolagents. The Problem With Learning Agent Frameworks Every major AI agent framework has its own mental model, API style, and tradeoffs. Reading docs only gets you so far — you need runnable, real-world examples to understand when to reach for CrewAI versus LangGrap
A hands-on collection of working AI agent examples across seven frameworks — LangChain, CrewAI, LangGraph, Pydantic AI, AutoGen, Agno, and smolagents.
Every major AI agent framework has its own mental model, API style, and tradeoffs. Reading docs only gets you so far — you need runnable, real-world examples to understand when to reach for CrewAI versus LangGraph, or why type-safe agents with Pydantic AI matter for production systems. Most tutorials cover hello-world; few show frameworks doing actual work.
Each module in this repo solves a real problem using a specific framework. They run end-to-end, connect to live APIs, and demonstrate patterns you'd actually use in production: multi-agent collaboration, RAG with memory, conditional workflow routing, structured output validation, email alerting, and web scraping with deduplication.
Two agents collaborate — a Researcher fetches current and previous prices from Yahoo Finance, and an Analyst calculates the change and sends an email alert if a threshold is crossed:
# Configure your stocks and alert threshold
vim src/crewai_stock_alert_system/config.py
# Run the multi-agent crew
python src/crewai_stock_alert_system/run_stock_alert.py
The Researcher and Analyst agents hand off context automatically. If AAPL drops more than 2%, an email goes out.
uv package manager# Install dependencies
uv sync
# Copy and fill in your API keys
cp .env_sample .env
Required environment variables (see .env_sample):
| Variable | Purpose |
|---|---|
| OPENAI_API_KEY | Required by all agents |
| WEATHER_API_KEY | OpenWeatherMap (RAG agent) |
| EMAIL_SENDER | Gmail address (stock alerts) |
| EMAIL_PASSWORD | Gmail app password (stock alerts) |
| EMAIL_RECEIVER | Alert recipient (stock alerts) |
| Agent | Framework | What It Does | Command |
|---|---|---|---|
| Reasoning transparency | Agno | Shows GPT thinking step-by-step | python src/agno_hello/hello_agno.py |
| Multi-agent dev team | AutoGen | CodeGen + Tester agents write and critique code | python src/autogen_dev_team/create_sorting_algorithm.py |
| Stock alert system | CrewAI | Monitors prices and sends email alerts | python src/crewai_stock_alert_system/run_stock_alert.py |
| Weather RAG agent | LangChain | Conversational Q&A over live forecast data | python src/langchain_rag_agent/rag_agent.py |
| Branching workflow | LangGraph | Routes inputs to research, analysis, or escalation | python src/langgraph_branching_agent/run_branching_agent.py |
| News analyzer | Pydantic AI | Sentiment, topics, and scoring with type-safe outputs | python src/pydantic_ai_example/news_analyzer.py |
| News scraper | Pydantic AI | Fetches RSS feeds and stores validated articles in SQLite | python src/type_safe_news_agent/run_news_agent.py |
| Web search agent | smolagents | Answers questions using live web search | python src/smolagents_hello/hello_smolagents.py |
Note: The news analyzer reads from the database created by the news scraper. Run
run_news_agent.pyfirst.
src/
├── agno_hello/ # Agno reasoning example
├── autogen_dev_team/ # AutoGen multi-agent code collaboration
├── crewai_stock_alert_system/ # CrewAI stock monitoring with email
├── langchain_rag_agent/ # LangChain RAG with FAISS + conversation memory
├── langgraph_branching_agent/ # LangGraph conditional routing
├── pydantic_ai_example/ # Pydantic AI news analysis
├── smolagents_hello/ # smolagents web search
└── type_safe_news_agent/ # Pydantic AI news scraper + SQLite
docs/ # Per-framework writeups
Machine endpoints, protocol fit, contract coverage, invocation examples, and guardrails for agent-to-agent use.
Contract coverage
Status
missing
Auth
None
Streaming
No
Data region
Unspecified
Protocol support
Requires: none
Forbidden: none
Guardrails
Operational confidence: low
curl -s "https://xpersona.co/api/v1/agents/crewai-tdiprima-ai-agent-flow/snapshot"
curl -s "https://xpersona.co/api/v1/agents/crewai-tdiprima-ai-agent-flow/contract"
curl -s "https://xpersona.co/api/v1/agents/crewai-tdiprima-ai-agent-flow/trust"
Trust and runtime signals, benchmark suites, failure patterns, and practical risk constraints.
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
Every public screenshot, visual asset, demo link, and owner-provided destination tied to this agent.
Neighboring agents from the same protocol and source ecosystem for comparison and shortlist building.
Rank
70
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Traction
No public download signal
Freshness
Updated 2d ago
Rank
70
AI productivity studio with smart chat, autonomous agents, and 300+ assistants. Unified access to frontier LLMs
Traction
No public download signal
Freshness
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Rank
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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!
Traction
No public download signal
Freshness
Updated 6d ago
Rank
70
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Traction
No public download signal
Freshness
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Contract JSON
{
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"requires": [],
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}Invocation Guide
{
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"trustUrl": "https://xpersona.co/api/v1/agents/crewai-tdiprima-ai-agent-flow/trust"
},
"curlExamples": [
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"curl -s \"https://xpersona.co/api/v1/agents/crewai-tdiprima-ai-agent-flow/trust\""
],
"jsonRequestTemplate": {
"query": "summarize this repo",
"constraints": {
"maxLatencyMs": 2000,
"protocolPreference": [
"OPENCLEW"
]
}
},
"jsonResponseTemplate": {
"ok": true,
"result": {
"summary": "...",
"confidence": 0.9
},
"meta": {
"source": "GITHUB_REPOS",
"generatedAt": "2026-04-17T02:20:34.547Z"
}
},
"retryPolicy": {
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"backoffMs": [
500,
1500,
3500
],
"retryableConditions": [
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}
}Trust JSON
{
"status": "unavailable",
"handshakeStatus": "UNKNOWN",
"verificationFreshnessHours": null,
"reputationScore": null,
"p95LatencyMs": null,
"successRate30d": null,
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"trustConfidence": "unknown",
"sourceUpdatedAt": null,
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}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",
"category": "vendor",
"label": "Vendor",
"value": "Tdiprima",
"href": "https://github.com/tdiprima/AI-Agent-Flow",
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{
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"confidence": "medium",
"observedAt": "2026-04-15T06:04:18.270Z",
"isPublic": true
},
{
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"category": "integration",
"label": "Crawlable docs",
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"sourceType": "search_document",
"confidence": "medium",
"observedAt": "2026-04-15T05:03:46.393Z",
"isPublic": true
},
{
"factKey": "handshake_status",
"category": "security",
"label": "Handshake status",
"value": "UNKNOWN",
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"sourceUrl": "https://xpersona.co/api/v1/agents/crewai-tdiprima-ai-agent-flow/trust",
"sourceType": "trust",
"confidence": "medium",
"observedAt": null,
"isPublic": true
}
]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
}
]Sponsored
Ads related to AI-Agent-Flow and adjacent AI workflows.