What can awesome-openclaw-examples do?

real-world openclaw agent example curation and documentation, clawhub skill discovery and integration pattern documentation, agent prompt engineering and template library, agent workflow automation use case library, agent performance benchmarking and kpi tracking, self-hosted agent deployment and configuration examples, multi-agent coordination and workflow orchestration patterns, agent testing and validation framework examples

awesome-openclaw-examples

AgentFree

Awesome OpenClaw examples: 100 tested, real-world OpenClaw usecases built with ClawHub skills, runnable scripts, prompts, KPIs, and sample outputs.

Open Source

/ 100

8 capabilities

Capabilities8 decomposed

real-world openclaw agent example curation and documentation

Medium confidence

Curates and documents 100+ tested, production-ready OpenClaw agent implementations across diverse use cases (automation, chatbots, workflows). Each example includes runnable scripts, prompt templates, performance KPIs, and sample outputs, enabling developers to understand agent patterns through concrete, executable reference implementations rather than abstract documentation.

Solves for

I need to see how other developers have built OpenClaw agents for my specific use caseI want to understand best practices for structuring OpenClaw skills and workflowsI need working code examples I can adapt for my own agent implementationI want to benchmark expected performance and outputs for similar agent tasks

Best for

developers building their first OpenClaw agents

teams evaluating OpenClaw as an agent framework

engineers migrating from other agent frameworks to OpenClaw

Requires

OpenClaw framework installed and configured

ClawHub account or self-hosted ClawHub instance for skill access

Python 3.8+ or Node.js 14+ depending on example language

Limitations

Examples are static snapshots — may not reflect latest OpenClaw API changes

Limited to use cases covered in the 100 examples; niche workflows may lack reference implementations

No interactive playground or live execution environment; examples require local setup

What makes it unique

Provides 100+ tested, end-to-end agent examples with actual outputs and KPIs rather than abstract tutorials — each example is a complete, runnable artifact that demonstrates skill composition, prompt engineering, and performance characteristics in production contexts

vs alternatives

More comprehensive and production-focused than OpenClaw's official documentation, offering real-world patterns and performance data that help developers avoid common pitfalls when building multi-skill agents

clawhub skill discovery and integration pattern documentation

Medium confidence

Documents how to discover, select, and compose ClawHub skills within OpenClaw agents through 100+ examples that demonstrate skill chaining, parameter passing, and error handling patterns. Examples show concrete integration points between agent orchestration logic and skill execution, enabling developers to understand the skill-to-agent binding architecture.

Solves for

I need to understand which ClawHub skills are available and how to use them in my agentI want to see examples of skill composition — how to chain multiple skills togetherI need to understand how to pass data between skills and handle skill failuresI want to learn the patterns for custom skill development within OpenClaw

Best for

developers new to the ClawHub skill ecosystem

teams building multi-skill agents requiring complex orchestration

engineers extending OpenClaw with custom skills

Requires

ClawHub instance (cloud or self-hosted) with skill registry access

OpenClaw agent framework configured with ClawHub connector

Understanding of skill schema and parameter types

Limitations

Examples may use deprecated or outdated ClawHub skills if not actively maintained

Skill availability varies by ClawHub instance; self-hosted instances may have different skill sets

No automated skill compatibility checking — developers must manually verify skill versions

What makes it unique

Demonstrates skill composition through executable examples showing actual data flow between skills, error handling, and parameter mapping — not just skill documentation but working orchestration patterns that reveal the skill binding and execution model

vs alternatives

More practical than ClawHub's skill catalog alone by showing how skills work together in real agents, including failure modes and data transformation patterns that developers encounter in production

agent prompt engineering and template library

Medium confidence

Provides 100+ tested prompt templates and engineering patterns for OpenClaw agents, including system prompts, task decomposition patterns, few-shot examples, and output formatting instructions. Each example includes the actual prompts used, enabling developers to understand how to structure agent instructions for different task types and skill combinations.

Solves for

I need effective system prompts for my OpenClaw agentI want to see how to structure prompts for multi-step task decompositionI need examples of few-shot prompting for specific agent behaviorsI want to understand how to guide agents to use specific skills effectively

Best for

prompt engineers optimizing agent behavior

developers new to agent-based LLM applications

teams building domain-specific agents

Requires

OpenClaw agent framework

LLM API access (OpenAI or compatible provider)

Understanding of prompt engineering fundamentals

Limitations

Prompts are optimized for specific LLM models (likely GPT-4/3.5); effectiveness varies with different model backends

Prompt engineering is empirical — examples may not transfer directly to different domains or skill sets

No automated prompt optimization or A/B testing framework included

What makes it unique

Provides actual prompts used in production agents with documented results, showing the relationship between prompt structure and agent behavior — not generic prompt advice but specific, tested templates for OpenClaw skill orchestration

vs alternatives

More specific to agent-based workflows than general prompt engineering guides, demonstrating how to structure prompts for multi-skill orchestration and task decomposition rather than single-turn LLM interactions

agent workflow automation use case library

Medium confidence

Catalogs 100+ real-world automation workflows implemented with OpenClaw agents, spanning domains like customer service, content generation, data processing, and business process automation. Each use case includes the complete workflow definition, skill composition, and performance metrics, enabling developers to understand how agents solve specific business problems.

Solves for

I need to understand what types of workflows OpenClaw agents can automateI want to see how other teams have solved similar business problems with agentsI need to estimate performance and cost for automating my specific workflowI want to understand the skill and infrastructure requirements for my use case

Best for

business teams evaluating agent automation for their workflows

developers building agents for specific industries or domains

teams migrating from traditional automation tools to agent-based approaches

Requires

OpenClaw framework and ClawHub skills matching the use case

Understanding of the specific business domain and workflow

Infrastructure to run agents (cloud or self-hosted)

Limitations

Use cases are domain-specific; applicability to different industries varies

Performance metrics may not reflect real-world conditions in different environments or at different scales

Examples assume specific skill availability; custom skill development may be required for some workflows

What makes it unique

Provides complete, end-to-end workflow examples with actual performance data and business context, showing how agents solve real problems rather than abstract capability demonstrations — each use case includes the full implementation path from requirements to production metrics

vs alternatives

More practical and business-focused than technical agent documentation, offering concrete ROI data and workflow patterns that help teams make adoption decisions and plan implementations

agent performance benchmarking and kpi tracking

Medium confidence

Includes performance metrics, KPIs, and benchmarking data for 100+ agent implementations, documenting execution time, cost per task, success rates, and skill utilization patterns. Enables developers to understand performance characteristics of different agent architectures and skill compositions, supporting capacity planning and optimization decisions.

Solves for

I need to understand the performance characteristics of OpenClaw agents for my use caseI want to benchmark my agent's performance against similar implementationsI need to estimate costs and resource requirements for scaling my agentI want to identify performance bottlenecks and optimization opportunities

Best for

teams optimizing agent performance and cost

developers planning infrastructure for agent deployment

builders evaluating agent frameworks based on performance characteristics

Requires

OpenClaw framework with performance monitoring enabled

LLM API access with usage tracking

Infrastructure to run agents and collect metrics

Limitations

Performance metrics are historical snapshots; actual performance varies with LLM model versions, skill implementations, and infrastructure

Benchmarks may not reflect performance under production load or with different data distributions

Limited guidance on performance optimization strategies or tuning parameters

What makes it unique

Provides actual performance data from production agent implementations with documented skill compositions and configurations, enabling direct performance comparison rather than theoretical estimates — metrics include execution time, cost, and success rates across diverse use cases

vs alternatives

More comprehensive than generic LLM benchmarks by including agent-specific metrics like skill utilization, orchestration overhead, and multi-step task performance that reflect real agent behavior

self-hosted agent deployment and configuration examples

Medium confidence

Demonstrates self-hosted deployment patterns for OpenClaw agents, including containerization, infrastructure setup, skill registry configuration, and operational considerations. Examples show how to deploy agents on-premises or in private cloud environments, with documentation of configuration options, scaling strategies, and monitoring setup.

Solves for

I need to deploy OpenClaw agents in my own infrastructureI want to understand the operational requirements for running agents at scaleI need to configure a self-hosted ClawHub instance for my organizationI want to understand security and compliance considerations for agent deployment

Best for

enterprises deploying agents in regulated environments

teams requiring on-premises or private cloud deployment

developers building self-hosted agent platforms

Requires

Container runtime (Docker) or Kubernetes cluster

Infrastructure to host OpenClaw and ClawHub (cloud VM, on-premises server, or Kubernetes)

Database for agent state and skill registry (PostgreSQL or compatible)

Limitations

Deployment examples may be specific to certain cloud providers or infrastructure platforms

Limited guidance on high-availability and disaster recovery configurations

No automated deployment or infrastructure-as-code templates provided

What makes it unique

Provides complete self-hosted deployment examples with operational considerations, not just installation instructions — includes scaling strategies, monitoring setup, and infrastructure patterns for production agent systems

vs alternatives

More comprehensive than OpenClaw's basic installation guide by covering operational aspects like monitoring, scaling, and multi-tenant configuration that teams need for production deployments

multi-agent coordination and workflow orchestration patterns

Medium confidence

Documents patterns for coordinating multiple OpenClaw agents within larger workflows, including agent-to-agent communication, state sharing, task delegation, and result aggregation. Examples demonstrate how to structure complex automation scenarios where multiple agents work together, with patterns for synchronization, error handling, and result validation.

Solves for

I need to coordinate multiple agents working on different parts of a workflowI want to understand how to pass results between agentsI need patterns for handling failures and retries in multi-agent workflowsI want to see examples of agents delegating tasks to other agents

Best for

teams building complex, multi-stage automation workflows

developers implementing agent hierarchies or specialized agent teams

builders creating agent-based business process automation

Requires

OpenClaw framework with multi-agent support

External state store or message queue for agent coordination (Redis, RabbitMQ, etc.)

Workflow orchestration layer (could be custom or third-party)

Limitations

Multi-agent coordination adds complexity; examples may not cover all edge cases

State management between agents requires external persistence; no built-in solution provided

Limited guidance on deadlock prevention or circular dependency handling

What makes it unique

Provides executable examples of multi-agent workflows with documented state management and synchronization patterns, showing how agents coordinate rather than just describing the concept — includes error handling and result aggregation patterns

vs alternatives

More practical than theoretical multi-agent frameworks by demonstrating concrete coordination patterns in OpenClaw, with working examples of agent communication and state sharing

agent testing and validation framework examples

Medium confidence

Demonstrates testing strategies for OpenClaw agents, including unit testing individual skills, integration testing skill compositions, and end-to-end testing of complete workflows. Examples show how to validate agent outputs, test error handling, and ensure deterministic behavior where needed, with patterns for test data generation and result validation.

Solves for

I need to test my OpenClaw agent before deploying to productionI want to validate that my agent produces correct outputs for different inputsI need to test error handling and recovery in my agentI want to ensure my agent's behavior is consistent and predictable

Best for

developers building production-grade agents

teams implementing CI/CD pipelines for agent code

builders ensuring quality and reliability of agent systems

Requires

OpenClaw framework with test support

Testing framework (pytest, Jest, or similar)

Test data and fixtures for your specific use case

Limitations

Testing LLM-based agents is inherently non-deterministic; examples may not cover all failure modes

No built-in test framework provided; examples use standard testing tools

Limited guidance on testing agents with external API dependencies

What makes it unique

Provides concrete testing examples for agent workflows including skill composition testing and end-to-end validation patterns, addressing the specific challenges of testing non-deterministic LLM-based systems

vs alternatives

More specialized than generic software testing guides by addressing agent-specific testing challenges like LLM non-determinism, skill composition validation, and multi-step workflow verification

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

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Best For

✓developers building their first OpenClaw agents
✓teams evaluating OpenClaw as an agent framework
✓engineers migrating from other agent frameworks to OpenClaw
✓builders prototyping multi-skill workflows
✓developers new to the ClawHub skill ecosystem
✓teams building multi-skill agents requiring complex orchestration
✓engineers extending OpenClaw with custom skills
✓builders evaluating skill coverage for their use case

Known Limitations

⚠Examples are static snapshots — may not reflect latest OpenClaw API changes
⚠Limited to use cases covered in the 100 examples; niche workflows may lack reference implementations
⚠No interactive playground or live execution environment; examples require local setup
⚠Documentation quality and depth varies across examples depending on contributor effort
⚠Examples may use deprecated or outdated ClawHub skills if not actively maintained
⚠Skill availability varies by ClawHub instance; self-hosted instances may have different skill sets

Requirements

OpenClaw framework installed and configuredClawHub account or self-hosted ClawHub instance for skill accessPython 3.8+ or Node.js 14+ depending on example languageAPI credentials for external services used in examples (OpenAI, etc.)ClawHub instance (cloud or self-hosted) with skill registry accessOpenClaw agent framework configured with ClawHub connectorUnderstanding of skill schema and parameter typesAPI credentials for skills requiring external service authentication

Input / Output

Accepts: example metadata (use case category, skill requirements), prompt templates (text), configuration files (YAML/JSON), skill names and parameter schemas (structured metadata), agent input data (text, structured objects), skill configuration (JSON/YAML), task descriptions (text), skill definitions and capabilities (structured metadata), example inputs and desired outputs (text, structured data), workflow input data (varies by use case: text, structured data, files), workflow configuration (YAML/JSON), skill parameters and settings (structured data), agent execution logs (text, JSON), LLM API usage data (structured data), skill execution traces (structured data), task input and output data (varies by use case), deployment configuration files (YAML, JSON), infrastructure specifications (CPU, memory, storage requirements), skill registry configuration (structured data), security and compliance requirements (text, structured data), workflow definitions with agent tasks (YAML, JSON), agent input data and task specifications (structured data), state and context shared between agents (structured data), error and retry policies (structured data), test cases and scenarios (structured data, text), test data and fixtures (varies by use case), expected outputs and validation rules (structured data), error scenarios and edge cases (text, structured data)

Produces: executable agent scripts (Python/Shell), sample agent outputs (text, structured data), performance metrics and KPIs (JSON), documentation and implementation guides (Markdown), skill execution results (varies by skill type), agent output combining multiple skill results (text, structured data), execution logs and error traces (text), system prompts (text), task decomposition instructions (text), output formatting templates (text, JSON schema), few-shot examples (text), workflow results (varies by use case: text, structured data, files), execution logs and audit trails (text, JSON), performance metrics and KPIs (JSON, CSV), cost and resource utilization reports (structured data), performance metrics (JSON, CSV), KPI reports (text, structured data), cost analysis and projections (structured data), performance comparison charts and visualizations (data for charting), deployment scripts and configurations (Shell, YAML), infrastructure documentation (Markdown, diagrams), operational runbooks (text), monitoring and alerting configurations (YAML, JSON), aggregated results from multiple agents (structured data), workflow execution logs and traces (text, JSON), state snapshots and checkpoints (structured data), error reports and recovery actions (text, structured data), test results and coverage reports (text, JSON), validation reports (text, structured data), error logs and failure analysis (text, JSON), performance test results (structured data)

UnfragileRank

Adoption21%(30% weight)

Quality45%(25% weight)

Ecosystem70%(20% weight)

Match Graph10%(20% weight)

Freshness75%(5% weight)

UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.

Type: Agent

8 capabilities

Visit awesome-openclaw-examples→

Repository Details

107

Stars

Forks

Shell

Language

MIT

License

Topics

ai-agentsai-automationawesome-listclawhubdeveloper-toolsllmopenaiopenclawopenclaw-pluginopenclaw-skillsproductivityself-hostedworkflow-automation

Last commit: Apr 5, 2026

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Awesome OpenClaw examples: 100 tested, real-world OpenClaw usecases built with ClawHub skills, runnable scripts, prompts, KPIs, and sample outputs.

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Capabilities8 decomposed

real-world openclaw agent example curation and documentation

Medium confidence

Solves for

Best for

developers building their first OpenClaw agents

teams evaluating OpenClaw as an agent framework

engineers migrating from other agent frameworks to OpenClaw

Requires

OpenClaw framework installed and configured

ClawHub account or self-hosted ClawHub instance for skill access

Python 3.8+ or Node.js 14+ depending on example language

Limitations

Examples are static snapshots — may not reflect latest OpenClaw API changes

Limited to use cases covered in the 100 examples; niche workflows may lack reference implementations

No interactive playground or live execution environment; examples require local setup

What makes it unique

vs alternatives

clawhub skill discovery and integration pattern documentation

Medium confidence

Solves for

Best for

developers new to the ClawHub skill ecosystem

teams building multi-skill agents requiring complex orchestration

engineers extending OpenClaw with custom skills

Requires

ClawHub instance (cloud or self-hosted) with skill registry access

OpenClaw agent framework configured with ClawHub connector

Understanding of skill schema and parameter types

Limitations

Examples may use deprecated or outdated ClawHub skills if not actively maintained

Skill availability varies by ClawHub instance; self-hosted instances may have different skill sets

No automated skill compatibility checking — developers must manually verify skill versions

What makes it unique

vs alternatives

More practical than ClawHub's skill catalog alone by showing how skills work together in real agents, including failure modes and data transformation patterns that developers encounter in production

agent prompt engineering and template library

Medium confidence

Solves for

Best for

prompt engineers optimizing agent behavior

developers new to agent-based LLM applications

teams building domain-specific agents

Requires

OpenClaw agent framework

LLM API access (OpenAI or compatible provider)

Understanding of prompt engineering fundamentals

Limitations

Prompts are optimized for specific LLM models (likely GPT-4/3.5); effectiveness varies with different model backends

Prompt engineering is empirical — examples may not transfer directly to different domains or skill sets

No automated prompt optimization or A/B testing framework included

What makes it unique

vs alternatives

agent workflow automation use case library

Medium confidence

Solves for

Best for

business teams evaluating agent automation for their workflows

developers building agents for specific industries or domains

teams migrating from traditional automation tools to agent-based approaches

Requires

OpenClaw framework and ClawHub skills matching the use case

Understanding of the specific business domain and workflow

Infrastructure to run agents (cloud or self-hosted)

Limitations

Use cases are domain-specific; applicability to different industries varies

Performance metrics may not reflect real-world conditions in different environments or at different scales

Examples assume specific skill availability; custom skill development may be required for some workflows

What makes it unique

vs alternatives

More practical and business-focused than technical agent documentation, offering concrete ROI data and workflow patterns that help teams make adoption decisions and plan implementations

agent performance benchmarking and kpi tracking

Medium confidence

Solves for

Best for

teams optimizing agent performance and cost

developers planning infrastructure for agent deployment

builders evaluating agent frameworks based on performance characteristics

Requires

OpenClaw framework with performance monitoring enabled

LLM API access with usage tracking

Infrastructure to run agents and collect metrics

Limitations

Performance metrics are historical snapshots; actual performance varies with LLM model versions, skill implementations, and infrastructure

Benchmarks may not reflect performance under production load or with different data distributions

Limited guidance on performance optimization strategies or tuning parameters

What makes it unique

vs alternatives

More comprehensive than generic LLM benchmarks by including agent-specific metrics like skill utilization, orchestration overhead, and multi-step task performance that reflect real agent behavior

self-hosted agent deployment and configuration examples

Medium confidence

Solves for

Best for

enterprises deploying agents in regulated environments

teams requiring on-premises or private cloud deployment

developers building self-hosted agent platforms

Requires

Container runtime (Docker) or Kubernetes cluster

Infrastructure to host OpenClaw and ClawHub (cloud VM, on-premises server, or Kubernetes)

Database for agent state and skill registry (PostgreSQL or compatible)

Limitations

Deployment examples may be specific to certain cloud providers or infrastructure platforms

Limited guidance on high-availability and disaster recovery configurations

No automated deployment or infrastructure-as-code templates provided

What makes it unique

vs alternatives

More comprehensive than OpenClaw's basic installation guide by covering operational aspects like monitoring, scaling, and multi-tenant configuration that teams need for production deployments

multi-agent coordination and workflow orchestration patterns

Medium confidence

Solves for

Best for

teams building complex, multi-stage automation workflows

developers implementing agent hierarchies or specialized agent teams

builders creating agent-based business process automation

Requires

OpenClaw framework with multi-agent support

External state store or message queue for agent coordination (Redis, RabbitMQ, etc.)

Workflow orchestration layer (could be custom or third-party)

Limitations

Multi-agent coordination adds complexity; examples may not cover all edge cases

State management between agents requires external persistence; no built-in solution provided

Limited guidance on deadlock prevention or circular dependency handling

What makes it unique

vs alternatives

More practical than theoretical multi-agent frameworks by demonstrating concrete coordination patterns in OpenClaw, with working examples of agent communication and state sharing

agent testing and validation framework examples

Medium confidence

Solves for

Best for

developers building production-grade agents

teams implementing CI/CD pipelines for agent code

builders ensuring quality and reliability of agent systems

Requires

OpenClaw framework with test support

Testing framework (pytest, Jest, or similar)

Test data and fixtures for your specific use case

Limitations

Testing LLM-based agents is inherently non-deterministic; examples may not cover all failure modes

No built-in test framework provided; examples use standard testing tools

Limited guidance on testing agents with external API dependencies

What makes it unique

vs alternatives

More specialized than generic software testing guides by addressing agent-specific testing challenges like LLM non-determinism, skill composition validation, and multi-step workflow verification

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

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awesome-openclaw-examples

Capabilities8 decomposed

real-world openclaw agent example curation and documentation

clawhub skill discovery and integration pattern documentation

agent prompt engineering and template library

agent workflow automation use case library

agent performance benchmarking and kpi tracking

self-hosted agent deployment and configuration examples

multi-agent coordination and workflow orchestration patterns

agent testing and validation framework examples

Related Artifactssharing capabilities

awesome-openclaw-usecases-zh

awesome-openclaw-agents

openclaw-superpowers

clawpanel

cashclaw

awesome-openclaw

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

Repository Details

About

Categories

Alternatives to awesome-openclaw-examples

Are you the builder of awesome-openclaw-examples?

Get the weekly brief

Data Sources

awesome-openclaw-examples

Capabilities8 decomposed

real-world openclaw agent example curation and documentation

clawhub skill discovery and integration pattern documentation

agent prompt engineering and template library

agent workflow automation use case library

agent performance benchmarking and kpi tracking

self-hosted agent deployment and configuration examples

multi-agent coordination and workflow orchestration patterns

agent testing and validation framework examples

Related Artifactssharing capabilities

awesome-openclaw-usecases-zh

awesome-openclaw-agents

openclaw-superpowers

clawpanel

cashclaw

awesome-openclaw

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

Repository Details

About

Categories

Alternatives to awesome-openclaw-examples

Are you the builder of awesome-openclaw-examples?

Get the weekly brief

Data Sources