Agent Catalog

Explore all available AgentMap agent types in this interactive catalog. Use the search and filters to find the perfect agent for your workflow needs.

AgentMap Agent Catalog

Browse all available agent types with examples and configurations

⚙️ Core

DefaultAgent

default

The simplest agent that logs its execution and returns a message with the prompt.

Input Fields: Any (unused)

Output Field: Returns a message including the agent's prompt

Prompt Usage: Included in output message

Protocols:BaseAgent

Services:None required

CSV Example:

TestGraph,Start,,Basic node,default,Next,,input,output,Hello World

⚙️ Core

EchoAgent

echo

Simply returns the input data unchanged.

Input Fields: Returns the first input field it finds

Output Field: The input data unchanged

Prompt Usage: Ignored

Protocols:BaseAgent

Services:None required

CSV Example:

TestGraph,Echo,,Echo node,echo,Next,,message,response,

⚙️ Core

BranchingAgent

branching

Used for testing conditional routing. Checks for success/failure indicators in inputs.

Input Fields: Looks for success, should_succeed, succeed, or branch fields

Output Field: Message describing the branching decision

Prompt Usage: Included in output message

Protocols:BaseAgent

Services:None required

CSV Example:

TestGraph,Branch,,Decision point,branching,SuccessPath,FailurePath,input,decision,Make a choice

⚙️ Core

SuccessAgent

success

Testing agent that always succeeds.

Input Fields: Any (unused)

Output Field: Confirmation message

Prompt Usage: Included in output message

Protocols:BaseAgent

Services:None required

CSV Example:

TestGraph,AlwaysSucceed,,Success node,success,Next,,input,result,I always succeed

⚙️ Core

FailureAgent

failure

Testing agent that always fails.

Input Fields: Any (unused)

Output Field: Confirmation message

Prompt Usage: Included in output message

Protocols:BaseAgent

Services:None required

CSV Example:

TestGraph,AlwaysFail,,Failure node,failure,Next,,input,result,I always fail

⚙️ Core

InputAgent

input

Prompts for user input during execution.

Input Fields: Any (unused)

Output Field: User's input response

Prompt Usage: Shown to user as input prompt

Protocols:BaseAgent

Services:None required

CSV Example:

TestGraph,GetInput,,User input node,input,Process,,message,user_input,Please enter your name:

🧠 LLM

LLMAgent

llm

Uses configurable LLM providers for text generation with intelligent routing support.

Input Fields: Used to format the prompt template

Output Field: LLM response

Prompt Usage: Used as prompt template or system message

Protocols:LLMCapableAgentPromptCapableAgent

Services:LLMService

Context Options:routing_enabledtask_typeprovidermodeltemperaturememory_key

CSV Example:

QAGraph,Question,{"routing_enabled": true, "task_type": "analysis"},Ask a question,llm,Answer,,question,response,Answer this question: {question}

🧠 LLM

OpenAIAgent

openai(aliases: gptchatgpt)

Backward compatibility wrapper for LLMAgent with OpenAI provider.

Input Fields: Used to format the prompt template

Output Field: LLM response

Prompt Usage: Used as prompt template

Protocols:LLMCapableAgent

Services:LLMService (OpenAI)

Context Options:modeltemperaturemax_tokens

CSV Example:

QAGraph,Question,{"model": "gpt-4", "temperature": 0.7},Ask a question,openai,Answer,,question,response,Answer this question: {question}

🧠 LLM

AnthropicAgent

claude(aliases: claude)

Backward compatibility wrapper for LLMAgent with Anthropic provider.

Input Fields: Used to format the prompt template

Output Field: LLM response

Prompt Usage: Used as prompt template

Protocols:LLMCapableAgent

Services:LLMService (Anthropic)

Context Options:modeltemperaturemax_tokens

CSV Example:

QAGraph,Summarize,{"model": "claude-3-sonnet-20240229"},Summarize text,claude,Next,,text,summary,Summarize this text in 3 bullet points: {text}

🧠 LLM

GoogleAgent

gemini(aliases: gemini)

Backward compatibility wrapper for LLMAgent with Google provider.

Input Fields: Used to format the prompt template

Output Field: LLM response

Prompt Usage: Used as prompt template

Protocols:LLMCapableAgent

Services:LLMService (Google)

Context Options:modeltemperaturemax_tokens

CSV Example:

QAGraph,Generate,{"model": "gemini-1.0-pro"},Generate content,gemini,Next,,prompt,content,Generate content based on: {prompt}

💾 Storage

CSVReaderAgent

csv_reader

Read from CSV files using the unified storage system.

Input Fields: Must contain collection (file path), optional document_id, query, path

Output Field: CSV data

Prompt Usage: Optional CSV path override

Protocols:StorageCapableAgentCSVCapableAgent

Services:StorageService

Context Options:formatid_fieldencodingdelimiter

CSV Example:

DataGraph,ReadCustomers,{"format": "records", "id_field": "customer_id"},Read customer data,csv_reader,Process,,collection,customers,data/customers.csv

💾 Storage

CSVWriterAgent

csv_writer

Write to CSV files using the unified storage system.

Input Fields: Must contain data and collection (file path)

Output Field: Operation result

Prompt Usage: Optional CSV path override

Protocols:StorageCapableAgentCSVCapableAgent

Services:StorageService

Context Options:formatmodeencodingdelimiter

CSV Example:

DataGraph,WriteResults,{"format": "records", "mode": "write"},Write processed data,csv_writer,End,,data,result,data/output.csv

💾 Storage

JSONDocumentReaderAgent

json_reader

Read from JSON files using the unified storage system.

Input Fields: Must contain collection (file path), optional document_id, path

Output Field: JSON data

Prompt Usage: Optional JSON path override

Protocols:StorageCapableAgentJSONCapableAgent

Services:StorageService

Context Options:formatencoding

CSV Example:

ConfigGraph,ReadConfig,{"format": "dict", "encoding": "utf-8"},Read configuration,json_reader,Process,,collection,config,config/app.json

💾 Storage

JSONDocumentWriterAgent

json_writer

Write to JSON files using the unified storage system.

Input Fields: Must contain data and collection (file path)

Output Field: Operation result

Prompt Usage: Optional JSON path override

Protocols:StorageCapableAgentJSONCapableAgent

Services:StorageService

Context Options:formatindentencoding

CSV Example:

ConfigGraph,SaveState,{"format": "dict", "indent": 2},Save application state,json_writer,End,,state,result,data/state.json

💾 Storage

VectorReaderAgent

vector_reader

Work with vector databases for semantic search and document retrieval.

Input Fields: query for similarity search

Output Field: Retrieved documents

Prompt Usage: Optional configuration override

Protocols:StorageCapableAgentVectorCapableAgent

Services:StorageService

Context Options:providerembedding_modelsimilarity_thresholdmax_results

CSV Example:

VectorGraph,Search,{"similarity_threshold": 0.8, "max_results": 5},Search for similar documents,vector_reader,Process,,query,search_results,

💾 Storage

VectorWriterAgent

vector_writer

Work with vector databases for embedding and storing documents.

Input Fields: document data

Output Field: Operation status

Prompt Usage: Optional configuration override

Protocols:StorageCapableAgentVectorCapableAgent

Services:StorageService

Context Options:providerembedding_modelcollection_name

CSV Example:

VectorGraph,LoadDocs,{"provider": "chroma", "embedding_model": "text-embedding-ada-002"},Load documents into vector store,vector_writer,Search,,documents,load_result,

📁 File

FileReaderAgent

file_reader

Reads and processes various document types with optional chunking and filtering.

Input Fields: collection (file path), optional document_id, query, path, format

Output Field: Document data with metadata

Prompt Usage: Not used

Protocols:StorageCapableAgentFileCapableAgent

Services:StorageService

Context Options:chunk_sizechunk_overlapshould_splitinclude_metadata

CSV Example:

DocGraph,ReadDocs,{"chunk_size": 1000, "chunk_overlap": 200, "should_split": true},Read documents,file_reader,Process,,collection,documents,

📁 File

FileWriterAgent

file_writer

Writes content to various text-based formats with different write modes.

Input Fields: collection (file path), data (content), optional mode

Output Field: Write operation result

Prompt Usage: Not used

Protocols:StorageCapableAgentFileCapableAgent

Services:StorageService

Context Options:modeencoding

CSV Example:

DocGraph,WriteFile,{"mode": "write", "encoding": "utf-8"},Write document,file_writer,Next,,data,result,path/to/output.txt

🔧 Specialized

OrchestrationAgent

orchestrator

Routes execution to one or more nodes based on context configuration.

Input Fields: available_nodes and routing data

Output Field: selected nodes

Prompt Usage: Not used

Protocols:BaseAgent

Services:None required

Context Options:nodes

CSV Example:

WorkflowGraph,Router,{"nodes": "ProcessA|ProcessB|ProcessC"},Route to processors,orchestrator,Collect,Error,available_nodes|data,selected_nodes,

🔧 Specialized

SummaryAgent

summary

Combines multiple inputs into a structured summary.

Input Fields: Multiple input fields to combine

Output Field: Combined summary result

Prompt Usage: Not used

Protocols:BaseAgent

Services:None required

Context Options:formatinclude_keys

CSV Example:

DataGraph,Combine,{"format": "{key}: {value}\n"},Combine results,summary,Next,Error,result_a|result_b|result_c,combined,

Found 20 of 20 agents. Need help? Check out the detailed agent documentation or quick start guide.

Quick Reference

Agent Categories

• 🏗️ Core Agents - Basic building blocks for workflow control and data flow
• 🧠 LLM Agents - AI-powered agents using language models from various providers
• 💾 Storage Agents - Data persistence and retrieval from various storage systems
• 📁 File Agents - File operations for reading and writing documents
• 🔧 Specialized Agents - Advanced workflow orchestration and data processing

Usage Tips

1. Copy CSV Examples - Click the copy button on any agent card to get ready-to-use CSV configuration
2. Search by Capability - Use keywords like "LLM", "storage", "file", or "routing" to find relevant agents
3. Filter by Category - Use category buttons to narrow down to specific agent types
4. Check Context Options - Review the context configuration options to understand customization possibilities

Next Steps

• Quick Start Guide - Build your first workflow using these agents
• CSV Schema Reference - Learn the complete CSV format for defining workflows
• CLI Commands - Use scaffolding to generate custom agent templates
• Agent Development - Create your own custom agents

Common Agent Combinations

Data Processing Pipeline

Pipeline,ReadData,,Read input data,csv_reader,ProcessData,Error,collection,raw_data,data/input.csv
Pipeline,ProcessData,,Transform the data,llm,WriteData,Error,raw_data,processed_data,Clean and format this data: {raw_data}
Pipeline,WriteData,,Save processed data,csv_writer,End,Error,processed_data,result,data/output.csv

Interactive Chatbot with Memory

ChatBot,GetInput,,Get user question,input,ProcessQuestion,End,,question,Enter your question:
ChatBot,ProcessQuestion,{"memory_key":"conversation","max_memory_messages":10},Process with AI,llm,GetInput,Error,question|conversation,response,You are a helpful assistant. Answer: {question}

Document Analysis with Summarization

Analysis,LoadDoc,,Load document,file_reader,AnalyzeDoc,Error,collection,document,
Analysis,AnalyzeDoc,,Analyze content,llm,CreateSummary,Error,document,analysis,Analyze and extract key insights: {document}
Analysis,CreateSummary,{"llm":"anthropic"},Create executive summary,summary,SaveResults,Error,analysis|document,executive_summary,Create a concise executive summary of the analysis
Analysis,SaveResults,,Save results,json_writer,End,Error,executive_summary,result,results/analysis.json

Intelligent Request Routing

Router,GetUserInput,,Get user request,input,RouteRequest,End,message,user_input,What can I help you with?
Router,RouteRequest,{"nodes":"WeatherService|NewsService|CalculatorService"},Route to appropriate service,orchestrator,DefaultHandler,Error,available_nodes|user_input,selected_node,Route the user request to the appropriate service
Router,WeatherService,,Get weather information,default,FormatResponse,Error,user_input,weather_data,Getting weather information
Router,NewsService,,Get latest news,default,FormatResponse,Error,user_input,news_data,Getting latest news
Router,CalculatorService,,Perform calculations,default,FormatResponse,Error,user_input,calc_result,Performing calculation
Router,FormatResponse,{"format":"{key}: {value}"},Format final response,summary,End,Error,weather_data|news_data|calc_result,formatted_response,
Router,DefaultHandler,,Handle unrecognized requests,default,End,Error,user_input,error_response,I don't understand that request
Router,End,,Complete the workflow,echo,,,formatted_response|error_response,final_output,

Parallel Processing with Consolidation

Parallel,SplitWork,,Split work into parallel tasks,default,TaskA|TaskB|TaskC,Error,input_data,split_data,
Parallel,TaskA,,Process part A,default,Consolidate,Error,split_data,result_a,Processing part A
Parallel,TaskB,,Process part B,default,Consolidate,Error,split_data,result_b,Processing part B
Parallel,TaskC,,Process part C,default,Consolidate,Error,split_data,result_c,Processing part C
Parallel,Consolidate,{"separator":"\n\n---\n\n"},Combine all results,summary,End,Error,result_a|result_b|result_c,final_result,
Parallel,End,,Output final results,echo,,,final_result,output,

Multi-Step LLM Analysis with Context

Analysis,LoadData,,Load source data,file_reader,ExtractEntities,Error,collection,raw_data,
Analysis,ExtractEntities,{"task_type":"analysis","routing_enabled":true},Extract key entities,llm,AnalyzeSentiment,Error,raw_data,entities,Extract all important entities and concepts from: {raw_data}
Analysis,AnalyzeSentiment,{"provider":"anthropic"},Analyze sentiment,llm,GenerateInsights,Error,raw_data|entities,sentiment,Analyze the sentiment of this content: {raw_data}
Analysis,GenerateInsights,{"temperature":0.3},Generate insights,llm,CreateReport,Error,entities|sentiment|raw_data,insights,Based on entities {entities} and sentiment {sentiment}, generate key insights about: {raw_data}
Analysis,CreateReport,{"llm":"anthropic"},Create final report,summary,SaveReport,Error,entities|sentiment|insights,executive_report,Create a comprehensive executive report combining all analysis
Analysis,SaveReport,,Save final report,file_writer,End,Error,executive_report,saved_report,reports/analysis_report.md
Analysis,End,,Analysis complete,echo,,,saved_report,completion_status,

Interactive Schema Validator

Want to validate your CSV before running it? Try this interactive validator workflow:

Validator,GetCSV,,Get CSV to validate,input,ValidateSchema,End,csv_content,Enter your CSV content:
Validator,ValidateSchema,,Validate CSV structure,default,ShowResults,Error,csv_content,validation_result,Validating CSV schema and structure
Validator,ShowResults,,Display validation results,echo,GetCSV,End,validation_result,results,

These examples demonstrate the power and flexibility of AgentMap's agent system. Each workflow shows different patterns:

• Linear Processing: Simple step-by-step data transformation
• Parallel Processing: Multiple tasks running simultaneously with consolidation
• Interactive Flows: User input driving workflow decisions
• Intelligent Routing: AI-powered decision making for request handling
• Memory Management: Maintaining context across interactions
• Multi-Modal Analysis: Combining different types of AI analysis

Advanced Configuration Examples

Vector Search with Document Processing

VectorSearch,LoadDocs,{"chunk_size":1000,"should_split":true},Load documents for indexing,file_reader,IndexDocs,Error,collection,documents,
VectorSearch,IndexDocs,{"provider":"chroma","embedding_model":"text-embedding-ada-002"},Index documents in vector store,vector_writer,SearchReady,Error,documents,index_result,
VectorSearch,SearchReady,,Ready for search queries,echo,ProcessQuery,End,index_result,ready_message,Vector search system ready
VectorSearch,ProcessQuery,,Get search query,input,PerformSearch,ProcessQuery,message,search_query,Enter your search query:
VectorSearch,PerformSearch,{"similarity_threshold":0.8,"max_results":5},Search for similar content,vector_reader,AnalyzeResults,Error,search_query,search_results,
VectorSearch,AnalyzeResults,{"routing_enabled":true,"task_type":"analysis"},Analyze search results,llm,DisplayResults,Error,search_query|search_results,analysis,Analyze these search results for query "{search_query}": {search_results}
VectorSearch,DisplayResults,,Display final results,echo,ProcessQuery,End,analysis,final_output,

Multi-Provider LLM Comparison

Comparison,GetPrompt,,Get prompt to compare,input,RunOpenAI|RunAnthropic|RunGoogle,End,message,prompt,Enter prompt to compare across providers:
Comparison,RunOpenAI,{"provider":"openai","model":"gpt-4"},Run with OpenAI,llm,CollectResults,Error,prompt,openai_result,{prompt}
Comparison,RunAnthropic,{"provider":"anthropic","model":"claude-3-sonnet-20240229"},Run with Anthropic,llm,CollectResults,Error,prompt,anthropic_result,{prompt}
Comparison,RunGoogle,{"provider":"google","model":"gemini-1.0-pro"},Run with Google,llm,CollectResults,Error,prompt,google_result,{prompt}
Comparison,CollectResults,{"format":"**{key}**:\n{value}\n\n"},Collect all results,summary,AnalyzeComparison,Error,openai_result|anthropic_result|google_result,all_results,
Comparison,AnalyzeComparison,{"routing_enabled":true},Analyze differences,llm,DisplayComparison,Error,prompt|all_results,comparison_analysis,Compare and analyze the differences between these LLM responses to "{prompt}": {all_results}
Comparison,DisplayComparison,,Show final comparison,echo,GetPrompt,End,comparison_analysis,final_comparison,