{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-kaymen99--langgraph-email-automation","slug":"kaymen99--langgraph-email-automation","name":"langgraph-email-automation","type":"agent","url":"https://github.com/kaymen99/langgraph-email-automation","page_url":"https://unfragile.ai/kaymen99--langgraph-email-automation","categories":["automation"],"tags":["ai-agents","ai-automation","ai-customer-service","ai-customer-support","customer-support","customer-support-automation","email-automation","gmail-api","langchain","langgraph","llama3","rag","rag-agents","rag-application"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-kaymen99--langgraph-email-automation__cap_0","uri":"capability://planning.reasoning.multi.agent.email.categorization.with.conditional.routing","name":"multi-agent email categorization with conditional routing","description":"Implements a LangGraph StateGraph-based workflow that routes incoming emails through specialized AI agents for intelligent classification into product_inquiry, complaint, feedback, or unrelated categories. Uses conditional routing nodes that branch the workflow based on categorization results, enabling different processing paths for each email type. The categorization agent leverages LangChain with Groq/Google APIs to analyze email content and metadata, with routing decisions persisted in a custom GraphState object that maintains context across workflow steps.","intents":["automatically classify incoming customer emails into actionable categories without manual triage","route different email types to specialized processing pipelines based on their content and intent","reduce manual email sorting overhead by 80%+ through intelligent multi-agent categorization"],"best_for":["customer support teams automating email triage at scale","SaaS companies with high-volume support inboxes needing intelligent routing","teams building custom support automation workflows on top of LangGraph"],"limitations":["categorization accuracy depends on quality of training data and agent prompts; no built-in active learning to improve over time","conditional routing adds ~100-200ms latency per email due to LLM inference for categorization","limited to predefined categories (product_inquiry, complaint, feedback, unrelated); custom categories require code modification","no fallback mechanism if categorization agent fails; requires external error handling"],"requires":["Python 3.9+","LangGraph 0.1.0+","LangChain 0.1.0+","API credentials for Groq or Google AI (for LLM inference)","Gmail API credentials with read/write access"],"input_types":["email text content","email subject line","sender metadata","email body with optional attachments"],"output_types":["category label (string: product_inquiry|complaint|feedback|unrelated)","confidence score (float 0-1)","routing decision (enum)","categorization reasoning (string)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-kaymen99--langgraph-email-automation__cap_1","uri":"capability://memory.knowledge.rag.powered.contextual.email.response.generation","name":"rag-powered contextual email response generation","description":"Generates customer support responses by combining retrieval-augmented generation (RAG) with ChromaDB vector store and Google Embeddings. For product_inquiry emails, the system retrieves relevant product documentation from the vector store using semantic similarity search, then passes retrieved context to a writing agent that generates contextually appropriate responses. Uses a two-stage pipeline: (1) embedding-based retrieval of top-k relevant documents from ChromaDB, (2) LLM-based response generation conditioned on retrieved context. The vector store is pre-populated via create_index.py which chunks and embeds product documentation.","intents":["generate accurate product-specific responses without manually searching documentation","ensure responses are grounded in actual product knowledge rather than hallucinated information","reduce response generation time by retrieving relevant context upfront rather than requiring agents to search"],"best_for":["support teams with extensive product documentation that needs to be referenced in responses","companies wanting to reduce hallucination risk in automated support responses","teams building knowledge-grounded chatbots or support agents"],"limitations":["RAG quality depends on documentation quality and chunking strategy; poorly written docs produce poor responses","vector store must be pre-indexed; no real-time document updates without re-indexing the entire store","semantic search may retrieve irrelevant documents if product documentation lacks clear structure or keywords","embedding model (Google Embeddings) adds ~200-500ms latency per retrieval operation","no built-in mechanism to handle out-of-domain queries where no relevant documentation exists"],"requires":["Python 3.9+","ChromaDB 0.3.0+","Google Embeddings API credentials","LangChain 0.1.0+","Product documentation in text/markdown format","Pre-indexed vector store (generated via create_index.py)"],"input_types":["email query text","product documentation (markdown/text)","customer context (optional)"],"output_types":["generated response text","retrieved context documents (list of chunks)","relevance scores for retrieved documents","response confidence metric"],"categories":["memory-knowledge","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-kaymen99--langgraph-email-automation__cap_10","uri":"capability://automation.workflow.standalone.batch.email.processing.mode","name":"standalone batch email processing mode","description":"Provides a standalone execution mode (main.py) that runs the email processing workflow as a continuous background process without requiring API deployment. The standalone mode fetches emails from Gmail in a loop (configurable polling interval), processes each email through the workflow, and sends responses. Useful for development, testing, and simple deployments where API infrastructure is not needed. Includes console logging for monitoring and debugging. Can be run as a systemd service or Docker container for production use.","intents":["run email automation as a simple background process without API infrastructure","test and debug the email processing workflow locally before deploying as a service","deploy email automation in simple environments where REST API is overkill"],"best_for":["small teams or solo developers testing email automation locally","simple deployments where API infrastructure is not needed","development and debugging workflows"],"limitations":["single-threaded execution; can only process one email at a time","no built-in scaling; cannot handle high email volumes","process crashes result in lost state; no automatic recovery","no built-in monitoring or alerting; requires external tools","polling-based approach has inherent latency (30-60 seconds typical)"],"requires":["Python 3.9+","LangGraph 0.1.0+","Gmail API credentials","All dependencies from requirements.txt","Optional: systemd or Docker for running as a service"],"input_types":["Gmail API credentials","polling interval (seconds)","optional: email filters (labels, senders)"],"output_types":["console logs (processing status, errors)","sent emails (in Gmail sent folder)","processed email count"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-kaymen99--langgraph-email-automation__cap_2","uri":"capability://safety.moderation.automated.email.quality.assurance.and.proofreading","name":"automated email quality assurance and proofreading","description":"Implements a quality assurance node that validates generated responses before sending using a specialized proofreading agent. The QA agent checks for grammatical errors, tone consistency, factual accuracy (by comparing against retrieved context), and compliance with support guidelines. Uses LangChain agents with Groq/Google APIs to perform multi-dimensional quality checks, returning a quality score and list of issues. If quality score falls below a threshold, the response is flagged for human review rather than auto-sent. The QA node is integrated into the workflow graph as a post-generation step before email sending.","intents":["prevent sending low-quality or inaccurate automated responses that damage customer trust","catch hallucinations or factual errors in generated responses before they reach customers","maintain consistent tone and brand voice across all automated support emails"],"best_for":["support teams with high quality standards where response errors are costly","companies in regulated industries (finance, healthcare) requiring compliance checks","teams wanting human-in-the-loop automation with automatic escalation for low-quality responses"],"limitations":["QA agent itself can hallucinate or miss subtle errors; not a replacement for human review","quality thresholds are hardcoded and require manual tuning based on domain-specific requirements","adds ~300-500ms latency per email due to additional LLM inference for quality checks","no built-in metrics for measuring QA agent accuracy or false positive/negative rates","cannot detect context-specific errors that require domain expertise beyond the agent's training"],"requires":["Python 3.9+","LangChain 0.1.0+","API credentials for Groq or Google AI","Pre-defined quality criteria and thresholds","Optional: human review queue system for flagged responses"],"input_types":["generated email response text","original customer email","retrieved context documents (for factual verification)","brand guidelines (optional)"],"output_types":["quality score (float 0-1)","list of issues found (array of strings)","pass/fail decision (boolean)","recommended actions (escalate|send|revise)"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-kaymen99--langgraph-email-automation__cap_3","uri":"capability://automation.workflow.continuous.gmail.inbox.monitoring.and.polling","name":"continuous gmail inbox monitoring and polling","description":"Implements a polling-based email monitoring system that continuously fetches new emails from Gmail inbox using the Gmail API with authenticated access. The monitoring node runs in a loop (configurable polling interval) and retrieves unread emails, parses email metadata (sender, subject, timestamp, body), and feeds them into the processing workflow. Uses Gmail API's label-based filtering to identify new emails and marks processed emails as read to avoid reprocessing. The polling mechanism is integrated into the main.py entry point for standalone deployment or exposed as an API endpoint in deploy_api.py for service-based deployment.","intents":["automatically detect and process new customer emails without manual intervention","maintain a continuous support automation pipeline that responds to emails in near real-time","avoid duplicate processing by tracking which emails have already been handled"],"best_for":["customer support teams wanting fully automated email processing without manual inbox checks","companies deploying email automation as a background service or microservice","teams integrating email automation into larger support infrastructure"],"limitations":["polling-based approach has inherent latency (configurable interval, typically 30-60 seconds); not true real-time","Gmail API rate limits apply (500 requests/minute for most accounts); high-volume inboxes may hit limits","no built-in deduplication if the same email is fetched multiple times due to API errors or network issues","requires Gmail API credentials and OAuth2 setup; cannot work with non-Gmail email providers without code changes","polling adds continuous load on Gmail API even during periods of no new emails"],"requires":["Python 3.9+","Gmail API credentials (OAuth2 client ID/secret)","Google authentication token with Gmail read/write scopes","Network connectivity to Gmail API","Configured polling interval (default 30-60 seconds)"],"input_types":["Gmail API credentials","OAuth2 tokens","Label filters (optional, defaults to INBOX)"],"output_types":["email objects (sender, subject, body, timestamp, message_id)","parsed email metadata","unread email count","polling status (success/failure)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-kaymen99--langgraph-email-automation__cap_4","uri":"capability://planning.reasoning.stateful.workflow.orchestration.with.langgraph.stategraph","name":"stateful workflow orchestration with langgraph stategraph","description":"Implements the core workflow orchestration using LangGraph's StateGraph primitive, which manages the entire email processing pipeline as a directed acyclic graph (DAG) of nodes and edges. Each node represents a processing step (categorization, retrieval, generation, QA, sending), and edges define the control flow between nodes. The custom GraphState object maintains workflow state across all steps, including email content, categorization results, retrieved context, generated response, and QA decisions. Conditional edges enable branching logic (e.g., route to different nodes based on email category). The StateGraph is compiled into an executable workflow that can be invoked synchronously or asynchronously.","intents":["orchestrate complex multi-step email processing pipelines without manual control flow management","maintain full context and state across all processing steps for debugging and auditing","enable conditional routing and branching based on intermediate results"],"best_for":["teams building complex AI agent workflows with multiple interdependent steps","developers needing transparent, debuggable workflow execution with full state visibility","systems requiring conditional routing and dynamic branching based on LLM outputs"],"limitations":["StateGraph execution is synchronous by default; async support requires additional configuration","state size grows with each processing step; large email volumes may cause memory issues","no built-in persistence; workflow state is lost if the process crashes (requires external state store)","debugging complex graphs with many conditional edges can be difficult without visualization tools","each node invocation adds ~50-100ms overhead for state serialization/deserialization"],"requires":["Python 3.9+","LangGraph 0.1.0+","LangChain 0.1.0+","Understanding of DAG-based workflow patterns","Optional: external state persistence layer (Redis, database) for production deployments"],"input_types":["email object (sender, subject, body)","workflow configuration (node definitions, edges, routing rules)"],"output_types":["final workflow state (GraphState object)","execution trace (list of executed nodes)","final email response (if applicable)","workflow status (success/failure/escalated)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-kaymen99--langgraph-email-automation__cap_5","uri":"capability://tool.use.integration.multi.llm.provider.abstraction.with.groq.and.google.ai","name":"multi-llm provider abstraction with groq and google ai","description":"Provides a unified interface for invoking multiple LLM providers (Groq and Google AI) through LangChain's abstraction layer, enabling agent implementations to be agnostic to the underlying LLM provider. The system uses LangChain's ChatGroq and ChatGoogle integrations to instantiate LLM instances, which are then passed to agent definitions. Agents can be configured to use different providers for different tasks (e.g., Groq for fast categorization, Google for higher-quality response generation). The provider selection is configurable via environment variables, allowing deployment-time switching without code changes.","intents":["switch between different LLM providers without rewriting agent code","optimize cost/latency by using different providers for different tasks (fast inference vs high quality)","avoid vendor lock-in by maintaining provider-agnostic agent implementations"],"best_for":["teams wanting to experiment with different LLM providers without code refactoring","cost-conscious teams needing to optimize LLM spend across different task types","organizations with multi-cloud or multi-vendor requirements"],"limitations":["different providers have different model capabilities, latency, and cost profiles; switching providers may require prompt tuning","API rate limits and quota management differ per provider; no built-in rate limiting or fallback logic","error handling is provider-specific; generic error handling may miss provider-specific failure modes","no automatic fallback if primary provider is unavailable; requires external circuit breaker logic","environment variable-based configuration is fragile; no runtime provider switching without process restart"],"requires":["Python 3.9+","LangChain 0.1.0+","API credentials for Groq (API key) and/or Google AI (API key)","Environment variables: GROQ_API_KEY, GOOGLE_API_KEY","Network connectivity to both provider APIs"],"input_types":["LLM provider name (string: groq|google)","model name (string, provider-specific)","prompt text","optional: temperature, max_tokens, other hyperparameters"],"output_types":["LLM response text","token usage (input/output tokens)","provider metadata (model used, latency)"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-kaymen99--langgraph-email-automation__cap_6","uri":"capability://memory.knowledge.vector.store.indexing.and.semantic.search.with.chromadb","name":"vector store indexing and semantic search with chromadb","description":"Implements a document indexing pipeline (create_index.py) that chunks product documentation, generates embeddings using Google Embeddings API, and stores them in ChromaDB vector store for later semantic retrieval. The indexing process: (1) reads product documentation files, (2) chunks documents into overlapping segments (configurable chunk size/overlap), (3) generates embeddings for each chunk using Google Embeddings, (4) stores chunks and embeddings in ChromaDB with metadata. During response generation, the RAG pipeline queries ChromaDB using semantic similarity search to retrieve top-k relevant chunks, which are then passed to the writing agent. ChromaDB provides in-memory or persistent storage options.","intents":["build a searchable knowledge base from product documentation without manual indexing","retrieve relevant documentation chunks based on semantic similarity rather than keyword matching","enable RAG-based response generation with grounded, up-to-date product information"],"best_for":["support teams with extensive product documentation that needs to be searchable","teams building knowledge-grounded AI agents or chatbots","organizations wanting to leverage existing documentation without manual knowledge base creation"],"limitations":["indexing is a one-time batch process; real-time documentation updates require full re-indexing","semantic search quality depends on embedding model quality and documentation structure; poorly written docs produce poor results","chunk size and overlap are hardcoded; suboptimal chunking can lead to missing context or redundant retrieval","no built-in deduplication; duplicate documentation will be indexed multiple times","ChromaDB in-memory storage is not persistent; persistent mode requires additional setup and may have performance overhead"],"requires":["Python 3.9+","ChromaDB 0.3.0+","Google Embeddings API credentials","Product documentation in text/markdown format","Disk space for vector store (typically 1-10GB depending on documentation size)"],"input_types":["product documentation files (markdown/text)","chunk size (int, default 1000)","chunk overlap (int, default 100)","metadata (optional, for filtering)"],"output_types":["ChromaDB collection object","indexed document count","vector store statistics (total chunks, embedding dimensions)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-kaymen99--langgraph-email-automation__cap_7","uri":"capability://automation.workflow.rest.api.deployment.with.fastapi.and.langserve","name":"rest api deployment with fastapi and langserve","description":"Provides a production-ready REST API deployment option (deploy_api.py) that wraps the LangGraph workflow in FastAPI endpoints using LangServe. The API exposes the email processing workflow as HTTP endpoints (e.g., POST /process-email) that accept email objects and return processed results. LangServe handles request/response serialization, async execution, and OpenAPI documentation generation. The API can be deployed as a standalone service or integrated into larger support infrastructure. Supports both synchronous and asynchronous invocation patterns.","intents":["deploy email automation as a scalable microservice that can be called from external systems","expose the workflow as REST endpoints for integration with existing support platforms","enable horizontal scaling by running multiple API instances behind a load balancer"],"best_for":["teams deploying email automation as a production service","organizations integrating email automation into larger support infrastructure","companies needing to scale email processing across multiple instances"],"limitations":["FastAPI/LangServe adds ~50-100ms overhead per request for serialization and HTTP handling","no built-in authentication or rate limiting; requires external API gateway for production security","async execution requires careful handling of state management and error propagation","API response time is bounded by the slowest workflow step (typically LLM inference); no streaming responses","no built-in request queuing or backpressure handling; high load may cause request timeouts"],"requires":["Python 3.9+","FastAPI 0.100+","LangServe 0.1.0+","LangGraph 0.1.0+","Uvicorn or other ASGI server","Optional: Docker for containerized deployment"],"input_types":["HTTP POST request with email object (JSON)","email fields: sender, subject, body, timestamp"],"output_types":["HTTP 200 response with processed result (JSON)","response fields: status, generated_response, category, quality_score","HTTP error responses (400, 500) for invalid input or processing failures"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-kaymen99--langgraph-email-automation__cap_8","uri":"capability://text.generation.language.agent.prompt.engineering.and.specialization","name":"agent prompt engineering and specialization","description":"Implements specialized AI agents for different tasks (categorization, writing, proofreading) using carefully crafted system prompts that define agent behavior, constraints, and output format. Each agent is instantiated with a specific prompt template (defined in src/prompts.py) that guides the LLM to perform its designated task. Prompts include task description, input/output format specifications, examples, and constraints (e.g., 'keep response under 200 words'). Agents are composed using LangChain's agent framework, which handles tool calling, reasoning, and output parsing. Prompts can be updated without code changes by modifying the prompt templates.","intents":["specialize LLM behavior for specific tasks without fine-tuning or custom models","ensure consistent output format and quality across different agent types","enable rapid iteration on agent behavior by updating prompts without redeploying code"],"best_for":["teams wanting to customize agent behavior without machine learning expertise","organizations needing to adapt agents to domain-specific requirements","developers building multi-agent systems with specialized roles"],"limitations":["prompt quality directly impacts agent performance; poorly written prompts produce poor results","no systematic way to measure prompt effectiveness or compare prompt versions","agents may ignore constraints or output format specifications if prompts are ambiguous","prompt engineering is largely empirical; no principled methodology for optimization","different LLM providers may interpret the same prompt differently, requiring provider-specific tuning"],"requires":["Python 3.9+","LangChain 0.1.0+","Understanding of prompt engineering best practices","Access to LLM provider APIs (Groq, Google AI)","Optional: prompt testing/evaluation framework"],"input_types":["prompt template (string with placeholders)","input data (email, context, etc.)","optional: few-shot examples"],"output_types":["agent output (structured or unstructured text)","reasoning trace (if agent uses chain-of-thought)","tool calls (if agent uses tools)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-kaymen99--langgraph-email-automation__cap_9","uri":"capability://automation.workflow.email.sending.with.gmail.api.integration","name":"email sending with gmail api integration","description":"Implements email sending functionality that uses the Gmail API to compose and send generated responses back to customers. The sending node constructs a properly formatted email message (with headers, body, optional attachments) and invokes the Gmail API's send endpoint. Includes error handling for common failures (invalid recipient, quota exceeded, authentication errors) and logging for audit trails. Supports both plain text and HTML-formatted responses. Integrates with the workflow state to track which emails have been sent and prevent duplicate sends.","intents":["automatically send generated responses to customers without manual copying/pasting","maintain audit trail of all sent emails for compliance and debugging","handle email formatting and delivery errors gracefully"],"best_for":["fully automated support systems where responses are sent without human review","teams wanting to close the loop on email automation by automatically sending responses","organizations with high email volume where manual sending is impractical"],"limitations":["Gmail API rate limits apply (500 requests/minute); high-volume sending may hit limits","no built-in retry logic for transient failures; requires external error handling","sent emails are marked as sent in Gmail but may not be delivered if recipient email is invalid","no support for email scheduling; responses are sent immediately","no built-in deduplication; same response may be sent multiple times if workflow is re-executed"],"requires":["Python 3.9+","Gmail API credentials with send scope","Google authentication token with Gmail send permissions","Network connectivity to Gmail API","Sender email address (configured in environment or workflow state)"],"input_types":["recipient email address","email subject","email body (plain text or HTML)","optional: attachments, cc/bcc recipients"],"output_types":["message ID (string)","send status (success/failure)","error details (if applicable)","timestamp of send"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":35,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","LangGraph 0.1.0+","LangChain 0.1.0+","API credentials for Groq or Google AI (for LLM inference)","Gmail API credentials with read/write access","ChromaDB 0.3.0+","Google Embeddings API credentials","Product documentation in text/markdown format","Pre-indexed vector store (generated via create_index.py)","Gmail API credentials"],"failure_modes":["categorization accuracy depends on quality of training data and agent prompts; no built-in active learning to improve over time","conditional routing adds ~100-200ms latency per email due to LLM inference for categorization","limited to predefined categories (product_inquiry, complaint, feedback, unrelated); custom categories require code modification","no fallback mechanism if categorization agent fails; requires external error handling","RAG quality depends on documentation quality and chunking strategy; poorly written docs produce poor responses","vector store must be pre-indexed; no real-time document updates without re-indexing the entire store","semantic search may retrieve irrelevant documents if product documentation lacks clear structure or keywords","embedding model (Google Embeddings) adds ~200-500ms latency per retrieval operation","no built-in mechanism to handle out-of-domain queries where no relevant documentation exists","single-threaded execution; can only process one email at a time","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.31070925294328944,"quality":0.32,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:21.550Z","last_scraped_at":"2026-05-03T13:59:57.743Z","last_commit":"2025-02-13T15:31:40Z"},"community":{"stars":242,"forks":69,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=kaymen99--langgraph-email-automation","compare_url":"https://unfragile.ai/compare?artifact=kaymen99--langgraph-email-automation"}},"signature":"LiR7L6SdgeX7dmN8DIRQ9kJFSAFhiT6S+BnPtOdGA6UrNvnIjhmNg2Tzeflh7wrnOpfaGGGkqyuAl/xJNQTYCw==","signedAt":"2026-06-20T21:34:43.532Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/kaymen99--langgraph-email-automation","artifact":"https://unfragile.ai/kaymen99--langgraph-email-automation","verify":"https://unfragile.ai/api/v1/verify?slug=kaymen99--langgraph-email-automation","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}