{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-kaymen99--sales-outreach-automation-langgraph","slug":"kaymen99--sales-outreach-automation-langgraph","name":"sales-outreach-automation-langgraph","type":"repo","url":"https://github.com/kaymen99/sales-outreach-automation-langgraph","page_url":"https://unfragile.ai/kaymen99--sales-outreach-automation-langgraph","categories":["automation"],"tags":["ai-agents","ai-automation","automation","crm-connections","hubspot","langchain","langgraph","lead-generation","leads-management","sales-automation"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-kaymen99--sales-outreach-automation-langgraph__cap_0","uri":"capability://tool.use.integration.multi.crm.lead.synchronization.with.standardized.adapter.pattern","name":"multi-crm lead synchronization with standardized adapter pattern","description":"Abstracts CRM connectivity through a base class pattern (src/lead_loaders/base.py) with concrete implementations for HubSpot, Airtable, and Google Sheets, enabling unified lead ingestion regardless of CRM backend. Each adapter implements standardized read/write interfaces that normalize heterogeneous CRM APIs into a common data model, allowing the workflow to operate CRM-agnostically while maintaining provider-specific field mapping and authentication.","intents":["I need to pull leads from multiple CRM systems without rewriting integration logic for each one","I want to switch CRM providers without refactoring my sales automation workflow","I need to sync lead qualification results back to the original CRM system"],"best_for":["sales teams using HubSpot, Airtable, or Google Sheets as primary lead sources","enterprises with multi-CRM environments needing unified lead processing","developers building extensible sales automation platforms"],"limitations":["Only three CRM providers supported out-of-box; custom adapters required for Salesforce, Pipedrive, or other systems","No real-time bidirectional sync — updates are batch-based and unidirectional (read leads, write results)","Field mapping must be manually configured per CRM instance; no automatic schema detection"],"requires":["Python 3.9+","API credentials for target CRM (HubSpot API key, Airtable personal access token, or Google Sheets service account)","Network connectivity to CRM APIs"],"input_types":["CRM API credentials (strings)","Lead identifiers (email, name, company)","Configuration objects specifying field mappings"],"output_types":["Structured lead objects (name, email, company, LinkedIn URL)","Qualification scores and analysis results written back to CRM"],"categories":["tool-use-integration","crm-connectors"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-kaymen99--sales-outreach-automation-langgraph__cap_1","uri":"capability://automation.workflow.langgraph.based.lead.processing.state.machine.with.conditional.routing","name":"langgraph-based lead processing state machine with conditional routing","description":"Orchestrates the entire lead lifecycle through a LangGraph StateGraph (src/graph.py) that chains discrete processing nodes (src/nodes.py) with conditional branching based on lead qualification scores and data availability. State flows through research → analysis → qualification → outreach generation stages, with each node updating a shared OutReachAutomationState object that persists context across the workflow, enabling resumable and debuggable multi-step automation.","intents":["I need to automate a complex multi-step sales process with clear decision points and error handling","I want to visualize and debug the lead processing workflow to understand where leads get stuck","I need to make the workflow resumable if a step fails (e.g., API timeout during research)"],"best_for":["sales operations teams building deterministic lead processing pipelines","developers implementing agentic workflows that require explicit state management","teams needing workflow observability and debugging capabilities"],"limitations":["State machine is sequential with limited parallelization — all leads process through the same linear path regardless of data availability","No built-in persistence layer — workflow state exists only in memory; crashes lose progress on in-flight leads","Conditional routing logic is hardcoded in graph definition; dynamic routing based on runtime conditions requires code changes"],"requires":["Python 3.9+","LangGraph library (installed via langgraph package)","LangChain for LLM integration","All upstream CRM and data source credentials"],"input_types":["Lead objects from CRM (structured data with name, email, company)","Configuration specifying which analysis steps to run"],"output_types":["Enriched lead objects with research data, qualification scores, and generated outreach materials","Execution logs showing which nodes processed each lead and timing information"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-kaymen99--sales-outreach-automation-langgraph__cap_10","uri":"capability://automation.workflow.batch.lead.processing.with.error.handling.and.logging","name":"batch lead processing with error handling and logging","description":"Processes multiple leads sequentially through the workflow with error handling and detailed logging at each step, enabling visibility into which leads succeeded, which failed, and why. The main execution loop (main.py) iterates through leads from the CRM, runs each through the LangGraph workflow, and logs results including processing time, errors, and generated content, providing operational visibility into the automation system.","intents":["I need to process hundreds of leads through the workflow and understand which ones succeeded or failed","I want detailed logs showing what happened to each lead at each workflow step","I need to identify and retry failed leads without reprocessing successful ones"],"best_for":["sales operations running daily/weekly lead processing batches","teams needing operational visibility into automation system health","organizations troubleshooting workflow failures and lead processing issues"],"limitations":["Sequential processing is slow for large batches — processing 1000 leads may take hours due to LLM API latency","No parallelization — cannot process multiple leads simultaneously to speed up batch execution","Error handling is basic (log and continue) — no automatic retry logic for transient failures","Logs are text-based without structured format — difficult to parse and analyze programmatically"],"requires":["Python 3.9+","All upstream dependencies (CRM credentials, LLM API keys, data sources)","Sufficient time to process all leads (typically 30-60 seconds per lead depending on LLM latency)"],"input_types":["List of leads from CRM","Workflow configuration (which steps to run, LLM provider selection)"],"output_types":["Processing logs showing success/failure for each lead","Generated content (analysis reports, outreach materials) for successful leads","Error messages and stack traces for failed leads"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-kaymen99--sales-outreach-automation-langgraph__cap_2","uri":"capability://data.processing.analysis.automated.lead.research.via.web.scraping.and.data.aggregation","name":"automated lead research via web scraping and data aggregation","description":"Collects lead intelligence by scraping LinkedIn profiles, company websites, and social media presence, then aggregates findings into structured research reports. The research node (src/nodes.py) orchestrates multiple external data sources and formats results as context for downstream LLM analysis, enabling personalized outreach based on recent company news, hiring activity, and professional background.","intents":["I need to automatically gather background information on leads without manual LinkedIn research","I want to identify recent company signals (funding, hiring, product launches) to personalize outreach","I need to create research reports that inform lead qualification decisions"],"best_for":["sales development teams automating lead research at scale","outbound sales organizations needing data enrichment before outreach","sales operations building intelligence-driven lead scoring"],"limitations":["LinkedIn scraping may violate Terms of Service and faces rate limiting; reliability depends on third-party scraping service stability","Company website analysis is shallow (basic text extraction) and cannot parse dynamic/JavaScript-heavy sites","No real-time data — research reflects historical snapshots; cannot detect intra-day hiring or funding announcements"],"requires":["Python 3.9+","Network access to external data sources (LinkedIn, company websites, news APIs)","Optional: API keys for premium data enrichment services (Hunter.io, RocketReach, etc.)","Lead data must include company name and/or LinkedIn profile URL"],"input_types":["Lead objects with name, email, company, LinkedIn URL","Configuration specifying which data sources to query"],"output_types":["Structured research reports (JSON/text) containing: professional background, company info, recent news, hiring signals","Raw HTML/text from scraped sources for LLM analysis"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-kaymen99--sales-outreach-automation-langgraph__cap_3","uri":"capability://planning.reasoning.ai.powered.lead.qualification.with.multi.llm.provider.support","name":"ai-powered lead qualification with multi-llm provider support","description":"Analyzes enriched lead data using configurable LLM providers (Gemini, OpenAI, Anthropic) to generate qualification scores and detailed analysis reports. The qualification node (src/nodes.py) sends structured prompts (src/prompts.py) containing lead research, company context, and business criteria to the LLM, which returns structured scores (0-100) and reasoning that determines whether the lead advances to outreach generation. Supports multiple LLM backends through a provider abstraction layer (src/utils.py) enabling cost/latency optimization.","intents":["I need to automatically score leads based on fit criteria without manual review","I want to use different LLM providers (cheaper models for simple scoring, advanced models for complex analysis)","I need qualification reasoning to explain why a lead was rejected or accepted"],"best_for":["sales teams automating lead qualification at scale (100+ leads/day)","organizations with variable LLM budgets wanting provider flexibility","teams building custom qualification criteria beyond standard firmographic scoring"],"limitations":["LLM quality varies significantly by provider and model version; no guarantee of consistent scoring across model updates","Hallucination risk — LLM may invent company details or misinterpret research data; requires human review for high-stakes decisions","Scoring is deterministic only within a single model version; switching providers may change qualification outcomes for identical leads","No feedback loop — qualification scores don't improve based on actual sales outcomes; requires manual prompt tuning"],"requires":["Python 3.9+","API keys for at least one LLM provider (OpenAI, Anthropic, Google Gemini)","Enriched lead data from research node (company info, professional background, signals)","Configured business criteria in prompts (src/prompts.py)"],"input_types":["Lead objects with research data (company background, news, hiring signals)","Qualification criteria configuration (target company size, industry, growth signals)","LLM provider selection and model parameters"],"output_types":["Qualification score (0-100 integer)","Structured analysis report (JSON) with reasoning, fit assessment, and risk factors","Pass/fail decision determining whether lead advances to outreach generation"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-kaymen99--sales-outreach-automation-langgraph__cap_4","uri":"capability://text.generation.language.personalized.outreach.content.generation.with.template.based.prompting","name":"personalized outreach content generation with template-based prompting","description":"Generates customized sales emails, interview scripts, and analysis reports by combining lead research data with structured prompt templates (src/prompts.py) sent to LLMs. The outreach generation node creates multiple content variants (email, call script, LinkedIn message) tailored to the lead's background, company signals, and business context, enabling sales teams to send personalized outreach at scale without manual copywriting.","intents":["I need to generate personalized sales emails for hundreds of leads without hiring copywriters","I want to create multiple outreach variants (email, call script, LinkedIn message) from a single lead profile","I need outreach content that references specific company signals and lead background to increase response rates"],"best_for":["outbound sales teams automating email/message generation at scale","sales development reps needing content templates they can customize","organizations testing multiple outreach variants for A/B testing"],"limitations":["Generated content quality depends entirely on LLM capability and prompt engineering; requires human review before sending to avoid brand damage","Personalization is surface-level (name, company, recent news) — cannot reason about deep business context or strategic fit","No A/B testing framework — generates single variant per lead; requires external tools to test multiple messages","Content may violate CAN-SPAM or GDPR if not manually reviewed; no built-in compliance checking"],"requires":["Python 3.9+","LLM API credentials (OpenAI, Anthropic, or Google Gemini)","Enriched lead data from research and qualification nodes","Configured outreach templates and business context in prompts (src/prompts.py)"],"input_types":["Qualified lead objects with research data (professional background, company info, signals)","Business context (product description, value proposition, call-to-action)","Outreach template configuration (tone, length, format preferences)"],"output_types":["Personalized email body (plain text or HTML)","Call script with talking points","LinkedIn message variant","Analysis report summarizing lead fit and outreach strategy"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-kaymen99--sales-outreach-automation-langgraph__cap_5","uri":"capability://tool.use.integration.google.docs.document.generation.and.crm.result.synchronization","name":"google docs document generation and crm result synchronization","description":"Exports generated analysis reports and outreach materials to Google Docs and writes qualification results back to the source CRM system. The document generation node creates formatted reports in Google Docs (enabling easy sharing and editing) while the CRM sync node updates lead records with qualification scores, analysis summaries, and generated content, creating a closed loop between automation and sales tools.","intents":["I need to save generated analysis reports to Google Docs for team review and sharing","I want qualification scores and outreach content automatically written back to my CRM","I need a centralized repository of lead analysis that sales reps can access and edit"],"best_for":["sales teams using Google Workspace and HubSpot/Airtable/Google Sheets","organizations needing audit trails of lead analysis and outreach decisions","teams sharing lead research across sales, marketing, and management"],"limitations":["Google Docs integration requires OAuth setup and service account credentials; adds authentication complexity","CRM field mapping must be manually configured; no automatic schema detection means custom fields may not sync","Document formatting is basic (text + simple tables); cannot generate complex layouts or embedded visualizations","No versioning or conflict resolution — if multiple processes write to same CRM record simultaneously, last write wins"],"requires":["Python 3.9+","Google Cloud project with Docs API enabled and service account credentials","CRM API credentials (HubSpot, Airtable, or Google Sheets)","Configured field mappings between generated content and CRM custom fields"],"input_types":["Generated analysis reports and outreach content (text/JSON)","Lead objects with CRM identifiers (HubSpot contact ID, Airtable record ID, etc.)","Configuration specifying which fields to sync to CRM"],"output_types":["Google Docs URLs containing formatted analysis reports","Updated CRM records with qualification scores, analysis summaries, and outreach content","Execution logs showing which fields were synced and any errors"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-kaymen99--sales-outreach-automation-langgraph__cap_6","uri":"capability://automation.workflow.configurable.workflow.customization.via.business.data.and.prompt.templates","name":"configurable workflow customization via business data and prompt templates","description":"Enables non-technical users to customize the entire sales automation workflow by editing business context (company description, value proposition, target criteria) and prompt templates (src/prompts.py) without modifying code. The system reads configuration from environment variables and prompt files, allowing sales operations teams to adjust qualification criteria, outreach messaging, and analysis focus by editing text files rather than Python code.","intents":["I need to adjust lead qualification criteria without asking engineers to modify code","I want to change outreach messaging and tone without redeploying the system","I need to customize analysis focus for different sales campaigns or product lines"],"best_for":["sales operations teams managing multiple campaigns with different criteria","organizations with non-technical sales leadership wanting to adjust automation rules","teams running A/B tests on qualification criteria or outreach messaging"],"limitations":["Configuration is file-based (environment variables, prompt text files); no UI for editing, requires file system access","Changes to prompts require restarting the workflow; no hot-reload capability for live configuration updates","No validation of configuration changes — invalid prompts or criteria may cause LLM failures mid-workflow","Prompt engineering requires understanding LLM behavior; poorly written prompts will degrade qualification and content quality"],"requires":["Python 3.9+","Access to configuration files (src/prompts.py, .env file)","Basic understanding of prompt engineering and LLM behavior","Ability to restart the workflow after configuration changes"],"input_types":["Text-based prompt templates (src/prompts.py)","Environment variables specifying business context (company name, value proposition, target criteria)","Configuration files defining workflow behavior (which steps to run, LLM provider selection)"],"output_types":["Modified workflow behavior reflecting new qualification criteria, outreach messaging, and analysis focus","Execution logs showing which configuration was used for each lead"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-kaymen99--sales-outreach-automation-langgraph__cap_7","uri":"capability://automation.workflow.execution.state.persistence.and.workflow.resumability","name":"execution state persistence and workflow resumability","description":"Maintains workflow state across processing steps through the OutReachAutomationState object (src/state.py) that persists lead data, research results, qualification scores, and generated content throughout the pipeline. This enables debugging individual leads, understanding where processing failed, and potentially resuming workflows after errors, though persistence is in-memory only and requires external storage for true durability.","intents":["I need to debug why a specific lead failed qualification or outreach generation","I want to understand what data was collected and analyzed for each lead","I need to resume processing if a step fails (e.g., LLM API timeout) without reprocessing all leads"],"best_for":["developers debugging workflow issues and lead processing problems","operations teams needing visibility into what happened to each lead","teams running long-running workflows that may experience transient failures"],"limitations":["State is in-memory only — workflow crashes lose all progress on in-flight leads; no automatic persistence to disk or database","No built-in resumability — if a step fails, the entire workflow must restart from the beginning","State object grows with each lead processed — memory usage scales linearly with number of leads, causing issues for large batches","No state versioning or rollback — cannot revert to previous state if a step produces incorrect results"],"requires":["Python 3.9+","LangGraph library with state management support","Sufficient memory to hold state for all in-flight leads (typically 1-10MB per lead)"],"input_types":["Lead objects and processing results from each workflow step","Configuration specifying which state fields to track"],"output_types":["Structured state object (OutReachAutomationState) containing all lead data and processing results","Execution logs showing state transitions and data flow between nodes"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-kaymen99--sales-outreach-automation-langgraph__cap_8","uri":"capability://tool.use.integration.multi.provider.llm.abstraction.with.cost.and.latency.optimization","name":"multi-provider llm abstraction with cost and latency optimization","description":"Abstracts LLM provider selection through a utility layer (src/utils.py) that routes requests to Gemini, OpenAI, or Anthropic based on configuration, enabling cost optimization by using cheaper models for simple tasks (lead research summarization) and advanced models for complex reasoning (qualification analysis). The abstraction handles provider-specific API differences, token counting, and error handling, allowing the workflow to switch providers without code changes.","intents":["I need to optimize LLM costs by using cheaper models for simple tasks and expensive models only when necessary","I want to switch LLM providers (e.g., from OpenAI to Anthropic) without rewriting the workflow","I need to compare LLM quality and cost across providers for the same task"],"best_for":["organizations running high-volume lead processing with tight LLM budgets","teams evaluating multiple LLM providers for quality and cost","developers building LLM-powered workflows that need provider flexibility"],"limitations":["Provider abstraction adds ~50-100ms latency per LLM call due to routing logic and error handling","Model capabilities vary significantly across providers — cheaper models may produce lower-quality qualification scores or outreach content","No automatic cost estimation — users must manually calculate costs for different provider/model combinations","Token counting is approximate; actual costs may vary based on provider's token calculation rules"],"requires":["Python 3.9+","API keys for at least one LLM provider (OpenAI, Anthropic, Google Gemini)","Configuration specifying which provider and model to use for each workflow step"],"input_types":["Prompts and context to send to LLM","Configuration specifying provider, model, and parameters (temperature, max_tokens)","Optional: cost budget and latency constraints"],"output_types":["LLM responses (text, structured JSON)","Metadata including provider, model, token usage, and cost"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-kaymen99--sales-outreach-automation-langgraph__cap_9","uri":"capability://text.generation.language.structured.prompt.engineering.with.task.specific.templates","name":"structured prompt engineering with task-specific templates","description":"Organizes LLM prompts into task-specific templates (src/prompts.py) for research summarization, lead qualification, and outreach generation, enabling consistent prompt structure across the workflow and making it easy to adjust prompts without modifying code. Each template includes context injection points for lead data, business criteria, and company information, allowing the same template to be reused for different leads with different data.","intents":["I need to ensure consistent prompt structure across all LLM calls to improve result quality","I want to adjust prompts for different campaigns without modifying code","I need to version and test different prompt variants to improve qualification accuracy"],"best_for":["teams optimizing LLM output quality through prompt engineering","organizations running multiple campaigns with different messaging and criteria","developers building LLM-powered workflows that need prompt flexibility"],"limitations":["Prompt quality is highly dependent on manual engineering — no automatic optimization or feedback loop","Templates are text-based without validation — invalid prompts may cause LLM failures mid-workflow","No A/B testing framework — requires external tools to test multiple prompt variants","Prompt changes require restarting the workflow; no hot-reload capability for live testing"],"requires":["Python 3.9+","Understanding of prompt engineering and LLM behavior","Access to prompt template files (src/prompts.py)"],"input_types":["Lead data (name, company, background, research findings)","Business context (value proposition, target criteria, campaign messaging)","Prompt template configuration"],"output_types":["Formatted prompts ready for LLM consumption","LLM responses (qualification scores, analysis reports, outreach content)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":40,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","API credentials for target CRM (HubSpot API key, Airtable personal access token, or Google Sheets service account)","Network connectivity to CRM APIs","LangGraph library (installed via langgraph package)","LangChain for LLM integration","All upstream CRM and data source credentials","All upstream dependencies (CRM credentials, LLM API keys, data sources)","Sufficient time to process all leads (typically 30-60 seconds per lead depending on LLM latency)","Network access to external data sources (LinkedIn, company websites, news APIs)","Optional: API keys for premium data enrichment services (Hunter.io, RocketReach, etc.)"],"failure_modes":["Only three CRM providers supported out-of-box; custom adapters required for Salesforce, Pipedrive, or other systems","No real-time bidirectional sync — updates are batch-based and unidirectional (read leads, write results)","Field mapping must be manually configured per CRM instance; no automatic schema detection","State machine is sequential with limited parallelization — all leads process through the same linear path regardless of data availability","No built-in persistence layer — workflow state exists only in memory; crashes lose progress on in-flight leads","Conditional routing logic is hardcoded in graph definition; dynamic routing based on runtime conditions requires code changes","Sequential processing is slow for large batches — processing 1000 leads may take hours due to LLM API latency","No parallelization — cannot process multiple leads simultaneously to speed up batch execution","Error handling is basic (log and continue) — no automatic retry logic for transient failures","Logs are text-based without structured format — difficult to parse and analyze programmatically","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.32516831865573664,"quality":0.47,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.9,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"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":"2025-01-15T14:04:52Z"},"community":{"stars":284,"forks":76,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=kaymen99--sales-outreach-automation-langgraph","compare_url":"https://unfragile.ai/compare?artifact=kaymen99--sales-outreach-automation-langgraph"}},"signature":"jrPeAWAjLkZCyH611S7C461J0hMNbhMMIwZzNkqIkssmPFpAiTslX0Z9kFNPyD8+1xJ4DVfavM7s3+cqcvC4AA==","signedAt":"2026-06-16T09:42:22.210Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/kaymen99--sales-outreach-automation-langgraph","artifact":"https://unfragile.ai/kaymen99--sales-outreach-automation-langgraph","verify":"https://unfragile.ai/api/v1/verify?slug=kaymen99--sales-outreach-automation-langgraph","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"}}