{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-autonomous-hr-chatbot","slug":"autonomous-hr-chatbot","name":"Autonomous HR Chatbot","type":"agent","url":"https://github.com/stepanogil/autonomous-hr-chatbot","page_url":"https://unfragile.ai/autonomous-hr-chatbot","categories":["chatbots-assistants"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-autonomous-hr-chatbot__cap_0","uri":"capability://planning.reasoning.langchain.orchestrated.multi.tool.agent.reasoning","name":"langchain-orchestrated multi-tool agent reasoning","description":"Implements a LangChain-based agent framework that interprets natural language HR queries and autonomously selects from three specialized tools (policy retrieval, employee data access, mathematical calculations) to compose answers. The agent uses chain-of-thought reasoning to decompose questions into tool invocations, managing context and tool outputs across multiple reasoning steps without human intervention.","intents":["Build an autonomous agent that can answer complex HR questions requiring multiple data sources","Enable the agent to decide which tools to use based on query intent without explicit routing","Chain tool outputs together to answer multi-step HR questions (e.g., 'calculate PTO balance for employee X based on policy Y')"],"best_for":["HR teams building self-service chatbots","Developers implementing autonomous agent systems with tool composition","Organizations wanting to reduce HR support ticket volume through automation"],"limitations":["LangChain 0.0.220 is outdated (as of 2025); newer versions have breaking API changes requiring migration","Agent reasoning is sequential and not optimized for parallel tool execution, adding latency for multi-tool queries","No built-in error recovery or fallback strategies if a tool fails mid-chain","Agent context window is limited by the underlying LLM (gpt-3.5-turbo has 4K tokens), constraining query complexity"],"requires":["Python 3.8+","LangChain 0.0.220","OpenAI API key or Azure OpenAI credentials","Pinecone API key for policy vector store","CSV file with employee data loaded as pandas DataFrame"],"input_types":["natural language text (HR queries)"],"output_types":["natural language text (agent-composed answers)","structured reasoning trace (tool selections and outputs)"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-autonomous-hr-chatbot__cap_1","uri":"capability://search.retrieval.semantic.hr.policy.retrieval.via.vector.embeddings","name":"semantic hr policy retrieval via vector embeddings","description":"Implements a RetrievalQA tool that converts HR policy documents into OpenAI text-embedding-ada-002 embeddings, stores them in Pinecone vector database, and retrieves semantically relevant policy excerpts at query time. The tool performs cosine similarity search to find policy sections matching the user's natural language question, enabling the agent to ground answers in actual HR documentation without hallucination.","intents":["Retrieve relevant HR policy sections when an employee asks about benefits, leave, or compensation","Ensure chatbot answers are grounded in actual company policies, not LLM hallucinations","Support policy updates by re-embedding and re-indexing documents without retraining the agent"],"best_for":["HR departments with large policy documents (handbooks, compliance guides)","Organizations needing audit trails showing which policies informed chatbot answers","Teams wanting to decouple policy updates from agent redeployment"],"limitations":["Embedding quality depends on document chunking strategy; poor chunk boundaries can fragment policy context across multiple embeddings","Pinecone vector search has a fixed dimensionality (1536 for ada-002); changing embedding models requires full re-indexing","No built-in handling of policy versioning or temporal queries (e.g., 'what was the PTO policy before 2024?')","Semantic search can retrieve irrelevant policies if query language differs significantly from policy document language"],"requires":["Pinecone account and API key","OpenAI API key for text-embedding-ada-002 model","HR policy documents in text format (PDF, DOCX, or plain text)","Jupyter notebook (store_embeddings_in_pinecone.ipynb) to pre-process and embed documents"],"input_types":["natural language HR policy questions","HR policy documents (text, PDF)"],"output_types":["relevant policy excerpts (text)","similarity scores (float 0-1)"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-autonomous-hr-chatbot__cap_2","uri":"capability://data.processing.analysis.employee.data.querying.via.python.repl.execution","name":"employee data querying via python repl execution","description":"Implements a PythonAstREPLTool that allows the agent to execute Python code against a pandas DataFrame containing employee records. The agent can generate and execute Python queries (e.g., 'df[df.name == \"John\"].salary') to access employee information, enabling dynamic data filtering without pre-defined query templates. The tool uses AST parsing to validate code safety before execution.","intents":["Query employee records (salary, department, hire date) based on natural language requests","Filter employee data by multiple criteria (e.g., 'all employees in Engineering hired after 2020')","Enable the agent to perform data transformations (grouping, aggregation) to answer analytical HR questions"],"best_for":["HR teams with employee data in CSV or database format","Organizations needing flexible employee data queries without writing SQL","Developers building agents that need to access structured employee records"],"limitations":["PythonAstREPLTool has security constraints; it blocks certain Python operations (imports, file I/O) to prevent code injection, limiting query complexity","Employee data is loaded into memory as a pandas DataFrame at startup; large datasets (>100K employees) may cause memory issues","No built-in support for real-time data sync; employee data is static until the agent is restarted","Agent-generated Python code may be inefficient (e.g., full table scans instead of indexed queries) compared to SQL"],"requires":["Employee data in CSV format with columns: name, department, salary, hire_date, etc.","pandas 2.0.3+","Python 3.8+","CSV file path accessible to the backend at runtime"],"input_types":["natural language employee data queries"],"output_types":["pandas DataFrame subsets (filtered/aggregated employee records)","scalar values (counts, sums, averages)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-autonomous-hr-chatbot__cap_3","uri":"capability://planning.reasoning.mathematical.calculation.via.llm.driven.math.chain","name":"mathematical calculation via llm-driven math chain","description":"Implements an LLMMathChain tool that allows the agent to perform mathematical calculations (e.g., PTO accrual, salary adjustments, benefit deductions) by having the LLM generate Python math expressions and executing them. The tool handles unit conversions and multi-step calculations, enabling the agent to answer HR questions requiring numerical reasoning without hardcoding calculation logic.","intents":["Calculate PTO accrual based on tenure and company policy","Compute net salary after tax and benefit deductions","Determine bonus amounts or salary adjustments based on performance metrics"],"best_for":["HR teams with complex compensation calculations","Organizations needing transparent, auditable calculation logic","Developers building agents that need to handle numerical HR queries"],"limitations":["LLMMathChain relies on the LLM to generate correct math expressions; complex calculations may produce incorrect results if the LLM misunderstands the formula","No built-in support for financial precision (rounding, currency handling); results may have floating-point errors","Calculations are not auditable at the formula level; the agent generates expressions dynamically, making it hard to verify correctness","No support for conditional logic (if-then calculations); all calculations are linear expressions"],"requires":["OpenAI API key","LangChain 0.0.220","Python 3.8+ for expression evaluation"],"input_types":["natural language calculation requests (e.g., 'calculate PTO balance for 5 years tenure')","numerical parameters (tenure, salary, tax rate)"],"output_types":["numerical results (float)","calculation explanation (text)"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-autonomous-hr-chatbot__cap_4","uri":"capability://text.generation.language.streamlit.based.conversational.chat.interface","name":"streamlit-based conversational chat interface","description":"Implements a Streamlit frontend (hr_agent_frontend.py) that renders a chat interface using the streamlit_chat component, allowing users to submit HR queries and view agent responses in a familiar conversation format. The frontend manages session state to maintain conversation history and handles streaming responses from the backend, providing real-time feedback to users.","intents":["Provide employees with an intuitive chat interface to ask HR questions","Display conversation history so users can reference previous answers","Enable real-time response streaming to show agent progress"],"best_for":["Non-technical HR employees and end users","Organizations wanting a low-code chat UI without custom frontend development","Teams prototyping HR chatbots quickly"],"limitations":["Streamlit 1.24.0 is outdated; newer versions may have breaking changes","streamlit_chat component is a third-party library with limited customization; styling is constrained to Streamlit's theming system","No built-in authentication or access control; all users see the same chat interface","Session state is stored in browser memory; conversation history is lost on page refresh unless explicitly persisted","Streamlit reruns the entire script on each interaction, causing latency for complex queries"],"requires":["Streamlit 1.24.0","streamlit-chat 0.1.1","Python 3.8+","Backend service (local or Azure) running and accessible"],"input_types":["natural language text (user queries)"],"output_types":["rendered chat messages (HTML/CSS)","conversation history (JSON)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-autonomous-hr-chatbot__cap_5","uri":"capability://automation.workflow.dual.backend.deployment.abstraction.local.and.azure","name":"dual-backend deployment abstraction (local and azure)","description":"Implements two backend modules (hr_agent_backend_local.py and hr_agent_backend_azure.py) that abstract the LLM provider and deployment environment, allowing the same agent logic to run against OpenAI API (local) or Azure OpenAI Service (cloud). Both backends use the same LangChain agent interface, enabling seamless switching between deployment targets without code changes to the agent logic.","intents":["Deploy the HR chatbot locally for development and testing without cloud dependencies","Deploy the HR chatbot to Azure for production with enterprise security and compliance","Switch between local and cloud deployments without rewriting agent code"],"best_for":["Organizations with hybrid deployment strategies (dev local, prod cloud)","Teams wanting to avoid vendor lock-in by supporting multiple LLM providers","Enterprises requiring Azure compliance (SOC 2, HIPAA) for HR data"],"limitations":["Both backends use gpt-3.5-turbo, which has a 4K token limit; longer conversations may exceed context window","Azure backend requires Azure OpenAI provisioning and quota management; setup is more complex than local OpenAI API","No automatic failover between backends; if Azure is unavailable, the system must be manually switched to local","API costs differ between local (OpenAI pay-as-you-go) and Azure (reserved capacity); cost optimization requires separate analysis"],"requires":["For local: OpenAI API key","For Azure: Azure subscription, Azure OpenAI resource, Azure credentials (connection string or managed identity)","LangChain 0.0.220","Python 3.8+"],"input_types":["agent queries (from frontend)"],"output_types":["agent responses (text)","tool execution traces"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-autonomous-hr-chatbot__cap_6","uri":"capability://data.processing.analysis.document.embedding.and.vector.indexing.pipeline","name":"document embedding and vector indexing pipeline","description":"Implements a Jupyter notebook (store_embeddings_in_pinecone.ipynb) that processes HR policy documents through a multi-step pipeline: splitting documents into semantic chunks, generating embeddings using OpenAI's text-embedding-ada-002 model, and storing embeddings in Pinecone with metadata. This pipeline runs offline before the agent starts, enabling fast semantic search at query time without embedding computation overhead.","intents":["Convert HR policy documents into searchable vector embeddings","Update the policy vector store when HR policies change","Prepare documents for semantic search without modifying the agent code"],"best_for":["HR teams managing large policy documents (100+ pages)","Organizations needing to update policies frequently without redeploying the agent","Developers implementing RAG systems with document preprocessing"],"limitations":["Document chunking strategy is not adaptive; fixed chunk sizes may fragment important policy context or create redundant overlapping chunks","Embedding generation is sequential; processing large document sets (1000+ pages) can take hours","No built-in deduplication; similar policy sections may be embedded multiple times, wasting Pinecone storage","Metadata is limited to document source; no support for policy versioning, effective dates, or deprecation tracking","Pipeline is manual (Jupyter notebook); no automation for periodic policy updates"],"requires":["Jupyter notebook environment","OpenAI API key for text-embedding-ada-002","Pinecone API key and index","HR policy documents in text format","Python 3.8+","langchain, pinecone-client, openai libraries"],"input_types":["HR policy documents (text, PDF)"],"output_types":["vector embeddings (1536-dimensional float arrays)","Pinecone index with metadata"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-autonomous-hr-chatbot__cap_7","uri":"capability://data.processing.analysis.csv.based.employee.data.loading.and.management","name":"csv-based employee data loading and management","description":"Implements employee data management through a CSV file that is loaded into a pandas DataFrame at agent startup. The system stores employee records with fields like name, department, salary, and hire_date, making employee data accessible to the PythonAstREPLTool for dynamic querying. This approach avoids database dependencies while supporting basic employee data operations.","intents":["Store employee records in a simple, version-controllable format (CSV)","Enable the agent to query employee data without a database backend","Support employee data updates by modifying the CSV file and restarting the agent"],"best_for":["Small to medium organizations (< 10K employees) with simple employee data structures","Teams wanting to avoid database setup and maintenance","Development and testing environments where data persistence is not critical"],"limitations":["CSV format has no schema enforcement; data type mismatches (e.g., salary as string) can cause query failures","No built-in data validation; invalid or missing employee records are not caught until query time","Entire employee dataset is loaded into memory; large datasets (> 100K employees) cause memory issues","No support for real-time data updates; employee data is static until the agent is restarted","CSV files are not suitable for sensitive data; employee records (salary, SSN) should be encrypted or stored in a secure database","No audit trail or change tracking; CSV modifications are not logged"],"requires":["CSV file with employee data (name, department, salary, hire_date columns)","pandas 2.0.3+","Python 3.8+","CSV file path accessible to the backend"],"input_types":["CSV file (text)"],"output_types":["pandas DataFrame (in-memory)","filtered/aggregated employee records"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-autonomous-hr-chatbot__cap_8","uri":"capability://planning.reasoning.multi.step.hr.query.decomposition.and.execution","name":"multi-step hr query decomposition and execution","description":"Enables the agent to decompose complex HR questions into multiple sequential steps, invoking different tools (policy retrieval, employee data queries, calculations) in a chain to compose final answers. For example, a query like 'What is the PTO balance for John Smith?' is decomposed into: (1) retrieve PTO policy, (2) query John's hire date, (3) calculate accrual based on tenure. The agent manages intermediate results and passes them between tool invocations.","intents":["Answer multi-step HR questions that require data from multiple sources","Compose answers by chaining tool outputs (e.g., policy + employee data + calculation)","Enable the agent to reason about which tools to invoke and in what order"],"best_for":["HR teams with complex questions requiring multiple data sources","Organizations needing transparent reasoning about how answers are composed","Developers building agents that need to handle multi-step workflows"],"limitations":["Sequential execution adds latency; if one tool is slow, the entire chain is blocked","No built-in error recovery; if a tool fails mid-chain, the agent may produce incomplete or incorrect answers","Agent reasoning is not optimized for complex chains; very long chains (> 5 steps) may exceed token limits","No support for parallel tool execution; tools that could run in parallel are executed sequentially","Intermediate results are not persisted; if the agent crashes mid-chain, all progress is lost"],"requires":["LangChain 0.0.220","OpenAI API key","All three tools configured (policy retrieval, employee data, calculator)"],"input_types":["natural language HR queries"],"output_types":["composed answers (text)","reasoning trace (tool invocations and outputs)"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-autonomous-hr-chatbot__cap_9","uri":"capability://memory.knowledge.conversation.history.management.and.context.preservation","name":"conversation history management and context preservation","description":"Manages conversation history in the Streamlit frontend, maintaining a record of user queries and agent responses across multiple turns. The agent uses this history to provide context for follow-up questions, enabling multi-turn conversations where the agent can reference previous answers. Session state is stored in Streamlit's session_state object, allowing history to persist within a single user session.","intents":["Enable users to ask follow-up questions that reference previous answers","Provide context to the agent about the conversation so far","Display conversation history to users for reference"],"best_for":["Chat interfaces where users ask multiple related questions","Organizations wanting to provide context-aware responses","Teams building conversational HR assistants"],"limitations":["Session state is stored in browser memory; conversation history is lost on page refresh unless explicitly persisted to a database","No built-in conversation persistence; history is not saved between user sessions","Streamlit reruns the entire script on each interaction, causing the agent to reprocess all history","No support for multi-user conversations or conversation sharing","Token limit constraints mean very long conversations may exceed the LLM's context window, causing older messages to be dropped"],"requires":["Streamlit 1.24.0","streamlit-chat 0.1.1","Backend service to process queries"],"input_types":["user queries (text)"],"output_types":["conversation history (list of messages)","rendered chat interface"],"categories":["memory-knowledge","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":26,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","LangChain 0.0.220","OpenAI API key or Azure OpenAI credentials","Pinecone API key for policy vector store","CSV file with employee data loaded as pandas DataFrame","Pinecone account and API key","OpenAI API key for text-embedding-ada-002 model","HR policy documents in text format (PDF, DOCX, or plain text)","Jupyter notebook (store_embeddings_in_pinecone.ipynb) to pre-process and embed documents","Employee data in CSV format with columns: name, department, salary, hire_date, etc."],"failure_modes":["LangChain 0.0.220 is outdated (as of 2025); newer versions have breaking API changes requiring migration","Agent reasoning is sequential and not optimized for parallel tool execution, adding latency for multi-tool queries","No built-in error recovery or fallback strategies if a tool fails mid-chain","Agent context window is limited by the underlying LLM (gpt-3.5-turbo has 4K tokens), constraining query complexity","Embedding quality depends on document chunking strategy; poor chunk boundaries can fragment policy context across multiple embeddings","Pinecone vector search has a fixed dimensionality (1536 for ada-002); changing embedding models requires full re-indexing","No built-in handling of policy versioning or temporal queries (e.g., 'what was the PTO policy before 2024?')","Semantic search can retrieve irrelevant policies if query language differs significantly from policy document language","PythonAstREPLTool has security constraints; it blocks certain Python operations (imports, file I/O) to prevent code injection, limiting query complexity","Employee data is loaded into memory as a pandas DataFrame at startup; large datasets (>100K employees) may cause memory issues","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.3,"ecosystem":0.39999999999999997,"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":"2026-06-17T09:51:02.371Z","last_scraped_at":"2026-05-03T14:00:10.321Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=autonomous-hr-chatbot","compare_url":"https://unfragile.ai/compare?artifact=autonomous-hr-chatbot"}},"signature":"wHmIB6fZRhhxr3lnow9pHV5uzOFIK40onbnRqgbeFE7/i807iuJyAH0Kcp4Lhrl0pkZ7jKQ3pIuQfX32KUH+Dg==","signedAt":"2026-06-20T21:43:54.087Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/autonomous-hr-chatbot","artifact":"https://unfragile.ai/autonomous-hr-chatbot","verify":"https://unfragile.ai/api/v1/verify?slug=autonomous-hr-chatbot","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"}}