{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"npm_npm-observeeagents","slug":"npm-observeeagents","name":"@observee/agents","type":"mcp","url":"https://www.npmjs.com/package/@observee/agents","page_url":"https://unfragile.ai/npm-observeeagents","categories":["mcp-servers"],"tags":["observee","mcp","agent","ai","llm","anthropic","openai","gemini","langchain","tools"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"npm_npm-observeeagents__cap_0","uri":"capability://tool.use.integration.multi.provider.llm.tool.calling.with.unified.schema","name":"multi-provider llm tool calling with unified schema","description":"Abstracts tool/function calling across multiple LLM providers (OpenAI, Anthropic, Gemini, Ollama) through a unified schema-based interface. Translates provider-specific function calling formats (OpenAI's tools array, Anthropic's tool_use blocks, Gemini's function calling) into a normalized capability model, handling request/response marshaling and provider-specific quirks automatically.","intents":["I want to write agent code once and run it against OpenAI, Anthropic, or Gemini without rewriting tool schemas","I need to handle tool calling responses differently per provider but abstract that complexity away","I want to add a new LLM provider to my agent without refactoring tool definitions"],"best_for":["teams building multi-model agent systems","developers avoiding vendor lock-in to a single LLM provider","builders prototyping agents and want to experiment with different models"],"limitations":["Provider-specific tool features (e.g., Anthropic's caching, OpenAI's parallel tool calls) may not be fully exposed through the abstraction","Schema translation adds ~50-100ms overhead per tool call round-trip","Limited to providers with explicit function/tool calling support; cannot emulate tool calling for models without native support"],"requires":["TypeScript 4.7+","Node.js 16+","API keys for at least one supported LLM provider (OpenAI, Anthropic, Google, or Ollama endpoint)"],"input_types":["JSON schema (tool definitions)","structured tool metadata (name, description, parameters)"],"output_types":["normalized tool call objects","provider-agnostic execution results"],"categories":["tool-use-integration","multi-provider-abstraction"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-observeeagents__cap_1","uri":"capability://tool.use.integration.mcp.model.context.protocol.server.integration","name":"mcp (model context protocol) server integration","description":"Implements the Model Context Protocol specification to expose tools and resources as standardized MCP servers that can be discovered and invoked by MCP-compatible clients. Handles MCP transport (stdio, SSE), resource management, tool registry, and request/response serialization according to the MCP specification, enabling interoperability with Claude Desktop, other MCP clients, and MCP-aware frameworks.","intents":["I want my agent tools to work with Claude Desktop and other MCP clients without custom integration code","I need to expose my tools as a standard MCP server that any MCP-compatible application can consume","I want to leverage existing MCP servers (web search, file access, database tools) in my agent without reimplementing them"],"best_for":["developers building tools for the Anthropic ecosystem","teams standardizing on MCP for tool interoperability","builders wanting their agents to work with Claude Desktop and other MCP clients"],"limitations":["MCP is primarily designed for Anthropic's Claude; support from other LLM providers is limited","Resource streaming and large file handling may have performance constraints depending on transport (stdio vs SSE)","Requires understanding of MCP specification and server lifecycle management"],"requires":["TypeScript 4.7+","Node.js 16+","MCP client library or compatible MCP client (Claude Desktop, etc.)"],"input_types":["tool definitions (name, description, input schema)","resource metadata (URIs, MIME types, descriptions)"],"output_types":["MCP-compliant JSON-RPC responses","tool execution results","resource content (text, binary)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-observeeagents__cap_2","uri":"capability://planning.reasoning.agent.execution.with.tool.use.orchestration","name":"agent execution with tool use orchestration","description":"Orchestrates agentic loops that repeatedly call LLMs, parse tool calls from responses, execute tools, and feed results back into the conversation context. Implements the core agent pattern with automatic tool call detection, execution, and result injection, supporting both streaming and non-streaming LLM responses, error handling for failed tool executions, and configurable stopping conditions (max iterations, tool call completion).","intents":["I want to build an agent that can autonomously use tools to accomplish tasks without manual orchestration code","I need to handle tool execution errors gracefully and retry or recover within the agent loop","I want to stream agent responses and tool execution progress to the user in real-time"],"best_for":["developers building autonomous agents with tool use","teams implementing ReAct or similar agentic patterns","builders prototyping multi-step task automation"],"limitations":["Agent loops add latency proportional to the number of tool calls; no built-in parallelization of independent tool calls","Context window management is the responsibility of the application; SDK does not automatically trim conversation history","No built-in memory or state persistence; requires external storage for multi-turn agent sessions"],"requires":["TypeScript 4.7+","Node.js 16+","LLM provider API key","tool definitions with execution handlers"],"input_types":["initial user prompt/task","tool definitions with execution functions","LLM configuration (model, temperature, max tokens)"],"output_types":["final agent response (text)","tool call history","execution results"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-observeeagents__cap_3","uri":"capability://automation.workflow.streaming.response.handling.with.tool.call.streaming","name":"streaming response handling with tool call streaming","description":"Handles streaming LLM responses and parses tool calls from streamed token sequences, enabling real-time display of agent reasoning and tool execution progress. Buffers streamed tokens, detects tool call boundaries (e.g., Anthropic's tool_use blocks in streaming), and yields partial results as they become available, supporting both text streaming and structured tool call extraction from incomplete streams.","intents":["I want to show users real-time agent reasoning and tool execution progress as it happens","I need to parse tool calls from streamed LLM responses without waiting for the full response","I want to display partial agent responses to users while tools are still executing"],"best_for":["developers building interactive agent UIs with real-time feedback","teams implementing streaming agent interfaces","builders wanting to reduce perceived latency in agent interactions"],"limitations":["Tool call detection from streams is provider-specific and may have edge cases with incomplete or malformed tool calls","Streaming adds complexity to error handling; partial tool calls may need to be buffered and retried","Not all LLM providers support streaming tool calls equally; some may require buffering the full response before tool call extraction"],"requires":["TypeScript 4.7+","Node.js 16+","LLM provider with streaming support (OpenAI, Anthropic, Gemini, Ollama)"],"input_types":["streaming LLM response (token stream)","tool call schema for parsing"],"output_types":["streamed text chunks","parsed tool call objects","partial execution results"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-observeeagents__cap_4","uri":"capability://tool.use.integration.tool.definition.and.schema.validation","name":"tool definition and schema validation","description":"Validates tool definitions against JSON Schema and provider-specific requirements, ensuring tools are compatible with the target LLM provider's tool calling format. Performs schema validation, parameter type checking, and provider-specific constraint validation (e.g., OpenAI's 4096-char description limit, Anthropic's input schema requirements), providing detailed error messages for schema violations.","intents":["I want to validate my tool schemas before deploying them to ensure they work with my LLM provider","I need to catch schema errors early and get clear error messages about what's wrong","I want to ensure my tool definitions are compatible with multiple LLM providers"],"best_for":["developers building tool-heavy agents","teams with strict validation requirements","builders wanting to catch schema errors at development time"],"limitations":["Validation is static and cannot detect runtime issues with tool execution","Provider-specific constraints may change with provider updates; SDK may lag behind provider API changes","Does not validate tool execution logic, only schema structure"],"requires":["TypeScript 4.7+","Node.js 16+","tool definitions in JSON Schema format"],"input_types":["tool definition objects","JSON Schema specifications"],"output_types":["validation results (pass/fail)","detailed error messages","provider compatibility report"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-observeeagents__cap_5","uri":"capability://memory.knowledge.context.management.and.conversation.history","name":"context management and conversation history","description":"Manages conversation history and context windows for multi-turn agent interactions, tracking messages, tool calls, and results in a structured format. Provides utilities for building conversation context, managing message ordering, and preparing context for LLM API calls, but does not include automatic context trimming or summarization; applications must manage context window limits explicitly.","intents":["I want to maintain conversation history across multiple agent interactions","I need to structure messages and tool results for LLM API calls","I want to track what tools were called and what results were returned in a conversation"],"best_for":["developers building multi-turn agent applications","teams implementing conversational agents","builders needing structured conversation tracking"],"limitations":["No automatic context window management; applications must manually trim or summarize history","No built-in persistence; conversation history is in-memory only","No automatic deduplication or compression of repeated context"],"requires":["TypeScript 4.7+","Node.js 16+","application-level context window management logic"],"input_types":["user messages","tool calls and results","system prompts"],"output_types":["structured message arrays","formatted context for LLM APIs"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-observeeagents__cap_6","uri":"capability://automation.workflow.error.handling.and.tool.execution.recovery","name":"error handling and tool execution recovery","description":"Implements error handling for tool execution failures, including automatic retry logic, error context injection into agent loops, and graceful degradation when tools fail. Catches tool execution exceptions, formats error messages, and optionally retries failed tool calls with exponential backoff, allowing agents to recover from transient failures or adapt when tools are unavailable.","intents":["I want my agent to handle tool execution failures gracefully without crashing","I need to retry failed tool calls automatically with backoff","I want the agent to inform the LLM about tool failures so it can adapt its strategy"],"best_for":["developers building production agents","teams needing robust error handling","builders working with unreliable external tools"],"limitations":["Retry logic is generic; does not distinguish between retryable and non-retryable errors","Exponential backoff may not be appropriate for all tool types","Error context injection may consume significant context window tokens"],"requires":["TypeScript 4.7+","Node.js 16+","tool execution handlers with error handling"],"input_types":["tool execution exceptions","error context"],"output_types":["formatted error messages","retry decisions","error context for LLM"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-observeeagents__cap_7","uri":"capability://code.generation.editing.typescript.type.safety.for.tool.definitions.and.responses","name":"typescript type safety for tool definitions and responses","description":"Provides TypeScript type definitions and generics for tool definitions, tool call responses, and agent outputs, enabling compile-time type checking and IDE autocomplete for tool parameters and results. Uses TypeScript's type system to enforce tool schema compatibility and provide type-safe tool execution handlers with inferred parameter types.","intents":["I want compile-time type checking for my tool definitions and execution handlers","I need IDE autocomplete for tool parameters and results","I want to catch type mismatches between tool schemas and execution logic at development time"],"best_for":["TypeScript developers building agents","teams with strict type safety requirements","developers wanting IDE support for tool definitions"],"limitations":["Type safety is limited to TypeScript; no runtime validation of types","Complex tool schemas may result in complex TypeScript types that are difficult to work with","Type inference may not work well with dynamic or conditional tool schemas"],"requires":["TypeScript 4.7+","Node.js 16+"],"input_types":["tool definition objects","JSON Schema specifications"],"output_types":["typed tool call objects","typed execution results"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-observeeagents__cap_8","uri":"capability://tool.use.integration.langchain.integration.and.compatibility","name":"langchain integration and compatibility","description":"Provides integration points with LangChain's agent framework, tool definitions, and LLM interfaces, allowing Observee agents to be used within LangChain applications or vice versa. Implements adapters for LangChain's Tool class, AgentExecutor, and LLM interfaces, enabling interoperability between Observee's agent orchestration and LangChain's broader ecosystem.","intents":["I want to use Observee agents within my existing LangChain application","I need to migrate from LangChain agents to Observee without rewriting tool definitions","I want to leverage LangChain tools in an Observee agent"],"best_for":["teams already invested in LangChain","developers migrating from LangChain to Observee","builders wanting to mix Observee and LangChain components"],"limitations":["Integration may not cover all LangChain features; some advanced LangChain patterns may not be compatible","Adapter overhead may add latency to tool calls","LangChain API changes may require SDK updates to maintain compatibility"],"requires":["TypeScript 4.7+","Node.js 16+","LangChain 0.1.0 or higher"],"input_types":["LangChain Tool objects","LangChain LLM instances"],"output_types":["Observee-compatible tool definitions","LangChain-compatible agent results"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":29,"verified":false,"data_access_risk":"moderate","permissions":["TypeScript 4.7+","Node.js 16+","API keys for at least one supported LLM provider (OpenAI, Anthropic, Google, or Ollama endpoint)","MCP client library or compatible MCP client (Claude Desktop, etc.)","LLM provider API key","tool definitions with execution handlers","LLM provider with streaming support (OpenAI, Anthropic, Gemini, Ollama)","tool definitions in JSON Schema format","application-level context window management logic","tool execution handlers with error handling"],"failure_modes":["Provider-specific tool features (e.g., Anthropic's caching, OpenAI's parallel tool calls) may not be fully exposed through the abstraction","Schema translation adds ~50-100ms overhead per tool call round-trip","Limited to providers with explicit function/tool calling support; cannot emulate tool calling for models without native support","MCP is primarily designed for Anthropic's Claude; support from other LLM providers is limited","Resource streaming and large file handling may have performance constraints depending on transport (stdio vs SSE)","Requires understanding of MCP specification and server lifecycle management","Agent loops add latency proportional to the number of tool calls; no built-in parallelization of independent tool calls","Context window management is the responsibility of the application; SDK does not automatically trim conversation history","No built-in memory or state persistence; requires external storage for multi-turn agent sessions","Tool call detection from streams is provider-specific and may have edge cases with incomplete or malformed tool calls","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.28,"ecosystem":0.5000000000000001,"match_graph":0.25,"freshness":0.6,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:23.904Z","last_scraped_at":"2026-05-03T14:23:57.517Z","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=npm-observeeagents","compare_url":"https://unfragile.ai/compare?artifact=npm-observeeagents"}},"signature":"SPBM2siMm9MbWD7xJz9JwPwQW87sFD1sA1IVTYvDVncX3CVuATl35H+PMo1mR7iJPrWHe7/pZX9CBYEBA8GxBg==","signedAt":"2026-06-20T00:31:15.872Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/npm-observeeagents","artifact":"https://unfragile.ai/npm-observeeagents","verify":"https://unfragile.ai/api/v1/verify?slug=npm-observeeagents","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"}}