{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-pguso--ai-agents-from-scratch","slug":"pguso--ai-agents-from-scratch","name":"ai-agents-from-scratch","type":"repo","url":"https://github.com/pguso/ai-agents-from-scratch","page_url":"https://unfragile.ai/pguso--ai-agents-from-scratch","categories":["ai-agents"],"tags":["ai-agents","educational","function-calling","llm","llm-agent","node-llama-cpp","react-agent","tutorial"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-pguso--ai-agents-from-scratch__cap_0","uri":"capability://text.generation.language.local.llm.inference.via.node.llama.cpp","name":"local-llm-inference-via-node-llama-cpp","description":"Executes quantized GGUF language models locally using node-llama-cpp bindings to the llama.cpp C++ runtime, with platform-specific acceleration (Metal on macOS, CUDA/Vulkan on Linux/Windows). Models run entirely on-device without cloud API calls, enabling privacy-preserving inference with configurable temperature, token limits, and streaming output. The architecture abstracts the underlying C++ runtime through JavaScript bindings, handling model loading, memory management, and token generation.","intents":["Run LLM inference locally without sending data to cloud providers","Build agents with privacy guarantees and no API rate limits","Experiment with different quantized models without cloud costs","Understand how LLM inference actually works at the binary level"],"best_for":["developers building privacy-sensitive AI agents","educators teaching LLM fundamentals without cloud dependencies","teams prototyping agents with cost constraints","researchers experimenting with model quantization and inference optimization"],"limitations":["Inference speed depends on local hardware; CPU-only inference is 10-50x slower than GPU-accelerated cloud APIs","Memory footprint scales with model size; 7B parameter models require ~8GB RAM minimum","No built-in batching or request queuing — single-threaded inference per model instance","Platform-specific binary compilation adds ~5-10 minutes to npm install on first setup","Limited to GGUF quantized models; cannot load full-precision or other formats without conversion"],"requires":["Node.js 18+","node-llama-cpp npm package (includes pre-compiled binaries)","8GB+ RAM for 7B models, 16GB+ for 13B+ models","GGUF quantized model file (e.g., Mistral, Llama 2, Phi) downloaded to ./models/ directory","macOS/Linux/Windows x64 with optional GPU drivers (CUDA 11.8+, Metal, or Vulkan)"],"input_types":["text prompts (string)","system prompts (string)","conversation history (array of message objects with role and content)"],"output_types":["text completion (string)","streaming tokens (async iterator)","structured JSON (when prompted with schema)"],"categories":["text-generation-language","local-inference"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-pguso--ai-agents-from-scratch__cap_1","uri":"capability://tool.use.integration.function.calling.with.tool.schema.binding","name":"function-calling-with-tool-schema-binding","description":"Implements structured function calling by embedding tool schemas in system prompts and parsing LLM-generated function calls from text output. The architecture defines tools as JavaScript objects with name, description, and parameters, then instructs the LLM to output function calls in a parseable format (typically JSON or XML). A tool execution framework intercepts these outputs, validates them against the schema, and executes the corresponding JavaScript functions, returning results back to the LLM for further reasoning.","intents":["Enable agents to call external tools (APIs, databases, file systems) based on reasoning","Understand how function calling works without relying on proprietary APIs like OpenAI's function_calling","Build agents that can dynamically choose which tools to use based on task requirements","Implement ReAct-style agents that reason about tool use before execution"],"best_for":["developers learning agent architecture from first principles","teams building agents with local LLMs that lack native function calling","educators demonstrating tool use patterns without cloud API dependencies","builders prototyping custom tool ecosystems for specialized domains"],"limitations":["Parsing function calls from text is fragile — LLM may generate malformed JSON or hallucinate function names","No built-in retry logic if parsing fails; requires manual error handling and re-prompting","Schema validation is manual; no automatic type coercion or constraint enforcement","Latency overhead from text parsing and validation adds ~50-200ms per tool call","Limited to tools defined in JavaScript; integrating external services requires wrapper functions"],"requires":["Node.js 18+","Tool definitions as JavaScript objects with {name, description, parameters, execute} structure","System prompt that instructs LLM to output function calls in a specific format (JSON/XML)","Parser function to extract and validate function calls from LLM output","Error handling for malformed or invalid function calls"],"input_types":["tool schema definitions (JavaScript objects)","user queries (text)","LLM-generated text containing function calls (string)"],"output_types":["parsed function calls (objects with name and arguments)","tool execution results (any type, returned to LLM)","agent responses (text after tool execution)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-pguso--ai-agents-from-scratch__cap_10","uri":"capability://text.generation.language.hybrid.local.cloud.model.switching","name":"hybrid-local-cloud-model-switching","description":"Enables switching between local LLMs (via node-llama-cpp) and cloud APIs (OpenAI, Anthropic) through a unified interface, allowing developers to compare quality/speed tradeoffs or fall back to cloud when local inference is insufficient. The architecture abstracts the model backend behind a common interface, with conditional logic to route requests to either local or cloud providers based on configuration. This pattern allows the same agent code to work with different model sources without modification.","intents":["Compare local vs cloud LLM quality and performance for specific tasks","Implement fallback strategies (use local by default, fall back to cloud on error)","Prototype with cloud APIs then migrate to local models for production","Understand tradeoffs between privacy (local) and capability (cloud)"],"best_for":["developers evaluating local vs cloud LLM tradeoffs","teams building hybrid systems with fallback capabilities","builders prototyping with cloud APIs then optimizing with local models","educators teaching model deployment strategies and architecture patterns"],"limitations":["Abstraction adds complexity; unified interface may not expose all model-specific features","Cost tracking becomes complex with multiple providers; requires separate accounting for local vs cloud usage","Quality differences between models may require different prompts or parameters; unified interface may not accommodate all variations","Latency differences are significant (local: 1-10s, cloud: 0.5-5s); fallback logic must handle timing differences","No automatic provider selection; developers must manually choose or implement selection logic"],"requires":["Node.js 18+","node-llama-cpp for local inference","OpenAI SDK or Anthropic SDK for cloud APIs","API keys for cloud providers (if using cloud fallback)","Unified model interface or adapter pattern to abstract provider differences","Configuration to specify which provider to use per request"],"input_types":["prompts (text)","provider preference (local/cloud/auto)","fallback strategy (fail, retry, switch provider)"],"output_types":["LLM responses (text)","provider metadata (which provider was used, latency, cost)","fallback logs (if provider switching occurred)"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-pguso--ai-agents-from-scratch__cap_11","uri":"capability://planning.reasoning.progressive.learning.path.with.modular.examples","name":"progressive-learning-path-with-modular-examples","description":"Structures agent development as a nine-module learning progression, where each module introduces exactly one new concept (basic LLM interaction → function calling → memory → ReAct). The architecture uses consistent module structure (executable .js file, detailed CODE.md walkthrough, conceptual CONCEPT.md explanation) to enable self-paced learning with multiple entry points. Each module builds on previous ones, creating a scaffolded learning experience from fundamentals to autonomous agents.","intents":["Learn agent development from first principles without skipping foundational concepts","Understand how each component (prompts, tools, memory, reasoning) contributes to agent behavior","Experiment with working code examples at each stage of complexity","Build mental models of agent architecture through hands-on implementation"],"best_for":["developers new to AI agents wanting to understand fundamentals","educators teaching agent development and LLM concepts","teams building custom agents and needing architectural understanding","self-taught learners who prefer hands-on code over theoretical material"],"limitations":["Learning path is linear; skipping modules may create knowledge gaps","Examples are educational, not production-ready; require significant refactoring for real use","No interactive exercises or automated testing; learners must manually verify understanding","Progression assumes JavaScript familiarity; not suitable for developers unfamiliar with async/await or Node.js","Module complexity increases rapidly; later modules (ReAct) require understanding of all previous concepts"],"requires":["Node.js 18+","JavaScript/async programming knowledge","Willingness to read and modify code","Local LLM model downloaded (for local examples)","Optional: OpenAI API key (for cloud examples)"],"input_types":["module code (JavaScript files)","module documentation (CODE.md, CONCEPT.md)","example prompts and queries (text)"],"output_types":["working agent implementations (JavaScript code)","understanding of agent architecture (conceptual knowledge)","executable examples demonstrating each concept (runnable code)"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-pguso--ai-agents-from-scratch__cap_2","uri":"capability://memory.knowledge.persistent.conversation.memory.with.message.history","name":"persistent-conversation-memory-with-message-history","description":"Maintains conversation state by storing message history (user and assistant messages) in memory or persistent storage, then including the full or windowed history in each LLM prompt. The architecture uses a message buffer that tracks role (user/assistant), content, and optionally metadata (timestamps, tool calls). Between turns, the system appends new user messages and LLM responses to this buffer, then passes the entire history to the LLM context window, enabling multi-turn reasoning and context awareness.","intents":["Build agents that remember previous interactions and maintain conversation context","Implement multi-turn reasoning where agents reference earlier steps or decisions","Create stateful agents that can learn from conversation history within a session","Understand how context management works without external vector databases or RAG"],"best_for":["developers building conversational agents with local LLMs","educators teaching context management and memory patterns","teams prototyping chatbots that need session-level memory","builders experimenting with conversation-based reasoning without cloud dependencies"],"limitations":["No automatic token counting — developers must manually track context window usage or implement token budgeting","Full history approach hits context window limits quickly; requires manual windowing or summarization for long conversations","No built-in persistence — memory is lost when process terminates unless explicitly saved to disk/database","No semantic deduplication or compression; redundant messages consume tokens unnecessarily","Scaling to thousands of messages requires external storage; in-memory arrays become unwieldy"],"requires":["Node.js 18+","Message history data structure (array of {role, content} objects)","LLM context window large enough for full history + new prompt (typically 2K-4K tokens for multi-turn)","Optional: file system or database for persistence (JSON, SQLite, etc.)","Token counting utility if implementing context window management"],"input_types":["user messages (text)","assistant responses (text)","message metadata (role, timestamp, tool calls)"],"output_types":["conversation history (array of message objects)","context-aware LLM responses (text)","persisted conversation logs (JSON or database records)"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-pguso--ai-agents-from-scratch__cap_3","uri":"capability://planning.reasoning.react.pattern.agent.orchestration","name":"react-pattern-agent-orchestration","description":"Implements the ReAct (Reasoning + Acting) pattern by orchestrating a loop where the LLM reasons about the next step, decides whether to call a tool or return a final answer, executes the tool if needed, and incorporates the result back into the conversation history. The architecture maintains a reasoning trace (visible to the LLM) that shows thought processes, tool calls, and observations, enabling the agent to self-correct and refine its approach iteratively. Each loop iteration appends the LLM's reasoning and tool results to the message history, creating a transparent audit trail.","intents":["Build autonomous agents that reason about multi-step problems and decide when to use tools","Implement agents that can self-correct by observing tool results and adjusting their approach","Create transparent agent execution with visible reasoning traces for debugging and auditing","Understand how production agent frameworks (LangChain, AutoGPT) orchestrate reasoning and action"],"best_for":["developers building autonomous agents with local LLMs","educators teaching agent orchestration patterns and reasoning loops","teams prototyping multi-step task automation (research, planning, execution)","builders experimenting with transparent agent reasoning for interpretability"],"limitations":["No built-in loop termination logic — requires manual max-iterations limit or explicit stop conditions to prevent infinite loops","Tool execution errors are not automatically handled; malformed tool calls or failed executions require explicit error recovery logic","Reasoning traces consume tokens rapidly; long reasoning chains can exhaust context window before reaching a solution","No cost optimization — every reasoning step and tool call consumes tokens; no built-in pruning or caching of intermediate results","Latency scales with number of reasoning steps; complex tasks may require 10-20 loop iterations, each adding 500ms-2s"],"requires":["Node.js 18+","Local LLM with sufficient context window (4K+ tokens recommended)","Tool definitions with schema and execution functions","System prompt that instructs LLM to output reasoning and tool calls in a parseable format","Loop orchestration code with max-iterations limit and termination conditions","Error handling for tool execution failures and malformed outputs"],"input_types":["user task or query (text)","tool definitions (JavaScript objects)","system prompt with ReAct instructions (text)"],"output_types":["reasoning trace (array of thought steps and tool calls)","final agent response (text)","execution log with tool results (structured data)"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-pguso--ai-agents-from-scratch__cap_4","uri":"capability://text.generation.language.streaming.token.generation.with.async.iteration","name":"streaming-token-generation-with-async-iteration","description":"Streams LLM output tokens in real-time using async iterators, allowing applications to display partial responses as they are generated rather than waiting for the full completion. The architecture uses node-llama-cpp's streaming API to yield tokens as they are produced by the inference engine, enabling progressive rendering, early stopping, and responsive user interfaces. Each token is yielded individually, allowing callers to accumulate them into a full response or process them incrementally.","intents":["Display LLM responses progressively to users without waiting for full completion","Implement responsive chat interfaces that show typing-like behavior","Enable early stopping or user interruption of long-running generations","Build real-time applications where partial responses are valuable (e.g., code generation, writing assistance)"],"best_for":["developers building interactive chat applications or web interfaces","teams creating responsive user experiences with LLM outputs","builders implementing real-time collaboration features with AI","educators demonstrating streaming and async patterns in JavaScript"],"limitations":["Streaming adds complexity to error handling — errors may occur mid-stream, requiring graceful degradation","Token-by-token processing prevents some optimizations (e.g., batch validation, structured output parsing)","Streaming output is harder to validate or constrain to a schema; requires post-processing or specialized parsing","Network latency becomes visible to users; slow connections show token-by-token delays rather than buffered responses","Memory usage is lower but CPU usage may be higher due to frequent context switches between token generation and yielding"],"requires":["Node.js 18+ with async/await and async iterator support","node-llama-cpp with streaming API enabled","Async function to consume the token stream (e.g., for loop with await)","Error handling for stream interruption or generation failures","Optional: buffering or debouncing logic to batch tokens for UI updates"],"input_types":["prompt text (string)","generation parameters (temperature, max_tokens, etc.)"],"output_types":["async iterator yielding individual tokens (string)","accumulated full response (string after consuming stream)","partial responses for progressive rendering (string)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-pguso--ai-agents-from-scratch__cap_5","uri":"capability://text.generation.language.system.prompt.specialization.for.task.adaptation","name":"system-prompt-specialization-for-task-adaptation","description":"Adapts LLM behavior by injecting task-specific system prompts that define role, constraints, output format, and reasoning style. The architecture treats system prompts as the primary control mechanism for agent specialization, allowing different prompts to transform the same base model into different specialized agents (translator, reasoner, code generator, etc.). System prompts are prepended to the message history and remain constant across conversation turns, establishing the agent's persona and operational guidelines.","intents":["Specialize a single LLM model for different tasks without fine-tuning","Control agent behavior and output format through prompt engineering","Create domain-specific agents (translator, mathematician, code reviewer) from a base model","Understand how system prompts shape LLM reasoning and output without model changes"],"best_for":["developers building multi-purpose agent systems with task switching","teams experimenting with prompt engineering for behavior control","educators teaching how LLMs respond to instructions and constraints","builders prototyping specialized agents without model fine-tuning"],"limitations":["Prompt engineering is empirical and non-deterministic — same prompt may produce different results across runs or models","System prompts consume tokens; complex prompts reduce available context for user input and conversation history","No guarantee that LLM will follow system prompt constraints; models may ignore instructions or hallucinate","Difficult to measure or optimize prompt effectiveness without extensive testing and evaluation","Prompt injection attacks possible if user input is not sanitized; malicious users can override system prompts"],"requires":["Node.js 18+","Well-crafted system prompt text (typically 100-500 tokens)","Understanding of target LLM's capabilities and limitations","Testing framework to evaluate prompt effectiveness","Optional: prompt versioning and A/B testing infrastructure"],"input_types":["system prompt text (string)","user queries (text)","task-specific constraints or format requirements (text)"],"output_types":["task-adapted LLM responses (text)","structured output matching prompt-specified format (JSON, markdown, etc.)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-pguso--ai-agents-from-scratch__cap_6","uri":"capability://automation.workflow.batch.parallel.processing.with.concurrent.inference","name":"batch-parallel-processing-with-concurrent-inference","description":"Processes multiple independent requests concurrently using Promise.all() or similar patterns, allowing multiple inference tasks to run in parallel (subject to hardware constraints). The architecture spawns multiple LLM inference tasks simultaneously, each with its own prompt and context, then collects results as they complete. This pattern is useful for embarrassingly parallel workloads (e.g., processing a batch of documents, generating multiple variations) where tasks are independent and can share the same model instance.","intents":["Process multiple independent LLM requests concurrently without sequential waiting","Implement batch processing pipelines for document analysis, content generation, or data transformation","Maximize hardware utilization by running multiple inference tasks in parallel","Build scalable agent systems that handle multiple user requests simultaneously"],"best_for":["developers building batch processing pipelines with local LLMs","teams processing large document collections or datasets with AI","builders implementing multi-user agent systems with resource sharing","educators demonstrating concurrent programming patterns with async/await"],"limitations":["Concurrency is limited by available RAM and CPU; too many parallel tasks cause memory exhaustion or context switching overhead","node-llama-cpp may not support true parallelism on all platforms; some implementations serialize inference despite async code","No built-in load balancing or queue management; developers must manually limit concurrent tasks to avoid resource exhaustion","Error in one task does not automatically propagate; requires Promise.allSettled() or explicit error handling to prevent silent failures","Latency may increase if tasks compete for CPU/GPU resources; parallel execution is not always faster than sequential for small batches"],"requires":["Node.js 18+ with Promise and async/await support","Sufficient RAM to load model once and run multiple inference tasks (typically 2-4x base model memory)","Task queue or concurrency limiter to prevent resource exhaustion (e.g., p-limit library)","Error handling for individual task failures without blocking other tasks","Monitoring to track concurrent task count and resource usage"],"input_types":["array of independent prompts or requests (array of strings or objects)","shared model instance (node-llama-cpp model)","generation parameters (temperature, max_tokens, etc.)"],"output_types":["array of responses (array of strings)","results with metadata (array of objects with response, status, timing)","error reports for failed tasks (array of error objects)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-pguso--ai-agents-from-scratch__cap_7","uri":"capability://planning.reasoning.model.selection.and.quantization.strategy.guidance","name":"model-selection-and-quantization-strategy-guidance","description":"Provides educational guidance on selecting appropriate quantized GGUF models based on task requirements, hardware constraints, and quality/speed tradeoffs. The architecture documents model characteristics (parameter count, quantization level, context window, inference speed) and helps developers choose between models like Mistral, Llama 2, Phi, and others. The repository includes a model download utility (npx node-llama-cpp pull) that surfaces model options and their specifications, enabling informed selection without trial-and-error.","intents":["Choose appropriate LLM models for specific tasks and hardware constraints","Understand quantization tradeoffs (model size vs quality vs speed)","Evaluate different models for agent development without extensive benchmarking","Learn how model selection impacts agent performance and resource usage"],"best_for":["developers new to local LLM deployment making first model selection","teams evaluating models for production agent systems","educators teaching model selection criteria and quantization concepts","builders optimizing for specific hardware (CPU-only, GPU-accelerated, edge devices)"],"limitations":["Guidance is general; optimal model depends on specific task and cannot be determined without testing","Quantization quality varies by model and quantization method; no universal quality metric","Inference speed benchmarks are hardware-dependent; speeds vary significantly across CPU/GPU configurations","Model availability changes over time; recommended models may become outdated or unavailable","No automated model evaluation or recommendation system; selection requires manual research and testing"],"requires":["Node.js 18+","npx node-llama-cpp pull command to browse available models","Understanding of model parameters (7B, 13B, 70B) and quantization levels (Q4, Q5, Q8)","Hardware specifications (RAM, GPU, CPU) to match against model requirements","Disk space for model storage (varies from 3GB for 7B Q4 to 50GB+ for 70B full precision)"],"input_types":["task description (text)","hardware specifications (CPU, RAM, GPU)","quality/speed preferences (qualitative)"],"output_types":["model recommendations (list of model names and specifications)","quantization strategy guidance (Q4 vs Q5 vs Q8 tradeoffs)","resource requirement estimates (RAM, disk, inference time)"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-pguso--ai-agents-from-scratch__cap_8","uri":"capability://text.generation.language.temperature.and.sampling.parameter.control","name":"temperature-and-sampling-parameter-control","description":"Exposes temperature, top-p, and other sampling parameters to control LLM output randomness and creativity. The architecture allows developers to tune these parameters per request, enabling different behaviors for different tasks (e.g., low temperature for deterministic code generation, high temperature for creative writing). Parameters are passed to the node-llama-cpp inference engine, which uses them to control the probability distribution over next tokens during generation.","intents":["Control LLM output randomness and creativity for different task types","Generate deterministic outputs for code or structured data (low temperature)","Generate diverse or creative outputs for brainstorming or content creation (high temperature)","Understand how sampling parameters affect LLM behavior and output quality"],"best_for":["developers fine-tuning agent behavior for specific tasks","teams experimenting with output quality and diversity tradeoffs","educators teaching LLM sampling and probability concepts","builders optimizing agents for different use cases (deterministic vs creative)"],"limitations":["Parameter effects are model-dependent; same temperature produces different results across models","No universal optimal values; requires empirical testing to find good parameters for specific tasks","Parameter tuning is manual; no automated optimization or adaptive sampling","Documentation of parameter effects is often vague; requires experimentation to understand impact","Some parameters (e.g., top-k, top-p) interact in complex ways; tuning one affects the impact of others"],"requires":["Node.js 18+","node-llama-cpp with sampling parameter support","Understanding of temperature, top-p, top-k, and other sampling parameters","Testing framework to evaluate output quality at different parameter values","Optional: parameter sweep or grid search to find optimal values"],"input_types":["temperature value (float, typically 0.0-2.0)","top-p value (float, typically 0.0-1.0)","top-k value (integer, typically 0-100)","other sampling parameters (repeat_penalty, etc.)"],"output_types":["LLM responses with controlled randomness (text)","parameter effectiveness metrics (quality, diversity, consistency)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-pguso--ai-agents-from-scratch__cap_9","uri":"capability://data.processing.analysis.token.counting.and.context.window.management","name":"token-counting-and-context-window-management","description":"Provides utilities and patterns for tracking token usage and managing context window constraints to prevent exceeding model limits. The architecture includes token counting logic (either through node-llama-cpp's built-in tokenizer or external libraries) that estimates prompt and response token counts before generation. Developers can use this information to implement context windowing strategies (e.g., dropping oldest messages when approaching limit) or warn users when approaching capacity.","intents":["Track token usage to understand cost and performance implications","Implement context window management for long conversations without exceeding limits","Optimize prompt length and conversation history to fit within model constraints","Understand how token counting affects agent design and memory management"],"best_for":["developers building agents with limited context windows (4K-8K tokens)","teams managing long-running conversations or multi-turn reasoning","builders optimizing for cost or latency in token-constrained scenarios","educators teaching token economics and context management"],"limitations":["Token counting is approximate; actual token count may differ from estimates due to tokenizer variations","No built-in context windowing strategies; developers must implement their own (e.g., sliding window, summarization)","Counting adds overhead; frequent token counting can impact performance","No automatic optimization; developers must manually decide what to drop when approaching limits","Different models use different tokenizers; token counts are not portable across models"],"requires":["Node.js 18+","Tokenizer implementation (node-llama-cpp's built-in or external library like js-tiktoken)","Token counting logic integrated into prompt building","Context window size for target model (e.g., 4096 for Mistral 7B)","Strategy for handling context overflow (drop messages, summarize, truncate)"],"input_types":["prompts and messages (text)","model context window size (integer)","token budget or limit (integer)"],"output_types":["token count estimates (integer)","context window utilization percentage (float)","warnings or errors when approaching limits (string)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":47,"verified":false,"data_access_risk":"high","permissions":["Node.js 18+","node-llama-cpp npm package (includes pre-compiled binaries)","8GB+ RAM for 7B models, 16GB+ for 13B+ models","GGUF quantized model file (e.g., Mistral, Llama 2, Phi) downloaded to ./models/ directory","macOS/Linux/Windows x64 with optional GPU drivers (CUDA 11.8+, Metal, or Vulkan)","Tool definitions as JavaScript objects with {name, description, parameters, execute} structure","System prompt that instructs LLM to output function calls in a specific format (JSON/XML)","Parser function to extract and validate function calls from LLM output","Error handling for malformed or invalid function calls","node-llama-cpp for local inference"],"failure_modes":["Inference speed depends on local hardware; CPU-only inference is 10-50x slower than GPU-accelerated cloud APIs","Memory footprint scales with model size; 7B parameter models require ~8GB RAM minimum","No built-in batching or request queuing — single-threaded inference per model instance","Platform-specific binary compilation adds ~5-10 minutes to npm install on first setup","Limited to GGUF quantized models; cannot load full-precision or other formats without conversion","Parsing function calls from text is fragile — LLM may generate malformed JSON or hallucinate function names","No built-in retry logic if parsing fails; requires manual error handling and re-prompting","Schema validation is manual; no automatic type coercion or constraint enforcement","Latency overhead from text parsing and validation adds ~50-200ms per tool call","Limited to tools defined in JavaScript; integrating external services requires wrapper functions","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.5645573302252876,"quality":0.49,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.75,"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":"2026-05-24T12:16:22.063Z","last_scraped_at":"2026-05-03T13:57:11.504Z","last_commit":"2026-04-26T18:21:54Z"},"community":{"stars":3462,"forks":520,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=pguso--ai-agents-from-scratch","compare_url":"https://unfragile.ai/compare?artifact=pguso--ai-agents-from-scratch"}},"signature":"a16pEHr695B16LHnMQYrThO59n6zkH3Pxy4ylHPKQDrKROp9Vbhd5sq3cVZ4n/HtL6LR6JZkAKSQC4Wkim7VCQ==","signedAt":"2026-06-20T18:55:37.271Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pguso--ai-agents-from-scratch","artifact":"https://unfragile.ai/pguso--ai-agents-from-scratch","verify":"https://unfragile.ai/api/v1/verify?slug=pguso--ai-agents-from-scratch","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"}}