{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-llm-agents","slug":"llm-agents","name":"LLM Agents","type":"repo","url":"https://github.com/mpaepper/llm_agents","page_url":"https://unfragile.ai/llm-agents","categories":["ai-agents"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-llm-agents__cap_0","uri":"capability://planning.reasoning.thought.action.observation.loop.orchestration","name":"thought-action-observation loop orchestration","description":"Implements an iterative reasoning loop where the agent maintains a previous_responses list accumulating all Thoughts, Actions, and Observations across iterations. Each cycle constructs an augmented prompt containing system instructions, tool descriptions, prior context, and the original user question, then parses the LLM response for Thought/Action/Action Input or Final Answer patterns, executing tools and feeding observations back until a Final Answer is produced or iteration limit is reached. This creates a stateful, multi-turn reasoning pattern that enables complex task decomposition.","intents":["Build an agent that can reason through multi-step problems by iteratively thinking, acting, and observing","Create autonomous systems that break down complex tasks into tool invocations with intermediate reasoning","Implement agents that accumulate context across multiple reasoning cycles without losing prior observations"],"best_for":["Python developers building autonomous reasoning systems","Teams implementing ReAct-style agent patterns for task automation","Builders prototyping LLM agents with minimal abstraction overhead"],"limitations":["No built-in context window management — long reasoning chains may exceed LLM token limits without manual truncation","Iteration limit is hardcoded; no adaptive stopping based on confidence or uncertainty metrics","No parallel tool execution — tools are invoked sequentially, limiting throughput for independent actions","Previous responses accumulate linearly in memory; no summarization or compression of old observations"],"requires":["Python 3.7+","OpenAI API key for ChatLLM integration","At least one ToolInterface implementation registered with the agent"],"input_types":["natural language user query (string)","system prompt (string)","tool definitions (ToolInterface objects)"],"output_types":["final answer (string)","conversation history with thoughts/actions/observations (list of dicts)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llm-agents__cap_1","uri":"capability://text.generation.language.llm.response.parsing.and.action.extraction","name":"llm response parsing and action extraction","description":"Parses unstructured LLM responses to extract structured Thought, Action, Action Input, and Final Answer fields using pattern matching or regex-based parsing. The parser identifies when the LLM intends to invoke a tool (Action: tool_name, Action Input: parameters) versus when it has reached a conclusion (Final Answer: result), enabling the agent to route responses to either tool execution or return-to-user paths. This decouples the LLM's natural language generation from the agent's control flow.","intents":["Extract tool invocation intent from LLM responses without requiring structured output or function calling APIs","Distinguish between intermediate reasoning steps and final answers in agent loops","Parse variable LLM response formats and normalize them into actionable agent commands"],"best_for":["Developers using LLMs without native function calling support","Teams wanting to avoid OpenAI function calling API costs or latency","Builders prototyping agents with multiple LLM providers that have inconsistent structured output"],"limitations":["Regex-based parsing is fragile to LLM output format variations; hallucinated or malformed Action/Action Input fields cause parsing failures","No validation that Action Input matches the tool's expected schema — type mismatches only surface at tool invocation time","Requires careful prompt engineering to ensure LLM consistently outputs expected Thought/Action/Final Answer format","No fallback mechanism if parsing fails; agent loop halts rather than recovering gracefully"],"requires":["LLM that follows prompt instructions for Thought/Action/Final Answer format","Python regex or string parsing library (built-in)"],"input_types":["raw LLM response (string)"],"output_types":["parsed action dict with keys: thought, action, action_input, final_answer","boolean indicating whether response contains valid action or final answer"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llm-agents__cap_10","uri":"capability://tool.use.integration.tool.invocation.routing.and.execution","name":"tool invocation routing and execution","description":"Implements a dispatch mechanism that matches the Action field from parsed LLM responses to registered ToolInterface instances by name, then invokes the matched tool's execute() method with the Action Input as a parameter. The tool's return value (observation) is captured and appended to the conversation history, completing the action phase of the reasoning loop. This decouples tool selection from tool execution, allowing the agent to support arbitrary tool sets.","intents":["Route tool invocation requests from the LLM to the correct tool implementation","Execute tools with parameters extracted from LLM responses","Capture tool results and feed them back into the reasoning loop"],"best_for":["Developers building agents with multiple tools","Teams creating extensible agent systems where tools are added dynamically","Builders prototyping agents with heterogeneous tool sets"],"limitations":["No validation that Action matches a registered tool; invalid tool names cause routing failures","No error handling for tool execution failures; exceptions propagate to the agent loop","No timeout mechanism; long-running tools block the entire agent","No retry logic for failed tool invocations; transient errors cause agent loop to fail","Tool execution is synchronous and blocking; no parallel execution of independent tools"],"requires":["Python 3.7+","Registered ToolInterface instances with unique names","Parsed LLM response with valid Action field"],"input_types":["action (string, tool name)","action_input (string, tool parameters)"],"output_types":["observation (string, tool execution result)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llm-agents__cap_11","uri":"capability://automation.workflow.iteration.limit.enforcement.and.loop.termination","name":"iteration limit enforcement and loop termination","description":"Enforces a configurable max_iterations parameter that terminates the reasoning loop if the iteration count exceeds the limit, even if no Final Answer has been produced. The agent tracks the current iteration number and checks it before each loop iteration, returning a timeout or max-iterations-exceeded message if the limit is reached. This prevents infinite loops and runaway agent behavior, but may prematurely terminate complex reasoning tasks.","intents":["Prevent infinite loops and runaway agent behavior","Set resource limits on agent execution time and LLM API calls","Ensure agents terminate within predictable time bounds"],"best_for":["Developers deploying agents in production with resource constraints","Teams wanting to limit API costs by capping LLM calls per agent run","Builders prototyping agents with unknown complexity"],"limitations":["Hard limit with no adaptive stopping; agents may terminate prematurely on complex tasks","No distinction between productive iterations and wasted iterations; all iterations count equally","No confidence-based stopping; agents cannot exit early if they're confident in a partial answer","Iteration limit is global; no per-tool or per-action iteration budgets","No graceful degradation; agents return incomplete answers rather than partial results"],"requires":["Python 3.7+","max_iterations parameter (int, default unspecified)"],"input_types":["max_iterations (int)"],"output_types":["termination status (success, max_iterations_exceeded, or error)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llm-agents__cap_2","uri":"capability://tool.use.integration.tool.interface.abstraction.and.custom.tool.registration","name":"tool interface abstraction and custom tool registration","description":"Defines a ToolInterface base class that standardizes how external tools are integrated into the agent. Developers implement ToolInterface with a name, description, and execute() method, then register tool instances with the agent. The agent automatically includes tool descriptions in the system prompt and routes Action commands to the corresponding tool's execute() method by name matching. This enables pluggable tool composition without modifying agent core logic.","intents":["Add custom tools to an agent without modifying the agent orchestration code","Create reusable tool implementations that can be shared across multiple agents","Enable the LLM to discover available tools through system prompt descriptions"],"best_for":["Python developers building extensible agent systems","Teams creating domain-specific tools (APIs, databases, computation engines)","Builders prototyping agents with heterogeneous tool sets"],"limitations":["No built-in tool validation — tools must implement execute() correctly or agent crashes at runtime","Tool descriptions are free-form strings; no schema validation ensures LLM understands parameter requirements","No error handling or retry logic for failed tool executions — exceptions propagate to agent loop","Tool execution is synchronous and blocking; long-running tools stall the entire agent"],"requires":["Python 3.7+","Inherit from ToolInterface base class","Implement execute(action_input: str) -> str method"],"input_types":["action_input (string, parsed from LLM response)"],"output_types":["observation (string, result of tool execution)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llm-agents__cap_3","uri":"capability://search.retrieval.multi.provider.search.tool.integration","name":"multi-provider search tool integration","description":"Provides pre-built search tool implementations (SerpAPITool, GoogleSearchTool, SearxSearchTool, HackerNewsSearchTool) that wrap different search APIs and backends. Each tool implements the ToolInterface, accepting a search query as action_input and returning formatted search results as observations. The library abstracts away API-specific authentication and response formatting, enabling developers to swap search providers by changing tool registration without modifying agent logic.","intents":["Enable agents to search the web or specialized sources (HackerNews) to gather information","Switch between search providers (SerpAPI, Google, Searx) without rewriting agent code","Provide agents with real-time information beyond their training data cutoff"],"best_for":["Developers building research or information-gathering agents","Teams wanting to avoid vendor lock-in by supporting multiple search backends","Builders prototyping agents that need current information"],"limitations":["Each search provider requires separate API keys and authentication setup","Search result formatting varies by provider; LLM may struggle with inconsistent result structures","No built-in result ranking, deduplication, or relevance filtering — raw results are passed to LLM","Rate limiting and quota management are provider-specific; no unified rate limiting across tools","HackerNews tool is limited to HN-specific content; no general web search alternative for that provider"],"requires":["Python 3.7+","API key for chosen search provider (SerpAPI, Google Custom Search, Searx instance, or HackerNews API)","Network connectivity to reach search provider APIs"],"input_types":["search query (string)"],"output_types":["formatted search results (string with title, snippet, URL per result)"],"categories":["search-retrieval","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llm-agents__cap_4","uri":"capability://code.generation.editing.python.code.execution.tool.with.repl.sandbox","name":"python code execution tool with repl sandbox","description":"Implements a PythonREPLTool that allows agents to execute arbitrary Python code in a sandboxed REPL environment. The tool accepts Python code as action_input, executes it in an isolated Python process or namespace, captures stdout/stderr, and returns execution results as observations. This enables agents to perform computations, data transformations, and logic that would be difficult to express in natural language or tool parameters.","intents":["Enable agents to execute Python code for calculations, data processing, or logic verification","Allow agents to test hypotheses or validate results programmatically","Provide agents with a general-purpose computation tool beyond domain-specific APIs"],"best_for":["Data science and research agents that need computational capabilities","Developers building agents for technical problem-solving","Teams prototyping agents that combine reasoning with code execution"],"limitations":["No true sandboxing — code executes in the same Python process, allowing access to agent internals and file system","No timeout mechanism; infinite loops or long-running code blocks the entire agent","No package isolation — code can only use pre-installed packages; no pip install during execution","Security risk if agent is exposed to untrusted user inputs; malicious code can be injected via action_input","Output capture may be incomplete for code that writes to files or makes network requests"],"requires":["Python 3.7+","Python interpreter available in runtime environment","No external dependencies for basic REPL functionality"],"input_types":["Python code (string)"],"output_types":["execution result (stdout/stderr combined, or error traceback)"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llm-agents__cap_5","uri":"capability://text.generation.language.openai.chatllm.integration.with.conversation.history","name":"openai chatllm integration with conversation history","description":"Implements a ChatLLM class that interfaces with OpenAI's Chat Completion API, maintaining a conversation history as a list of message dicts with role (system/user/assistant) and content fields. The class accepts accumulated context (system prompt, previous thoughts/actions/observations, current query) and constructs a messages array that respects OpenAI's message format. It handles API authentication via OPENAI_API_KEY environment variable and returns raw LLM responses for parsing by the agent.","intents":["Integrate OpenAI's GPT models into the agent reasoning loop","Maintain multi-turn conversation history with proper message formatting","Leverage OpenAI's API for LLM inference without building custom API clients"],"best_for":["Developers using OpenAI as their primary LLM provider","Teams building agents that require GPT-3.5 or GPT-4 capabilities","Builders prototyping agents with minimal LLM integration overhead"],"limitations":["Hardcoded to OpenAI API; no support for other providers (Anthropic, Ollama, local models) without code modification","No token counting or context window management — developers must manually track token usage to avoid exceeding limits","No streaming support; entire response is buffered before returning, adding latency for long outputs","No retry logic for API failures; transient errors cause agent loop to fail","API costs scale with conversation length because full history is sent with each request"],"requires":["Python 3.7+","OpenAI API key set in OPENAI_API_KEY environment variable","openai Python package (version unspecified in docs)"],"input_types":["system prompt (string)","accumulated context with thoughts/actions/observations (string)","user query (string)"],"output_types":["LLM response (string)"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llm-agents__cap_6","uri":"capability://automation.workflow.agent.initialization.and.configuration","name":"agent initialization and configuration","description":"Provides an Agent class constructor that accepts an LLM instance (ChatLLM), a list of ToolInterface implementations, and optional parameters like max_iterations and system_prompt. The constructor validates inputs, registers tools, and initializes internal state (previous_responses list, iteration counter). This enables developers to declaratively configure agents with specific LLM and tool combinations without writing boilerplate initialization code.","intents":["Create agent instances with specific LLM and tool configurations","Set agent-level parameters like maximum iterations and system instructions","Initialize agent state and prepare it for the reasoning loop"],"best_for":["Python developers building multiple agent instances with different configurations","Teams creating agent factories or agent templates","Builders prototyping agents with varying tool sets"],"limitations":["No validation that tools are compatible with the LLM's capabilities","No default system prompt; developers must provide one or agent behavior is undefined","max_iterations is a hard limit with no adaptive stopping; agents may terminate prematurely on complex tasks","No configuration persistence; agent state is not serializable to disk for later resumption"],"requires":["Python 3.7+","ChatLLM instance (or compatible LLM interface)","List of ToolInterface implementations"],"input_types":["llm (ChatLLM instance)","tools (list of ToolInterface objects)","max_iterations (int, optional)","system_prompt (string, optional)"],"output_types":["Agent instance ready for run() method"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llm-agents__cap_7","uri":"capability://automation.workflow.agent.execution.and.result.retrieval","name":"agent execution and result retrieval","description":"Implements an Agent.run(user_query: str) method that executes the thought-action-observation loop until a Final Answer is produced or max_iterations is reached. The method returns the final answer string and optionally the full conversation history (previous_responses list). This provides a simple synchronous interface for running agents end-to-end without exposing the internal loop mechanics.","intents":["Execute an agent from user query to final answer in a single method call","Retrieve both the final answer and the full reasoning history for debugging or logging","Run agents synchronously without managing the reasoning loop manually"],"best_for":["Developers building simple agent applications with synchronous execution","Teams prototyping agents and needing quick feedback loops","Builders integrating agents into existing Python applications"],"limitations":["Synchronous execution blocks the calling thread; no async/await support for concurrent agent runs","No streaming of intermediate results; entire reasoning process is hidden until final answer is returned","No cancellation mechanism; running agents cannot be interrupted mid-loop","Exception handling is not specified; tool failures or LLM errors may crash the entire run() call","No progress reporting or logging of intermediate steps to external systems"],"requires":["Python 3.7+","Initialized Agent instance with LLM and tools configured"],"input_types":["user_query (string)"],"output_types":["final_answer (string)","conversation_history (list of dicts with thought/action/observation, optional)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llm-agents__cap_8","uri":"capability://text.generation.language.system.prompt.and.tool.description.injection","name":"system prompt and tool description injection","description":"Automatically constructs an augmented system prompt that includes the original system_prompt parameter, formatted descriptions of all registered tools (name, description, expected input format), and instructions for the Thought-Action-Observation format. This augmented prompt is prepended to each LLM request, enabling the LLM to discover available tools and understand how to invoke them without hardcoding tool information in the base system prompt.","intents":["Automatically include tool descriptions in the LLM context without manual prompt engineering","Enable the LLM to discover available tools dynamically based on registered ToolInterface instances","Standardize the format for tool invocation instructions across all agents"],"best_for":["Developers building agents with dynamic tool sets that change at runtime","Teams wanting to avoid manual prompt engineering for tool descriptions","Builders prototyping agents with many tools"],"limitations":["Tool descriptions are free-form strings; no schema validation ensures LLM understands parameter requirements","Augmented prompt grows linearly with number of tools; many tools may exceed LLM context window","No optimization for tool descriptions; verbose descriptions waste tokens","Tool descriptions are static; no adaptive descriptions based on query context or tool usage history"],"requires":["Python 3.7+","System prompt (string)","Registered ToolInterface instances with name and description attributes"],"input_types":["system_prompt (string)","tools (list of ToolInterface objects)"],"output_types":["augmented_prompt (string with system instructions and tool descriptions)"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llm-agents__cap_9","uri":"capability://memory.knowledge.conversation.history.accumulation.and.context.management","name":"conversation history accumulation and context management","description":"Maintains a previous_responses list that accumulates all Thought, Action, Observation, and Final Answer entries across agent iterations. Each iteration appends the new thought/action/observation to this list before constructing the next LLM prompt, creating a growing context window that includes the full reasoning history. The list is passed to the LLM as part of the augmented prompt, enabling the LLM to reference prior observations and reasoning steps.","intents":["Maintain full reasoning history for debugging and transparency","Enable the LLM to reference prior observations and reasoning steps in subsequent iterations","Provide conversation history for logging, auditing, or post-hoc analysis"],"best_for":["Developers building agents that need transparent reasoning trails","Teams auditing or debugging agent behavior","Builders prototyping agents where reasoning history is valuable for learning"],"limitations":["No context window management; history grows unbounded and may exceed LLM token limits","No summarization or compression of old observations; entire history is sent with each request","No selective history pruning; developers cannot remove irrelevant observations to save tokens","Memory usage grows linearly with agent iterations; long-running agents consume significant memory","No persistence; conversation history is lost when agent instance is garbage collected"],"requires":["Python 3.7+","Agent instance with reasoning loop"],"input_types":["thought (string)","action (string)","action_input (string)","observation (string)"],"output_types":["previous_responses (list of dicts with thought/action/observation entries)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"high","permissions":["Python 3.7+","OpenAI API key for ChatLLM integration","At least one ToolInterface implementation registered with the agent","LLM that follows prompt instructions for Thought/Action/Final Answer format","Python regex or string parsing library (built-in)","Registered ToolInterface instances with unique names","Parsed LLM response with valid Action field","max_iterations parameter (int, default unspecified)","Inherit from ToolInterface base class","Implement execute(action_input: str) -> str method"],"failure_modes":["No built-in context window management — long reasoning chains may exceed LLM token limits without manual truncation","Iteration limit is hardcoded; no adaptive stopping based on confidence or uncertainty metrics","No parallel tool execution — tools are invoked sequentially, limiting throughput for independent actions","Previous responses accumulate linearly in memory; no summarization or compression of old observations","Regex-based parsing is fragile to LLM output format variations; hallucinated or malformed Action/Action Input fields cause parsing failures","No validation that Action Input matches the tool's expected schema — type mismatches only surface at tool invocation time","Requires careful prompt engineering to ensure LLM consistently outputs expected Thought/Action/Final Answer format","No fallback mechanism if parsing fails; agent loop halts rather than recovering gracefully","No validation that Action matches a registered tool; invalid tool names cause routing failures","No error handling for tool execution failures; exceptions propagate to the agent loop","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.34,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"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":"2026-06-17T09:51:03.577Z","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=llm-agents","compare_url":"https://unfragile.ai/compare?artifact=llm-agents"}},"signature":"jijSQvt4YHqbrLXw26sP1+Ijst3R1RMt8NC8X76mzBdrHnhFhcdmZXvxVrrusVdGt4gfutkMJlMW1EZJMTlXCQ==","signedAt":"2026-06-21T22:15:58.214Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/llm-agents","artifact":"https://unfragile.ai/llm-agents","verify":"https://unfragile.ai/api/v1/verify?slug=llm-agents","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"}}