{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-agentforge","slug":"agentforge","name":"AgentForge","type":"repo","url":"https://github.com/DataBassGit/AgentForge","page_url":"https://unfragile.ai/agentforge","categories":["ai-agents"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-agentforge__cap_0","uri":"capability://automation.workflow.yaml.driven.agent.configuration.with.hot.reloading","name":"yaml-driven agent configuration with hot-reloading","description":"AgentForge uses a Config singleton that loads and parses YAML files from a .agentforge directory, enabling agents and workflows to be defined declaratively without code changes. The ConfigManager builds structured configuration objects that support dynamic model selection and prompt updates at runtime without restarting the application, using a file-watching pattern for hot-reload capability.","intents":["Define agent behavior and prompts in YAML without touching Python code","Swap LLM models or adjust agent parameters between runs without redeployment","Enable non-technical team members to iterate on agent configurations","Rapidly prototype multiple agent variants by editing configuration files"],"best_for":["teams building LLM agents who want configuration-as-code practices","non-technical domain experts who need to tune agent behavior","rapid prototyping workflows where iteration speed matters more than compiled optimization"],"limitations":["YAML parsing adds startup latency when loading large configuration directories","No built-in schema validation — malformed YAML fails at runtime rather than parse time","Hot-reloading requires file system monitoring which may not work reliably on network drives or containerized environments"],"requires":["Python 3.8+",".agentforge directory in project root with YAML files","PyYAML library for parsing"],"input_types":["YAML configuration files","text prompts","model identifiers"],"output_types":["structured configuration objects","agent instances","cog instances"],"categories":["automation-workflow","configuration-management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-agentforge__cap_1","uri":"capability://automation.workflow.multi.agent.workflow.orchestration.via.cog.abstraction","name":"multi-agent workflow orchestration via cog abstraction","description":"AgentForge provides a Cog class that orchestrates multiple Agent instances in a defined workflow sequence, managing execution order, data flow between agents, and memory context propagation. Cogs are configured via YAML flow definitions that specify which agents run, in what order, and how outputs from one agent feed into the next, with the MemoryManager automatically injecting contextual information before each agent executes.","intents":["Chain multiple specialized agents together to solve complex multi-step tasks","Define agent workflows declaratively without writing orchestration code","Ensure each agent in a pipeline has access to relevant context from previous steps","Build hierarchical agent systems where some agents coordinate others"],"best_for":["teams building multi-step AI workflows (e.g., research → analysis → summarization)","applications requiring agent specialization where different agents handle different domains","projects where workflow topology changes frequently and needs to be configurable"],"limitations":["Sequential execution only — no built-in parallelization of independent agents","Data flow between agents is implicit through memory system rather than explicit in code, making debugging harder","No conditional branching or dynamic agent selection based on runtime conditions"],"requires":["Python 3.8+","Agent instances configured in .agentforge/agents/","Cog flow definition in YAML with agent references"],"input_types":["YAML cog configuration","agent instances","initial input data"],"output_types":["final agent output","execution trace","memory state updates"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-agentforge__cap_10","uri":"capability://memory.knowledge.chroma.vector.database.integration.for.semantic.memory.storage","name":"chroma vector database integration for semantic memory storage","description":"AgentForge uses Chroma as the default storage backend for all memory types, providing vector-based semantic search capabilities. The integration handles embedding generation, vector storage, and retrieval, enabling agents to find relevant memories based on semantic similarity rather than exact keyword matching. Chroma can be deployed locally or remotely, supporting both development and production scenarios.","intents":["Store agent memories in a vector database for semantic retrieval","Find relevant context based on meaning rather than keywords","Scale memory storage to large conversation histories","Enable semantic search across agent interactions"],"best_for":["applications with large conversation histories requiring semantic search","systems where exact keyword matching is insufficient for memory retrieval","production deployments requiring scalable memory storage"],"limitations":["Vector storage adds latency — ~100-300ms per semantic search","Embedding quality depends on the embedding model used","Chroma local mode is not suitable for multi-process deployments","No built-in memory deduplication — similar memories may be stored multiple times"],"requires":["Python 3.8+","Chroma library (local or remote instance)","Embedding model (OpenAI, local, or other)","Storage configuration in .agentforge/system/"],"input_types":["text to embed and store","semantic queries","memory metadata"],"output_types":["stored embeddings","retrieved memories","similarity scores"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-agentforge__cap_11","uri":"capability://data.processing.analysis.parsing.and.output.processing.for.structured.extraction","name":"parsing and output processing for structured extraction","description":"AgentForge includes a parsing processor that extracts structured data from agent outputs, handling JSON parsing, regex extraction, and custom parsing logic. The processor enables agents to generate structured outputs (JSON, YAML, etc.) that are automatically parsed into Python objects, with error handling for malformed outputs and fallback strategies.","intents":["Extract structured data from LLM outputs","Validate agent outputs against expected schemas","Convert agent text outputs into machine-readable formats","Handle parsing errors gracefully with fallbacks"],"best_for":["workflows requiring structured agent outputs","systems where agent outputs feed into downstream processing","applications needing reliable data extraction from LLM responses"],"limitations":["LLMs frequently generate malformed JSON — fallback strategies may not always succeed","Complex nested structures may not parse correctly","No schema validation — parsed data is not validated against expected types"],"requires":["Python 3.8+","Parsing configuration in agent prompts or code"],"input_types":["agent text outputs","JSON strings","structured text"],"output_types":["parsed Python objects","dictionaries","lists"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-agentforge__cap_2","uri":"capability://tool.use.integration.llm.agnostic.provider.abstraction.with.multi.model.support","name":"llm-agnostic provider abstraction with multi-model support","description":"AgentForge implements a base API layer that abstracts away provider-specific details (OpenAI, Anthropic, Ollama, etc.), allowing agents to be written once and run against any supported LLM without code changes. The framework handles provider-specific API differences, authentication, and model parameter mapping through a unified interface, with model selection configurable per-agent via YAML.","intents":["Switch between LLM providers (OpenAI to Anthropic to local Ollama) without rewriting agent code","Run cost optimization experiments by testing the same agent logic against cheaper models","Avoid vendor lock-in by building agents that work across multiple LLM providers","Use local models for development/testing and cloud models for production"],"best_for":["teams wanting to avoid LLM vendor lock-in","cost-conscious projects that need to experiment with different model tiers","organizations with hybrid cloud/on-premise infrastructure","developers building agent frameworks that need to support multiple LLM backends"],"limitations":["Abstraction layer adds ~50-100ms latency per API call due to parameter translation","Advanced provider-specific features (vision, function calling variants) may not be fully exposed through the abstraction","Model parameter differences (temperature ranges, max tokens) still require per-provider tuning"],"requires":["Python 3.8+","API keys for at least one supported provider (OpenAI, Anthropic, Ollama, etc.)","Model configuration in .agentforge/models/ YAML"],"input_types":["agent prompts","model identifiers","provider credentials"],"output_types":["LLM completions","token usage metrics","provider-agnostic response objects"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-agentforge__cap_3","uri":"capability://memory.knowledge.multi.tier.memory.system.with.specialized.memory.types","name":"multi-tier memory system with specialized memory types","description":"AgentForge implements a MemoryManager that coordinates three distinct memory types: Persona Memory (agent identity/instructions), Chat History Memory (conversation context), and ScratchPad Memory (working state). Each memory type is backed by a pluggable storage backend (Chroma vector DB by default) and is automatically injected into agent prompts before execution, enabling agents to maintain context across multiple invocations without explicit state management.","intents":["Give agents persistent identity and behavioral instructions across conversations","Maintain conversation history so agents can reference previous exchanges","Provide agents with a working area to store intermediate reasoning or state","Enable memory retrieval based on semantic similarity rather than exact matching"],"best_for":["conversational agents that need to remember user preferences and interaction history","multi-turn workflows where agents need to reference earlier steps","systems requiring agent personalization or role-based behavior","applications where memory needs to be queryable by semantic meaning"],"limitations":["Vector storage (Chroma) adds latency for memory retrieval — ~100-300ms per semantic search","No built-in memory eviction or TTL — memory grows unbounded unless manually pruned","Semantic memory retrieval can return irrelevant results if embeddings are poor quality","No cross-agent memory sharing — each agent has isolated memory unless explicitly configured"],"requires":["Python 3.8+","Chroma vector database (local or remote)","Embedding model (OpenAI, local, or other)","Memory configuration in .agentforge/memory/"],"input_types":["text prompts","conversation history","agent state data","semantic queries"],"output_types":["augmented prompts with memory context","retrieved memory entries","memory persistence confirmations"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-agentforge__cap_4","uri":"capability://tool.use.integration.declarative.action.tool.system.with.schema.based.function.calling","name":"declarative action/tool system with schema-based function calling","description":"AgentForge provides an Actions system (note: marked as deprecated in docs but still present) that enables agents to call external functions and tools through a schema-based registry. Tools are defined declaratively with input/output schemas, and the framework handles marshaling arguments from LLM outputs into function calls, with support for multiple tool providers and custom tool implementations.","intents":["Allow agents to call external APIs, databases, or custom functions","Define tool interfaces declaratively so agents know what tools are available","Automatically parse LLM outputs into structured function calls","Build agents that can take actions in external systems"],"best_for":["agents that need to interact with APIs or external services","workflows requiring database queries or file system operations","systems where agents need to take actions beyond text generation"],"limitations":["Action system is marked as deprecated — may be removed in future versions","No built-in retry logic for failed tool calls","Tool schema validation is basic — complex nested schemas may not be fully supported","No sandboxing — agents can call any registered tool without restrictions"],"requires":["Python 3.8+","Tool definitions in .agentforge/actions/ or custom tool implementations","Agent configuration that references available tools"],"input_types":["tool schema definitions","LLM outputs with tool calls","function arguments"],"output_types":["function call results","tool execution traces","error messages"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-agentforge__cap_5","uri":"capability://text.generation.language.prompt.templating.and.processing.with.variable.interpolation","name":"prompt templating and processing with variable interpolation","description":"AgentForge includes a prompt processor that handles template variable interpolation, memory context injection, and prompt formatting. Prompts are stored as templates in YAML files with placeholders for variables, memory content, and dynamic values that are resolved at agent execution time, enabling reusable prompt templates that adapt to different contexts.","intents":["Define reusable prompt templates with placeholders for dynamic content","Automatically inject memory context and conversation history into prompts","Format prompts consistently across multiple agents","Modify prompts without changing agent code"],"best_for":["teams managing multiple agents with similar prompt structures","applications requiring consistent prompt formatting across agents","workflows where prompts need frequent iteration"],"limitations":["Template syntax is basic — no conditional logic or loops in templates","Large prompts with extensive memory injection can exceed token limits","No built-in prompt optimization or compression"],"requires":["Python 3.8+","Prompt templates in .agentforge/prompts/","Variable definitions in agent configuration"],"input_types":["YAML prompt templates","variable values","memory context"],"output_types":["formatted prompts","interpolated text"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-agentforge__cap_6","uri":"capability://automation.workflow.built.in.testing.framework.for.agent.validation","name":"built-in testing framework for agent validation","description":"AgentForge includes a testing framework that enables developers to write tests for agents, validating outputs against expected results, checking memory state changes, and verifying workflow execution. Tests are integrated with the configuration system so agents can be tested in isolation or as part of larger workflows, with support for mocking external dependencies.","intents":["Write unit tests for individual agents to verify behavior","Test multi-agent workflows end-to-end","Validate that agents produce expected outputs for given inputs","Ensure agent behavior remains consistent across configuration changes"],"best_for":["teams building production agent systems that require reliability","projects where agent behavior needs to be validated before deployment","continuous integration pipelines for agent workflows"],"limitations":["Testing LLM outputs is inherently non-deterministic — tests may be flaky","No built-in support for testing with multiple LLM providers simultaneously","Limited mocking capabilities for external dependencies"],"requires":["Python 3.8+","pytest or unittest framework","Test configuration in .agentforge/tests/"],"input_types":["test cases","agent configurations","expected outputs"],"output_types":["test results","assertion failures","execution traces"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-agentforge__cap_7","uri":"capability://automation.workflow.structured.logging.and.execution.tracing","name":"structured logging and execution tracing","description":"AgentForge implements a logging system that captures agent execution traces, including inputs, outputs, memory state changes, and timing information. Logs are structured and queryable, enabling debugging of agent behavior and performance analysis. The logging system integrates with the configuration system to enable per-agent log levels and output destinations.","intents":["Debug agent behavior by examining execution traces","Monitor agent performance and identify bottlenecks","Audit agent decisions and memory state changes","Analyze patterns in agent outputs for improvement"],"best_for":["teams debugging complex multi-agent workflows","production systems requiring audit trails","performance optimization efforts"],"limitations":["Verbose logging can impact performance — ~5-10% overhead per agent execution","Log storage grows quickly with high-volume agent execution","No built-in log aggregation or centralized logging support"],"requires":["Python 3.8+","Logging configuration in .agentforge/system/"],"input_types":["agent execution events","memory operations","API calls"],"output_types":["structured log entries","execution traces","performance metrics"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-agentforge__cap_8","uri":"capability://tool.use.integration.discord.integration.for.agent.deployment","name":"discord integration for agent deployment","description":"AgentForge includes utilities for deploying agents to Discord as bots, handling message parsing, response formatting, and conversation context management. The Discord integration maps Discord messages to agent inputs and agent outputs to Discord messages, enabling agents to be accessed through Discord without additional wrapper code.","intents":["Deploy agents as Discord bots for team collaboration","Enable non-technical users to interact with agents through Discord","Build Discord-native workflows that leverage agent capabilities","Maintain conversation context across Discord messages"],"best_for":["teams using Discord for communication who want to integrate agents","projects requiring low-friction agent deployment","collaborative workflows where agents assist team members"],"limitations":["Discord message length limits (2000 characters) may truncate agent outputs","No support for Discord threads or advanced message features","Rate limiting on Discord API can cause agent response delays","Conversation context is limited to Discord message history"],"requires":["Python 3.8+","Discord bot token","discord.py library","Discord server with bot permissions"],"input_types":["Discord messages","user mentions","message attachments"],"output_types":["Discord messages","formatted responses","embedded content"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-agentforge__cap_9","uri":"capability://text.generation.language.persona.based.agent.identity.and.behavior.customization","name":"persona-based agent identity and behavior customization","description":"AgentForge implements Persona Memory that stores agent identity, behavioral instructions, and role-specific information. Personas are defined in YAML and automatically injected into agent prompts, enabling agents to adopt different roles, communication styles, and expertise areas without code changes. Multiple personas can be swapped at runtime for the same agent logic.","intents":["Give agents distinct personalities and communication styles","Define role-specific behavior (e.g., expert vs. beginner-friendly)","Maintain consistent agent identity across conversations","Swap agent personas without changing underlying logic"],"best_for":["applications requiring multiple agent personas (customer service, technical support, etc.)","systems where agent behavior needs to adapt to different user contexts","teams building agent systems with distinct roles"],"limitations":["Persona injection adds tokens to every prompt, increasing API costs","No built-in conflict resolution if persona instructions contradict task instructions","Personas are static — no dynamic persona adaptation based on user feedback"],"requires":["Python 3.8+","Persona definitions in .agentforge/prompts/persona/","Agent configuration referencing a persona"],"input_types":["persona definitions","agent configuration"],"output_types":["persona-augmented prompts","persona-consistent outputs"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+",".agentforge directory in project root with YAML files","PyYAML library for parsing","Agent instances configured in .agentforge/agents/","Cog flow definition in YAML with agent references","Chroma library (local or remote instance)","Embedding model (OpenAI, local, or other)","Storage configuration in .agentforge/system/","Parsing configuration in agent prompts or code","API keys for at least one supported provider (OpenAI, Anthropic, Ollama, etc.)"],"failure_modes":["YAML parsing adds startup latency when loading large configuration directories","No built-in schema validation — malformed YAML fails at runtime rather than parse time","Hot-reloading requires file system monitoring which may not work reliably on network drives or containerized environments","Sequential execution only — no built-in parallelization of independent agents","Data flow between agents is implicit through memory system rather than explicit in code, making debugging harder","No conditional branching or dynamic agent selection based on runtime conditions","Vector storage adds latency — ~100-300ms per semantic search","Embedding quality depends on the embedding model used","Chroma local mode is not suitable for multi-process deployments","No built-in memory deduplication — similar memories may be stored multiple times","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":"2026-06-17T09:51:02.370Z","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=agentforge","compare_url":"https://unfragile.ai/compare?artifact=agentforge"}},"signature":"DivZFAoRzqRoEYwAdJVxcusGDqh8AmEJ+NAegyK/964RJdB63C25xsyUJeqVfZyLK7e2blDIpj2+upZK/uSjDw==","signedAt":"2026-06-20T11:00:54.811Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/agentforge","artifact":"https://unfragile.ai/agentforge","verify":"https://unfragile.ai/api/v1/verify?slug=agentforge","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"}}