{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-symbolicai","slug":"symbolicai","name":"SymbolicAI","type":"framework","url":"https://github.com/ExtensityAI/symbolicai","page_url":"https://unfragile.ai/symbolicai","categories":["app-builders"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-symbolicai__cap_0","uri":"capability://planning.reasoning.symbolic.expression.composition.with.llm.integration","name":"symbolic expression composition with llm integration","description":"Enables declarative construction of neuro-symbolic computation graphs where LLM calls are composed as first-class symbolic expressions. Uses a domain-specific language (DSL) approach to represent prompts, chains, and reasoning steps as composable objects that can be inspected, validated, and executed. The framework treats language model operations as symbolic primitives that can be combined with logical operators, control flow, and external tools into larger symbolic programs.","intents":["Build complex LLM workflows as composable symbolic expressions rather than imperative code","Create reusable prompt templates and reasoning chains that can be inspected and modified at runtime","Combine symbolic reasoning with neural LLM capabilities in a unified framework","Define multi-step reasoning pipelines with explicit control flow and composition"],"best_for":["Teams building neuro-symbolic AI applications requiring interpretability","Researchers prototyping hybrid symbolic-neural reasoning systems","Developers creating complex LLM chains with explicit composition semantics"],"limitations":["Symbolic expression overhead adds latency compared to direct API calls","Requires learning DSL syntax and composition patterns specific to the framework","Limited to LLM providers with supported API integrations","No built-in distributed execution — single-machine symbolic graph evaluation"],"requires":["Python 3.8+","API credentials for at least one LLM provider (OpenAI, Anthropic, or compatible)","Understanding of symbolic computation and expression trees"],"input_types":["text prompts","structured symbolic expressions","configuration objects"],"output_types":["symbolic expression results","text from LLM","structured data from parsing"],"categories":["planning-reasoning","neuro-symbolic-ai"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-symbolicai__cap_1","uri":"capability://text.generation.language.type.safe.prompt.templating.with.variable.binding","name":"type-safe prompt templating with variable binding","description":"Implements a templating system that binds variables to prompt strings with type checking and validation at definition time. Supports parameterized prompt construction where variables are declared with types and constraints, then bound at execution time with automatic validation. The system prevents prompt injection and type mismatches by validating inputs against declared schemas before passing to LLMs.","intents":["Create reusable prompt templates with typed parameters and validation","Prevent prompt injection attacks by validating variable inputs against schemas","Build prompt libraries with explicit contracts about expected inputs","Compose prompts dynamically while maintaining type safety"],"best_for":["Teams requiring production-grade prompt safety and reusability","Applications handling untrusted user inputs to LLMs","Organizations building prompt libraries with strict contracts"],"limitations":["Type validation adds overhead to prompt execution","Schema definition required upfront — less flexible for ad-hoc prompting","Limited to Python type system — no cross-language type sharing","Complex nested types may require verbose schema definitions"],"requires":["Python 3.8+","Understanding of type annotations and schema validation"],"input_types":["prompt strings with placeholders","typed variable values","schema definitions"],"output_types":["validated prompt strings","type validation errors"],"categories":["text-generation-language","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-symbolicai__cap_10","uri":"capability://automation.workflow.symbolic.expression.serialization.and.persistence","name":"symbolic expression serialization and persistence","description":"Serializes symbolic expressions to persistent storage formats (JSON, YAML, pickle) and deserializes them for later execution. Enables saving and loading of reasoning chains, prompts, and knowledge graphs. Supports versioning and migration of symbolic expressions across framework versions.","intents":["Save reasoning chains and prompts for reuse across sessions","Version control symbolic programs and knowledge graphs","Share symbolic expressions between team members","Persist execution results and traces for analysis"],"best_for":["Teams building reusable prompt and reasoning chain libraries","Applications requiring persistence of symbolic state","Systems with version control requirements for AI artifacts"],"limitations":["Serialization overhead for complex symbolic expressions","Deserialization may fail if framework version changes","Large knowledge graphs may produce large serialized files","No built-in encryption for sensitive symbolic data"],"requires":["Python 3.8+","File system or object storage for persistence"],"input_types":["symbolic expressions","reasoning chains","knowledge graphs"],"output_types":["serialized formats (JSON, YAML, pickle)","deserialized symbolic objects"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-symbolicai__cap_11","uri":"capability://automation.workflow.symbolic.batch.processing.and.parallel.execution","name":"symbolic batch processing and parallel execution","description":"Executes multiple symbolic reasoning chains in parallel or batch mode with result aggregation and error handling. Implements batch scheduling, parallel execution with resource limits, and result collection. Supports both data-parallel (same chain on multiple inputs) and task-parallel (different chains) execution patterns.","intents":["Process multiple inputs through the same reasoning chain in parallel","Execute different reasoning chains concurrently with resource management","Aggregate results from parallel executions","Handle failures in individual batch items without stopping the entire batch"],"best_for":["Applications processing large datasets through LLM chains","Systems requiring efficient resource utilization for multiple chains","Batch processing pipelines with LLM components"],"limitations":["Parallel execution limited by LLM provider rate limits","No automatic load balancing across providers","Batch processing adds complexity to error handling and result collection","Resource limits must be manually configured"],"requires":["Python 3.8+","Sufficient API quota for parallel LLM calls","Optional: async/await support for concurrent execution"],"input_types":["batch of inputs","reasoning chains","execution parameters"],"output_types":["aggregated results","error reports per item","execution statistics"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-symbolicai__cap_2","uri":"capability://tool.use.integration.multi.provider.llm.abstraction.with.unified.interface","name":"multi-provider llm abstraction with unified interface","description":"Abstracts multiple LLM providers (OpenAI, Anthropic, local models, etc.) behind a unified Python interface, allowing model swapping without changing application code. Implements provider-specific adapters that translate between the framework's canonical request/response format and each provider's API contract. Handles provider-specific features (function calling, streaming, token counting) through a capability detection system.","intents":["Switch between LLM providers without refactoring application code","Build applications that work with multiple models simultaneously","Avoid vendor lock-in by abstracting provider-specific APIs","Test applications with different models and compare outputs"],"best_for":["Teams wanting to avoid LLM provider lock-in","Applications requiring model flexibility and A/B testing","Researchers comparing behavior across multiple LLM providers"],"limitations":["Abstraction layer adds latency (~50-100ms per request) due to translation overhead","Provider-specific features (advanced function calling, vision) may not be fully exposed","Requires maintaining adapter code as providers update APIs","Some advanced provider features may be unavailable through the unified interface"],"requires":["Python 3.8+","API keys for desired LLM providers","Network connectivity to provider APIs or local model server"],"input_types":["prompts","messages","function schemas"],"output_types":["text completions","structured function calls","token counts"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-symbolicai__cap_3","uri":"capability://memory.knowledge.symbolic.memory.and.context.management","name":"symbolic memory and context management","description":"Manages conversation history and context as symbolic data structures that can be inspected, filtered, and composed. Implements context windows as symbolic expressions where messages, embeddings, and metadata are first-class objects. Supports context compression, selective retrieval, and composition of multiple context streams into unified reasoning chains.","intents":["Maintain and inspect conversation history as structured symbolic data","Compress or filter context to fit within token limits while preserving meaning","Compose multiple conversation threads or knowledge sources into unified context","Retrieve relevant context based on semantic similarity or metadata"],"best_for":["Multi-turn conversational agents requiring context management","Applications with large knowledge bases needing selective retrieval","Systems requiring interpretable context composition decisions"],"limitations":["In-memory context storage — no built-in persistence to databases","Context compression heuristics may lose important information","Semantic retrieval requires embedding model integration","Scaling to very large context windows (>100k tokens) requires external storage"],"requires":["Python 3.8+","Optional: embedding model for semantic context retrieval","Optional: vector store for large-scale context indexing"],"input_types":["messages","embeddings","metadata"],"output_types":["filtered context","composed context windows","context metadata"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-symbolicai__cap_4","uri":"capability://planning.reasoning.symbolic.reasoning.chain.execution.with.backtracking","name":"symbolic reasoning chain execution with backtracking","description":"Executes symbolic reasoning chains with support for backtracking, branching, and alternative path exploration. Implements a symbolic execution engine that can explore multiple reasoning paths, evaluate their validity, and backtrack to try alternatives when constraints are violated. Chains are represented as symbolic expressions that can be inspected before execution and modified based on intermediate results.","intents":["Implement complex reasoning workflows with multiple decision points and alternatives","Explore alternative reasoning paths and select the best outcome","Backtrack and retry with different approaches when reasoning fails","Validate reasoning chains against constraints before committing to results"],"best_for":["Applications requiring robust error recovery and alternative reasoning","Complex problem-solving agents with multiple solution strategies","Systems where reasoning validity must be verified before execution"],"limitations":["Backtracking exploration increases LLM API costs exponentially with path count","No automatic pruning of low-probability branches — requires manual strategy definition","Execution time grows with number of alternative paths explored","Requires explicit constraint definitions for validity checking"],"requires":["Python 3.8+","Clear constraint definitions for reasoning validity","Sufficient API quota for multiple LLM calls per reasoning chain"],"input_types":["symbolic reasoning chains","constraints","initial problem state"],"output_types":["valid reasoning result","execution trace with alternatives","constraint violation reports"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-symbolicai__cap_5","uri":"capability://tool.use.integration.symbolic.tool.calling.with.schema.based.function.binding","name":"symbolic tool calling with schema-based function binding","description":"Enables LLMs to call external tools through a schema-based function registry where tools are defined as symbolic objects with type signatures and validation. Implements automatic schema generation from Python function signatures, validation of tool arguments against schemas, and error handling with automatic retry logic. Supports both synchronous and asynchronous tool execution with result composition back into reasoning chains.","intents":["Allow LLMs to call external functions and APIs with type-safe argument passing","Define tool libraries with explicit contracts and validation","Automatically generate function schemas from Python code","Handle tool execution errors with retry logic and fallbacks"],"best_for":["Agents requiring access to external APIs and tools","Applications with strict type safety requirements for tool calling","Systems where tool availability and capabilities must be discoverable"],"limitations":["Schema generation from Python signatures may not capture all constraints","Tool execution latency depends on external service availability","No built-in rate limiting or quota management for tool calls","Async tool execution requires event loop management"],"requires":["Python 3.8+","Tool functions with type annotations","External services/APIs for tools to call"],"input_types":["Python functions","type signatures","tool parameters"],"output_types":["function schemas","tool execution results","error reports"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-symbolicai__cap_6","uri":"capability://memory.knowledge.symbolic.knowledge.graph.construction.and.querying","name":"symbolic knowledge graph construction and querying","description":"Builds and queries knowledge graphs as symbolic data structures extracted from LLM outputs or external sources. Implements entity extraction, relationship inference, and graph traversal as symbolic operations that can be composed into larger reasoning chains. Supports both explicit graph construction (from structured data) and implicit graph building (from LLM-extracted facts).","intents":["Extract structured knowledge from unstructured text via LLM","Build knowledge graphs that can be queried and reasoned over","Compose knowledge graph queries into larger reasoning chains","Validate extracted knowledge against constraints and consistency rules"],"best_for":["Applications requiring structured knowledge extraction from text","Systems building domain-specific knowledge bases","Reasoning systems that need to query and traverse relationships"],"limitations":["Knowledge graph construction from LLM outputs may contain hallucinations","Graph querying performance degrades with large graphs (>100k nodes)","No built-in graph persistence — requires external storage","Relationship inference requires careful prompt engineering to avoid errors"],"requires":["Python 3.8+","LLM for entity and relationship extraction","Optional: graph database for large-scale storage"],"input_types":["text documents","structured data","extraction prompts"],"output_types":["knowledge graphs","query results","extracted entities and relationships"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-symbolicai__cap_7","uri":"capability://code.generation.editing.symbolic.program.synthesis.from.specifications","name":"symbolic program synthesis from specifications","description":"Generates executable code or symbolic programs from high-level specifications using LLMs with validation and refinement loops. Implements specification parsing, code generation, testing, and iterative refinement as symbolic operations. Supports synthesis of both Python code and symbolic expressions that can be executed within the framework.","intents":["Generate code from natural language specifications","Synthesize symbolic programs that solve specified problems","Iteratively refine generated code based on test results","Validate synthesized programs against specifications"],"best_for":["Low-code/no-code platforms leveraging LLMs for code generation","Automated programming assistants for domain-specific languages","Research systems exploring program synthesis with neural models"],"limitations":["Generated code quality depends heavily on specification clarity","Synthesis requires multiple LLM calls for refinement — expensive","Testing and validation logic must be manually defined","No guarantees of correctness — generated code requires human review"],"requires":["Python 3.8+","Clear specifications for synthesis","Test cases or validation logic"],"input_types":["natural language specifications","test cases","domain constraints"],"output_types":["generated code","symbolic programs","validation reports"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-symbolicai__cap_8","uri":"capability://planning.reasoning.symbolic.constraint.satisfaction.and.optimization","name":"symbolic constraint satisfaction and optimization","description":"Solves constraint satisfaction and optimization problems by representing constraints as symbolic expressions and using LLMs to explore solution spaces. Implements constraint propagation, solution validation, and iterative refinement to find solutions that satisfy all constraints. Supports both hard constraints (must be satisfied) and soft constraints (preferences).","intents":["Solve constraint satisfaction problems using LLM reasoning","Optimize solutions against multiple objectives and constraints","Validate candidate solutions against constraint specifications","Iteratively refine solutions to improve constraint satisfaction"],"best_for":["Optimization and planning problems with complex constraints","Applications requiring flexible constraint-based reasoning","Systems where constraint satisfaction must be interpretable"],"limitations":["LLM-based constraint solving may not find optimal solutions","Constraint propagation requires careful specification to avoid conflicts","Scaling to large constraint sets (>1000 constraints) is inefficient","No guarantees of solution optimality or completeness"],"requires":["Python 3.8+","Explicit constraint definitions","Optimization objectives or preferences"],"input_types":["constraints","optimization objectives","candidate solutions"],"output_types":["valid solutions","constraint satisfaction reports","optimization metrics"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-symbolicai__cap_9","uri":"capability://planning.reasoning.symbolic.debugging.and.execution.tracing","name":"symbolic debugging and execution tracing","description":"Provides detailed execution traces of symbolic reasoning chains with step-by-step inspection of intermediate results, decisions, and LLM outputs. Implements breakpoints, conditional tracing, and result inspection at each symbolic operation. Traces can be visualized, logged, and analyzed to understand reasoning behavior and debug failures.","intents":["Debug complex reasoning chains by inspecting intermediate results","Understand LLM decision-making at each step of a chain","Identify where reasoning chains fail or produce unexpected results","Log and analyze execution traces for optimization"],"best_for":["Developers building and debugging complex reasoning chains","Teams requiring interpretability and auditability of AI decisions","Research systems studying LLM reasoning behavior"],"limitations":["Detailed tracing adds significant overhead (~10-20% latency increase)","Trace storage grows linearly with chain complexity","Visualization tools may not scale to very deep chains (>100 steps)","Tracing requires explicit instrumentation of symbolic operations"],"requires":["Python 3.8+","Optional: visualization tools for trace display"],"input_types":["symbolic reasoning chains","breakpoint specifications"],"output_types":["execution traces","intermediate results","decision logs"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":26,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","API credentials for at least one LLM provider (OpenAI, Anthropic, or compatible)","Understanding of symbolic computation and expression trees","Understanding of type annotations and schema validation","File system or object storage for persistence","Sufficient API quota for parallel LLM calls","Optional: async/await support for concurrent execution","API keys for desired LLM providers","Network connectivity to provider APIs or local model server","Optional: embedding model for semantic context retrieval"],"failure_modes":["Symbolic expression overhead adds latency compared to direct API calls","Requires learning DSL syntax and composition patterns specific to the framework","Limited to LLM providers with supported API integrations","No built-in distributed execution — single-machine symbolic graph evaluation","Type validation adds overhead to prompt execution","Schema definition required upfront — less flexible for ad-hoc prompting","Limited to Python type system — no cross-language type sharing","Complex nested types may require verbose schema definitions","Serialization overhead for complex symbolic expressions","Deserialization may fail if framework version changes","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":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:04.049Z","last_scraped_at":"2026-05-03T14:00:20.516Z","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=symbolicai","compare_url":"https://unfragile.ai/compare?artifact=symbolicai"}},"signature":"JVQLEw2jWmkBdsHLANz5ZZs/bpCJ8b5EzuoT2aVoUogRYxrRwEmSzkBKrS86bz1uwpQFXBKZKaanbfq+PKbxCA==","signedAt":"2026-06-22T15:46:13.741Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/symbolicai","artifact":"https://unfragile.ai/symbolicai","verify":"https://unfragile.ai/api/v1/verify?slug=symbolicai","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"}}