Build agents via YAML with Prolog validation and 110 built-in tools
AgentI'm one of the creators of The Edge Agent (TEA). We built this because we needed a way to deploy agents that was verifiable and robust enough for production/edge cases, moving away from loose scripts.The architecture aims to solve critical gaps in deterministic orchestration identified by
Capabilities11 decomposed
yaml-based agent configuration with declarative syntax
Medium confidenceEnables agent definition through YAML configuration files rather than imperative code, parsing the YAML structure into an internal agent representation that maps to tool registries and execution pipelines. The declarative approach abstracts away boilerplate orchestration logic, allowing non-developers to compose multi-step workflows by declaring agent goals, tools, and execution constraints in human-readable YAML syntax.
Uses YAML as the primary agent definition language rather than Python/JavaScript DSLs, lowering barrier to entry for non-developers while maintaining full integration with 110 built-in tools
Simpler configuration syntax than LangChain's Python-based agent builders or AutoGen's multi-agent frameworks, enabling faster iteration for configuration-driven use cases
prolog-based agent validation and constraint checking
Medium confidenceIntegrates Prolog logic programming to validate agent configurations before execution, checking for logical consistency, constraint satisfaction, and goal reachability. The validation engine translates YAML agent definitions into Prolog predicates, then queries the Prolog engine to detect configuration errors (unreachable goals, circular dependencies, missing tool bindings) before the agent runs, preventing runtime failures.
Uses Prolog logic programming for agent validation rather than simple schema validation, enabling detection of logical inconsistencies and constraint violations that imperative validators would miss
More rigorous than JSON schema validation used by most agent frameworks; catches logical errors before runtime, reducing debugging time in production deployments
agent performance monitoring and metrics collection
Medium confidenceCollects metrics on agent execution performance (latency per step, tool invocation counts, success rates, error rates) and exposes them for monitoring and alerting. The metrics system tracks execution time, tool usage patterns, and failure modes, enabling operators to identify performance bottlenecks and detect anomalies in agent behavior.
Correlates performance metrics with Prolog constraint validation results, identifying whether performance issues are due to constraint overhead or underlying tool latency
More detailed than basic execution logging; provides structured metrics enabling automated performance analysis and anomaly detection
110 built-in tool integration with unified calling interface
Medium confidenceProvides a pre-integrated library of 110 tools (APIs, functions, external services) accessible through a unified function-calling interface. Tools are registered in a central registry with standardized schemas (name, description, parameters, return type), allowing agents to discover and invoke tools via a single abstraction layer without custom integration code for each tool.
Provides 110 pre-integrated tools in a unified registry with standardized schemas, eliminating per-tool integration boilerplate that developers would otherwise write for each external service
Broader tool coverage than most agent frameworks' default toolsets; reduces time-to-first-working-agent by providing immediate access to common utilities and APIs without custom adapters
agent execution orchestration with step-by-step planning
Medium confidenceOrchestrates agent execution by decomposing high-level goals into discrete steps, planning tool invocations, and managing state across execution steps. The orchestrator maintains an execution context (current goal, available tools, previous results) and iteratively selects the next tool to invoke based on agent reasoning, handling tool outputs and updating state until the goal is achieved or a termination condition is met.
Combines YAML-defined workflows with Prolog validation to ensure each execution step is logically consistent with agent constraints, providing both flexibility and safety guarantees
More structured than ReAct-style agents that lack explicit planning; provides better visibility and control than black-box LLM-only orchestration
agent goal decomposition and subgoal generation
Medium confidenceAutomatically breaks down high-level agent goals into smaller, achievable subgoals by analyzing the goal statement and available tools. The decomposition engine uses heuristics or reasoning to identify prerequisite steps, dependencies between subgoals, and optimal ordering, generating an execution plan that guides the agent through sequential subgoal achievement.
Integrates goal decomposition with Prolog validation to ensure generated subgoals are logically achievable and satisfy agent constraints before execution begins
More explicit than ReAct agents that decompose goals implicitly during execution; enables pre-execution validation and optimization that reduces runtime failures
tool parameter binding and schema validation
Medium confidenceValidates and binds parameters to tool invocations by matching agent-generated parameters against tool schemas (expected types, required fields, constraints). The binding engine performs type coercion, validates parameter values against constraints, and generates clear error messages when parameters are invalid, preventing malformed tool calls from reaching the underlying tool.
Combines schema-based validation with Prolog constraint checking to ensure tool parameters not only match type schemas but also satisfy logical constraints defined in agent configuration
More rigorous than simple type checking used by most frameworks; catches semantic parameter errors (e.g., invalid combinations) that type systems alone would miss
agent execution tracing and debugging output
Medium confidenceRecords detailed execution traces showing each step of agent reasoning, tool invocations, parameters, results, and state changes. The tracing system captures execution metadata (timestamps, latency, tool selection rationale) and outputs structured logs or interactive visualizations, enabling developers to understand agent behavior and diagnose failures without modifying code.
Integrates execution tracing with Prolog validation results, showing not only what the agent did but also why each step satisfied logical constraints and passed validation checks
More detailed than basic logging; provides structured traces that enable automated analysis and visualization of agent behavior across multiple execution runs
constraint-based tool selection and filtering
Medium confidenceFilters available tools based on agent-defined constraints (resource limits, permission levels, execution context) before presenting them to the reasoning engine. The filtering system evaluates Prolog constraints against the current execution context, removing tools that violate constraints and ensuring only valid tools are considered for selection at each step.
Uses Prolog constraints to dynamically filter tools based on execution context, enabling fine-grained access control that adapts to runtime conditions rather than static tool permissions
More flexible than role-based access control; enables context-aware tool restrictions that respond to execution state (budget, mode, user context) without code changes
multi-agent coordination and message passing
Medium confidenceEnables multiple agents to coordinate by passing messages and sharing execution context. Agents can invoke other agents as tools, passing goals and receiving results, with a message queue or event bus managing communication between agents. The coordination layer handles agent discovery, message routing, and context propagation across agent boundaries.
Integrates multi-agent coordination with Prolog validation, ensuring that agent delegation chains satisfy logical constraints and prevent circular dependencies before execution
More structured than ad-hoc agent communication; provides validation and coordination guarantees that prevent common multi-agent failure modes
agent configuration versioning and rollback
Medium confidenceManages versions of agent YAML configurations, enabling rollback to previous versions and comparison between configurations. The versioning system tracks configuration changes, stores historical versions, and provides diff tools to understand what changed between versions, supporting safe configuration updates and quick recovery from problematic deployments.
Integrates configuration versioning with Prolog validation, automatically validating each historical version to ensure rollback targets are logically consistent
More sophisticated than simple Git-based configuration management; provides automated validation of historical versions and prevents rollback to invalid configurations
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
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Hey HN, we're Jon and Kristiane, and we're building Orloj (https://orloj.dev), an open-source orchestration runtime for multi-agent AI systems. You define agents, tools, policies, and workflows in declarative YAML manifests, and Orloj handles scheduling, execution, governance, an
Best For
- ✓teams building internal automation tools with non-technical operators
- ✓organizations seeking configuration-driven agent deployment
- ✓rapid prototyping scenarios where code iteration is expensive
- ✓teams deploying agents in production where validation prevents costly failures
- ✓systems requiring formal verification of agent behavior before execution
- ✓organizations with strict compliance requirements for automated decision-making
- ✓production systems requiring operational visibility into agent performance
- ✓teams optimizing agent efficiency and cost
Known Limitations
- ⚠YAML syntax limits expressiveness compared to imperative languages — complex conditional logic requires workarounds
- ⚠No built-in IDE support for schema validation or autocomplete in standard editors
- ⚠Debugging YAML-defined agents requires parsing error messages back to configuration lines
- ⚠Prolog validation adds startup latency — complex agent graphs may require seconds to validate
- ⚠Prolog's declarative nature makes it difficult to express temporal constraints or stateful validations
- ⚠Limited to logical consistency checks — cannot validate semantic correctness or real-world feasibility
Requirements
Input / Output
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