Summary

AgenticGoKit has evolved significantly with many core features already implemented, including multi-agent orchestration, workflow visualization, and comprehensive CLI tooling. Below is the current implementation status and future roadmap.

---

Implementation Status

Component	Status	Notes
Core Runner & Orchestration	✅ COMPLETED	Event system, Runner core, Route/Collaborate modes
Multi-Agent Orchestration	✅ COMPLETED	Collaborative, Sequential, Loop, and Mixed patterns
Workflow Visualization	✅ COMPLETED	Automatic Mermaid diagram generation
CLI Tooling	✅ COMPLETED	Project scaffolding, orchestration modes, visualization
ModelProvider Interface	✅ COMPLETED	OpenAI, Azure OpenAI, Ollama adapters
MCP Integration	✅ COMPLETED	Tool discovery, execution, server management
Tool Integration	✅ COMPLETED	Dynamic tool discovery via MCP protocol
Memory & State Management	✅ COMPLETED	Session storage, state management
Error Handling & Resilience	✅ COMPLETED	Retry policies, circuit breakers, fault tolerance
Developer Tooling	✅ COMPLETED	Comprehensive CLI, project templates
REST API	🔄 IN PROGRESS	HTTP endpoints for programmatic access
Developer UI	📋 PLANNED	Dashboard for workflow visualization and debugging
Advanced RAG	📋 PLANNED	Vector database integration, retrieval workflows

---

Recently Completed Features

✅ Multi-Agent Orchestration (Completed)

• Collaborative Mode: All agents process events in parallel with result aggregation
• Sequential Mode: Agents process events in pipeline order with state passing
• Loop Mode: Single agent repeats execution until conditions are met
• Mixed Mode: Combine collaborative and sequential patterns in one workflow

✅ Workflow Visualization (Completed)

• Automatic Mermaid Generation: CLI generates workflow diagrams automatically
• Multiple Diagram Types: Support for all orchestration patterns
• Custom Configuration: Configurable diagram styles, directions, and metadata
• File Export: Save diagrams as .mmd files for documentation

✅ Enhanced CLI Tooling (Completed)

• Orchestration Mode Flags: --orchestration-mode, --collaborative-agents, --sequential-agents, --loop-agent
• Visualization Flags: --visualize, --visualize-output
• Configuration Options: --failure-threshold, --max-concurrency, --orchestration-timeout
• Project Templates: Complete multi-agent project scaffolding

✅ Production-Ready Features (Completed)

• Fault Tolerance: Configurable failure thresholds and error handling
• Retry Policies: Exponential backoff, circuit breakers, jitter
• State Management: Comprehensive state passing between agents
• Callback System: Pre/post execution hooks for monitoring
• Observability: Structured logging and tracing throughout

---

Original Roadmap (Historical)

Sprint 1 (Weeks 1–2): Core Runner & Orchestration ✅ COMPLETED

Epic: Establish the heart of the framework—the Runner—that ingests events and dispatches to agents in different orchestration modes.

1. User Story: As a developer, I want an Event interface so that all agent workflows can receive a uniform input structure.
   Acceptance Criteria:

   • Define Go interface Event with common fields (ID, payload, metadata).
   • Unit tests for JSON (un)marshalling of Event.

2. User Story: As a developer, I want a Runner core service to register and emit events so workflows can start.
   Acceptance Criteria:

   • Implement Runner.RegisterAgent(name string, agent Agent).
   • Implement Runner.Emit(event Event).
   • In-memory event queue with FIFO ordering.

3. User Story: As a developer, I want a “route” mode so a single event is forwarded to exactly one agent.
   Acceptance Criteria:

   • RouteOrchestrator picks agent by round-robin or weighted rules.
   • Unit tests covering routing logic.

4. User Story: As a developer, I want a “collaborate” mode so multiple agents can process the same event in parallel.
   Acceptance Criteria:

   • CollaborateOrchestrator invokes all registered agents concurrently.
   • Proper error aggregation and timeout handling.

---

Sprint 2 (Weeks 3–4): Deterministic Workflow Agents

Epic: Build non-LLM workflow primitives for structured pipelines, enabling hybrid flows without external model calls.

1. User Story: As a developer, I want a SequentialAgent that runs a list of sub-agents in order.
   Acceptance Criteria:

   • Accepts []Agent, executes one after another, passing state forward.
   • Error short-circuit: stops on first failure.

2. User Story: As a developer, I want a ParallelAgent that runs sub-agents concurrently and aggregates results.
   Acceptance Criteria:

   • Executes []Agent with Go routines and channels.
   • Collects outputs and errors into a slice.

3. User Story: As a developer, I want a LoopAgent that repeats a sub-agent until a condition is met.
   Acceptance Criteria:

   • Accepts a ConditionFunc(State) bool.
   • Safety cap on max iterations.

4. User Story: As a developer, I want the workflow agents wired into the Runner so they can be used in orchestrator modes.
   Acceptance Criteria:

   • Runner can dispatch to any Agent (including workflow agents).
   • Integration tests for nested workflows.

---

Sprint 3 (Weeks 5–6): ModelProvider & Tool Integration

Epic: Abstract LLM backends and build in essential tools for agents to call.

1. User Story: As a developer, I want a ModelProvider interface so any LLM can be plugged in.
   Acceptance Criteria:

   • Define methods Call(ctx, Prompt) (Response, error).
   • Mock implementation for tests.

2. User Story: As a developer, I want an OpenAIAdapter that implements ModelProvider for OpenAI’s APIs.
   Acceptance Criteria:

   • Supports streaming and non-streaming calls.
   • Configurable via environment variables.

3. User Story: As a developer, I want an OllamaAdapter for local LLM hosting (Ollama).
   Acceptance Criteria:

   • HTTP client to local Ollama server.
   • Fallback to file-based prompts if server unavailable.

4. User Story: As a developer, I want a generic FunctionTool so agents can execute external functions.
   Acceptance Criteria:

   • Register named functions with signature func(Context) (Output, error).
   • Example tools: web search stub, code executor.

---

Sprint 4 (Weeks 7–8): Memory & Artifact Management

Epic: Build short-term session storage, long-term memory via vector DB, and file artifact handling.

1. User Story: As a developer, I want an in-memory SessionStore to keep per-user conversation state.
   Acceptance Criteria:

   • CRUD operations for Session and State objects.
   • Concurrency-safe with mutexes or sync.Map.

2. User Story: As a developer, I want a VectorMemory interface with a Pinecone driver.
   Acceptance Criteria:

   • Methods Store(embedding, metadata), Query(embedding, k).
   • Integration tests with Pinecone sandbox.

3. User Story: As a developer, I want a file ArtifactService to save and version artifacts like logs or images.
   Acceptance Criteria:

   • Local FS backend storing files under /artifacts/{sessionID}/.
   • Metadata JSON manifest for each artifact.

4. User Story: As a developer, I want the memory and artifact services injected into Runner so agents can access them.
   Acceptance Criteria:

   • Dependency injection via RunnerConfig.
   • Sample agent that writes a debug log artifact.

---

Sprint 5 (Weeks 9–10): Debugging, Callbacks & CLI

Epic: Enable deep introspection with hooks, traces, and a command-line inspector.

1. User Story: As a developer, I want pre- and post-call callbacks around model and tool invocations.
   Acceptance Criteria:

   • Register callbacks OnBeforeCall, OnAfterCall.
   • Callback context includes input, output, timestamps.

2. User Story: As a developer, I want a TraceLogger that records each execution step into an in-memory log.
   Acceptance Criteria:

   • Structured log entries for each agent invocation.
   • APIs to stream or retrieve trace logs by session ID.

3. User Story: As a developer, I want a CLI command agentcli trace <sessionID> to replay an execution trace.
   Acceptance Criteria:

   • Tabular or tree-view output in the terminal.
   • Flags for filtering by agent name or time range.

4. User Story: As a developer, I want tests covering callback registration and trace retrieval.
   Acceptance Criteria:

   • End-to-end test: run a sample workflow, assert trace contains expected entries.

---

Sprint 6 (Weeks 11–12): REST API, Dev UI & Deployment

Epic: Expose framework functionality via HTTP, build a minimal dashboard, and prepare containerization.

1. User Story: As a developer, I want REST endpoints to submit events and retrieve session state.
   Acceptance Criteria:

   • POST /v1/events, GET /v1/sessions/{id}.
   • OpenAPI spec generated.

2. User Story: As a developer, I want a simple React-based Developer UI to visualize trace logs and session memory.
   Acceptance Criteria:

   • Dashboard page listing active sessions.
   • On-click drill-down to trace view and memory snapshots.

3. User Story: As a developer, I want Dockerfiles and Helm charts so the framework can be deployed to Kubernetes.
   Acceptance Criteria:

   • Multi-stage Docker build with Go binary and static assets.
   • Helm chart values for customizable replicas, resource limits.

4. User Story: As a DevOps engineer, I want a GitHub Actions pipeline that lints code, runs tests, builds Docker image, and publishes to registry.
   Acceptance Criteria:

   • Workflow triggers on push to main.
   • Artifacts include test coverage report and Docker image tag.

---

MCP Integration (Proposed Sprint)

Epic: Add support for Model Context Protocol (MCP) tools to enhance the framework's tool ecosystem and context management capabilities.

1. User Story: As a developer, I want to define an MCPTool interface to standardize MCP tool integration. Acceptance Criteria:

   • Define MCPTool interface with methods for tool invocation and configuration.
   • Unit tests for the interface implementation.

2. User Story: As a developer, I want to extend the ToolRegistry to support MCP tools. Acceptance Criteria:

   • Add methods to register and retrieve MCP tools.
   • Ensure compatibility with existing tools.

3. User Story: As a developer, I want an adapter to wrap MCP tools for seamless integration. Acceptance Criteria:

   • Implement an MCPToolAdapter to bridge MCP tools with the framework.
   • Unit tests for adapter functionality.

4. User Story: As a developer, I want to manage MCP-specific context using the State interface. Acceptance Criteria:

   • Add methods to get and set MCP context in State.
   • Ensure context is passed correctly during tool invocation.

5. User Story: As a developer, I want tracing hooks for MCP tool invocations to ensure observability. Acceptance Criteria:

   • Add tracing hooks for MCP tool calls.
   • Log inputs, outputs, and errors for each invocation.

6. User Story: As a developer, I want an example workflow demonstrating MCP tool usage. Acceptance Criteria:

   • Create a new example in the examples/mcp_tool/ folder.
   • Document the workflow in the examples README.

7. User Story: As a developer, I want to discover MCP servers dynamically to register their tools. Acceptance Criteria:

   • Implement an MCPServerDiscovery interface for discovering MCP servers.
   • Provide implementations for mDNS-based discovery and static configuration.
   • Unit tests for discovery mechanisms.

8. User Story: As a developer, I want to query discovered MCP servers for available tools. Acceptance Criteria:

   • Implement an MCPClient to query MCP servers for tool information.
   • Ensure compatibility with the ToolRegistry for dynamic registration.
   • Integration tests for querying and registering tools.

9. User Story: As a developer, I want to dynamically register tools from discovered MCP servers. Acceptance Criteria:

   • Extend the ToolRegistry to support dynamic registration of MCP tools.
   • Add an adapter for invoking tools from MCP servers.
   • Unit tests for dynamic registration and invocation.

10. User Story: As a developer, I want tracing hooks for MCP server discovery and tool registration. Acceptance Criteria:

    • Add tracing hooks to log discovered servers and registered tools.
    • Integration tests for tracing functionality.

11. User Story: As a developer, I want an example workflow demonstrating MCP server discovery and tool usage. Acceptance Criteria:

    • Create a new example in the examples/mcp_discovery/ folder.
    • Document the workflow in the examples README.

---

RAG Integration (Proposed Sprint)

Epic: Add support for Retrieval-Augmented Generation (RAG) to enhance the framework's ability to combine retrieval mechanisms with generative AI models.

1. User Story: As a developer, I want to define a Retriever interface to standardize retrieval operations. Acceptance Criteria:

   • Define Retriever interface with methods for querying and retrieving documents.
   • Unit tests for the interface implementation.

2. User Story: As a developer, I want to implement retriever backends for popular systems like Pinecone and Elasticsearch. Acceptance Criteria:

   • Implement PineconeRetriever and ElasticsearchRetriever.
   • Integration tests for each backend.

3. User Story: As a developer, I want a RAGAgent that combines retrieval and generation workflows. Acceptance Criteria:

   • Implement RAGAgent to query retrievers and pass retrieved context to LLMs.
   • Unit tests for the agent's logic.

4. User Story: As a developer, I want to extend the State interface to manage retrieved context. Acceptance Criteria:

   • Add methods to store and retrieve RAG-specific context in State.
   • Ensure compatibility with existing agents.

5. User Story: As a developer, I want tracing hooks for RAG workflows to ensure observability. Acceptance Criteria:

   • Add tracing hooks to log retrieval queries, results, and LLM outputs.
   • Integration tests for tracing functionality.

6. User Story: As a developer, I want an example workflow demonstrating RAG usage. Acceptance Criteria:

   • Create a new example in the examples/rag_workflow/ folder.
   • Document the workflow in the examples README.