Version: 1.0 Status: Draft Last Updated: June 2026 Author: AI Engineer (Portfolio Project)
AgentForge is a self-hosted, open-source platform that enables developers and teams to visually design, deploy, execute, and monitor multi-agent AI workflows. It provides a drag-and-drop DAG (Directed Acyclic Graph) editor for composing agent pipelines, a real-time execution engine powered by LangGraph, and built-in observability for tracing, cost tracking, and debugging.
The product targets the gap between "building agents in notebooks" and "running agents in production." Most developers today struggle to move beyond single-agent prototypes. AgentForge makes multi-agent orchestration accessible, observable, and production-ready — without writing orchestration boilerplate.
Tagline: "Design agent workflows visually. Run them reliably. Monitor everything."
- Multi-agent orchestration is hard to build from scratch. Developers spend 60%+ of AI engineering time on orchestration plumbing — state management, error handling, retry logic, context passing — instead of agent logic.
- No visual tooling exists for agent composition. Unlike CI/CD (GitHub Actions UI) or data pipelines (Airflow UI), agent workflows are still composed entirely in code. Debugging requires printf-ing through LLM calls.
- Cost visibility is near-zero. Teams running multi-agent systems have no idea which agent consumed how many tokens, which workflow step is the most expensive, or when they'll hit budget limits.
- Agent templates are not reusable. Every team rebuilds the same "summarizer agent" or "router agent" from scratch. There's no marketplace or registry for sharing battle-tested agent components.
- Execution debugging is painful. When an agent workflow fails at step 7 of 12, developers have no way to replay execution, inspect intermediate state, or understand why a specific decision was made.
- 57% of organizations now deploy multi-step agent workflows (2026 data)
- 90% of engineers are shifting toward AI orchestration roles
- No dominant open-source visual orchestrator exists for multi-agent systems
- MCP (5,000+ servers) and A2A protocols create a natural integration surface
- Building AI-powered products or internal tools
- Need multi-agent workflows but don't want to build orchestration infrastructure
- Comfortable with Docker, APIs, and YAML configuration
- Use case: "I want to build a customer support pipeline with 4 specialized agents but I don't want to write 2,000 lines of LangGraph boilerplate."
- Already using LangGraph or CrewAI in code
- Want visibility, cost tracking, and team collaboration on top of existing agents
- Need RBAC for multi-person teams
- Use case: "Our team of 6 runs 15 agent workflows. We need a control plane."
- Non-coders who want to understand and modify agent workflows
- Use the visual editor to inspect and tweak existing workflows
- Use case: "I want to see why the sales agent escalates so many leads to the human reviewer."
| Goal | Metric | Target |
|---|---|---|
| Reduce time-to-first-workflow | Time from install to running first agent workflow | < 15 minutes |
| Make orchestration visual | % of workflows created via visual editor vs code-only | > 60% |
| Provide full cost visibility | % of workflows with active cost tracking | 100% |
| Enable community reuse | Number of community-contributed agent templates | 50+ within 3 months of launch |
| Production reliability | Workflow execution success rate on deployed workflows | > 95% |
| Open-source traction | GitHub stars within 6 months of public release | 2,000+ |
- Drag-and-drop canvas for composing agent workflows
- Node types:
- Agent Node — Executes an LLM-powered agent with configurable system prompt, tools, and model
- Tool Node — Calls an external tool (MCP server, HTTP API, database query)
- Router Node — Conditional branching based on agent output (if/else, switch)
- Evaluator Node — Runs quality checks on agent output (schema validation, LLM-as-judge)
- Human-in-the-Loop Node — Pauses execution for human review/approval
- Input/Output Nodes — Define workflow inputs and outputs
- Edge connections define data flow between nodes
- Each node has a configuration panel (prompt editor, model selector, tool bindings, timeout settings)
- Workflow validation before execution (cycle detection, missing connections, type mismatches)
- Powered by LangGraph under the hood
- Each workflow compiles to a LangGraph state graph at runtime
- Supports all 4 orchestration patterns:
- Sequential — Linear chain of agents
- Parallel — Fan-out/fan-in for independent agents
- Hierarchical — Planner agent delegates to worker agents
- Conditional — Router-based branching
- Execution modes:
- Run — Execute the full workflow
- Step — Execute one node at a time for debugging
- Resume — Continue from a paused human-in-the-loop node
- State persistence across execution (PostgreSQL-backed)
- Timeout and retry configuration per node
- Live visualization of workflow execution on the DAG canvas
- Nodes light up as they execute (color-coded: running/success/failed/paused)
- Data flow animation along edges
- Execution timeline view:
- Each node shows: start time, end time, duration, token usage, cost
- Click any node to inspect: input state, output state, LLM prompts used, tool calls made
- Error inspection:
- Full stack trace on failure
- Agent "thought process" (reasoning trace) visible
- Retry button on individual failed nodes
- Per-node token counting (input tokens + output tokens)
- Per-workflow cost aggregation
- Per-run cost breakdown (which node was most expensive)
- Budget alerts: set max cost per workflow run, get notified or auto-pause
- Cost dashboard:
- Total spend over time (daily/weekly/monthly)
- Cost per workflow
- Cost per agent template
- Projected monthly spend based on current usage
- Built-in library of pre-configured agent templates:
- Summarizer, Classifier, Extractor, Translator, Router, Code Generator, etc.
- Each template includes: system prompt, recommended model, tool bindings, example usage
- Community contribution system:
- Users can publish their agents as templates
- Templates are versioned (semver)
- Review/approval workflow for community templates
- Signed/verified badge for trusted contributors
- Template marketplace UI with search, categories, ratings, download count
- MCP server registry within the platform
- Users register MCP servers by providing connection config (URL, auth, capabilities)
- Registered MCP servers appear as available tools in the Tool Node configuration
- MCP server health monitoring (connection status, latency, error rate)
- Built-in support for common MCP servers:
- File system, HTTP client, PostgreSQL, MongoDB, web search, code execution
- Every workflow exposes a REST API endpoint for programmatic execution
- Webhook support: trigger workflows from external events (GitHub push, Stripe payment, Slack message)
- API key management per workflow
- Rate limiting and throttling per API key
- Webhook payload automatically injected as workflow input
- Email/password + OAuth (GitHub, Google) authentication
- Workspace-based team management
- Roles:
- Owner — Full access, billing, workspace settings
- Admin — Manage workflows, templates, team members
- Editor — Create and edit workflows, execute
- Viewer — View workflows and execution logs, read-only
- Audit log of all actions (who edited what, who triggered which workflow)
These features are explicitly deferred to future versions:
| Feature | Reason for Deferral |
|---|---|
| A2A protocol support | Complex to implement, low initial demand |
| Kubernetes operator / Helm chart | Docker Compose sufficient for MVP |
| Visual model fine-tuning | Out of orchestration scope |
| On-premise enterprise edition | Requires sales/licensing infrastructure |
| Mobile app | Desktop-first for DAG editing UX |
| Workflow versioning with diff view | Complex UI, simple versioning in MVP |
| Multi-cloud deployment wizard | Docker Compose covers initial deployment |
Install via Docker Compose
→ Open browser to localhost:3000
→ Create account (or OAuth login)
→ Onboarding wizard: "Create your first workflow"
→ Template picker: select "Customer Support Pipeline" template
→ DAG editor loads with pre-configured agents
→ User customizes prompts and tool connections
→ Click "Run" → watch execution animate on canvas
→ View cost breakdown and execution timeline
Dashboard → "New Workflow" → Empty canvas
→ Drag Agent Node onto canvas → Configure (name, prompt, model, tools)
→ Drag Router Node → Connect from Agent → Set conditions
→ Drag another Agent Node → Connect from Router branch A
→ Drag Tool Node → Connect from Router branch B
→ Connect all outputs to Output Node
→ Validate (green check) → Save → Run
Dashboard → Workflow → "Executions" tab → Select failed run
→ DAG canvas shows red on failed node
→ Click failed node → Inspector panel opens
→ Shows: input state, LLM prompt sent, LLM response, error message
→ User identifies issue → Edits node configuration
→ Click "Re-run from this node" → Execution resumes from that point
Workflow → Select agent node → "Publish as Template"
→ Template editor: name, description, category, version
→ Configure which parameters are user-customizable vs locked
→ Submit for review → Template appears in registry after approval
| Requirement | Target |
|---|---|
| Latency | Workflow compilation < 500ms; single node execution < 30s (LLM-dependent) |
| Concurrency | Support 10 concurrent workflow executions per instance |
| Reliability | Workflow state survives process crash (PostgreSQL persistence) |
| Scalability | Single instance handles 50 active users; horizontal scaling via multiple workers |
| Security | OWASP AST10 compliant; secrets encrypted at rest; API keys hashed; sandboxed agent execution |
| Browser Support | Chrome 90+, Firefox 90+, Edge 90+, Safari 15+ |
| Accessibility | WCAG 2.1 AA for core workflows |
| Deployment | Single Docker Compose command; < 5 minutes to running state |
| Product | Overlap | Differentiator for AgentForge |
|---|---|---|
| n8n | Visual workflow automation | n8n is for traditional automation (APIs, webhooks). AgentForge is purpose-built for AI agent orchestration with LLM-native concepts. |
| LangFlow | Visual LangChain builder | LangFlow focuses on chain composition. AgentForge adds production features: cost tracking, RBAC, execution replay, template marketplace. |
| Dify | Visual AI workflow builder | Dify is more chatbot-focused. AgentForge targets multi-agent orchestration with full observability. |
| LangGraph Studio | LangGraph visualization | LangGraph Studio is a debugger, not a builder. AgentForge combines visual building + execution + monitoring. |
| CrewAI Enterprise | Agent orchestration | CrewAI is code-first. AgentForge adds a visual layer on top of orchestration engines. |
- License: Apache 2.0 (permissive, enterprise-friendly)
- Monetization path (future): Managed cloud hosting (AgentForge Cloud), enterprise features (SSO, audit compliance, priority support)
- Community features from day one:
- GitHub Discussions for support
- Contribution guide for agent templates
- "Good first issue" labels for onboarding contributors
- Template marketplace as community hub
| Risk | Impact | Mitigation |
|---|---|---|
| LangGraph API changes break compilation | High | Abstract orchestration behind an adapter layer; pin LangGraph version |
| LLM API costs make platform expensive for users | Medium | Cost tracking + budget alerts + model routing (cheap models for simple tasks) |
| DAG editor UX too complex for non-developers | Medium | Template-first onboarding; progressive disclosure of advanced features |
| Agent execution security (sandbox escape) | High | Container-based sandboxing per agent; network egress controls; OWASP AST10 review |
| Community template quality varies | Medium | Review/approval workflow; automated testing of templates; verified badges |
| Competing with well-funded alternatives | Medium | Open-source community + production features that demos lack |
The MVP is considered shippable when:
- Docker Compose installs and runs successfully on Linux, macOS, and Windows (WSL)
- User can create an account and log in
- User can build a 3-node workflow via the visual editor
- User can execute the workflow and see real-time animation
- User can inspect execution details (input/output per node)
- Cost tracking displays token usage and estimated cost per run
- At least 5 built-in agent templates are available
- At least 2 MCP servers can be registered and used as tools
- Workflow API endpoint accepts POST requests and returns results
- Basic RBAC (owner/editor/viewer) functions correctly
- No critical or high-severity security vulnerabilities (OWASP scan)
- Documentation covers: installation, quickstart, architecture, API reference