Goal: Build an AI-powered Connector Hub — Standalone (portal assistant), Copilot (embedded in host system), Hub (central cross-system orchestration). Principles: Provider-agnostic (no vendor lock-in), minimal-abstraction, protocol-first, connector-first (integration is the core value).
Product Vision
FIM Agent is an AI Connector Hub that serves three progressive modes:| Step | Mode | What happens |
|---|---|---|
| Land | Copilot | Embed into one system, prove value inside their UI |
| Expand | Copilot → Hub | Roll out to more systems; Hub aggregates them |
Shipped Versions
v0.1 (2025-08-01) — MVP: ReAct + DAG Planner
- ReActAgent with tools (calculator, python_exec, web_search)
- DAG Planner (LLM generates dependency graphs)
- Portal UI with streaming + KaTeX
v0.2 (2025-09-15) — Multi-Model + Memory
- Retry / rate limiting / usage tracking
- Native function calling (no JSON-only parsing)
- Multi-model support (fast + main LLM)
- Memory: WindowMemory, SummaryMemory
- FastAPI backend with SSE streaming
v0.3 (2025-10-20) — Web Tools + MCP
- Web tools (web_search, web_fetch) via Jina/Tavily/Brave
- File operations tool
- MCP client (standard tool integration)
- Tool auto-discovery + categories
- DAG visualization with click-to-scroll
- Code exec in Docker (
--network=none)
v0.4 (2025-11-15) — Multi-Turn + Agents
- Multi-turn conversations (DbMemory)
- Tool step folding UI
- HTTP request + shell exec tools
- Agent management (create, configure, publish)
- JWT authentication
- Per-agent execution mode + temperature control
v0.5 (2025-12-20) — Full RAG + Grounded Gen
- Full RAG pipeline (embedding + vector store + FTS + RRF + reranker)
- Grounded Generation (citations, conflict detection, confidence scores)
- Knowledge base document management (CRUD, search, retry, schema migration)
- ContextGuard + pinned messages (token budget manager)
- DbMemory persistence + LLM Compact
- DAG Re-Planning (up to 3 rounds)
v0.6 (2026-01-10) — Connector Platform
- Connector CRUD: create, read, update, delete
- ConnectorToolAdapter: converts Connector → BaseTool
- Per-user credentials: AES-GCM encryption
- Confirmation gate: write operation approval
- Audit logging: all tool calls recorded
- Circuit breaker: graceful degradation on failures
- Utility tools: email_send, json_transform, template_render, text_utils
- Embedding options: Jina, OpenAI, custom providers
v0.7 (2026-02-07) — Admin Platform + Multi-Tenant
- Admin Platform: user management, role toggle, password reset, account enable/disable
- Invite-only registration: three modes (open/invite/disabled) + invite code CRUD
- Storage management: per-user disk usage, clear, orphan cleanup
- Conversation moderation: admin list/delete all
- Per-user force logout: revoke all tokens
- API health dashboard: system stats, connector metrics
- First-run setup wizard: guided admin account creation
- Personal Center: per-user global instructions, language preference
- JWT auth: token-based SSE auth, conversation ownership
- Global MCP servers: admin-provisioned, loaded in all sessions
- Backward-compat: registration_enabled → registration_mode auto-migration
v0.7.x (2026-02-21 onwards) — Stability + Polish
- Invite code management
- Per-user quotas (429 enforcement)
- Structured audit logging
- Sensitive word filtering
- Admin login history
- Admin file browser
- Enhanced admin views (model_name, tools, kb_ids fields)
- Docker Compose deployment (single image, named volumes)
- OAuth auto-detection from window.location
Planned Versions
v0.8 — Connector Declarative Config + RBAC
Goal: Make it easier to define connectors without writing Python code.- YAML/JSON connector config: platform auto-generates MCP server
- Connector import/export: share connector templates
- Connector fork: clone + customize existing connectors
- Database connectors: direct SQL access (PostgreSQL, MySQL, Oracle)
- Message push: Lark, WeCom, Slack, Email notification actions
- RBAC: per-user/role connector access control
- Operation audit: detailed logging of who did what
v0.9 — Observability + Production Hardening
Goal: Production-grade operations and debugging.- Distributed tracing: OpenTelemetry integration
- Circuit breaker: exponential backoff, failure detection
- Observability: metrics (latency, success rate, token usage)
- Connector analytics: usage patterns, failure modes
- Sandbox hardening: v2 improvements to code execution isolation
- Docker Compose: full deployment stack
- Performance testing: concurrent load benchmarks
v1.0 — Hot-Plug + Embeddable
Goal: Zero-restart connector addition and embedded delivery.- Hot-plug connectors: upload OpenAPI spec, AI generates config, live in 5 minutes (no restart)
- Connector marketplace: community-shared templates
- Embeddable widget:
<script src="fim-agent.js">injected into host page - Page context injection: widget reads host page context (current ID, URL, DOM selectors)
- Scheduled jobs: cron-like DAG triggers
- Webhooks: inbound event triggers
- Batch execution: process 1000+ items via DAG
- Admin dashboard: full management UI
- Enterprise security: IP whitelisting, encryption at rest, SSO
- Semantic memory: cross-session memory retrieval
- Memory lifecycle: TTL, importance scoring, semantic similarity
Frozen Features (Shipped, Maintain Only)
Per the Orthogonality Strategy, these features are shipped and working but will not receive new capabilities (bug fixes only):| Feature | Version | Why frozen |
|---|---|---|
| ReAct Agent | v0.1 | Models now have native tool calling |
| DAG Planning / Re-Planning | v0.1, v0.5 | Model reasoning capabilities improving; decomposition becoming single-shot |
| Memory (Window, Summary, Compact) | v0.2, v0.5 | Context windows growing (200K+); less need for external memory management |
| RAG pipeline | v0.5 | Providers building retrieval natively (OpenAI file_search, Gemini Search Grounding) |
| Grounded Generation | v0.5 | Models improving at citations; 5-stage pipeline adds diminishing value |
| ContextGuard / Pinned Messages | v0.5 | Shipping as-is; no new features |
Consider (Deferred Indefinitely)
Per the Orthogonality Strategy, these would be high-effort and face absorption risk:| Feature | Why deferred |
|---|---|
| Multi-Agent Orchestration | Providers building natively (OpenAI Swarm, Claude Code Teams, Google A2A) |
| Semantic Memory Store | Context windows growing; providers adding native memory (ChatGPT Memory, Claude Projects) |
| Memory Lifecycle | Same as above; engineering cost high relative to shrinking gap |
How Versions Align With Modes
| Version | Standalone | Copilot | Hub | Notes |
|---|---|---|---|---|
| v0.1–v0.3 | Working | Not yet | Not yet | Portal-only, single-user |
| v0.4 | Working | Not yet | Not yet | Multi-conversation, agent management |
| v0.5 | Working | Not yet | Not yet | Knowledge base + RAG |
| v0.6 | Working | Possible | Possible | Connectors ship; Copilot/Hub possible with manual wiring |
| v0.7 | Working | Ready | Ready | Admin platform; multi-tenant auth; ready for production |
| v0.8 | Working | Ready | Optimized | RBAC + audit log per-system; easier to onboard |
| v0.9 | Working | Ready | Production | Observability, performance, hardening |
| v1.0 | Working | Optimized | Enterprise | Hot-plug, marketplace, scheduled jobs, webhooks, batch |
Resource Allocation (v0.8–v1.0)
The Orthogonality Strategy shapes where effort goes:| Category | Allocation | Versions | Why |
|---|---|---|---|
| Connector Platform (v0.6+) | 60% | Ongoing | Core differentiation; no absorption risk |
| Enterprise Features (RBAC, audit, security) | 25% | v0.8–v1.0 | Boring but durable; production requirement |
| Embedded/Delivery (widget, hot-plug) | 10% | v0.9–v1.0 | Strategic for land-and-expand GTM |
| v0.1–v0.5 maintenance | 5% | Ongoing | Bug fixes only; no new features |
Metric-Driven Milestones
Success is measured by:| Metric | v0.7 Target | v0.8 Target | v1.0 Target |
|---|---|---|---|
| Connectors deployed | 5 | 20+ | 100+ |
| Enterprise customers | 1–2 | 5–10 | 20+ |
| Avg connector setup time | 2 weeks | 2 days | 5 minutes (hot-plug) |
| Token efficiency (DAG vs ReAct-only) | 30% reduction | 40% reduction | 50% reduction |
| Uptime SLA | 99.5% | 99.9% | 99.95% |
| Support ticket themes | Integration, setup | Connector custom logic | Hot-plug, scaling |
Open Questions / TBD
- Marketplace moderation: How to validate community connectors? (v1.0)
- Token economics: How to price multi-user, multi-agent scenarios? (v1.0)
- Telemetry opt-out: How to honor privacy preferences? (v0.8)
- Connector versioning: How to manage breaking changes in connector APIs? (v0.8)
- Rate limiting: Per-connector, per-user, or global? (v0.8)