The /model command was only updating the router's currentTier but not
the agent's currentTier. Since NativeAgent.chatWithRouter() passes its
own tier to router.chat(), switching to 'local' still sent requests
through the default (Anthropic) client first.
- Extract createClientFromConfig() to dispatch on provider field instead
of hardcoding all tiers as AnthropicClient
- Add fallback/fallbackReason metadata to ChatResponse and ChatStreamEvent
so callers know when a fallback model was used
- Enhance doctor check to report full model stack and warn on missing
API keys for cloud providers
- Log fallback warnings in NativeAgent and display them in TUI
- Support tier names and local_providers entries in fallback_chain
- Add 8 tests for createClientFromConfig covering all provider types
Document all 6 CLI commands with examples, cron scheduling config
format with field reference, doctor diagnostics output and check
details. Update architecture diagram. Add CHANGELOG covering
Phase 5a and the initial 0.1.0 release.
Implements CronScheduler as a ChannelAdapter that fires InboundMessages
on cron schedules and routes agent responses to configured output
channels (e.g. Telegram). Includes 9 tests.
Replace doctor stub with full implementation including checks for:
config existence, YAML parsing, schema validation, env vars,
data directory writability, session DB, model config, Telegram,
MCP servers, and skills loading.
Implement a three-tier skill system (bundled/managed/workspace) that
extends Flynn's abilities via SKILL.md instructions injected into the
system prompt.
- SkillManifest/Skill types with requirements gating (OS, binaries, env)
- Loader: discovers skills from directories, validates manifests,
checks system requirements, infers manifest from SKILL.md if missing
- SkillRegistry: holds skills, generates system prompt additions,
supports override by name (workspace > managed > bundled)
- SkillInstaller: copies/removes skills in managed directory with
upgrade support
- Config: add skills.workspace_dir, managed_dir, bundled_dir options
- Daemon: loads all skills at startup, injects available skill
instructions into the system prompt
- Tests: 45 new tests (loader 22, registry 11, installer 12)
Implement Model Context Protocol (MCP) support so Flynn can spawn MCP
server processes, discover their tools, and make them available to the
agent alongside builtin tools.
- McpClient: wraps @modelcontextprotocol/sdk with StdioClientTransport
for process lifecycle, tool discovery (listTools), and invocation (callTool)
- McpManager: lifecycle management for multiple MCP servers with
startAll/stopAll/restart, tool bridging into ToolRegistry
- Bridge: converts MCP tools to Flynn Tool interface with mcp:<server>:<tool>
namespacing to avoid collisions with builtin tools
- Config: add env and cwd fields to mcp server schema
- ToolRegistry: add unregister() method for MCP server cleanup
- Daemon: wire McpManager into startup and shutdown lifecycle
- Tests: 28 new tests (bridge, manager, registry unregister)
Refactor GatewayServer to serve HTTP and WebSocket on a shared
http.Server. Add static file serving with path traversal protection,
a dark-themed dashboard (system health, sessions, tools) and a
WebSocket chat interface with streaming tool events and markdown
rendering.
Phase 2 of the Flynn roadmap. Adds a WebSocket gateway server that
starts alongside the Telegram bot, providing real-time API access to
the agent, sessions, and tools.
Protocol: JSON-RPC-like (request/response/event) over WebSocket.
8 methods: agent.send, agent.cancel, sessions.list, sessions.history,
sessions.create, tools.list, tools.invoke, system.health.
Auth: Bearer token + Tailscale identity header support.
Session bridge: per-connection agent instances with shared model router.
New files: src/gateway/ (protocol, router, server, auth, session-bridge,
handlers for agent/sessions/tools/system).
57 new tests (181 total), typecheck clean.
Telegram bot now shows tool status during execution:
- Sends status message when tool starts (tool name + args snippet)
- Edits status message with result on completion
- Keeps typing indicator active during tool execution
- Adds setOnToolUse() to NativeAgent for per-message callback control
TUI now creates a NativeAgent with tool registry/executor and uses
agent.process() for message handling. Tool calls display status lines
showing tool name, args, and success/error result. Falls back to
direct model client when agent is not configured.
Lists available tools (shell, file ops, web fetch) so the model knows
what it can do. Keeps SOUL.md as the single source of truth for the
system prompt.
Tests the full agent -> tool execution -> model feedback loop:
- Shell tool execution with mock model
- Multi-tool chaining (file.write -> file.read) across iterations
- Verification that tool results are correctly passed back to model
Rewrites NativeAgent.process() from single-turn to an iterative tool
loop. When toolRegistry and toolExecutor are provided, the agent calls
the model, executes any requested tool calls, feeds results back, and
loops until the model returns a text response or max iterations hit.
- Backward compatible: works exactly as before without tools
- Supports onToolUse callback for frontend status display
- Max iterations (default 10) prevents infinite loops
- Handles multiple tool calls per model response
- 5 new tests (8 total)
William Valentin