Resolved merge conflicts in agents.py and test_task_queue.py:
- Keep full chat-to-task pipeline (agent/priority extraction, question
filtering, context injection) over simpler main version
- Incorporate test_briefing_task_queue_summary from main
- All 64 task queue tests pass
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Addresses 14 bugs from 3 rounds of deep chat evaluation:
- Add chat-to-task pipeline in agents.py with regex-based intent detection,
agent extraction, priority extraction, and title cleaning
- Filter meta-questions ("how do I create a task?") from task creation
- Inject real-time date/time context into every chat message
- Inject live queue state when user asks about tasks
- Ground system prompts with agent roster, honesty guardrails, self-knowledge,
math delegation template, anti-filler rules, values-conflict guidance
- Add CSS for markdown code blocks, inline code, lists, blockquotes in chat
- Add highlight.js CDN for syntax highlighting in chat responses
- Reduce small-model memory context budget (4000→2000) for expanded prompt
- Add 27 comprehensive tests covering the full chat-to-task pipeline
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Chat messages like "add X to the queue" or "create a task" are
intercepted and create a task_queue entry with pending_approval
status instead of going through to the LLM
- Briefing engine now gathers task queue stats (pending, running,
completed, failed) and includes them in the morning briefing prompt
- 7 new tests covering detection patterns, chat integration, and
briefing summary
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Task Queue system:
- New /tasks page with three-column layout (Pending/Active/Completed)
- Full CRUD API at /api/tasks with approve/veto/modify/pause/cancel/retry
- SQLite persistence in task_queue table
- WebSocket live updates via ws_manager
- Create task modal with agent assignment and priority
- Auto-approve rules for low-risk tasks
- HTMX polling for real-time column updates
- HOME TASK buttons now link to task queue with agent pre-selected
- MARKET HIRE buttons link to task queue with agent pre-selected
Work Order system:
- External submission API for agents/users (POST /work-orders/submit)
- Risk scoring and configurable auto-execution thresholds
- Dashboard at /work-orders/queue with approve/reject/execute flow
- Integration with swarm task system for execution
UI & Dashboard bug fixes:
- EVENTS: add startup event so page is never empty
- LEDGER: fix empty filter params in URL
- MISSION CONTROL: LLM backend and model now read from /health
- MISSION CONTROL: agent count fallback to /swarm/agents
- SWARM: HTMX fallback loads initial data if WebSocket is slow
- MEMORY: add edit/delete buttons for personal facts
- UPGRADES: add empty state guidance with links
- BRIEFING: add regenerate button and POST /briefing/regenerate endpoint
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Add sandboxed calculator tool to Timmy's toolkit so arithmetic questions
get exact answers instead of LLM hallucinations
- Update system prompts (lite + full) to instruct Timmy to always use the
calculator and never attempt multi-digit math in his head
- Add self-contradiction guard to both prompts ("commit to your facts")
- Render Timmy's chat responses as markdown via marked.js + DOMPurify
instead of raw escaped text
- Suppress empty briefing notification on startup when there are 0
pending approval items
- Add calculator to session response sanitizer regex
- 18 new calculator tests, 2 updated briefing notification tests
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Remove show_tool_calls kwarg (not in Agno 2.5.3), which crashed Agent.__init__
- Guard memory_search against top_k=None from model, return formatted string
- Skip Telegram/Discord startup silently when no token configured
- Replace placeholder MEMORY.md with proper structured hot memory document
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add fixtures required by functional test suite:
- docker_stack: Docker container test URL (skips if FUNCTIONAL_DOCKER != 1)
- serve_client: FastAPI TestClient for timmy-serve app
- tdd_runner: Alias for self_tdd_runner
Fixes CI errors in test_docker_swarm.py, test_l402_flow.py, test_cli.py
- Fix recall_personal_facts() call - remove unsupported limit parameter
- Replace 4 slow E2E test files with single fast test file
- All 6 E2E tests complete in ~9 seconds (was 60+ seconds)
- Reuse browser session across tests (module-scoped fixture)
- Combine related checks into single tests
- Add HTTP-only smoke test for speed
- YAML-based provider configuration (config/providers.yaml)
- Priority-ordered provider routing
- Circuit breaker pattern for failing providers
- Health check and availability monitoring
- Metrics tracking (latency, errors, success rates)
- Support for Ollama, OpenAI, Anthropic, AirLLM providers
- Automatic failover on rate limits or errors
- REST API endpoints for monitoring and control
- 41 comprehensive tests
API Endpoints:
- POST /api/v1/router/complete - Chat completion with failover
- GET /api/v1/router/status - Provider health status
- GET /api/v1/router/metrics - Detailed metrics
- GET /api/v1/router/providers - List all providers
- POST /api/v1/router/providers/{name}/control - Enable/disable/reset
- POST /api/v1/router/health-check - Run health checks
- GET /api/v1/router/config - View configuration
Timmy was exhibiting severe incoherence (no memory between messages, tool call
leakage, chain-of-thought narration, random tool invocations) due to creating
a brand new agent per HTTP request and giving a 3B model (llama3.2) a 73-line
system prompt with complex tool-calling instructions it couldn't follow.
Key changes:
- Add session.py singleton with stable session_id for conversation continuity
- Add _model_supports_tools() to strip tools from small models (< 7B)
- Add two-tier prompts: lite (12 lines) for small models, full for capable ones
- Add response sanitizer to strip leaked JSON tool calls and CoT narration
- Set show_tool_calls=False to prevent raw tool JSON in output
- Wire ConversationManager for user name extraction
- Deprecate orphaned memory_layers.py (unused 4-layer system)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit replaces the previous memory_layers.py with a proper three-tier
memory system as specified by the user:
## Tier 1 — Hot Memory (MEMORY.md)
- Single flat file always loaded into system context
- Contains: current status, standing rules, agent roster, key decisions
- ~300 lines max, pruned monthly
- Managed by HotMemory class
## Tier 2 — Structured Vault (memory/)
- Directory with three namespaces:
• self/ — identity.md, user_profile.md, methodology.md
• notes/ — session logs, AARs, research
• aar/ — post-task retrospectives
- Markdown format, Obsidian-compatible
- Append-only, date-stamped
- Managed by VaultMemory class
## Handoff Protocol
- last-session-handoff.md written at session end
- Contains: summary, key decisions, open items, next steps
- Auto-loaded at next session start
- Maintains continuity across resets
## Implementation
### New Files:
- src/timmy/memory_system.py — Core memory system
- MEMORY.md — Hot memory template
- memory/self/*.md — Identity, user profile, methodology
### Modified:
- src/timmy/agent.py — Integrated with memory system
- create_timmy() injects memory context
- TimmyWithMemory class with automatic fact extraction
- tests/test_agent.py — Updated for memory context
## Key Principles
- Hot memory = small and curated
- Vault = append-only, never delete
- Handoffs = continuity mechanism
- Flat files = human-readable, portable
## Usage
All 973 tests pass.
Keep Mission Control link from this branch alongside SWARM and SPARK
links from main. All 939 tests pass.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The POST /swarm/tasks endpoint now triggers an automatic auction via
asyncio.create_task. Tests must allow tasks to be in bidding, assigned,
or failed status since the background auction may resolve before the
follow-up GET query.
All 895 tests pass.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Adds SelfModifyLoop — an edit→validate→test→commit cycle that can read
its own failure reports, diagnose root causes, and restart autonomously.
Key capabilities:
- Multi-backend LLM: Anthropic Claude API, Ollama, or auto-detect
- Syntax validation via compile() before writing to disk
- Autonomous self-correction loop with configurable max cycles
- XML-based output format to avoid triple-quote delimiter conflicts
- Branch creation skipped by default to prevent container restarts
- CLI: self-modify run "instruction" --backend auto --autonomous
- 939 tests passing, 30 skipped
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
selenium depends on websocket-client which installs a top-level
`websocket` package that shadows our src/websocket/ module on CI.
Renaming to ws_manager eliminates the conflict entirely — no more
sys.path hacks needed in conftest or Selenium tests.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
selenium depends on websocket-client which installs a top-level
`websocket` package that shadows our src/websocket/ module. Ensure
src/ is inserted at the front of sys.path in conftest so the project
module wins the import race. Fixes collection errors for
test_websocket.py and test_websocket_extended.py on GitHub Actions.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add an ollama service (behind --profile ollama) to the test compose stack
and a new test suite that verifies real LLM inference end-to-end:
- docker-compose.test.yml: add ollama/ollama service with health check,
make OLLAMA_URL and OLLAMA_MODEL configurable via env vars
- tests/functional/test_ollama_chat.py: session-scoped fixture that
brings up Ollama + dashboard, pulls qwen2.5:0.5b (~400MB, CPU-only),
and runs chat/history/multi-turn tests against the live stack
- Makefile: add `make test-ollama` target
Run with: make test-ollama (or FUNCTIONAL_DOCKER=1 pytest tests/functional/test_ollama_chat.py -v)
https://claude.ai/code/session_01NTEzfRHSZQCfkfypxgyHKk
Introduces a vendor-agnostic chat platform architecture:
- chat_bridge/base.py: ChatPlatform ABC, ChatMessage, ChatThread
- chat_bridge/registry.py: PlatformRegistry singleton
- chat_bridge/invite_parser.py: QR + Ollama vision invite extraction
- chat_bridge/vendors/discord.py: DiscordVendor with native threads
Workflow: paste a screenshot of a Discord invite or QR code at
POST /discord/join → Timmy extracts the invite automatically.
Every Discord conversation gets its own thread, keeping channels clean.
Bot responds to @mentions and DMs, routes through Timmy agent.
43 new tests (base classes, registry, invite parser, vendor, routes).
https://claude.ai/code/session_01WU4h3cQQiouMwmgYmAgkMM
Three-tier functional test infrastructure:
- CLI tests via Typer CliRunner (timmy, timmy-serve, self-tdd)
- Dashboard integration tests with real TestClient, real SQLite, real
coordinator (no patch/mock — Ollama offline = graceful degradation)
- Docker compose container-level tests (gated by FUNCTIONAL_DOCKER=1)
- End-to-end L402 payment flow with real mock-lightning backend
42 new tests (8 Docker tests skipped without FUNCTIONAL_DOCKER=1).
All 849 tests pass.
https://claude.ai/code/session_01WU4h3cQQiouMwmgYmAgkMM
- Fix /serve/chat AttributeError: split Request and ChatRequest params
so auth headers are read from HTTP request, not Pydantic body
- Add regression tests for the serve_chat endpoint bug
- Add agent_core and lightning to pyproject.toml wheel includes
- Replace Apache 2.0 LICENSE with MIT to match pyproject.toml
- Update test count from "228" to "600+" across README, docs, AGENTS.md
- Add 5 missing subsystems to README table (Spark, Creative, Tools,
Telegram, agent_core/lightning)
- Update AGENTS.md project structure with 6 missing modules
- Mark completed v2 roadmap items (personas, MCP tools) in AGENTS.md
https://claude.ai/code/session_01GMiccXbo77GkV3TA69x6KS
Build real PNG, WAV, and MP4 fixtures (no AI models) and exercise the
full assembler and Creative Director pipeline end-to-end. Fix MoviePy v2
crossfade API (vfx.CrossFadeIn) and font resolution (DejaVu-Sans).
14 new integration tests — 638 total, all passing.
https://claude.ai/code/session_01KJm6jQkNi3aA3yoQJn636c
Adds 3 new personas (Pixel, Lyra, Reel) and 5 new tool modules:
- Git/DevOps tools (GitPython): clone, status, diff, log, blame, branch,
add, commit, push, pull, stash — wired to Forge and Helm personas
- Image generation (FLUX via diffusers): text-to-image, storyboards,
variations — Pixel persona
- Music generation (ACE-Step 1.5): full songs with vocals+instrumentals,
instrumental tracks, vocal-only tracks — Lyra persona
- Video generation (Wan 2.1 via diffusers): text-to-video, image-to-video
clips — Reel persona
- Creative Director pipeline: multi-step orchestration that chains
storyboard → music → video → assembly into 3+ minute final videos
- Video assembler (MoviePy + FFmpeg): stitch clips, overlay audio,
title cards, subtitles, final export
Also includes:
- Spark Intelligence tool-level + creative pipeline event capture
- Creative Studio dashboard page (/creative/ui) with 4 tabs
- Config settings for all new models and output directories
- pyproject.toml creative optional extra for GPU dependencies
- 107 new tests covering all modules (624 total, all passing)
https://claude.ai/code/session_01KJm6jQkNi3aA3yoQJn636c
Mission Control Dashboard:
- /swarm/mission-control page with real-time system status
- Sovereignty score display with visual progress bar
- Dependency health grid (Ollama, Redis, Lightning, SQLite)
- Recommendations based on dependency status
- Heartbeat monitor with tick counter
- System metrics: uptime, agents, tasks, sats earned
Health Endpoints:
- /health/sovereignty - Full sovereignty audit report
- /health/components - Component status and config
Tests (TDD approach):
- 11 Mission Control tests (all passing)
- 23 scary path tests for production scenarios
- Concurrent load, memory persistence, edge cases
Total: 525 tests passing
Add full pytest-cov configuration with fail_under=60% threshold,
HTML/XML report targets, and proper exclude_lines. Fix websocket
history test to use public broadcast() API instead of manually
manipulating internals. Audit confirmed 491 tests at 71.2% coverage.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Introduce a feedback loop where task outcomes (win/loss, success/failure)
feed back into agent bidding strategy. Borrows the "learn from outcomes"
concept from Spark Intelligence but builds it natively on Timmy's existing
SQLite + swarm architecture.
New module: src/swarm/learner.py
- Records every bid outcome with task description context
- Computes per-agent metrics: win rate, success rate, keyword performance
- suggest_bid() adjusts bids based on historical performance
- learned_keywords() discovers what task types agents actually excel at
Changes:
- persona_node: _compute_bid() now consults learner for adaptive adjustments
- coordinator: complete_task/fail_task feed results into learner
- coordinator: run_auction_and_assign records all bid outcomes
- routes/swarm: add /swarm/insights and /swarm/insights/{agent_id} endpoints
- routes/swarm: add POST /swarm/tasks/{task_id}/fail endpoint
All 413 tests pass (23 new + 390 existing).
https://claude.ai/code/session_01E5jhTCwSUnJk9p9zrTMVUJ
