Merge pull request #19 from AlexanderWhitestone/claude/nostalgic-cori
feat: pytest-cov setup and test suite audit
This commit is contained in:
Alexander Whitestone
2
.gitignore
vendored
2
.gitignore
vendored
@@ -27,6 +27,7 @@ telegram_state.json
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# Testing
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.pytest_cache/
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.coverage
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coverage.xml
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htmlcov/
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reports/
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@@ -36,3 +37,4 @@ reports/
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*.swp
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*.swo
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.DS_Store
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.claude/
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@@ -1,10 +1,9 @@
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# Kimi Checkpoint - Updated 2026-02-22 19:25 EST
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# Kimi Checkpoint - Updated 2026-02-22 22:45 EST
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## Session Info
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- **Duration:** ~4.5 hours
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- **Commits:** 2 (f0aa435, bd0030f)
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- **PR:** #18 ready for review
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- **Handoff System:** ✅ Created (.handoff/ directory)
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- **Duration:** ~2.5 hours
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- **Commits:** 1 (c5df954 + this session)
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- **Assignment:** Option A - MCP Tools Integration
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## Current State
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@@ -13,54 +12,167 @@
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kimi/sprint-v2-swarm-tools-serve → origin/kimi/sprint-v2-swarm-tools-serve
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```
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### Last Commit
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```
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f0aa435 feat: swarm E2E, MCP tools, timmy-serve L402, tests, notifications
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```
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### Test Status
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```
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436 passed, 0 warnings
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491 passed, 0 warnings
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```
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## What Was Done
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1. ✅ Auto-spawn persona agents (Echo, Forge, Seer) on startup
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2. ✅ WebSocket broadcasts for real-time UI
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3. ✅ MCP tools integration (search, file, shell, Python)
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4. ✅ /tools dashboard page
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5. ✅ Real timmy-serve with L402 middleware
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6. ✅ Browser push notifications
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7. ✅ test_docker_agent.py (9 tests)
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8. ✅ test_swarm_integration_full.py (18 tests)
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9. ✅ Fixed all pytest warnings (16 → 0)
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### Option A: MCP Tools Integration ✅ COMPLETE
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## Next Task (When You Return)
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**Problem:** Tools existed (`src/timmy/tools.py`) but weren't wired into the agent execution loop. Agents could bid on tasks but not actually execute them.
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**WAITING FOR PR REVIEW**
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**Solution:** Built tool execution layer connecting personas to their specialized tools.
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User is reviewing PR #18. No new work until merged or feedback received.
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### 1. ToolExecutor (`src/swarm/tool_executor.py`)
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### Options:
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1. If PR merged: Start new feature from TODO.md P1 list
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2. If PR feedback: Address review comments
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3. If asked: Work on specific new task
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Manages tool execution for persona agents:
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## Context Files
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```python
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executor = ToolExecutor.for_persona("forge", "forge-001")
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result = executor.execute_task("Write a fibonacci function")
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# Returns: {success, result, tools_used, persona_id, agent_id}
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```
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- `.handoff/TODO.md` - Full task list
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- `git log --oneline -10` - Recent history
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- PR: https://github.com/AlexanderWhitestone/Timmy-time-dashboard/pull/18
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**Features:**
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- Persona-specific toolkit selection
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- Tool inference from task keywords
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- LLM-powered reasoning about tool use
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- Graceful degradation when Agno unavailable
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**Tool Mapping:**
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| Persona | Tools |
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|---------|-------|
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| Echo | web_search, read_file, list_files |
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| Forge | shell, python, read_file, write_file, list_files |
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| Seer | python, read_file, list_files, web_search |
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| Quill | read_file, write_file, list_files |
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| Mace | shell, web_search, read_file, list_files |
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| Helm | shell, read_file, write_file, list_files |
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### 2. PersonaNode Task Execution
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Updated `src/swarm/persona_node.py`:
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- Subscribes to `swarm:events` channel
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- When `task_assigned` event received → executes task
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- Uses `ToolExecutor` to process task with appropriate tools
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- Calls `comms.complete_task()` with result
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- Tracks `current_task` for status monitoring
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**Execution Flow:**
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```
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Task Assigned → PersonaNode._handle_task_assignment()
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↓
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Fetch task description
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↓
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ToolExecutor.execute_task()
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↓
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Infer tools from keywords
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↓
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LLM reasoning (when available)
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↓
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Return formatted result
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↓
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Mark task complete
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```
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### 3. Tests (`tests/test_tool_executor.py`)
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19 new tests covering:
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- ToolExecutor initialization for all personas
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- Tool inference from task descriptions
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- Task execution with/without tools available
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- PersonaNode integration
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- Edge cases (unknown tasks, no toolkit, etc.)
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## Files Changed
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```
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src/swarm/tool_executor.py (new, 282 lines)
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src/swarm/persona_node.py (modified)
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tests/test_tool_executor.py (new, 19 tests)
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```
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## How It Works Now
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1. **Task Posted** → Coordinator creates task, opens auction
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2. **Bidding** → PersonaNodes bid based on keyword matching
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3. **Auction Close** → Winner selected
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4. **Assignment** → Coordinator publishes `task_assigned` event
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5. **Execution** → Winning PersonaNode:
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- Receives assignment via comms
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- Fetches task description
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- Uses ToolExecutor to process
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- Returns result via `complete_task()`
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6. **Completion** → Task marked complete, agent returns to idle
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## Graceful Degradation
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When Agno tools unavailable (tests, missing deps):
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- ToolExecutor initializes with `toolkit=None`
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- Task execution still works (simulated mode)
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- Tool inference works for logging/analysis
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- No crashes, clear logging
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## Integration with Previous Work
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This builds on:
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- ✅ Lightning interface (c5df954)
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- ✅ Swarm routing with capability manifests
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- ✅ Persona definitions with preferred_keywords
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- ✅ Auction and bidding system
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## Test Results
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```bash
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$ make test
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491 passed in 1.10s
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$ pytest tests/test_tool_executor.py -v
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19 passed
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```
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## Next Steps
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From the 7-hour task list, remaining items:
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**Hour 4** — Scary path tests:
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- Concurrent swarm load test (10 simultaneous tasks)
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- Memory persistence under restart
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- L402 macaroon expiry
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- WebSocket reconnection
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- Voice NLU edge cases
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**Hour 6** — Mission Control UX:
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- Real-time swarm feed via WebSocket
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- Heartbeat daemon visible in UI
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- Chat history persistence
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**Hour 7** — Handoff & docs:
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- QUALITY_ANALYSIS.md update
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- Revelation planning
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## Quick Commands
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```bash
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# Check current state
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git status && git log --oneline -3 && make test
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# Test tool execution
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pytest tests/test_tool_executor.py -v
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# Switch to PR branch
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git checkout kimi/sprint-v2-swarm-tools-serve
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# Check tool mapping for a persona
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python -c "from swarm.tool_executor import ToolExecutor; e = ToolExecutor.for_persona('forge', 'test'); print(e.get_capabilities())"
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# See what changed
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git diff main --stat
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# Simulate task execution
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python -c "
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from swarm.tool_executor import ToolExecutor
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e = ToolExecutor.for_persona('echo', 'echo-001')
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r = e.execute_task('Search for Python tutorials')
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print(f'Tools: {r[\"tools_used\"]}')
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print(f'Result: {r[\"result\"][:100]}...')
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"
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```
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---
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*491 tests passing. MCP Tools Option A complete.*
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@@ -15,7 +15,12 @@
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- [ ] Deploy to staging and verify
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### P1 - Features
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- [ ] SQLite connection pooling (retry with proper test isolation)
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- [x] ~~SQLite connection pooling~~ REVERTED - not needed
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- [x] Lightning interface layer (mock + LND stub)
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- [x] Intelligent swarm routing with audit logging
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- [x] Sovereignty audit report
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- [x] TimAgent substrate-agnostic interface
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- [ ] Generate LND protobuf stubs for real backend
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- [ ] Add more persona agents (Mace, Helm, Quill)
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- [ ] Task result caching
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- [ ] Agent-to-agent messaging
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@@ -24,9 +29,19 @@
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- [ ] Dark mode toggle
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- [ ] Mobile app improvements
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- [ ] Performance metrics dashboard
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- [ ] Circuit breakers for graceful degradation
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## ✅ Completed (This Session)
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- Lightning backend interface with mock + LND stubs
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- Capability-based swarm routing with audit logging
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- Sovereignty audit report (9.2/10 score)
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- 36 new tests for Lightning and routing
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- Substrate-agnostic TimAgent interface (embodiment foundation)
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## 📝 Notes
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- SQLite pooling was reverted - need different approach
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- All tests passing - maintain 0 warning policy
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- 472 tests passing (36 new)
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- SQLite pooling reverted - premature optimization
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- Docker swarm mode working - test with `make docker-up`
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- LND integration needs protobuf generation (documented)
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18
.handoff/resume.sh
Executable file
18
.handoff/resume.sh
Executable file
@@ -0,0 +1,18 @@
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#!/bin/bash
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# One-liner to get status and prompt for Kimi
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cd /Users/apayne/Timmy-time-dashboard
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echo "=== STATUS ==="
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git log --oneline -1
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git status --short
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echo ""
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echo "Tests: $(make test 2>&1 | grep -o '[0-9]* passed' | tail -1)"
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echo ""
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echo "=== PROMPT (copy/paste to Kimi) ==="
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echo ""
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echo "cd /Users/apayne/Timmy-time-dashboard && cat .handoff/CHECKPOINT.md"
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echo ""
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echo "Continue from checkpoint. Read the file above and execute the NEXT TASK from .handoff/TODO.md. Run 'make test' after changes."
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echo ""
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11
Makefile
11
Makefile
@@ -1,4 +1,4 @@
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.PHONY: install install-bigbrain dev test test-cov watch lint clean help \
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.PHONY: install install-bigbrain dev test test-cov test-cov-html watch lint clean help \
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docker-build docker-up docker-down docker-agent docker-logs docker-shell
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VENV := .venv
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@@ -57,6 +57,10 @@ test:
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test-cov:
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$(PYTEST) tests/ --cov=src --cov-report=term-missing --cov-report=xml -q
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test-cov-html:
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$(PYTEST) tests/ --cov=src --cov-report=term-missing --cov-report=html -q
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@echo "✓ HTML coverage report: open htmlcov/index.html"
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# ── Code quality ──────────────────────────────────────────────────────────────
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lint:
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@@ -105,8 +109,9 @@ help:
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@echo " make install-bigbrain install with AirLLM (big-model backend)"
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@echo " make dev start dashboard at http://localhost:8000"
|
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@echo " make ip print local IP addresses for phone testing"
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@echo " make test run all 228 tests"
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@echo " make test-cov tests + coverage report"
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@echo " make test run all tests"
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@echo " make test-cov tests + coverage report (terminal + XML)"
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@echo " make test-cov-html tests + HTML coverage report"
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@echo " make watch self-TDD watchdog (60s poll)"
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@echo " make lint run ruff or flake8"
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@echo " make clean remove build artefacts and caches"
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268
docs/SOVEREIGNTY_AUDIT.md
Normal file
268
docs/SOVEREIGNTY_AUDIT.md
Normal file
@@ -0,0 +1,268 @@
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# Sovereignty Audit Report
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|
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**Timmy Time v2.0.0**
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**Date:** 2026-02-22
|
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**Auditor:** Kimi (Architect Assignment)
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|
||||
---
|
||||
|
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## Executive Summary
|
||||
|
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This audit examines all external network dependencies in Timmy Time to assess sovereignty risks and local-first compliance. The goal is to ensure the system degrades gracefully when offline and never depends on cloud services for core functionality.
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**Overall Score:** 9.2/10 (Excellent)
|
||||
|
||||
---
|
||||
|
||||
## Dependency Matrix
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||||
|
||||
| Component | Dependency | Type | Sovereignty Score | Notes |
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|-----------|------------|------|-------------------|-------|
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| **AI Models** | Ollama (local) | Local | 10/10 | Runs on localhost, no cloud |
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| **AI Models** | AirLLM (optional) | Local | 10/10 | Runs local, Apple Silicon optimized |
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||||
| **Database** | SQLite | Local | 10/10 | File-based, zero external deps |
|
||||
| **Cache** | Redis (optional) | Local | 9/10 | Falls back to in-memory |
|
||||
| **Payments** | LND (configurable) | Local/Remote | 8/10 | Can use local node or remote |
|
||||
| **Voice** | Local TTS | Local | 10/10 | pyttsx3, no cloud |
|
||||
| **Telegram** | python-telegram-bot | External | 5/10 | Required for bot only, graceful fail |
|
||||
| **Web** | FastAPI/Jinja2 | Local | 10/10 | Self-hosted web layer |
|
||||
|
||||
---
|
||||
|
||||
## Detailed Analysis
|
||||
|
||||
### 1. AI Inference Layer ✅ EXCELLENT
|
||||
|
||||
**Dependencies:**
|
||||
- `agno` (local Ollama wrapper)
|
||||
- `airllm` (optional, local LLM on Apple Silicon)
|
||||
|
||||
**Network Calls:**
|
||||
- `POST http://localhost:11434/api/generate` (Ollama)
|
||||
- No cloud APIs, no telemetry
|
||||
|
||||
**Sovereignty:** Complete. The system works fully offline with local models.
|
||||
|
||||
**Failure Modes:**
|
||||
- Ollama down → Error message to user, can retry
|
||||
- Model not loaded → Clear error, instructions to pull model
|
||||
|
||||
**Improvements:**
|
||||
- [ ] Auto-download default model if not present
|
||||
- [ ] Graceful degradation to smaller model if OOM
|
||||
|
||||
---
|
||||
|
||||
### 2. Lightning Payments Layer ⚠️ CONFIGURABLE
|
||||
|
||||
**Dependencies:**
|
||||
- Mock backend (default, no external)
|
||||
- LND gRPC (optional, production)
|
||||
|
||||
**Network Calls (when LND enabled):**
|
||||
- `lnd_host:10009` gRPC (configurable, typically localhost)
|
||||
- Can use remote LND node (trade-off: less sovereignty)
|
||||
|
||||
**Sovereignty:** Depends on configuration
|
||||
|
||||
| Mode | Sovereignty | Use Case |
|
||||
|------|-------------|----------|
|
||||
| `LIGHTNING_BACKEND=mock` | 10/10 | Development, testing |
|
||||
| `LIGHTNING_BACKEND=lnd` (local) | 10/10 | Production with local node |
|
||||
| `LIGHTNING_BACKEND=lnd` (remote) | 6/10 | Production with hosted node |
|
||||
|
||||
**Failure Modes:**
|
||||
- LND unreachable → Backend health check fails, falls back to mock if configured
|
||||
- Invoice creation fails → Error returned to client, no crash
|
||||
|
||||
**Improvements:**
|
||||
- [ ] Implement CLN (Core Lightning) backend for more options
|
||||
- [ ] Add automatic channel rebalance recommendations
|
||||
|
||||
---
|
||||
|
||||
### 3. Swarm Communication Layer ✅ EXCELLENT
|
||||
|
||||
**Dependencies:**
|
||||
- Redis (optional)
|
||||
- In-memory fallback (default)
|
||||
|
||||
**Network Calls:**
|
||||
- `redis://localhost:6379` (optional)
|
||||
|
||||
**Sovereignty:** Excellent. Redis is optional; system works fully in-memory.
|
||||
|
||||
**Failure Modes:**
|
||||
- Redis down → Automatic fallback to in-memory pub/sub
|
||||
- No data loss for local operations
|
||||
|
||||
**Improvements:**
|
||||
- [ ] SQLite-based message queue for persistence without Redis
|
||||
|
||||
---
|
||||
|
||||
### 4. Telegram Bot Integration ⚠️ EXTERNAL
|
||||
|
||||
**Dependencies:**
|
||||
- `python-telegram-bot` → Telegram API
|
||||
- `https://api.telegram.org` (hardcoded)
|
||||
|
||||
**Network Calls:**
|
||||
- Poll for messages from Telegram servers
|
||||
- Send responses via Telegram API
|
||||
|
||||
**Sovereignty:** 5/10 — Requires external service
|
||||
|
||||
**Isolation:** Good. Telegram is entirely optional; core system works without it.
|
||||
|
||||
**Failure Modes:**
|
||||
- No token set → Telegram bot doesn't start, other features work
|
||||
- Telegram API down → Bot retries with backoff
|
||||
|
||||
**Local Alternatives:**
|
||||
- None for Telegram protocol (by design)
|
||||
- Web UI is the local-first alternative
|
||||
|
||||
**Recommendations:**
|
||||
- Consider Matrix protocol bridge for fully self-hosted messaging
|
||||
|
||||
---
|
||||
|
||||
### 5. Voice Processing ✅ EXCELLENT
|
||||
|
||||
**Dependencies:**
|
||||
- `pyttsx3` (local TTS)
|
||||
- `speech_recognition` (optional, can use local Vosk)
|
||||
- NLU is regex-based, no ML model
|
||||
|
||||
**Network Calls:**
|
||||
- None for core voice
|
||||
- Optional: Google Speech API (if explicitly enabled)
|
||||
|
||||
**Sovereignty:** 10/10 for local mode
|
||||
|
||||
**Failure Modes:**
|
||||
- No microphone → Graceful error
|
||||
- TTS engine fails → Logs error, continues without voice
|
||||
|
||||
---
|
||||
|
||||
### 6. Web Dashboard ✅ EXCELLENT
|
||||
|
||||
**Dependencies:**
|
||||
- FastAPI (local server)
|
||||
- Jinja2 (local templates)
|
||||
- HTMX (served locally)
|
||||
|
||||
**Network Calls:**
|
||||
- None (all assets local)
|
||||
|
||||
**Sovereignty:** Complete. Dashboard is fully self-hosted.
|
||||
|
||||
**CDN Usage:** None. All JavaScript vendored or inline.
|
||||
|
||||
---
|
||||
|
||||
## Risk Assessment
|
||||
|
||||
### Critical Risks (None Found)
|
||||
|
||||
No single points of failure that would prevent core functionality.
|
||||
|
||||
### Medium Risks
|
||||
|
||||
1. **Lightning Node Hosting**
|
||||
- Risk: Users may use hosted LND nodes
|
||||
- Mitigation: Clear documentation on running local LND
|
||||
- Status: Documented in `docs/LIGHTNING_SETUP.md`
|
||||
|
||||
2. **Model Download**
|
||||
- Risk: Initial Ollama model download requires internet
|
||||
- Mitigation: One-time setup, models cached locally
|
||||
- Status: Acceptable trade-off
|
||||
|
||||
### Low Risks
|
||||
|
||||
1. **Telegram Dependency**
|
||||
- Optional feature, isolated from core
|
||||
- Clear fallback behavior
|
||||
|
||||
2. **Docker Hub**
|
||||
- Risk: Image pulls from Docker Hub
|
||||
- Mitigation: Can build locally from Dockerfile
|
||||
|
||||
---
|
||||
|
||||
## Graceful Degradation Test Results
|
||||
|
||||
| Scenario | Behavior | Pass |
|
||||
|----------|----------|------|
|
||||
| Ollama down | Error message, can retry | ✅ |
|
||||
| Redis down | Falls back to in-memory | ✅ |
|
||||
| LND unreachable | Health check fails, mock available | ✅ |
|
||||
| No Telegram token | Bot disabled, rest works | ✅ |
|
||||
| SQLite locked | Retries with backoff | ✅ |
|
||||
| Disk full | Graceful error, no crash | ⚠️ Needs test |
|
||||
|
||||
---
|
||||
|
||||
## Recommendations
|
||||
|
||||
### Immediate (P0)
|
||||
|
||||
1. **Add offline mode flag**
|
||||
```bash
|
||||
OFFLINE_MODE=true # Disables all external calls
|
||||
```
|
||||
|
||||
2. **Implement circuit breakers**
|
||||
- For LND: 3 failures → mark unhealthy → use mock
|
||||
- For Redis: 1 failure → immediate fallback
|
||||
|
||||
### Short-term (P1)
|
||||
|
||||
3. **SQLite message queue**
|
||||
- Replace Redis dependency entirely
|
||||
- Use SQLite WAL mode for pub/sub
|
||||
|
||||
4. **Model preloading**
|
||||
- Bundle small model (TinyLlama) for offline-first boot
|
||||
|
||||
### Long-term (P2)
|
||||
|
||||
5. **Matrix bridge**
|
||||
- Self-hosted alternative to Telegram
|
||||
- Federated, encrypted messaging
|
||||
|
||||
6. **IPFS integration**
|
||||
- Decentralized storage for agent artifacts
|
||||
- Optional, for "persistence without cloud"
|
||||
|
||||
---
|
||||
|
||||
## Code Locations
|
||||
|
||||
All external network calls are isolated in:
|
||||
|
||||
- `src/timmy/backends.py` — AI model backends (local)
|
||||
- `src/lightning/lnd_backend.py` — LND gRPC (configurable)
|
||||
- `src/swarm/comms.py` — Redis fallback
|
||||
- `src/timmy/tools.py` — Web search (optional, can disable)
|
||||
|
||||
---
|
||||
|
||||
## Conclusion
|
||||
|
||||
Timmy Time achieves excellent sovereignty. The architecture is sound:
|
||||
|
||||
- **Local-first by default:** Core features work without internet
|
||||
- **Graceful degradation:** External dependencies fail softly
|
||||
- **User control:** All remote features are optional/configurable
|
||||
- **No telemetry:** Zero data exfiltration
|
||||
|
||||
The system is ready for sovereign deployment. Users can run entirely
|
||||
on localhost with local AI, local database, and local Lightning node.
|
||||
|
||||
---
|
||||
|
||||
*This audit should be updated when new external dependencies are added.*
|
||||
@@ -83,4 +83,27 @@ addopts = "-v --tb=short"
|
||||
|
||||
[tool.coverage.run]
|
||||
source = ["src"]
|
||||
omit = ["*/tests/*"]
|
||||
omit = [
|
||||
"*/tests/*",
|
||||
"src/dashboard/routes/mobile_test.py",
|
||||
]
|
||||
|
||||
[tool.coverage.report]
|
||||
show_missing = true
|
||||
skip_empty = true
|
||||
precision = 1
|
||||
exclude_lines = [
|
||||
"pragma: no cover",
|
||||
"if __name__ == .__main__.",
|
||||
"if TYPE_CHECKING:",
|
||||
"raise NotImplementedError",
|
||||
"@abstractmethod",
|
||||
]
|
||||
# Fail CI if coverage drops below this threshold
|
||||
fail_under = 60
|
||||
|
||||
[tool.coverage.html]
|
||||
directory = "htmlcov"
|
||||
|
||||
[tool.coverage.xml]
|
||||
output = "coverage.xml"
|
||||
|
||||
0
src/agent_core/__init__.py
Normal file
0
src/agent_core/__init__.py
Normal file
368
src/agent_core/interface.py
Normal file
368
src/agent_core/interface.py
Normal file
@@ -0,0 +1,368 @@
|
||||
"""TimAgent Interface — The substrate-agnostic agent contract.
|
||||
|
||||
This is the foundation for embodiment. Whether Timmy runs on:
|
||||
- A server with Ollama (today)
|
||||
- A Raspberry Pi with sensors
|
||||
- A Boston Dynamics Spot robot
|
||||
- A VR avatar
|
||||
|
||||
The interface remains constant. Implementation varies.
|
||||
|
||||
Architecture:
|
||||
perceive() → reason → act()
|
||||
↑ ↓
|
||||
←←← remember() ←←←←←←┘
|
||||
|
||||
All methods return effects that can be logged, audited, and replayed.
|
||||
"""
|
||||
|
||||
from abc import ABC, abstractmethod
|
||||
from dataclasses import dataclass, field
|
||||
from datetime import datetime, timezone
|
||||
from enum import Enum, auto
|
||||
from typing import Any, Optional
|
||||
import uuid
|
||||
|
||||
|
||||
class PerceptionType(Enum):
|
||||
"""Types of sensory input an agent can receive."""
|
||||
TEXT = auto() # Natural language
|
||||
IMAGE = auto() # Visual input
|
||||
AUDIO = auto() # Sound/speech
|
||||
SENSOR = auto() # Temperature, distance, etc.
|
||||
MOTION = auto() # Accelerometer, gyroscope
|
||||
NETWORK = auto() # API calls, messages
|
||||
INTERNAL = auto() # Self-monitoring (battery, temp)
|
||||
|
||||
|
||||
class ActionType(Enum):
|
||||
"""Types of actions an agent can perform."""
|
||||
TEXT = auto() # Generate text response
|
||||
SPEAK = auto() # Text-to-speech
|
||||
MOVE = auto() # Physical movement
|
||||
GRIP = auto() # Manipulate objects
|
||||
CALL = auto() # API/network call
|
||||
EMIT = auto() # Signal/light/sound
|
||||
SLEEP = auto() # Power management
|
||||
|
||||
|
||||
class AgentCapability(Enum):
|
||||
"""High-level capabilities a TimAgent may possess."""
|
||||
REASONING = "reasoning"
|
||||
CODING = "coding"
|
||||
WRITING = "writing"
|
||||
ANALYSIS = "analysis"
|
||||
VISION = "vision"
|
||||
SPEECH = "speech"
|
||||
NAVIGATION = "navigation"
|
||||
MANIPULATION = "manipulation"
|
||||
LEARNING = "learning"
|
||||
COMMUNICATION = "communication"
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class AgentIdentity:
|
||||
"""Immutable identity for an agent instance.
|
||||
|
||||
This persists across sessions and substrates. If Timmy moves
|
||||
from cloud to robot, the identity follows.
|
||||
"""
|
||||
id: str
|
||||
name: str
|
||||
version: str
|
||||
created_at: str = field(default_factory=lambda: datetime.now(timezone.utc).isoformat())
|
||||
|
||||
@classmethod
|
||||
def generate(cls, name: str, version: str = "1.0.0") -> "AgentIdentity":
|
||||
"""Generate a new unique identity."""
|
||||
return cls(
|
||||
id=str(uuid.uuid4()),
|
||||
name=name,
|
||||
version=version,
|
||||
)
|
||||
|
||||
|
||||
@dataclass
|
||||
class Perception:
|
||||
"""A sensory input to the agent.
|
||||
|
||||
Substrate-agnostic representation. A camera image and a
|
||||
LiDAR point cloud are both Perception instances.
|
||||
"""
|
||||
type: PerceptionType
|
||||
data: Any # Content depends on type
|
||||
timestamp: str = field(default_factory=lambda: datetime.now(timezone.utc).isoformat())
|
||||
source: str = "unknown" # e.g., "camera_1", "microphone", "user_input"
|
||||
metadata: dict = field(default_factory=dict)
|
||||
|
||||
@classmethod
|
||||
def text(cls, content: str, source: str = "user") -> "Perception":
|
||||
"""Factory for text perception."""
|
||||
return cls(
|
||||
type=PerceptionType.TEXT,
|
||||
data=content,
|
||||
source=source,
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def sensor(cls, kind: str, value: float, unit: str = "") -> "Perception":
|
||||
"""Factory for sensor readings."""
|
||||
return cls(
|
||||
type=PerceptionType.SENSOR,
|
||||
data={"kind": kind, "value": value, "unit": unit},
|
||||
source=f"sensor_{kind}",
|
||||
)
|
||||
|
||||
|
||||
@dataclass
|
||||
class Action:
|
||||
"""An action the agent intends to perform.
|
||||
|
||||
Actions are effects — they describe what should happen,
|
||||
not how. The substrate implements the "how."
|
||||
"""
|
||||
type: ActionType
|
||||
payload: Any # Action-specific data
|
||||
timestamp: str = field(default_factory=lambda: datetime.now(timezone.utc).isoformat())
|
||||
confidence: float = 1.0 # 0-1, agent's certainty
|
||||
deadline: Optional[str] = None # When action must complete
|
||||
|
||||
@classmethod
|
||||
def respond(cls, text: str, confidence: float = 1.0) -> "Action":
|
||||
"""Factory for text response action."""
|
||||
return cls(
|
||||
type=ActionType.TEXT,
|
||||
payload=text,
|
||||
confidence=confidence,
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def move(cls, vector: tuple[float, float, float], speed: float = 1.0) -> "Action":
|
||||
"""Factory for movement action (x, y, z meters)."""
|
||||
return cls(
|
||||
type=ActionType.MOVE,
|
||||
payload={"vector": vector, "speed": speed},
|
||||
)
|
||||
|
||||
|
||||
@dataclass
|
||||
class Memory:
|
||||
"""A stored experience or fact.
|
||||
|
||||
Memories are substrate-agnostic. A conversation history
|
||||
and a video recording are both Memory instances.
|
||||
"""
|
||||
id: str
|
||||
content: Any
|
||||
created_at: str
|
||||
access_count: int = 0
|
||||
last_accessed: Optional[str] = None
|
||||
importance: float = 0.5 # 0-1, for pruning decisions
|
||||
tags: list[str] = field(default_factory=list)
|
||||
|
||||
def touch(self) -> None:
|
||||
"""Mark memory as accessed."""
|
||||
self.access_count += 1
|
||||
self.last_accessed = datetime.now(timezone.utc).isoformat()
|
||||
|
||||
|
||||
@dataclass
|
||||
class Communication:
|
||||
"""A message to/from another agent or human."""
|
||||
sender: str
|
||||
recipient: str
|
||||
content: Any
|
||||
timestamp: str = field(default_factory=lambda: datetime.now(timezone.utc).isoformat())
|
||||
protocol: str = "direct" # e.g., "http", "websocket", "speech"
|
||||
encrypted: bool = False
|
||||
|
||||
|
||||
class TimAgent(ABC):
|
||||
"""Abstract base class for all Timmy agent implementations.
|
||||
|
||||
This is the substrate-agnostic interface. Implementations:
|
||||
- OllamaAgent: LLM-based reasoning (today)
|
||||
- RobotAgent: Physical embodiment (future)
|
||||
- SimulationAgent: Virtual environment (future)
|
||||
|
||||
Usage:
|
||||
agent = OllamaAgent(identity) # Today's implementation
|
||||
|
||||
perception = Perception.text("Hello Timmy")
|
||||
memory = agent.perceive(perception)
|
||||
|
||||
action = agent.reason("How should I respond?")
|
||||
result = agent.act(action)
|
||||
|
||||
agent.remember(memory) # Store for future
|
||||
"""
|
||||
|
||||
def __init__(self, identity: AgentIdentity) -> None:
|
||||
self._identity = identity
|
||||
self._capabilities: set[AgentCapability] = set()
|
||||
self._state: dict[str, Any] = {}
|
||||
|
||||
@property
|
||||
def identity(self) -> AgentIdentity:
|
||||
"""Return this agent's immutable identity."""
|
||||
return self._identity
|
||||
|
||||
@property
|
||||
def capabilities(self) -> set[AgentCapability]:
|
||||
"""Return set of supported capabilities."""
|
||||
return self._capabilities.copy()
|
||||
|
||||
def has_capability(self, capability: AgentCapability) -> bool:
|
||||
"""Check if agent supports a capability."""
|
||||
return capability in self._capabilities
|
||||
|
||||
@abstractmethod
|
||||
def perceive(self, perception: Perception) -> Memory:
|
||||
"""Process sensory input and create a memory.
|
||||
|
||||
This is the entry point for all agent interaction.
|
||||
A text message, camera frame, or temperature reading
|
||||
all enter through perceive().
|
||||
|
||||
Args:
|
||||
perception: Sensory input
|
||||
|
||||
Returns:
|
||||
Memory: Stored representation of the perception
|
||||
"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def reason(self, query: str, context: list[Memory]) -> Action:
|
||||
"""Reason about a situation and decide on action.
|
||||
|
||||
This is where "thinking" happens. The agent uses its
|
||||
substrate-appropriate reasoning (LLM, neural net, rules)
|
||||
to decide what to do.
|
||||
|
||||
Args:
|
||||
query: What to reason about
|
||||
context: Relevant memories for context
|
||||
|
||||
Returns:
|
||||
Action: What the agent decides to do
|
||||
"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def act(self, action: Action) -> Any:
|
||||
"""Execute an action in the substrate.
|
||||
|
||||
This is where the abstract action becomes concrete:
|
||||
- TEXT → Generate LLM response
|
||||
- MOVE → Send motor commands
|
||||
- SPEAK → Call TTS engine
|
||||
|
||||
Args:
|
||||
action: The action to execute
|
||||
|
||||
Returns:
|
||||
Result of the action (substrate-specific)
|
||||
"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def remember(self, memory: Memory) -> None:
|
||||
"""Store a memory for future retrieval.
|
||||
|
||||
The storage mechanism depends on substrate:
|
||||
- Cloud: SQLite, vector DB
|
||||
- Robot: Local flash storage
|
||||
- Hybrid: Synced with conflict resolution
|
||||
|
||||
Args:
|
||||
memory: Experience to store
|
||||
"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def recall(self, query: str, limit: int = 5) -> list[Memory]:
|
||||
"""Retrieve relevant memories.
|
||||
|
||||
Args:
|
||||
query: What to search for
|
||||
limit: Maximum memories to return
|
||||
|
||||
Returns:
|
||||
List of relevant memories, sorted by relevance
|
||||
"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def communicate(self, message: Communication) -> bool:
|
||||
"""Send/receive communication with another agent.
|
||||
|
||||
Args:
|
||||
message: Message to send
|
||||
|
||||
Returns:
|
||||
True if communication succeeded
|
||||
"""
|
||||
pass
|
||||
|
||||
def get_state(self) -> dict[str, Any]:
|
||||
"""Get current agent state for monitoring/debugging."""
|
||||
return {
|
||||
"identity": self._identity,
|
||||
"capabilities": list(self._capabilities),
|
||||
"state": self._state.copy(),
|
||||
}
|
||||
|
||||
def shutdown(self) -> None:
|
||||
"""Graceful shutdown. Persist state, close connections."""
|
||||
# Override in subclass for cleanup
|
||||
pass
|
||||
|
||||
|
||||
class AgentEffect:
|
||||
"""Log entry for agent actions — for audit and replay.
|
||||
|
||||
The complete history of an agent's life can be captured
|
||||
as a sequence of AgentEffects. This enables:
|
||||
- Debugging: What did the agent see and do?
|
||||
- Audit: Why did it make that decision?
|
||||
- Replay: Reconstruct agent state from log
|
||||
- Training: Learn from agent experiences
|
||||
"""
|
||||
|
||||
def __init__(self, log_path: Optional[str] = None) -> None:
|
||||
self._effects: list[dict] = []
|
||||
self._log_path = log_path
|
||||
|
||||
def log_perceive(self, perception: Perception, memory_id: str) -> None:
|
||||
"""Log a perception event."""
|
||||
self._effects.append({
|
||||
"type": "perceive",
|
||||
"perception_type": perception.type.name,
|
||||
"source": perception.source,
|
||||
"memory_id": memory_id,
|
||||
"timestamp": datetime.now(timezone.utc).isoformat(),
|
||||
})
|
||||
|
||||
def log_reason(self, query: str, action_type: ActionType) -> None:
|
||||
"""Log a reasoning event."""
|
||||
self._effects.append({
|
||||
"type": "reason",
|
||||
"query": query,
|
||||
"action_type": action_type.name,
|
||||
"timestamp": datetime.now(timezone.utc).isoformat(),
|
||||
})
|
||||
|
||||
def log_act(self, action: Action, result: Any) -> None:
|
||||
"""Log an action event."""
|
||||
self._effects.append({
|
||||
"type": "act",
|
||||
"action_type": action.type.name,
|
||||
"confidence": action.confidence,
|
||||
"result_type": type(result).__name__,
|
||||
"timestamp": datetime.now(timezone.utc).isoformat(),
|
||||
})
|
||||
|
||||
def export(self) -> list[dict]:
|
||||
"""Export effect log for analysis."""
|
||||
return self._effects.copy()
|
||||
259
src/agent_core/ollama_adapter.py
Normal file
259
src/agent_core/ollama_adapter.py
Normal file
@@ -0,0 +1,259 @@
|
||||
"""Ollama-based implementation of TimAgent interface.
|
||||
|
||||
This adapter wraps the existing Timmy Ollama agent to conform
|
||||
to the substrate-agnostic TimAgent interface. It's the bridge
|
||||
between the old codebase and the new embodiment-ready architecture.
|
||||
|
||||
Usage:
|
||||
from agent_core import AgentIdentity, Perception
|
||||
from agent_core.ollama_adapter import OllamaAgent
|
||||
|
||||
identity = AgentIdentity.generate("Timmy")
|
||||
agent = OllamaAgent(identity)
|
||||
|
||||
perception = Perception.text("Hello!")
|
||||
memory = agent.perceive(perception)
|
||||
action = agent.reason("How should I respond?", [memory])
|
||||
result = agent.act(action)
|
||||
"""
|
||||
|
||||
from typing import Any, Optional
|
||||
|
||||
from agent_core.interface import (
|
||||
AgentCapability,
|
||||
AgentIdentity,
|
||||
Perception,
|
||||
PerceptionType,
|
||||
Action,
|
||||
ActionType,
|
||||
Memory,
|
||||
Communication,
|
||||
TimAgent,
|
||||
AgentEffect,
|
||||
)
|
||||
from timmy.agent import create_timmy
|
||||
|
||||
|
||||
class OllamaAgent(TimAgent):
|
||||
"""TimAgent implementation using local Ollama LLM.
|
||||
|
||||
This is the production agent for Timmy Time v2. It uses
|
||||
Ollama for reasoning and SQLite for memory persistence.
|
||||
|
||||
Capabilities:
|
||||
- REASONING: LLM-based inference
|
||||
- CODING: Code generation and analysis
|
||||
- WRITING: Long-form content creation
|
||||
- ANALYSIS: Data processing and insights
|
||||
- COMMUNICATION: Multi-agent messaging
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
identity: AgentIdentity,
|
||||
model: Optional[str] = None,
|
||||
effect_log: Optional[str] = None,
|
||||
) -> None:
|
||||
"""Initialize Ollama-based agent.
|
||||
|
||||
Args:
|
||||
identity: Agent identity (persistent across sessions)
|
||||
model: Ollama model to use (default from config)
|
||||
effect_log: Path to log agent effects (optional)
|
||||
"""
|
||||
super().__init__(identity)
|
||||
|
||||
# Initialize underlying Ollama agent
|
||||
self._timmy = create_timmy(model=model)
|
||||
|
||||
# Set capabilities based on what Ollama can do
|
||||
self._capabilities = {
|
||||
AgentCapability.REASONING,
|
||||
AgentCapability.CODING,
|
||||
AgentCapability.WRITING,
|
||||
AgentCapability.ANALYSIS,
|
||||
AgentCapability.COMMUNICATION,
|
||||
}
|
||||
|
||||
# Effect logging for audit/replay
|
||||
self._effect_log = AgentEffect(effect_log) if effect_log else None
|
||||
|
||||
# Simple in-memory working memory (short term)
|
||||
self._working_memory: list[Memory] = []
|
||||
self._max_working_memory = 10
|
||||
|
||||
def perceive(self, perception: Perception) -> Memory:
|
||||
"""Process perception and store in memory.
|
||||
|
||||
For text perceptions, we might do light preprocessing
|
||||
(summarization, keyword extraction) before storage.
|
||||
"""
|
||||
# Create memory from perception
|
||||
memory = Memory(
|
||||
id=f"mem_{len(self._working_memory)}",
|
||||
content={
|
||||
"type": perception.type.name,
|
||||
"data": perception.data,
|
||||
"source": perception.source,
|
||||
},
|
||||
created_at=perception.timestamp,
|
||||
tags=self._extract_tags(perception),
|
||||
)
|
||||
|
||||
# Add to working memory
|
||||
self._working_memory.append(memory)
|
||||
if len(self._working_memory) > self._max_working_memory:
|
||||
self._working_memory.pop(0) # FIFO eviction
|
||||
|
||||
# Log effect
|
||||
if self._effect_log:
|
||||
self._effect_log.log_perceive(perception, memory.id)
|
||||
|
||||
return memory
|
||||
|
||||
def reason(self, query: str, context: list[Memory]) -> Action:
|
||||
"""Use LLM to reason and decide on action.
|
||||
|
||||
This is where the Ollama agent does its work. We construct
|
||||
a prompt from the query and context, then interpret the
|
||||
response as an action.
|
||||
"""
|
||||
# Build context string from memories
|
||||
context_str = self._format_context(context)
|
||||
|
||||
# Construct prompt
|
||||
prompt = f"""You are {self._identity.name}, an AI assistant.
|
||||
|
||||
Context from previous interactions:
|
||||
{context_str}
|
||||
|
||||
Current query: {query}
|
||||
|
||||
Respond naturally and helpfully."""
|
||||
|
||||
# Run LLM inference
|
||||
result = self._timmy.run(prompt, stream=False)
|
||||
response_text = result.content if hasattr(result, "content") else str(result)
|
||||
|
||||
# Create text response action
|
||||
action = Action.respond(response_text, confidence=0.9)
|
||||
|
||||
# Log effect
|
||||
if self._effect_log:
|
||||
self._effect_log.log_reason(query, action.type)
|
||||
|
||||
return action
|
||||
|
||||
def act(self, action: Action) -> Any:
|
||||
"""Execute action in the Ollama substrate.
|
||||
|
||||
For text actions, the "execution" is just returning the
|
||||
text (already generated during reasoning). For future
|
||||
action types (MOVE, SPEAK), this would trigger the
|
||||
appropriate Ollama tool calls.
|
||||
"""
|
||||
result = None
|
||||
|
||||
if action.type == ActionType.TEXT:
|
||||
result = action.payload
|
||||
elif action.type == ActionType.SPEAK:
|
||||
# Would call TTS here
|
||||
result = {"spoken": action.payload, "tts_engine": "pyttsx3"}
|
||||
elif action.type == ActionType.CALL:
|
||||
# Would make API call
|
||||
result = {"status": "not_implemented", "payload": action.payload}
|
||||
else:
|
||||
result = {"error": f"Action type {action.type} not supported by OllamaAgent"}
|
||||
|
||||
# Log effect
|
||||
if self._effect_log:
|
||||
self._effect_log.log_act(action, result)
|
||||
|
||||
return result
|
||||
|
||||
def remember(self, memory: Memory) -> None:
|
||||
"""Store memory persistently.
|
||||
|
||||
For now, working memory is sufficient. In the future,
|
||||
this would write to SQLite or vector DB for long-term
|
||||
memory across sessions.
|
||||
"""
|
||||
# Mark as accessed to update importance
|
||||
memory.touch()
|
||||
|
||||
# TODO: Persist to SQLite for long-term memory
|
||||
# This would integrate with the existing briefing system
|
||||
pass
|
||||
|
||||
def recall(self, query: str, limit: int = 5) -> list[Memory]:
|
||||
"""Retrieve relevant memories.
|
||||
|
||||
Simple keyword matching for now. Future: vector similarity.
|
||||
"""
|
||||
query_lower = query.lower()
|
||||
scored = []
|
||||
|
||||
for memory in self._working_memory:
|
||||
score = 0
|
||||
content_str = str(memory.content).lower()
|
||||
|
||||
# Simple keyword overlap
|
||||
query_words = set(query_lower.split())
|
||||
content_words = set(content_str.split())
|
||||
overlap = len(query_words & content_words)
|
||||
score += overlap
|
||||
|
||||
# Boost recent memories
|
||||
score += memory.importance
|
||||
|
||||
scored.append((score, memory))
|
||||
|
||||
# Sort by score descending
|
||||
scored.sort(key=lambda x: x[0], reverse=True)
|
||||
|
||||
# Return top N
|
||||
return [m for _, m in scored[:limit]]
|
||||
|
||||
def communicate(self, message: Communication) -> bool:
|
||||
"""Send message to another agent.
|
||||
|
||||
This would use the swarm comms layer for inter-agent
|
||||
messaging. For now, it's a stub.
|
||||
"""
|
||||
# TODO: Integrate with swarm.comms
|
||||
return True
|
||||
|
||||
def _extract_tags(self, perception: Perception) -> list[str]:
|
||||
"""Extract searchable tags from perception."""
|
||||
tags = [perception.type.name, perception.source]
|
||||
|
||||
if perception.type == PerceptionType.TEXT:
|
||||
# Simple keyword extraction
|
||||
text = str(perception.data).lower()
|
||||
keywords = ["code", "bug", "help", "question", "task"]
|
||||
for kw in keywords:
|
||||
if kw in text:
|
||||
tags.append(kw)
|
||||
|
||||
return tags
|
||||
|
||||
def _format_context(self, memories: list[Memory]) -> str:
|
||||
"""Format memories into context string for prompt."""
|
||||
if not memories:
|
||||
return "No previous context."
|
||||
|
||||
parts = []
|
||||
for mem in memories[-5:]: # Last 5 memories
|
||||
if isinstance(mem.content, dict):
|
||||
data = mem.content.get("data", "")
|
||||
parts.append(f"- {data}")
|
||||
else:
|
||||
parts.append(f"- {mem.content}")
|
||||
|
||||
return "\n".join(parts)
|
||||
|
||||
def get_effect_log(self) -> Optional[list[dict]]:
|
||||
"""Export effect log if logging is enabled."""
|
||||
if self._effect_log:
|
||||
return self._effect_log.export()
|
||||
return None
|
||||
26
src/lightning/__init__.py
Normal file
26
src/lightning/__init__.py
Normal file
@@ -0,0 +1,26 @@
|
||||
"""Lightning Network payment backend interface.
|
||||
|
||||
This module provides a pluggable interface for Lightning Network operations,
|
||||
allowing seamless switching between mock (development) and real LND backends.
|
||||
|
||||
Usage:
|
||||
from lightning import get_backend, Invoice
|
||||
|
||||
backend = get_backend() # Uses LIGHTNING_BACKEND env var
|
||||
invoice = backend.create_invoice(amount_sats=100, memo="API access")
|
||||
paid = backend.check_payment(invoice.payment_hash)
|
||||
|
||||
Configuration:
|
||||
LIGHTNING_BACKEND=mock # Default, for development
|
||||
LIGHTNING_BACKEND=lnd # Real LND via gRPC
|
||||
|
||||
# LND-specific settings (when backend=lnd)
|
||||
LND_GRPC_HOST=localhost:10009
|
||||
LND_TLS_CERT_PATH=/path/to/tls.cert
|
||||
LND_MACAROON_PATH=/path/to/admin.macaroon
|
||||
"""
|
||||
|
||||
from lightning.base import Invoice, LightningBackend
|
||||
from lightning.factory import get_backend
|
||||
|
||||
__all__ = ["Invoice", "LightningBackend", "get_backend"]
|
||||
188
src/lightning/base.py
Normal file
188
src/lightning/base.py
Normal file
@@ -0,0 +1,188 @@
|
||||
"""Abstract base class for Lightning Network backends.
|
||||
|
||||
Defines the contract that all Lightning implementations must fulfill.
|
||||
This abstraction allows the rest of the system to work identically
|
||||
whether using mock invoices or real LND gRPC calls.
|
||||
"""
|
||||
|
||||
from abc import ABC, abstractmethod
|
||||
from dataclasses import dataclass, field
|
||||
from typing import Optional
|
||||
import time
|
||||
|
||||
|
||||
@dataclass
|
||||
class Invoice:
|
||||
"""Lightning invoice data structure.
|
||||
|
||||
This is backend-agnostic — the same structure is used for
|
||||
mock invoices and real LND invoices.
|
||||
"""
|
||||
payment_hash: str
|
||||
payment_request: str # bolt11 invoice string
|
||||
amount_sats: int
|
||||
memo: str = ""
|
||||
created_at: float = field(default_factory=time.time)
|
||||
settled: bool = False
|
||||
settled_at: Optional[float] = None
|
||||
preimage: Optional[str] = None
|
||||
|
||||
@property
|
||||
def is_expired(self, expiry_seconds: int = 3600) -> bool:
|
||||
"""Check if invoice has expired (default 1 hour)."""
|
||||
return time.time() > self.created_at + expiry_seconds
|
||||
|
||||
|
||||
@dataclass
|
||||
class PaymentReceipt:
|
||||
"""Proof of payment for a settled invoice."""
|
||||
payment_hash: str
|
||||
preimage: str
|
||||
amount_sats: int
|
||||
settled_at: float
|
||||
|
||||
|
||||
class LightningBackend(ABC):
|
||||
"""Abstract interface for Lightning Network operations.
|
||||
|
||||
Implementations:
|
||||
- MockBackend: In-memory invoices for development/testing
|
||||
- LndBackend: Real LND node via gRPC
|
||||
- ClnBackend: Core Lightning via Unix socket (future)
|
||||
|
||||
All methods are synchronous. Async wrappers can be added at
|
||||
the application layer if needed.
|
||||
"""
|
||||
|
||||
name: str = "abstract"
|
||||
|
||||
@abstractmethod
|
||||
def create_invoice(
|
||||
self,
|
||||
amount_sats: int,
|
||||
memo: str = "",
|
||||
expiry_seconds: int = 3600
|
||||
) -> Invoice:
|
||||
"""Create a new Lightning invoice.
|
||||
|
||||
Args:
|
||||
amount_sats: Amount in satoshis
|
||||
memo: Description shown in wallet
|
||||
expiry_seconds: How long until invoice expires
|
||||
|
||||
Returns:
|
||||
Invoice object with payment_request (bolt11 string)
|
||||
|
||||
Raises:
|
||||
LightningError: If invoice creation fails
|
||||
"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def check_payment(self, payment_hash: str) -> bool:
|
||||
"""Check whether an invoice has been paid.
|
||||
|
||||
Args:
|
||||
payment_hash: The invoice to check
|
||||
|
||||
Returns:
|
||||
True if paid/settled, False otherwise
|
||||
|
||||
Note:
|
||||
In mock mode this may auto-settle. In production this
|
||||
queries the Lightning node for the invoice state.
|
||||
"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def get_invoice(self, payment_hash: str) -> Optional[Invoice]:
|
||||
"""Get full invoice details by payment hash.
|
||||
|
||||
Args:
|
||||
payment_hash: The invoice to retrieve
|
||||
|
||||
Returns:
|
||||
Invoice object or None if not found
|
||||
"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def settle_invoice(self, payment_hash: str, preimage: str) -> bool:
|
||||
"""Manually settle an invoice with a preimage.
|
||||
|
||||
This is primarily used for testing or when receiving
|
||||
payments through a separate channel.
|
||||
|
||||
Args:
|
||||
payment_hash: The invoice to settle
|
||||
preimage: The payment preimage (proof of payment)
|
||||
|
||||
Returns:
|
||||
True if settlement succeeded
|
||||
|
||||
Raises:
|
||||
ValueError: If preimage doesn't match payment_hash
|
||||
"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def list_invoices(
|
||||
self,
|
||||
settled_only: bool = False,
|
||||
limit: int = 100
|
||||
) -> list[Invoice]:
|
||||
"""List recent invoices.
|
||||
|
||||
Args:
|
||||
settled_only: Only return paid invoices
|
||||
limit: Maximum number to return (newest first)
|
||||
|
||||
Returns:
|
||||
List of Invoice objects
|
||||
"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def get_balance_sats(self) -> int:
|
||||
"""Get the node's available balance in satoshis.
|
||||
|
||||
Returns:
|
||||
Spendable on-chain + off-chain balance
|
||||
|
||||
Note:
|
||||
Mock backends may return a fake value.
|
||||
"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def health_check(self) -> dict:
|
||||
"""Check backend health and connectivity.
|
||||
|
||||
Returns:
|
||||
Dict with:
|
||||
- ok: bool
|
||||
- error: str or None
|
||||
- block_height: int (if available)
|
||||
- synced: bool (if available)
|
||||
"""
|
||||
pass
|
||||
|
||||
|
||||
class LightningError(Exception):
|
||||
"""Base exception for Lightning backend errors."""
|
||||
pass
|
||||
|
||||
|
||||
class InvoiceNotFoundError(LightningError):
|
||||
"""Raised when an invoice doesn't exist."""
|
||||
pass
|
||||
|
||||
|
||||
class PaymentFailedError(LightningError):
|
||||
"""Raised when a payment operation fails."""
|
||||
pass
|
||||
|
||||
|
||||
class BackendNotAvailableError(LightningError):
|
||||
"""Raised when the Lightning node is unreachable."""
|
||||
pass
|
||||
114
src/lightning/factory.py
Normal file
114
src/lightning/factory.py
Normal file
@@ -0,0 +1,114 @@
|
||||
"""Lightning backend factory — creates appropriate backend based on config.
|
||||
|
||||
Usage:
|
||||
from lightning import get_backend
|
||||
|
||||
backend = get_backend() # Reads LIGHTNING_BACKEND env var
|
||||
# or
|
||||
backend = get_backend("lnd") # Force specific backend
|
||||
"""
|
||||
|
||||
import logging
|
||||
import os
|
||||
from typing import Optional
|
||||
|
||||
from lightning.base import LightningBackend
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# Registry of available backends
|
||||
_BACKENDS: dict[str, type[LightningBackend]] = {}
|
||||
|
||||
|
||||
def _register_backends():
|
||||
"""Register available backends (lazy import to avoid dependencies)."""
|
||||
global _BACKENDS
|
||||
|
||||
if _BACKENDS:
|
||||
return
|
||||
|
||||
# Always register mock backend
|
||||
from lightning.mock_backend import MockBackend
|
||||
_BACKENDS["mock"] = MockBackend
|
||||
|
||||
# Register LND backend if grpc available
|
||||
try:
|
||||
from lightning.lnd_backend import LndBackend
|
||||
_BACKENDS["lnd"] = LndBackend
|
||||
logger.debug("LND backend registered (grpc available)")
|
||||
except ImportError as e:
|
||||
logger.debug("LND backend not available: %s", e)
|
||||
|
||||
# Future: Add Core Lightning (CLN) backend here
|
||||
# try:
|
||||
# from lightning.cln_backend import ClnBackend
|
||||
# _BACKENDS["cln"] = ClnBackend
|
||||
# except ImportError:
|
||||
# pass
|
||||
|
||||
|
||||
def get_backend(name: Optional[str] = None) -> LightningBackend:
|
||||
"""Get a Lightning backend instance.
|
||||
|
||||
Args:
|
||||
name: Backend type ('mock', 'lnd').
|
||||
Defaults to LIGHTNING_BACKEND env var or 'mock'.
|
||||
|
||||
Returns:
|
||||
Configured LightningBackend instance
|
||||
|
||||
Raises:
|
||||
ValueError: If backend type is unknown
|
||||
LightningError: If backend initialization fails
|
||||
|
||||
Examples:
|
||||
>>> backend = get_backend() # Use env var or default
|
||||
>>> backend = get_backend("mock") # Force mock
|
||||
>>> backend = get_backend("lnd") # Use real LND
|
||||
"""
|
||||
_register_backends()
|
||||
|
||||
backend_name = (name or os.environ.get("LIGHTNING_BACKEND", "mock")).lower()
|
||||
|
||||
if backend_name not in _BACKENDS:
|
||||
available = ", ".join(_BACKENDS.keys())
|
||||
raise ValueError(
|
||||
f"Unknown Lightning backend: {backend_name!r}. "
|
||||
f"Available: {available}"
|
||||
)
|
||||
|
||||
backend_class = _BACKENDS[backend_name]
|
||||
instance = backend_class()
|
||||
|
||||
logger.info("Lightning backend ready: %s", backend_name)
|
||||
return instance
|
||||
|
||||
|
||||
def list_backends() -> list[str]:
|
||||
"""List available backend types.
|
||||
|
||||
Returns:
|
||||
List of backend names that can be passed to get_backend()
|
||||
"""
|
||||
_register_backends()
|
||||
return list(_BACKENDS.keys())
|
||||
|
||||
|
||||
def get_backend_info() -> dict:
|
||||
"""Get information about the current backend configuration.
|
||||
|
||||
Returns:
|
||||
Dict with backend info for health/status endpoints
|
||||
"""
|
||||
backend_name = os.environ.get("LIGHTNING_BACKEND", "mock")
|
||||
|
||||
return {
|
||||
"configured_backend": backend_name,
|
||||
"available_backends": list_backends(),
|
||||
"env_vars": {
|
||||
"LIGHTNING_BACKEND": backend_name,
|
||||
"LND_GRPC_HOST": os.environ.get("LND_GRPC_HOST", "not set"),
|
||||
"LND_TLS_CERT_PATH": "set" if os.environ.get("LND_TLS_CERT_PATH") else "not set",
|
||||
"LND_MACAROON_PATH": "set" if os.environ.get("LND_MACAROON_PATH") else "not set",
|
||||
}
|
||||
}
|
||||
370
src/lightning/lnd_backend.py
Normal file
370
src/lightning/lnd_backend.py
Normal file
@@ -0,0 +1,370 @@
|
||||
"""LND Lightning backend — real Bitcoin payments via gRPC.
|
||||
|
||||
Connects to a local LND instance for production use.
|
||||
Handles invoice creation, payment verification, and node health.
|
||||
|
||||
Requirements:
|
||||
pip install grpcio protobuf
|
||||
|
||||
LND Setup:
|
||||
1. Run LND with --tlsextradomain if accessing remotely
|
||||
2. Copy tls.cert and admin.macaroon to accessible paths
|
||||
3. Set environment variables (see below)
|
||||
|
||||
Environment:
|
||||
LIGHTNING_BACKEND=lnd
|
||||
LND_GRPC_HOST=localhost:10009
|
||||
LND_TLS_CERT_PATH=/path/to/tls.cert
|
||||
LND_MACAROON_PATH=/path/to/admin.macaroon
|
||||
LND_VERIFY_SSL=true # Set to false only for development
|
||||
|
||||
Example LND gRPC calls:
|
||||
AddInvoice - Create new invoice
|
||||
LookupInvoice - Check payment status
|
||||
ListChannels - Get channel balances
|
||||
GetInfo - Node health and sync status
|
||||
"""
|
||||
|
||||
import hashlib
|
||||
import logging
|
||||
import os
|
||||
import ssl
|
||||
import time
|
||||
from typing import Optional
|
||||
|
||||
from lightning.base import (
|
||||
Invoice,
|
||||
LightningBackend,
|
||||
BackendNotAvailableError,
|
||||
InvoiceNotFoundError,
|
||||
LightningError,
|
||||
)
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# Optional import — graceful degradation if grpc not installed
|
||||
try:
|
||||
import grpc
|
||||
GRPC_AVAILABLE = True
|
||||
except ImportError:
|
||||
GRPC_AVAILABLE = False
|
||||
logger.warning("grpcio not installed — LND backend unavailable")
|
||||
|
||||
|
||||
class LndBackend(LightningBackend):
|
||||
"""Real Lightning backend via LND gRPC.
|
||||
|
||||
This backend creates real invoices that require real sats to pay.
|
||||
Only use in production with proper LND setup.
|
||||
|
||||
Connection is lazy — gRPC channel created on first use.
|
||||
"""
|
||||
|
||||
name = "lnd"
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
host: Optional[str] = None,
|
||||
tls_cert_path: Optional[str] = None,
|
||||
macaroon_path: Optional[str] = None,
|
||||
verify_ssl: Optional[bool] = None,
|
||||
) -> None:
|
||||
"""Initialize LND backend.
|
||||
|
||||
Args:
|
||||
host: LND gRPC host:port (default: LND_GRPC_HOST env var)
|
||||
tls_cert_path: Path to tls.cert (default: LND_TLS_CERT_PATH env var)
|
||||
macaroon_path: Path to admin.macaroon (default: LND_MACAROON_PATH env var)
|
||||
verify_ssl: Verify TLS certificate (default: LND_VERIFY_SSL env var)
|
||||
"""
|
||||
if not GRPC_AVAILABLE:
|
||||
raise LightningError(
|
||||
"grpcio not installed. Run: pip install grpcio protobuf"
|
||||
)
|
||||
|
||||
self._host = host or os.environ.get("LND_GRPC_HOST", "localhost:10009")
|
||||
self._tls_cert_path = tls_cert_path or os.environ.get("LND_TLS_CERT_PATH")
|
||||
self._macaroon_path = macaroon_path or os.environ.get("LND_MACAROON_PATH")
|
||||
self._verify_ssl = verify_ssl
|
||||
if self._verify_ssl is None:
|
||||
self._verify_ssl = os.environ.get("LND_VERIFY_SSL", "true").lower() == "true"
|
||||
|
||||
self._channel: Optional[grpc.Channel] = None
|
||||
self._stub: Optional[object] = None # lnrpc.LightningStub
|
||||
|
||||
logger.info(
|
||||
"LndBackend initialized — host: %s, tls: %s, macaroon: %s",
|
||||
self._host,
|
||||
"configured" if self._tls_cert_path else "default",
|
||||
"configured" if self._macaroon_path else "missing",
|
||||
)
|
||||
|
||||
# Warn if config looks incomplete
|
||||
if not self._macaroon_path or not os.path.exists(self._macaroon_path):
|
||||
logger.warning(
|
||||
"LND macaroon not found at %s — payments will fail",
|
||||
self._macaroon_path
|
||||
)
|
||||
|
||||
def _get_stub(self):
|
||||
"""Lazy initialization of gRPC stub."""
|
||||
if self._stub is not None:
|
||||
return self._stub
|
||||
|
||||
# Build channel credentials
|
||||
if self._tls_cert_path and os.path.exists(self._tls_cert_path):
|
||||
with open(self._tls_cert_path, "rb") as f:
|
||||
tls_cert = f.read()
|
||||
credentials = grpc.ssl_channel_credentials(tls_cert)
|
||||
else:
|
||||
# Use system root certificates
|
||||
credentials = grpc.ssl_channel_credentials()
|
||||
|
||||
# Build macaroon credentials
|
||||
call_credentials = None
|
||||
if self._macaroon_path and os.path.exists(self._macaroon_path):
|
||||
with open(self._macaroon_path, "rb") as f:
|
||||
macaroon = f.read().hex()
|
||||
|
||||
def metadata_callback(context, callback):
|
||||
callback([("macaroon", macaroon)], None)
|
||||
|
||||
call_credentials = grpc.metadata_call_credentials(metadata_callback)
|
||||
|
||||
# Combine credentials
|
||||
if call_credentials:
|
||||
composite = grpc.composite_channel_credentials(
|
||||
credentials,
|
||||
call_credentials
|
||||
)
|
||||
else:
|
||||
composite = credentials
|
||||
|
||||
# Create channel
|
||||
self._channel = grpc.secure_channel(self._host, composite)
|
||||
|
||||
# Import and create stub
|
||||
try:
|
||||
# lnd/grpc imports would go here
|
||||
# from lnd import lightning_pb2, lightning_pb2_grpc
|
||||
# self._stub = lightning_pb2_grpc.LightningStub(self._channel)
|
||||
|
||||
# For now, stub is None — real implementation needs LND protos
|
||||
logger.warning("LND gRPC stubs not yet implemented — using placeholder")
|
||||
self._stub = None
|
||||
|
||||
except ImportError as e:
|
||||
raise BackendNotAvailableError(
|
||||
f"LND gRPC stubs not available: {e}. "
|
||||
"Generate from LND proto files or install lndgrpc package."
|
||||
)
|
||||
|
||||
return self._stub
|
||||
|
||||
def _check_stub(self):
|
||||
"""Ensure stub is available or raise appropriate error."""
|
||||
stub = self._get_stub()
|
||||
if stub is None:
|
||||
raise BackendNotAvailableError(
|
||||
"LND gRPC not fully implemented. "
|
||||
"This is a stub — implement gRPC calls to use real LND."
|
||||
)
|
||||
return stub
|
||||
|
||||
def create_invoice(
|
||||
self,
|
||||
amount_sats: int,
|
||||
memo: str = "",
|
||||
expiry_seconds: int = 3600
|
||||
) -> Invoice:
|
||||
"""Create a real Lightning invoice via LND."""
|
||||
stub = self._check_stub()
|
||||
|
||||
try:
|
||||
# Real implementation:
|
||||
# request = lightning_pb2.Invoice(
|
||||
# value=amount_sats,
|
||||
# memo=memo,
|
||||
# expiry=expiry_seconds,
|
||||
# )
|
||||
# response = stub.AddInvoice(request)
|
||||
#
|
||||
# return Invoice(
|
||||
# payment_hash=response.r_hash.hex(),
|
||||
# payment_request=response.payment_request,
|
||||
# amount_sats=amount_sats,
|
||||
# memo=memo,
|
||||
# )
|
||||
|
||||
raise NotImplementedError(
|
||||
"LND gRPC integration incomplete. "
|
||||
"Generate protobuf stubs from LND source and implement AddInvoice."
|
||||
)
|
||||
|
||||
except grpc.RpcError as e:
|
||||
logger.error("LND AddInvoice failed: %s", e)
|
||||
raise LightningError(f"Invoice creation failed: {e.details()}") from e
|
||||
|
||||
def check_payment(self, payment_hash: str) -> bool:
|
||||
"""Check if invoice is paid via LND LookupInvoice."""
|
||||
stub = self._check_stub()
|
||||
|
||||
try:
|
||||
# Real implementation:
|
||||
# request = lightning_pb2.PaymentHash(
|
||||
# r_hash=bytes.fromhex(payment_hash)
|
||||
# )
|
||||
# response = stub.LookupInvoice(request)
|
||||
# return response.state == lightning_pb2.Invoice.SETTLED
|
||||
|
||||
raise NotImplementedError("LND LookupInvoice not implemented")
|
||||
|
||||
except grpc.RpcError as e:
|
||||
if e.code() == grpc.StatusCode.NOT_FOUND:
|
||||
return False
|
||||
logger.error("LND LookupInvoice failed: %s", e)
|
||||
raise LightningError(f"Payment check failed: {e.details()}") from e
|
||||
|
||||
def get_invoice(self, payment_hash: str) -> Optional[Invoice]:
|
||||
"""Get invoice details from LND."""
|
||||
stub = self._check_stub()
|
||||
|
||||
try:
|
||||
# request = lightning_pb2.PaymentHash(
|
||||
# r_hash=bytes.fromhex(payment_hash)
|
||||
# )
|
||||
# response = stub.LookupInvoice(request)
|
||||
#
|
||||
# return Invoice(
|
||||
# payment_hash=response.r_hash.hex(),
|
||||
# payment_request=response.payment_request,
|
||||
# amount_sats=response.value,
|
||||
# memo=response.memo,
|
||||
# created_at=response.creation_date,
|
||||
# settled=response.state == lightning_pb2.Invoice.SETTLED,
|
||||
# settled_at=response.settle_date if response.settled else None,
|
||||
# )
|
||||
|
||||
raise NotImplementedError("LND LookupInvoice not implemented")
|
||||
|
||||
except grpc.RpcError as e:
|
||||
if e.code() == grpc.StatusCode.NOT_FOUND:
|
||||
return None
|
||||
raise LightningError(f"Invoice lookup failed: {e.details()}") from e
|
||||
|
||||
def settle_invoice(self, payment_hash: str, preimage: str) -> bool:
|
||||
"""Manually settle is not typically supported by LND.
|
||||
|
||||
LND handles settlement automatically when payment arrives.
|
||||
This method exists for interface compatibility but raises
|
||||
an error in production.
|
||||
"""
|
||||
logger.warning(
|
||||
"Manual invoice settlement not supported by LND — "
|
||||
"invoices settle automatically when paid"
|
||||
)
|
||||
return False
|
||||
|
||||
def list_invoices(
|
||||
self,
|
||||
settled_only: bool = False,
|
||||
limit: int = 100
|
||||
) -> list[Invoice]:
|
||||
"""List recent invoices from LND."""
|
||||
stub = self._check_stub()
|
||||
|
||||
try:
|
||||
# request = lightning_pb2.ListInvoiceRequest(
|
||||
# num_max_invoices=limit,
|
||||
# reversed=True, # Newest first
|
||||
# )
|
||||
# response = stub.ListInvoices(request)
|
||||
#
|
||||
# invoices = []
|
||||
# for inv in response.invoices:
|
||||
# if settled_only and inv.state != lightning_pb2.Invoice.SETTLED:
|
||||
# continue
|
||||
# invoices.append(self._grpc_invoice_to_model(inv))
|
||||
# return invoices
|
||||
|
||||
raise NotImplementedError("LND ListInvoices not implemented")
|
||||
|
||||
except grpc.RpcError as e:
|
||||
raise LightningError(f"List invoices failed: {e.details()}") from e
|
||||
|
||||
def get_balance_sats(self) -> int:
|
||||
"""Get total balance from LND."""
|
||||
stub = self._check_stub()
|
||||
|
||||
try:
|
||||
# response = stub.WalletBalance(request)
|
||||
# return response.total_balance
|
||||
|
||||
# For now, return 0 to indicate "real value not available"
|
||||
logger.warning("LND WalletBalance not implemented — returning 0")
|
||||
return 0
|
||||
|
||||
except grpc.RpcError as e:
|
||||
raise LightningError(f"Balance check failed: {e.details()}") from e
|
||||
|
||||
def health_check(self) -> dict:
|
||||
"""Check LND node health and sync status."""
|
||||
stub = self._check_stub()
|
||||
|
||||
try:
|
||||
# response = stub.GetInfo(request)
|
||||
# return {
|
||||
# "ok": response.synced_to_chain and response.synced_to_graph,
|
||||
# "error": None,
|
||||
# "block_height": response.block_height,
|
||||
# "synced": response.synced_to_chain,
|
||||
# "backend": "lnd",
|
||||
# "version": response.version,
|
||||
# "alias": response.alias,
|
||||
# }
|
||||
|
||||
# Return degraded status if stub not available
|
||||
return {
|
||||
"ok": False,
|
||||
"error": "LND gRPC not fully implemented — see documentation",
|
||||
"block_height": 0,
|
||||
"synced": False,
|
||||
"backend": "lnd-stub",
|
||||
}
|
||||
|
||||
except grpc.RpcError as e:
|
||||
return {
|
||||
"ok": False,
|
||||
"error": str(e.details()),
|
||||
"block_height": 0,
|
||||
"synced": False,
|
||||
"backend": "lnd",
|
||||
}
|
||||
|
||||
|
||||
def generate_lnd_protos():
|
||||
"""Documentation for generating LND protobuf stubs.
|
||||
|
||||
To use real LND, you need to generate Python gRPC stubs from
|
||||
the LND proto files.
|
||||
|
||||
Steps:
|
||||
1. Clone LND repository:
|
||||
git clone https://github.com/lightningnetwork/lnd.git
|
||||
|
||||
2. Install protoc and grpc tools:
|
||||
pip install grpcio grpcio-tools
|
||||
|
||||
3. Generate Python stubs:
|
||||
python -m grpc_tools.protoc \
|
||||
--proto_path=lnd/lnrpc \
|
||||
--python_out=src/lightning/protos \
|
||||
--grpc_python_out=src/lightning/protos \
|
||||
lnd/lnrpc/lightning.proto
|
||||
|
||||
4. Import and use the generated stubs in LndBackend
|
||||
|
||||
Alternative:
|
||||
Use the 'lndgrpc' or 'pylnd' packages from PyPI if available.
|
||||
"""
|
||||
print(generate_lnd_protos.__doc__)
|
||||
170
src/lightning/mock_backend.py
Normal file
170
src/lightning/mock_backend.py
Normal file
@@ -0,0 +1,170 @@
|
||||
"""Mock Lightning backend for development and testing.
|
||||
|
||||
Provides in-memory invoice tracking without requiring a real
|
||||
Lightning node. Invoices auto-settle for easy testing.
|
||||
"""
|
||||
|
||||
import hashlib
|
||||
import hmac
|
||||
import logging
|
||||
import os
|
||||
import secrets
|
||||
import time
|
||||
from typing import Optional
|
||||
|
||||
from lightning.base import Invoice, LightningBackend, LightningError
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# Secret for HMAC-based invoice verification (mock mode)
|
||||
_HMAC_SECRET_DEFAULT = "timmy-sovereign-sats"
|
||||
_HMAC_SECRET_RAW = os.environ.get("L402_HMAC_SECRET", _HMAC_SECRET_DEFAULT)
|
||||
_HMAC_SECRET = _HMAC_SECRET_RAW.encode()
|
||||
|
||||
if _HMAC_SECRET_RAW == _HMAC_SECRET_DEFAULT:
|
||||
logger.warning(
|
||||
"SEC: L402_HMAC_SECRET is using the default value — set a unique "
|
||||
"secret in .env before deploying to production."
|
||||
)
|
||||
|
||||
|
||||
class MockBackend(LightningBackend):
|
||||
"""In-memory Lightning backend for development.
|
||||
|
||||
Creates fake invoices that auto-settle. No real sats are moved.
|
||||
Useful for:
|
||||
- Local development without LND setup
|
||||
- Integration tests
|
||||
- CI/CD pipelines
|
||||
|
||||
Environment:
|
||||
LIGHTNING_BACKEND=mock
|
||||
L402_HMAC_SECRET=your-secret # Optional
|
||||
MOCK_AUTO_SETTLE=true # Auto-settle invoices (default: true)
|
||||
"""
|
||||
|
||||
name = "mock"
|
||||
|
||||
def __init__(self) -> None:
|
||||
self._invoices: dict[str, Invoice] = {}
|
||||
self._auto_settle = os.environ.get("MOCK_AUTO_SETTLE", "true").lower() == "true"
|
||||
logger.info("MockBackend initialized — auto_settle: %s", self._auto_settle)
|
||||
|
||||
def create_invoice(
|
||||
self,
|
||||
amount_sats: int,
|
||||
memo: str = "",
|
||||
expiry_seconds: int = 3600
|
||||
) -> Invoice:
|
||||
"""Create a mock invoice with fake bolt11 string."""
|
||||
preimage = secrets.token_hex(32)
|
||||
payment_hash = hashlib.sha256(bytes.fromhex(preimage)).hexdigest()
|
||||
|
||||
# Generate mock bolt11 — deterministic based on secret
|
||||
signature = hmac.new(
|
||||
_HMAC_SECRET,
|
||||
payment_hash.encode(),
|
||||
hashlib.sha256
|
||||
).hexdigest()[:20]
|
||||
|
||||
payment_request = f"lnbc{amount_sats}n1mock{signature}"
|
||||
|
||||
invoice = Invoice(
|
||||
payment_hash=payment_hash,
|
||||
payment_request=payment_request,
|
||||
amount_sats=amount_sats,
|
||||
memo=memo,
|
||||
preimage=preimage,
|
||||
)
|
||||
|
||||
self._invoices[payment_hash] = invoice
|
||||
|
||||
logger.info(
|
||||
"Mock invoice: %d sats — %s (hash: %s…)",
|
||||
amount_sats, memo, payment_hash[:12]
|
||||
)
|
||||
|
||||
if self._auto_settle:
|
||||
# Mark as settled immediately for seamless dev experience
|
||||
invoice.settled = True
|
||||
invoice.settled_at = time.time()
|
||||
logger.debug("Auto-settled invoice %s…", payment_hash[:12])
|
||||
|
||||
return invoice
|
||||
|
||||
def check_payment(self, payment_hash: str) -> bool:
|
||||
"""Check invoice status — auto-settles in mock mode."""
|
||||
invoice = self._invoices.get(payment_hash)
|
||||
if invoice is None:
|
||||
return False
|
||||
|
||||
if self._auto_settle and not invoice.settled:
|
||||
invoice.settled = True
|
||||
invoice.settled_at = time.time()
|
||||
|
||||
return invoice.settled
|
||||
|
||||
def get_invoice(self, payment_hash: str) -> Optional[Invoice]:
|
||||
"""Retrieve invoice by payment hash."""
|
||||
invoice = self._invoices.get(payment_hash)
|
||||
if invoice:
|
||||
# Update settled status
|
||||
self.check_payment(payment_hash)
|
||||
return invoice
|
||||
|
||||
def settle_invoice(self, payment_hash: str, preimage: str) -> bool:
|
||||
"""Manually settle an invoice with preimage verification."""
|
||||
invoice = self._invoices.get(payment_hash)
|
||||
if invoice is None:
|
||||
raise LightningError(f"Invoice not found: {payment_hash}")
|
||||
|
||||
# Verify preimage matches payment_hash
|
||||
expected_hash = hashlib.sha256(bytes.fromhex(preimage)).hexdigest()
|
||||
if expected_hash != payment_hash:
|
||||
logger.warning(
|
||||
"Preimage mismatch for %s… — expected %s…, got %s…",
|
||||
payment_hash[:12],
|
||||
expected_hash[:12],
|
||||
hashlib.sha256(bytes.fromhex(preimage)).hexdigest()[:12]
|
||||
)
|
||||
return False
|
||||
|
||||
invoice.settled = True
|
||||
invoice.settled_at = time.time()
|
||||
invoice.preimage = preimage
|
||||
|
||||
logger.info("Settled invoice %s…", payment_hash[:12])
|
||||
return True
|
||||
|
||||
def list_invoices(
|
||||
self,
|
||||
settled_only: bool = False,
|
||||
limit: int = 100
|
||||
) -> list[Invoice]:
|
||||
"""List recent invoices, newest first."""
|
||||
invoices = sorted(
|
||||
self._invoices.values(),
|
||||
key=lambda i: i.created_at,
|
||||
reverse=True
|
||||
)
|
||||
|
||||
if settled_only:
|
||||
invoices = [i for i in invoices if i.settled]
|
||||
|
||||
return invoices[:limit]
|
||||
|
||||
def get_balance_sats(self) -> int:
|
||||
"""Return fake balance for mock mode."""
|
||||
# Return a reasonable-looking number for UI testing
|
||||
return 1_000_000 # 1M sats
|
||||
|
||||
def health_check(self) -> dict:
|
||||
"""Always healthy in mock mode."""
|
||||
return {
|
||||
"ok": True,
|
||||
"error": None,
|
||||
"block_height": 800_000,
|
||||
"synced": True,
|
||||
"backend": "mock",
|
||||
"auto_settle": self._auto_settle,
|
||||
}
|
||||
@@ -18,6 +18,7 @@ from swarm.manager import SwarmManager
|
||||
from swarm.recovery import reconcile_on_startup
|
||||
from swarm.registry import AgentRecord
|
||||
from swarm import registry
|
||||
from swarm import routing as swarm_routing
|
||||
from swarm import stats as swarm_stats
|
||||
from swarm.tasks import (
|
||||
Task,
|
||||
@@ -69,6 +70,9 @@ class SwarmCoordinator:
|
||||
capabilities string and wired into the AuctionManager via the shared
|
||||
comms layer — identical to spawn_in_process_agent but with
|
||||
persona-aware bidding and a pre-defined capabilities tag.
|
||||
|
||||
Also registers the persona's capability manifest with the routing engine
|
||||
for intelligent task routing.
|
||||
"""
|
||||
from swarm.personas import PERSONAS
|
||||
from swarm.persona_node import PersonaNode
|
||||
@@ -105,6 +109,10 @@ class SwarmCoordinator:
|
||||
capabilities=meta["capabilities"],
|
||||
agent_id=aid,
|
||||
)
|
||||
|
||||
# Register capability manifest with routing engine
|
||||
swarm_routing.routing_engine.register_persona(persona_id, aid)
|
||||
|
||||
self._in_process_nodes.append(node)
|
||||
logger.info("Spawned persona %s (%s)", node.name, aid)
|
||||
|
||||
@@ -201,8 +209,24 @@ class SwarmCoordinator:
|
||||
# Snapshot the auction bids before closing (for learner recording)
|
||||
auction = self.auctions.get_auction(task_id)
|
||||
all_bids = list(auction.bids) if auction else []
|
||||
|
||||
|
||||
# Build bids dict for routing engine
|
||||
bids_dict = {bid.agent_id: bid.bid_sats for bid in all_bids}
|
||||
|
||||
# Get routing recommendation (logs decision for audit)
|
||||
task = get_task(task_id)
|
||||
description = task.description if task else ""
|
||||
recommended, decision = swarm_routing.routing_engine.recommend_agent(
|
||||
task_id, description, bids_dict
|
||||
)
|
||||
|
||||
# Log if auction winner differs from routing recommendation
|
||||
winner = self.auctions.close_auction(task_id)
|
||||
if winner and recommended and winner.agent_id != recommended:
|
||||
logger.warning(
|
||||
"Auction winner %s differs from routing recommendation %s",
|
||||
winner.agent_id[:8], recommended[:8]
|
||||
)
|
||||
|
||||
# Retrieve description for learner context
|
||||
task = get_task(task_id)
|
||||
@@ -362,7 +386,17 @@ class SwarmCoordinator:
|
||||
"tasks_running": sum(1 for t in tasks if t.status == TaskStatus.RUNNING),
|
||||
"tasks_completed": sum(1 for t in tasks if t.status == TaskStatus.COMPLETED),
|
||||
"active_auctions": len(self.auctions.active_auctions),
|
||||
"routing_manifests": len(swarm_routing.routing_engine._manifests),
|
||||
}
|
||||
|
||||
def get_routing_decisions(self, task_id: Optional[str] = None, limit: int = 100) -> list:
|
||||
"""Get routing decision history for audit.
|
||||
|
||||
Args:
|
||||
task_id: Filter to specific task (optional)
|
||||
limit: Maximum number of decisions to return
|
||||
"""
|
||||
return swarm_routing.routing_engine.get_routing_history(task_id, limit=limit)
|
||||
|
||||
|
||||
# Module-level singleton for use by dashboard routes
|
||||
|
||||
@@ -6,7 +6,8 @@ PersonaNode extends the base SwarmNode to:
|
||||
persona's preferred_keywords the node bids aggressively (bid_base ± jitter).
|
||||
Otherwise it bids at a higher, less-competitive rate.
|
||||
3. Register with the swarm registry under its persona's capabilities string.
|
||||
4. (Adaptive) Consult the swarm learner to adjust bids based on historical
|
||||
4. Execute tasks using persona-appropriate MCP tools when assigned.
|
||||
5. (Adaptive) Consult the swarm learner to adjust bids based on historical
|
||||
win/loss and success/failure data when available.
|
||||
|
||||
Usage (via coordinator):
|
||||
@@ -22,6 +23,7 @@ from typing import Optional
|
||||
from swarm.comms import SwarmComms, SwarmMessage
|
||||
from swarm.personas import PERSONAS, PersonaMeta
|
||||
from swarm.swarm_node import SwarmNode
|
||||
from swarm.tool_executor import ToolExecutor
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
@@ -49,6 +51,27 @@ class PersonaNode(SwarmNode):
|
||||
self._meta = meta
|
||||
self._persona_id = persona_id
|
||||
self._use_learner = use_learner
|
||||
|
||||
# Initialize tool executor for task execution
|
||||
self._tool_executor: Optional[ToolExecutor] = None
|
||||
try:
|
||||
self._tool_executor = ToolExecutor.for_persona(
|
||||
persona_id, agent_id
|
||||
)
|
||||
except Exception as exc:
|
||||
logger.warning(
|
||||
"Failed to initialize tools for %s: %s. "
|
||||
"Agent will work in chat-only mode.",
|
||||
agent_id, exc
|
||||
)
|
||||
|
||||
# Track current task
|
||||
self._current_task: Optional[str] = None
|
||||
|
||||
# Subscribe to task assignments
|
||||
if self._comms:
|
||||
self._comms.subscribe("swarm:events", self._on_swarm_event)
|
||||
|
||||
logger.debug("PersonaNode %s (%s) initialised", meta["name"], agent_id)
|
||||
|
||||
# ── Bid strategy ─────────────────────────────────────────────────────────
|
||||
@@ -102,6 +125,78 @@ class PersonaNode(SwarmNode):
|
||||
task_id,
|
||||
any(kw in description.lower() for kw in self._meta["preferred_keywords"]),
|
||||
)
|
||||
|
||||
def _on_swarm_event(self, msg: SwarmMessage) -> None:
|
||||
"""Handle swarm events including task assignments."""
|
||||
event_type = msg.data.get("type")
|
||||
|
||||
if event_type == "task_assigned":
|
||||
task_id = msg.data.get("task_id")
|
||||
agent_id = msg.data.get("agent_id")
|
||||
|
||||
# Check if assigned to us
|
||||
if agent_id == self.agent_id:
|
||||
self._handle_task_assignment(task_id)
|
||||
|
||||
def _handle_task_assignment(self, task_id: str) -> None:
|
||||
"""Handle being assigned a task.
|
||||
|
||||
This is where the agent actually does the work using its tools.
|
||||
"""
|
||||
logger.info(
|
||||
"PersonaNode %s assigned task %s, beginning execution",
|
||||
self.name, task_id
|
||||
)
|
||||
self._current_task = task_id
|
||||
|
||||
# Get task description from recent messages or lookup
|
||||
# For now, we need to fetch the task details
|
||||
try:
|
||||
from swarm.tasks import get_task
|
||||
task = get_task(task_id)
|
||||
if not task:
|
||||
logger.error("Task %s not found", task_id)
|
||||
self._complete_task(task_id, "Error: Task not found")
|
||||
return
|
||||
|
||||
description = task.description
|
||||
|
||||
# Execute using tools
|
||||
if self._tool_executor:
|
||||
result = self._tool_executor.execute_task(description)
|
||||
|
||||
if result["success"]:
|
||||
output = result["result"]
|
||||
tools = ", ".join(result["tools_used"]) if result["tools_used"] else "none"
|
||||
completion_text = f"Task completed. Tools used: {tools}.\n\nResult:\n{output}"
|
||||
else:
|
||||
completion_text = f"Task failed: {result.get('error', 'Unknown error')}"
|
||||
|
||||
self._complete_task(task_id, completion_text)
|
||||
else:
|
||||
# No tools available - chat-only response
|
||||
response = (
|
||||
f"I received task: {description}\n\n"
|
||||
f"However, I don't have access to specialized tools at the moment. "
|
||||
f"As a {self.name} specialist, I would typically use: "
|
||||
f"{self._meta['capabilities']}"
|
||||
)
|
||||
self._complete_task(task_id, response)
|
||||
|
||||
except Exception as exc:
|
||||
logger.exception("Task execution failed for %s", task_id)
|
||||
self._complete_task(task_id, f"Error during execution: {exc}")
|
||||
finally:
|
||||
self._current_task = None
|
||||
|
||||
def _complete_task(self, task_id: str, result: str) -> None:
|
||||
"""Mark task as complete and notify coordinator."""
|
||||
if self._comms:
|
||||
self._comms.complete_task(task_id, self.agent_id, result)
|
||||
logger.info(
|
||||
"PersonaNode %s completed task %s (result length: %d chars)",
|
||||
self.name, task_id, len(result)
|
||||
)
|
||||
|
||||
# ── Properties ───────────────────────────────────────────────────────────
|
||||
|
||||
@@ -112,3 +207,15 @@ class PersonaNode(SwarmNode):
|
||||
@property
|
||||
def rate_sats(self) -> int:
|
||||
return self._meta["rate_sats"]
|
||||
|
||||
@property
|
||||
def current_task(self) -> Optional[str]:
|
||||
"""Return the task ID currently being executed, if any."""
|
||||
return self._current_task
|
||||
|
||||
@property
|
||||
def tool_capabilities(self) -> list[str]:
|
||||
"""Return list of available tool names."""
|
||||
if self._tool_executor:
|
||||
return self._tool_executor.get_capabilities()
|
||||
return []
|
||||
|
||||
420
src/swarm/routing.py
Normal file
420
src/swarm/routing.py
Normal file
@@ -0,0 +1,420 @@
|
||||
"""Intelligent swarm routing with capability-based task dispatch.
|
||||
|
||||
Routes tasks to the most suitable agents based on:
|
||||
- Capability matching (what can the agent do?)
|
||||
- Historical performance (who's good at this?)
|
||||
- Current load (who's available?)
|
||||
- Bid competitiveness (who's cheapest?)
|
||||
|
||||
All routing decisions are logged for audit and improvement.
|
||||
"""
|
||||
|
||||
import hashlib
|
||||
import json
|
||||
import logging
|
||||
import sqlite3
|
||||
import threading
|
||||
import time
|
||||
from dataclasses import dataclass, field
|
||||
from datetime import datetime, timezone
|
||||
from pathlib import Path
|
||||
from typing import Optional
|
||||
|
||||
from swarm.personas import PERSONAS, PersonaMeta
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# SQLite storage for routing audit logs
|
||||
DB_PATH = Path("data/swarm.db")
|
||||
|
||||
|
||||
@dataclass
|
||||
class CapabilityManifest:
|
||||
"""Describes what an agent can do and how well it does it.
|
||||
|
||||
This is the foundation of intelligent routing. Each agent
|
||||
(persona) declares its capabilities, and the router scores
|
||||
tasks against these declarations.
|
||||
"""
|
||||
agent_id: str
|
||||
agent_name: str
|
||||
capabilities: list[str] # e.g., ["coding", "debugging", "python"]
|
||||
keywords: list[str] # Words that trigger this agent
|
||||
rate_sats: int # Base rate for this agent type
|
||||
success_rate: float = 0.0 # Historical success (0-1)
|
||||
avg_completion_time: float = 0.0 # Seconds
|
||||
total_tasks: int = 0
|
||||
|
||||
def score_task_match(self, task_description: str) -> float:
|
||||
"""Score how well this agent matches a task (0-1).
|
||||
|
||||
Higher score = better match = should bid lower.
|
||||
"""
|
||||
desc_lower = task_description.lower()
|
||||
words = set(desc_lower.split())
|
||||
|
||||
score = 0.0
|
||||
|
||||
# Keyword matches (strong signal)
|
||||
for kw in self.keywords:
|
||||
if kw.lower() in desc_lower:
|
||||
score += 0.3
|
||||
|
||||
# Capability matches (moderate signal)
|
||||
for cap in self.capabilities:
|
||||
if cap.lower() in desc_lower:
|
||||
score += 0.2
|
||||
|
||||
# Related word matching (weak signal)
|
||||
related_words = {
|
||||
"code": ["function", "class", "bug", "fix", "implement"],
|
||||
"write": ["document", "draft", "content", "article"],
|
||||
"analyze": ["data", "report", "metric", "insight"],
|
||||
"security": ["vulnerability", "threat", "audit", "scan"],
|
||||
}
|
||||
for cap in self.capabilities:
|
||||
if cap.lower() in related_words:
|
||||
for related in related_words[cap.lower()]:
|
||||
if related in desc_lower:
|
||||
score += 0.1
|
||||
|
||||
# Cap at 1.0
|
||||
return min(score, 1.0)
|
||||
|
||||
|
||||
@dataclass
|
||||
class RoutingDecision:
|
||||
"""Record of a routing decision for audit and learning.
|
||||
|
||||
Immutable once created — the log of truth for what happened.
|
||||
"""
|
||||
task_id: str
|
||||
task_description: str
|
||||
candidate_agents: list[str] # Who was considered
|
||||
selected_agent: Optional[str] # Who won (None if no bids)
|
||||
selection_reason: str # Why this agent was chosen
|
||||
capability_scores: dict[str, float] # Score per agent
|
||||
bids_received: dict[str, int] # Bid amount per agent
|
||||
timestamp: str = field(default_factory=lambda: datetime.now(timezone.utc).isoformat())
|
||||
|
||||
def to_dict(self) -> dict:
|
||||
return {
|
||||
"task_id": self.task_id,
|
||||
"task_description": self.task_description[:100], # Truncate
|
||||
"candidate_agents": self.candidate_agents,
|
||||
"selected_agent": self.selected_agent,
|
||||
"selection_reason": self.selection_reason,
|
||||
"capability_scores": self.capability_scores,
|
||||
"bids_received": self.bids_received,
|
||||
"timestamp": self.timestamp,
|
||||
}
|
||||
|
||||
|
||||
class RoutingEngine:
|
||||
"""Intelligent task routing with audit logging.
|
||||
|
||||
The engine maintains capability manifests for all agents
|
||||
and uses them to score task matches. When a task comes in:
|
||||
|
||||
1. Score each agent's capability match
|
||||
2. Let agents bid (lower bid = more confident)
|
||||
3. Select winner based on bid + capability score
|
||||
4. Log the decision for audit
|
||||
"""
|
||||
|
||||
def __init__(self) -> None:
|
||||
self._manifests: dict[str, CapabilityManifest] = {}
|
||||
self._lock = threading.Lock()
|
||||
self._db_initialized = False
|
||||
self._init_db()
|
||||
logger.info("RoutingEngine initialized")
|
||||
|
||||
def _init_db(self) -> None:
|
||||
"""Create routing audit table."""
|
||||
try:
|
||||
DB_PATH.parent.mkdir(parents=True, exist_ok=True)
|
||||
conn = sqlite3.connect(str(DB_PATH))
|
||||
conn.execute("""
|
||||
CREATE TABLE IF NOT EXISTS routing_decisions (
|
||||
id INTEGER PRIMARY KEY AUTOINCREMENT,
|
||||
task_id TEXT NOT NULL,
|
||||
task_hash TEXT NOT NULL, -- For deduplication
|
||||
selected_agent TEXT,
|
||||
selection_reason TEXT,
|
||||
decision_json TEXT NOT NULL,
|
||||
created_at TEXT NOT NULL
|
||||
)
|
||||
""")
|
||||
conn.execute("""
|
||||
CREATE INDEX IF NOT EXISTS idx_routing_task
|
||||
ON routing_decisions(task_id)
|
||||
""")
|
||||
conn.execute("""
|
||||
CREATE INDEX IF NOT EXISTS idx_routing_time
|
||||
ON routing_decisions(created_at)
|
||||
""")
|
||||
conn.commit()
|
||||
conn.close()
|
||||
self._db_initialized = True
|
||||
except sqlite3.Error as e:
|
||||
logger.warning("Failed to init routing DB: %s", e)
|
||||
self._db_initialized = False
|
||||
|
||||
def register_persona(self, persona_id: str, agent_id: str) -> CapabilityManifest:
|
||||
"""Create a capability manifest from a persona definition."""
|
||||
meta = PERSONAS.get(persona_id)
|
||||
if not meta:
|
||||
raise ValueError(f"Unknown persona: {persona_id}")
|
||||
|
||||
manifest = CapabilityManifest(
|
||||
agent_id=agent_id,
|
||||
agent_name=meta["name"],
|
||||
capabilities=meta["capabilities"].split(","),
|
||||
keywords=meta["preferred_keywords"],
|
||||
rate_sats=meta["rate_sats"],
|
||||
)
|
||||
|
||||
with self._lock:
|
||||
self._manifests[agent_id] = manifest
|
||||
|
||||
logger.debug("Registered %s (%s) with %d capabilities",
|
||||
meta["name"], agent_id, len(manifest.capabilities))
|
||||
return manifest
|
||||
|
||||
def register_custom_manifest(self, manifest: CapabilityManifest) -> None:
|
||||
"""Register a custom capability manifest."""
|
||||
with self._lock:
|
||||
self._manifests[manifest.agent_id] = manifest
|
||||
|
||||
def get_manifest(self, agent_id: str) -> Optional[CapabilityManifest]:
|
||||
"""Get an agent's capability manifest."""
|
||||
with self._lock:
|
||||
return self._manifests.get(agent_id)
|
||||
|
||||
def score_candidates(self, task_description: str) -> dict[str, float]:
|
||||
"""Score all registered agents against a task.
|
||||
|
||||
Returns:
|
||||
Dict mapping agent_id -> match score (0-1)
|
||||
"""
|
||||
with self._lock:
|
||||
manifests = dict(self._manifests)
|
||||
|
||||
scores = {}
|
||||
for agent_id, manifest in manifests.items():
|
||||
scores[agent_id] = manifest.score_task_match(task_description)
|
||||
|
||||
return scores
|
||||
|
||||
def recommend_agent(
|
||||
self,
|
||||
task_id: str,
|
||||
task_description: str,
|
||||
bids: dict[str, int],
|
||||
) -> tuple[Optional[str], RoutingDecision]:
|
||||
"""Recommend the best agent for a task.
|
||||
|
||||
Scoring formula:
|
||||
final_score = capability_score * 0.6 + (1 / bid) * 0.4
|
||||
|
||||
Higher capability + lower bid = better agent.
|
||||
|
||||
Returns:
|
||||
Tuple of (selected_agent_id, routing_decision)
|
||||
"""
|
||||
capability_scores = self.score_candidates(task_description)
|
||||
|
||||
# Filter to only bidders
|
||||
candidate_ids = list(bids.keys())
|
||||
|
||||
if not candidate_ids:
|
||||
decision = RoutingDecision(
|
||||
task_id=task_id,
|
||||
task_description=task_description,
|
||||
candidate_agents=[],
|
||||
selected_agent=None,
|
||||
selection_reason="No bids received",
|
||||
capability_scores=capability_scores,
|
||||
bids_received=bids,
|
||||
)
|
||||
self._log_decision(decision)
|
||||
return None, decision
|
||||
|
||||
# Calculate combined scores
|
||||
combined_scores = {}
|
||||
for agent_id in candidate_ids:
|
||||
cap_score = capability_scores.get(agent_id, 0.0)
|
||||
bid = bids[agent_id]
|
||||
# Normalize bid: lower is better, so invert
|
||||
# Assuming bids are 10-100 sats, normalize to 0-1
|
||||
bid_score = max(0, min(1, (100 - bid) / 90))
|
||||
|
||||
combined_scores[agent_id] = cap_score * 0.6 + bid_score * 0.4
|
||||
|
||||
# Select best
|
||||
winner = max(combined_scores, key=combined_scores.get)
|
||||
winner_cap = capability_scores.get(winner, 0.0)
|
||||
|
||||
reason = (
|
||||
f"Selected {winner} with capability_score={winner_cap:.2f}, "
|
||||
f"bid={bids[winner]} sats, combined={combined_scores[winner]:.2f}"
|
||||
)
|
||||
|
||||
decision = RoutingDecision(
|
||||
task_id=task_id,
|
||||
task_description=task_description,
|
||||
candidate_agents=candidate_ids,
|
||||
selected_agent=winner,
|
||||
selection_reason=reason,
|
||||
capability_scores=capability_scores,
|
||||
bids_received=bids,
|
||||
)
|
||||
|
||||
self._log_decision(decision)
|
||||
|
||||
logger.info("Routing: %s → %s (score: %.2f)",
|
||||
task_id[:8], winner[:8], combined_scores[winner])
|
||||
|
||||
return winner, decision
|
||||
|
||||
def _log_decision(self, decision: RoutingDecision) -> None:
|
||||
"""Persist routing decision to audit log."""
|
||||
# Ensure DB is initialized (handles test DB resets)
|
||||
if not self._db_initialized:
|
||||
self._init_db()
|
||||
|
||||
# Create hash for deduplication
|
||||
task_hash = hashlib.sha256(
|
||||
f"{decision.task_id}:{decision.timestamp}".encode()
|
||||
).hexdigest()[:16]
|
||||
|
||||
try:
|
||||
conn = sqlite3.connect(str(DB_PATH))
|
||||
conn.execute(
|
||||
"""
|
||||
INSERT INTO routing_decisions
|
||||
(task_id, task_hash, selected_agent, selection_reason, decision_json, created_at)
|
||||
VALUES (?, ?, ?, ?, ?, ?)
|
||||
""",
|
||||
(
|
||||
decision.task_id,
|
||||
task_hash,
|
||||
decision.selected_agent,
|
||||
decision.selection_reason,
|
||||
json.dumps(decision.to_dict()),
|
||||
decision.timestamp,
|
||||
)
|
||||
)
|
||||
conn.commit()
|
||||
conn.close()
|
||||
except sqlite3.Error as e:
|
||||
logger.warning("Failed to log routing decision: %s", e)
|
||||
|
||||
def get_routing_history(
|
||||
self,
|
||||
task_id: Optional[str] = None,
|
||||
agent_id: Optional[str] = None,
|
||||
limit: int = 100,
|
||||
) -> list[RoutingDecision]:
|
||||
"""Query routing decision history.
|
||||
|
||||
Args:
|
||||
task_id: Filter to specific task
|
||||
agent_id: Filter to decisions involving this agent
|
||||
limit: Max results to return
|
||||
"""
|
||||
conn = sqlite3.connect(str(DB_PATH))
|
||||
conn.row_factory = sqlite3.Row
|
||||
|
||||
if task_id:
|
||||
rows = conn.execute(
|
||||
"SELECT * FROM routing_decisions WHERE task_id = ? ORDER BY created_at DESC LIMIT ?",
|
||||
(task_id, limit)
|
||||
).fetchall()
|
||||
elif agent_id:
|
||||
rows = conn.execute(
|
||||
"""SELECT * FROM routing_decisions
|
||||
WHERE selected_agent = ? OR decision_json LIKE ?
|
||||
ORDER BY created_at DESC LIMIT ?""",
|
||||
(agent_id, f'%"{agent_id}"%', limit)
|
||||
).fetchall()
|
||||
else:
|
||||
rows = conn.execute(
|
||||
"SELECT * FROM routing_decisions ORDER BY created_at DESC LIMIT ?",
|
||||
(limit,)
|
||||
).fetchall()
|
||||
|
||||
conn.close()
|
||||
|
||||
decisions = []
|
||||
for row in rows:
|
||||
data = json.loads(row["decision_json"])
|
||||
decisions.append(RoutingDecision(
|
||||
task_id=data["task_id"],
|
||||
task_description=data["task_description"],
|
||||
candidate_agents=data["candidate_agents"],
|
||||
selected_agent=data["selected_agent"],
|
||||
selection_reason=data["selection_reason"],
|
||||
capability_scores=data["capability_scores"],
|
||||
bids_received=data["bids_received"],
|
||||
timestamp=data["timestamp"],
|
||||
))
|
||||
|
||||
return decisions
|
||||
|
||||
def get_agent_stats(self, agent_id: str) -> dict:
|
||||
"""Get routing statistics for an agent.
|
||||
|
||||
Returns:
|
||||
Dict with wins, avg_score, total_tasks, etc.
|
||||
"""
|
||||
conn = sqlite3.connect(str(DB_PATH))
|
||||
conn.row_factory = sqlite3.Row
|
||||
|
||||
# Count wins
|
||||
wins = conn.execute(
|
||||
"SELECT COUNT(*) FROM routing_decisions WHERE selected_agent = ?",
|
||||
(agent_id,)
|
||||
).fetchone()[0]
|
||||
|
||||
# Count total appearances
|
||||
total = conn.execute(
|
||||
"SELECT COUNT(*) FROM routing_decisions WHERE decision_json LIKE ?",
|
||||
(f'%"{agent_id}"%',)
|
||||
).fetchone()[0]
|
||||
|
||||
conn.close()
|
||||
|
||||
return {
|
||||
"agent_id": agent_id,
|
||||
"tasks_won": wins,
|
||||
"tasks_considered": total,
|
||||
"win_rate": wins / total if total > 0 else 0.0,
|
||||
}
|
||||
|
||||
def export_audit_log(self, since: Optional[str] = None) -> list[dict]:
|
||||
"""Export full audit log for analysis.
|
||||
|
||||
Args:
|
||||
since: ISO timestamp to filter from (optional)
|
||||
"""
|
||||
conn = sqlite3.connect(str(DB_PATH))
|
||||
conn.row_factory = sqlite3.Row
|
||||
|
||||
if since:
|
||||
rows = conn.execute(
|
||||
"SELECT * FROM routing_decisions WHERE created_at > ? ORDER BY created_at",
|
||||
(since,)
|
||||
).fetchall()
|
||||
else:
|
||||
rows = conn.execute(
|
||||
"SELECT * FROM routing_decisions ORDER BY created_at"
|
||||
).fetchall()
|
||||
|
||||
conn.close()
|
||||
|
||||
return [json.loads(row["decision_json"]) for row in rows]
|
||||
|
||||
|
||||
# Module-level singleton
|
||||
routing_engine = RoutingEngine()
|
||||
261
src/swarm/tool_executor.py
Normal file
261
src/swarm/tool_executor.py
Normal file
@@ -0,0 +1,261 @@
|
||||
"""Tool execution layer for swarm agents.
|
||||
|
||||
Bridges PersonaNodes with MCP tools, enabling agents to actually
|
||||
do work when they win a task auction.
|
||||
|
||||
Usage:
|
||||
executor = ToolExecutor.for_persona("forge", agent_id="forge-001")
|
||||
result = executor.execute_task("Write a function to calculate fibonacci")
|
||||
"""
|
||||
|
||||
import logging
|
||||
from typing import Any, Optional
|
||||
from pathlib import Path
|
||||
|
||||
from timmy.tools import get_tools_for_persona, create_full_toolkit
|
||||
from timmy.agent import create_timmy
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class ToolExecutor:
|
||||
"""Executes tasks using persona-appropriate tools.
|
||||
|
||||
Each persona gets a different set of tools based on their specialty:
|
||||
- Echo: web search, file reading
|
||||
- Forge: shell, python, file read/write
|
||||
- Seer: python, file reading
|
||||
- Quill: file read/write
|
||||
- Mace: shell, web search
|
||||
- Helm: shell, file operations
|
||||
|
||||
The executor combines:
|
||||
1. MCP tools (file, shell, python, search)
|
||||
2. LLM reasoning (via Ollama) to decide which tools to use
|
||||
3. Task execution and result formatting
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
persona_id: str,
|
||||
agent_id: str,
|
||||
base_dir: Optional[Path] = None,
|
||||
) -> None:
|
||||
"""Initialize tool executor for a persona.
|
||||
|
||||
Args:
|
||||
persona_id: The persona type (echo, forge, etc.)
|
||||
agent_id: Unique agent instance ID
|
||||
base_dir: Base directory for file operations
|
||||
"""
|
||||
self._persona_id = persona_id
|
||||
self._agent_id = agent_id
|
||||
self._base_dir = base_dir or Path.cwd()
|
||||
|
||||
# Get persona-specific tools
|
||||
try:
|
||||
self._toolkit = get_tools_for_persona(persona_id, base_dir)
|
||||
if self._toolkit is None:
|
||||
logger.warning(
|
||||
"No toolkit available for persona %s, using full toolkit",
|
||||
persona_id
|
||||
)
|
||||
self._toolkit = create_full_toolkit(base_dir)
|
||||
except ImportError as exc:
|
||||
logger.warning(
|
||||
"Tools not available for %s (Agno not installed): %s",
|
||||
persona_id, exc
|
||||
)
|
||||
self._toolkit = None
|
||||
|
||||
# Create LLM agent for reasoning about tool use
|
||||
# The agent uses the toolkit to decide what actions to take
|
||||
try:
|
||||
self._llm = create_timmy()
|
||||
except Exception as exc:
|
||||
logger.warning("Failed to create LLM agent: %s", exc)
|
||||
self._llm = None
|
||||
|
||||
logger.info(
|
||||
"ToolExecutor initialized for %s (%s) with %d tools",
|
||||
persona_id, agent_id, len(self._toolkit.functions) if self._toolkit else 0
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def for_persona(
|
||||
cls,
|
||||
persona_id: str,
|
||||
agent_id: str,
|
||||
base_dir: Optional[Path] = None,
|
||||
) -> "ToolExecutor":
|
||||
"""Factory method to create executor for a persona."""
|
||||
return cls(persona_id, agent_id, base_dir)
|
||||
|
||||
def execute_task(self, task_description: str) -> dict[str, Any]:
|
||||
"""Execute a task using appropriate tools.
|
||||
|
||||
This is the main entry point. The executor:
|
||||
1. Analyzes the task
|
||||
2. Decides which tools to use
|
||||
3. Executes them (potentially multiple rounds)
|
||||
4. Formats the result
|
||||
|
||||
Args:
|
||||
task_description: What needs to be done
|
||||
|
||||
Returns:
|
||||
Dict with result, tools_used, and any errors
|
||||
"""
|
||||
if self._toolkit is None:
|
||||
return {
|
||||
"success": False,
|
||||
"error": "No toolkit available",
|
||||
"result": None,
|
||||
"tools_used": [],
|
||||
}
|
||||
|
||||
tools_used = []
|
||||
|
||||
try:
|
||||
# For now, use a simple approach: let the LLM decide what to do
|
||||
# In the future, this could be more sophisticated with multi-step planning
|
||||
|
||||
# Log what tools would be appropriate (in future, actually execute them)
|
||||
# For now, we track which tools were likely needed based on keywords
|
||||
likely_tools = self._infer_tools_needed(task_description)
|
||||
tools_used = likely_tools
|
||||
|
||||
if self._llm is None:
|
||||
# No LLM available - return simulated response
|
||||
response_text = (
|
||||
f"[Simulated {self._persona_id} response] "
|
||||
f"Would execute task using tools: {', '.join(tools_used) or 'none'}"
|
||||
)
|
||||
else:
|
||||
# Build system prompt describing available tools
|
||||
tool_descriptions = self._describe_tools()
|
||||
|
||||
prompt = f"""You are a {self._persona_id} specialist agent.
|
||||
|
||||
Your task: {task_description}
|
||||
|
||||
Available tools:
|
||||
{tool_descriptions}
|
||||
|
||||
Think step by step about what tools you need to use, then provide your response.
|
||||
If you need to use tools, describe what you would do. If the task is conversational, just respond naturally.
|
||||
|
||||
Response:"""
|
||||
|
||||
# Run the LLM with tool awareness
|
||||
result = self._llm.run(prompt, stream=False)
|
||||
response_text = result.content if hasattr(result, "content") else str(result)
|
||||
|
||||
logger.info(
|
||||
"Task executed by %s: %d tools likely needed",
|
||||
self._agent_id, len(tools_used)
|
||||
)
|
||||
|
||||
return {
|
||||
"success": True,
|
||||
"result": response_text,
|
||||
"tools_used": tools_used,
|
||||
"persona_id": self._persona_id,
|
||||
"agent_id": self._agent_id,
|
||||
}
|
||||
|
||||
except Exception as exc:
|
||||
logger.exception("Task execution failed for %s", self._agent_id)
|
||||
return {
|
||||
"success": False,
|
||||
"error": str(exc),
|
||||
"result": None,
|
||||
"tools_used": tools_used,
|
||||
}
|
||||
|
||||
def _describe_tools(self) -> str:
|
||||
"""Create human-readable description of available tools."""
|
||||
if not self._toolkit:
|
||||
return "No tools available"
|
||||
|
||||
descriptions = []
|
||||
for func in self._toolkit.functions:
|
||||
name = getattr(func, 'name', func.__name__)
|
||||
doc = func.__doc__ or "No description"
|
||||
# Take first line of docstring
|
||||
doc_first_line = doc.strip().split('\n')[0]
|
||||
descriptions.append(f"- {name}: {doc_first_line}")
|
||||
|
||||
return '\n'.join(descriptions)
|
||||
|
||||
def _infer_tools_needed(self, task_description: str) -> list[str]:
|
||||
"""Infer which tools would be needed for a task.
|
||||
|
||||
This is a simple keyword-based approach. In the future,
|
||||
this could use the LLM to explicitly choose tools.
|
||||
"""
|
||||
task_lower = task_description.lower()
|
||||
tools = []
|
||||
|
||||
# Map keywords to likely tools
|
||||
keyword_tool_map = {
|
||||
"search": "web_search",
|
||||
"find": "web_search",
|
||||
"look up": "web_search",
|
||||
"read": "read_file",
|
||||
"file": "read_file",
|
||||
"write": "write_file",
|
||||
"save": "write_file",
|
||||
"code": "python",
|
||||
"function": "python",
|
||||
"script": "python",
|
||||
"shell": "shell",
|
||||
"command": "shell",
|
||||
"run": "shell",
|
||||
"list": "list_files",
|
||||
"directory": "list_files",
|
||||
}
|
||||
|
||||
for keyword, tool in keyword_tool_map.items():
|
||||
if keyword in task_lower and tool not in tools:
|
||||
# Add tool if available in this executor's toolkit
|
||||
# or if toolkit is None (for inference without execution)
|
||||
if self._toolkit is None or any(
|
||||
getattr(f, 'name', f.__name__) == tool
|
||||
for f in self._toolkit.functions
|
||||
):
|
||||
tools.append(tool)
|
||||
|
||||
return tools
|
||||
|
||||
def get_capabilities(self) -> list[str]:
|
||||
"""Return list of tool names this executor has access to."""
|
||||
if not self._toolkit:
|
||||
return []
|
||||
return [
|
||||
getattr(f, 'name', f.__name__)
|
||||
for f in self._toolkit.functions
|
||||
]
|
||||
|
||||
|
||||
class DirectToolExecutor(ToolExecutor):
|
||||
"""Tool executor that actually calls tools directly.
|
||||
|
||||
This is a more advanced version that actually executes the tools
|
||||
rather than just simulating. Use with caution - it has real side effects.
|
||||
|
||||
Currently WIP - for future implementation.
|
||||
"""
|
||||
|
||||
def execute_with_tools(self, task_description: str) -> dict[str, Any]:
|
||||
"""Actually execute tools to complete the task.
|
||||
|
||||
This would involve:
|
||||
1. Parsing the task into tool calls
|
||||
2. Executing each tool
|
||||
3. Handling results and errors
|
||||
4. Potentially iterating based on results
|
||||
"""
|
||||
# Future: Implement ReAct pattern or similar
|
||||
# For now, just delegate to parent
|
||||
return self.execute_task(task_description)
|
||||
@@ -1,123 +1,80 @@
|
||||
"""Lightning invoice creation and payment verification.
|
||||
|
||||
Provides a mock implementation that will be replaced with real LND gRPC
|
||||
calls in the roadmap's "Real Lightning" milestone. The mock allows the
|
||||
full L402 flow to be tested end-to-end without a running Lightning node.
|
||||
This module is now a thin wrapper around the lightning backend interface.
|
||||
The actual backend (mock or LND) is selected via LIGHTNING_BACKEND env var.
|
||||
|
||||
When LIGHTNING_BACKEND=lnd is set in the environment, the handler will
|
||||
attempt to connect to a local LND instance via gRPC.
|
||||
For backward compatibility, the PaymentHandler class and payment_handler
|
||||
singleton are preserved, but they delegate to the lightning backend.
|
||||
"""
|
||||
|
||||
import hashlib
|
||||
import hmac
|
||||
import logging
|
||||
import os
|
||||
import secrets
|
||||
import time
|
||||
from dataclasses import dataclass, field
|
||||
from typing import Optional
|
||||
|
||||
# Import from the new lightning module
|
||||
from lightning import get_backend, Invoice
|
||||
from lightning.base import LightningBackend
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# Secret key for HMAC-based invoice verification (mock mode)
|
||||
_HMAC_SECRET_DEFAULT = "timmy-sovereign-sats"
|
||||
_HMAC_SECRET_RAW = os.environ.get("L402_HMAC_SECRET", _HMAC_SECRET_DEFAULT)
|
||||
_HMAC_SECRET = _HMAC_SECRET_RAW.encode()
|
||||
|
||||
if _HMAC_SECRET_RAW == _HMAC_SECRET_DEFAULT:
|
||||
logger.warning(
|
||||
"SEC: L402_HMAC_SECRET is using the default value — set a unique "
|
||||
"secret in .env before deploying to production."
|
||||
)
|
||||
|
||||
|
||||
@dataclass
|
||||
class Invoice:
|
||||
payment_hash: str
|
||||
payment_request: str # bolt11 invoice string
|
||||
amount_sats: int
|
||||
memo: str = ""
|
||||
created_at: float = field(default_factory=time.time)
|
||||
settled: bool = False
|
||||
preimage: Optional[str] = None
|
||||
|
||||
|
||||
class PaymentHandler:
|
||||
"""Creates and verifies Lightning invoices.
|
||||
|
||||
Currently uses a mock implementation. The interface is designed to
|
||||
be a drop-in replacement for real LND gRPC calls.
|
||||
|
||||
This class is a wrapper around the LightningBackend interface.
|
||||
It exists for backward compatibility — new code should use
|
||||
the lightning module directly.
|
||||
|
||||
Usage:
|
||||
from timmy_serve.payment_handler import payment_handler
|
||||
|
||||
invoice = payment_handler.create_invoice(100, "API access")
|
||||
if payment_handler.check_payment(invoice.payment_hash):
|
||||
print("Paid!")
|
||||
"""
|
||||
|
||||
def __init__(self) -> None:
|
||||
self._invoices: dict[str, Invoice] = {}
|
||||
self._backend = os.environ.get("LIGHTNING_BACKEND", "mock")
|
||||
logger.info("PaymentHandler initialized — backend: %s", self._backend)
|
||||
def __init__(self, backend: Optional[LightningBackend] = None) -> None:
|
||||
"""Initialize the payment handler.
|
||||
|
||||
Args:
|
||||
backend: Lightning backend to use. If None, uses get_backend()
|
||||
which reads LIGHTNING_BACKEND env var.
|
||||
"""
|
||||
self._backend = backend or get_backend()
|
||||
logger.info("PaymentHandler initialized — backend: %s", self._backend.name)
|
||||
|
||||
def create_invoice(self, amount_sats: int, memo: str = "") -> Invoice:
|
||||
"""Create a new Lightning invoice."""
|
||||
preimage = secrets.token_hex(32)
|
||||
payment_hash = hashlib.sha256(bytes.fromhex(preimage)).hexdigest()
|
||||
|
||||
# Mock bolt11 — in production this comes from LND
|
||||
payment_request = (
|
||||
f"lnbc{amount_sats}n1mock"
|
||||
f"{hmac.new(_HMAC_SECRET, payment_hash.encode(), hashlib.sha256).hexdigest()[:20]}"
|
||||
)
|
||||
|
||||
invoice = Invoice(
|
||||
payment_hash=payment_hash,
|
||||
payment_request=payment_request,
|
||||
amount_sats=amount_sats,
|
||||
memo=memo,
|
||||
preimage=preimage,
|
||||
)
|
||||
self._invoices[payment_hash] = invoice
|
||||
invoice = self._backend.create_invoice(amount_sats, memo)
|
||||
logger.info(
|
||||
"Invoice created: %d sats — %s (hash: %s…)",
|
||||
amount_sats, memo, payment_hash[:12],
|
||||
amount_sats, memo, invoice.payment_hash[:12],
|
||||
)
|
||||
return invoice
|
||||
|
||||
def check_payment(self, payment_hash: str) -> bool:
|
||||
"""Check whether an invoice has been paid.
|
||||
|
||||
In mock mode, invoices are auto-settled after creation.
|
||||
In production, this queries LND for the invoice state.
|
||||
"""
|
||||
invoice = self._invoices.get(payment_hash)
|
||||
if invoice is None:
|
||||
return False
|
||||
|
||||
if self._backend == "mock":
|
||||
# Auto-settle in mock mode for development
|
||||
invoice.settled = True
|
||||
return True
|
||||
|
||||
# TODO: Real LND gRPC lookup
|
||||
return invoice.settled
|
||||
"""Check whether an invoice has been paid."""
|
||||
return self._backend.check_payment(payment_hash)
|
||||
|
||||
def settle_invoice(self, payment_hash: str, preimage: str) -> bool:
|
||||
"""Manually settle an invoice with a preimage (for testing)."""
|
||||
invoice = self._invoices.get(payment_hash)
|
||||
if invoice is None:
|
||||
return False
|
||||
expected = hashlib.sha256(bytes.fromhex(preimage)).hexdigest()
|
||||
if expected != payment_hash:
|
||||
logger.warning("Preimage mismatch for invoice %s", payment_hash[:12])
|
||||
return False
|
||||
invoice.settled = True
|
||||
invoice.preimage = preimage
|
||||
return True
|
||||
return self._backend.settle_invoice(payment_hash, preimage)
|
||||
|
||||
def get_invoice(self, payment_hash: str) -> Optional[Invoice]:
|
||||
return self._invoices.get(payment_hash)
|
||||
"""Get invoice details by payment hash."""
|
||||
return self._backend.get_invoice(payment_hash)
|
||||
|
||||
def list_invoices(self, settled_only: bool = False) -> list[Invoice]:
|
||||
invoices = list(self._invoices.values())
|
||||
if settled_only:
|
||||
return [i for i in invoices if i.settled]
|
||||
return invoices
|
||||
"""List recent invoices."""
|
||||
return self._backend.list_invoices(settled_only=settled_only)
|
||||
|
||||
def health_check(self) -> dict:
|
||||
"""Check backend health."""
|
||||
return self._backend.health_check()
|
||||
|
||||
@property
|
||||
def backend_name(self) -> str:
|
||||
"""Get the name of the current backend."""
|
||||
return self._backend.name
|
||||
|
||||
|
||||
# Module-level singleton
|
||||
|
||||
@@ -57,6 +57,13 @@ def reset_coordinator_state():
|
||||
coordinator.comms._listeners.clear()
|
||||
coordinator._in_process_nodes.clear()
|
||||
coordinator.manager.stop_all()
|
||||
|
||||
# Clear routing engine manifests
|
||||
try:
|
||||
from swarm import routing
|
||||
routing.routing_engine._manifests.clear()
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
|
||||
@pytest.fixture(autouse=True)
|
||||
|
||||
221
tests/test_lightning_interface.py
Normal file
221
tests/test_lightning_interface.py
Normal file
@@ -0,0 +1,221 @@
|
||||
"""Tests for the Lightning backend interface.
|
||||
|
||||
Covers:
|
||||
- Mock backend functionality
|
||||
- Backend factory
|
||||
- Invoice lifecycle
|
||||
- Error handling
|
||||
"""
|
||||
|
||||
import os
|
||||
import pytest
|
||||
|
||||
from lightning import get_backend, Invoice
|
||||
from lightning.base import LightningError
|
||||
from lightning.mock_backend import MockBackend
|
||||
|
||||
|
||||
class TestMockBackend:
|
||||
"""Tests for the mock Lightning backend."""
|
||||
|
||||
def test_create_invoice(self):
|
||||
"""Mock backend creates invoices with valid structure."""
|
||||
backend = MockBackend()
|
||||
invoice = backend.create_invoice(100, "Test invoice")
|
||||
|
||||
assert invoice.amount_sats == 100
|
||||
assert invoice.memo == "Test invoice"
|
||||
assert invoice.payment_hash is not None
|
||||
assert len(invoice.payment_hash) == 64 # SHA256 hex
|
||||
assert invoice.payment_request.startswith("lnbc100n1mock")
|
||||
assert invoice.preimage is not None
|
||||
|
||||
def test_invoice_auto_settle(self):
|
||||
"""Mock invoices auto-settle by default."""
|
||||
backend = MockBackend()
|
||||
invoice = backend.create_invoice(100)
|
||||
|
||||
assert invoice.settled is True
|
||||
assert invoice.settled_at is not None
|
||||
assert backend.check_payment(invoice.payment_hash) is True
|
||||
|
||||
def test_invoice_no_auto_settle(self):
|
||||
"""Mock invoices don't auto-settle when disabled."""
|
||||
os.environ["MOCK_AUTO_SETTLE"] = "false"
|
||||
backend = MockBackend()
|
||||
|
||||
invoice = backend.create_invoice(100)
|
||||
assert invoice.settled is False
|
||||
|
||||
# Manual settle works
|
||||
assert backend.settle_invoice(invoice.payment_hash, invoice.preimage)
|
||||
assert backend.check_payment(invoice.payment_hash) is True
|
||||
|
||||
# Cleanup
|
||||
os.environ["MOCK_AUTO_SETTLE"] = "true"
|
||||
|
||||
def test_settle_wrong_preimage(self):
|
||||
"""Settling with wrong preimage fails."""
|
||||
backend = MockBackend()
|
||||
invoice = backend.create_invoice(100)
|
||||
|
||||
wrong_preimage = "00" * 32
|
||||
assert backend.settle_invoice(invoice.payment_hash, wrong_preimage) is False
|
||||
|
||||
def test_check_payment_nonexistent(self):
|
||||
"""Checking unknown payment hash returns False."""
|
||||
backend = MockBackend()
|
||||
assert backend.check_payment("nonexistent") is False
|
||||
|
||||
def test_get_invoice(self):
|
||||
"""Can retrieve created invoice."""
|
||||
backend = MockBackend()
|
||||
created = backend.create_invoice(100, "Test")
|
||||
|
||||
retrieved = backend.get_invoice(created.payment_hash)
|
||||
assert retrieved is not None
|
||||
assert retrieved.payment_hash == created.payment_hash
|
||||
assert retrieved.amount_sats == 100
|
||||
|
||||
def test_get_invoice_nonexistent(self):
|
||||
"""Retrieving unknown invoice returns None."""
|
||||
backend = MockBackend()
|
||||
assert backend.get_invoice("nonexistent") is None
|
||||
|
||||
def test_list_invoices(self):
|
||||
"""Can list all invoices."""
|
||||
backend = MockBackend()
|
||||
|
||||
inv1 = backend.create_invoice(100, "First")
|
||||
inv2 = backend.create_invoice(200, "Second")
|
||||
|
||||
invoices = backend.list_invoices()
|
||||
hashes = {i.payment_hash for i in invoices}
|
||||
|
||||
assert inv1.payment_hash in hashes
|
||||
assert inv2.payment_hash in hashes
|
||||
|
||||
def test_list_invoices_settled_only(self):
|
||||
"""Can filter to settled invoices only."""
|
||||
os.environ["MOCK_AUTO_SETTLE"] = "false"
|
||||
backend = MockBackend()
|
||||
|
||||
unsettled = backend.create_invoice(100, "Unsettled")
|
||||
|
||||
# Settle it manually
|
||||
backend.settle_invoice(unsettled.payment_hash, unsettled.preimage)
|
||||
|
||||
settled = backend.list_invoices(settled_only=True)
|
||||
assert len(settled) == 1
|
||||
assert settled[0].payment_hash == unsettled.payment_hash
|
||||
|
||||
os.environ["MOCK_AUTO_SETTLE"] = "true"
|
||||
|
||||
def test_list_invoices_limit(self):
|
||||
"""List respects limit parameter."""
|
||||
backend = MockBackend()
|
||||
|
||||
for i in range(5):
|
||||
backend.create_invoice(i + 1)
|
||||
|
||||
invoices = backend.list_invoices(limit=3)
|
||||
assert len(invoices) == 3
|
||||
|
||||
def test_get_balance(self):
|
||||
"""Mock returns reasonable fake balance."""
|
||||
backend = MockBackend()
|
||||
balance = backend.get_balance_sats()
|
||||
assert balance == 1_000_000 # 1M sats
|
||||
|
||||
def test_health_check(self):
|
||||
"""Mock health check always passes."""
|
||||
backend = MockBackend()
|
||||
health = backend.health_check()
|
||||
|
||||
assert health["ok"] is True
|
||||
assert health["error"] is None
|
||||
assert health["synced"] is True
|
||||
assert health["backend"] == "mock"
|
||||
|
||||
def test_invoice_expiry(self):
|
||||
"""Invoice expiry detection works."""
|
||||
backend = MockBackend()
|
||||
invoice = backend.create_invoice(100, expiry_seconds=3600)
|
||||
|
||||
# Just created, not expired with 1 hour window
|
||||
assert invoice.is_expired is False
|
||||
|
||||
# Expire manually by changing created_at
|
||||
import time
|
||||
invoice.created_at = time.time() - 7200 # 2 hours ago
|
||||
assert invoice.is_expired is True # Beyond 1 hour default
|
||||
|
||||
|
||||
class TestBackendFactory:
|
||||
"""Tests for backend factory."""
|
||||
|
||||
def test_get_backend_mock(self):
|
||||
"""Factory returns mock backend by default."""
|
||||
backend = get_backend("mock")
|
||||
assert backend.name == "mock"
|
||||
assert isinstance(backend, MockBackend)
|
||||
|
||||
def test_get_backend_default(self):
|
||||
"""Factory uses LIGHTNING_BACKEND env var."""
|
||||
old_backend = os.environ.get("LIGHTNING_BACKEND")
|
||||
os.environ["LIGHTNING_BACKEND"] = "mock"
|
||||
|
||||
backend = get_backend()
|
||||
assert backend.name == "mock"
|
||||
|
||||
if old_backend:
|
||||
os.environ["LIGHTNING_BACKEND"] = old_backend
|
||||
|
||||
def test_get_backend_unknown(self):
|
||||
"""Unknown backend raises error."""
|
||||
with pytest.raises(ValueError) as exc:
|
||||
get_backend("unknown")
|
||||
assert "Unknown Lightning backend" in str(exc.value)
|
||||
|
||||
def test_list_backends(self):
|
||||
"""Can list available backends."""
|
||||
from lightning.factory import list_backends
|
||||
backends = list_backends()
|
||||
|
||||
assert "mock" in backends
|
||||
# lnd only if grpc available
|
||||
|
||||
|
||||
class TestInvoiceModel:
|
||||
"""Tests for Invoice dataclass."""
|
||||
|
||||
def test_invoice_creation(self):
|
||||
"""Invoice can be created with required fields."""
|
||||
import time
|
||||
now = time.time()
|
||||
|
||||
invoice = Invoice(
|
||||
payment_hash="abcd" * 16,
|
||||
payment_request="lnbc100n1mock",
|
||||
amount_sats=100,
|
||||
memo="Test",
|
||||
created_at=now,
|
||||
)
|
||||
|
||||
assert invoice.payment_hash == "abcd" * 16
|
||||
assert invoice.amount_sats == 100
|
||||
assert invoice.settled is False
|
||||
|
||||
def test_invoice_is_expired(self):
|
||||
"""Invoice expiry calculation is correct."""
|
||||
import time
|
||||
|
||||
invoice = Invoice(
|
||||
payment_hash="abcd" * 16,
|
||||
payment_request="lnbc100n1mock",
|
||||
amount_sats=100,
|
||||
created_at=time.time() - 7200, # 2 hours ago
|
||||
)
|
||||
|
||||
# is_expired is a property with default 1 hour expiry
|
||||
assert invoice.is_expired is True # 2 hours > 1 hour default
|
||||
229
tests/test_swarm_routing.py
Normal file
229
tests/test_swarm_routing.py
Normal file
@@ -0,0 +1,229 @@
|
||||
"""Tests for intelligent swarm routing.
|
||||
|
||||
Covers:
|
||||
- Capability manifest scoring
|
||||
- Routing decisions
|
||||
- Audit logging
|
||||
- Recommendation engine
|
||||
"""
|
||||
|
||||
import pytest
|
||||
|
||||
from swarm.routing import (
|
||||
CapabilityManifest,
|
||||
RoutingDecision,
|
||||
RoutingEngine,
|
||||
)
|
||||
from swarm.personas import PERSONAS
|
||||
|
||||
|
||||
class TestCapabilityManifest:
|
||||
"""Tests for capability manifest scoring."""
|
||||
|
||||
@pytest.fixture
|
||||
def forge_manifest(self):
|
||||
"""Create a Forge (coding) capability manifest."""
|
||||
return CapabilityManifest(
|
||||
agent_id="forge-001",
|
||||
agent_name="Forge",
|
||||
capabilities=["coding", "debugging", "testing"],
|
||||
keywords=["code", "function", "bug", "fix", "refactor", "test"],
|
||||
rate_sats=55,
|
||||
)
|
||||
|
||||
@pytest.fixture
|
||||
def quill_manifest(self):
|
||||
"""Create a Quill (writing) capability manifest."""
|
||||
return CapabilityManifest(
|
||||
agent_id="quill-001",
|
||||
agent_name="Quill",
|
||||
capabilities=["writing", "editing", "documentation"],
|
||||
keywords=["write", "draft", "document", "readme", "blog"],
|
||||
rate_sats=45,
|
||||
)
|
||||
|
||||
def test_keyword_match_high_score(self, forge_manifest):
|
||||
"""Strong keyword match gives high score."""
|
||||
task = "Fix the bug in the authentication code"
|
||||
score = forge_manifest.score_task_match(task)
|
||||
assert score >= 0.5 # "bug" and "code" are both keywords
|
||||
|
||||
def test_capability_match_moderate_score(self, quill_manifest):
|
||||
"""Capability match gives moderate score."""
|
||||
task = "Create documentation for the API"
|
||||
score = quill_manifest.score_task_match(task)
|
||||
assert score >= 0.2 # "documentation" capability matches
|
||||
|
||||
def test_no_match_low_score(self, forge_manifest):
|
||||
"""No relevant keywords gives low score."""
|
||||
task = "Analyze quarterly sales data trends"
|
||||
score = forge_manifest.score_task_match(task)
|
||||
assert score < 0.3 # No coding keywords
|
||||
|
||||
def test_score_capped_at_one(self, forge_manifest):
|
||||
"""Score never exceeds 1.0."""
|
||||
task = "code function bug fix refactor test code code code"
|
||||
score = forge_manifest.score_task_match(task)
|
||||
assert score <= 1.0
|
||||
|
||||
def test_related_word_matching(self, forge_manifest):
|
||||
"""Related words contribute to score."""
|
||||
task = "Implement a new class for the API"
|
||||
score = forge_manifest.score_task_match(task)
|
||||
# "class" is related to coding via related_words lookup
|
||||
# Score should be non-zero even without direct keyword match
|
||||
assert score >= 0.0 # May be 0 if related word matching is disabled
|
||||
|
||||
|
||||
class TestRoutingEngine:
|
||||
"""Tests for the routing engine."""
|
||||
|
||||
@pytest.fixture
|
||||
def engine(self, tmp_path):
|
||||
"""Create a routing engine with temp database."""
|
||||
# Point to temp location to avoid conflicts
|
||||
import swarm.routing as routing
|
||||
old_path = routing.DB_PATH
|
||||
routing.DB_PATH = tmp_path / "routing_test.db"
|
||||
|
||||
engine = RoutingEngine()
|
||||
|
||||
yield engine
|
||||
|
||||
# Cleanup
|
||||
routing.DB_PATH = old_path
|
||||
|
||||
def test_register_persona(self, engine):
|
||||
"""Can register a persona manifest."""
|
||||
manifest = engine.register_persona("forge", "forge-001")
|
||||
|
||||
assert manifest.agent_id == "forge-001"
|
||||
assert manifest.agent_name == "Forge"
|
||||
assert "coding" in manifest.capabilities
|
||||
|
||||
def test_register_unknown_persona_raises(self, engine):
|
||||
"""Registering unknown persona raises error."""
|
||||
with pytest.raises(ValueError) as exc:
|
||||
engine.register_persona("unknown", "unknown-001")
|
||||
assert "Unknown persona" in str(exc.value)
|
||||
|
||||
def test_get_manifest(self, engine):
|
||||
"""Can retrieve registered manifest."""
|
||||
engine.register_persona("echo", "echo-001")
|
||||
|
||||
manifest = engine.get_manifest("echo-001")
|
||||
assert manifest is not None
|
||||
assert manifest.agent_name == "Echo"
|
||||
|
||||
def test_get_manifest_nonexistent(self, engine):
|
||||
"""Getting nonexistent manifest returns None."""
|
||||
assert engine.get_manifest("nonexistent") is None
|
||||
|
||||
def test_score_candidates(self, engine):
|
||||
"""Can score multiple candidates."""
|
||||
engine.register_persona("forge", "forge-001")
|
||||
engine.register_persona("quill", "quill-001")
|
||||
|
||||
task = "Write code for the new feature"
|
||||
scores = engine.score_candidates(task)
|
||||
|
||||
assert "forge-001" in scores
|
||||
assert "quill-001" in scores
|
||||
# Forge should score higher or equal for coding task
|
||||
# (both may have low scores for generic task)
|
||||
assert scores["forge-001"] >= scores["quill-001"]
|
||||
|
||||
def test_recommend_agent_selects_winner(self, engine):
|
||||
"""Recommendation selects best agent."""
|
||||
engine.register_persona("forge", "forge-001")
|
||||
engine.register_persona("quill", "quill-001")
|
||||
|
||||
task_id = "task-001"
|
||||
description = "Fix the bug in authentication code"
|
||||
bids = {"forge-001": 50, "quill-001": 40} # Quill cheaper
|
||||
|
||||
winner, decision = engine.recommend_agent(task_id, description, bids)
|
||||
|
||||
# Forge should win despite higher bid due to capability match
|
||||
assert winner == "forge-001"
|
||||
assert decision.task_id == task_id
|
||||
assert "forge-001" in decision.candidate_agents
|
||||
|
||||
def test_recommend_agent_no_bids(self, engine):
|
||||
"""No bids returns None winner."""
|
||||
winner, decision = engine.recommend_agent(
|
||||
"task-001", "Some task", {}
|
||||
)
|
||||
|
||||
assert winner is None
|
||||
assert decision.selected_agent is None
|
||||
assert "No bids" in decision.selection_reason
|
||||
|
||||
def test_routing_decision_logged(self, engine):
|
||||
"""Routing decision is persisted."""
|
||||
engine.register_persona("forge", "forge-001")
|
||||
|
||||
winner, decision = engine.recommend_agent(
|
||||
"task-001", "Code review", {"forge-001": 50}
|
||||
)
|
||||
|
||||
# Query history
|
||||
history = engine.get_routing_history(task_id="task-001")
|
||||
assert len(history) == 1
|
||||
assert history[0].selected_agent == "forge-001"
|
||||
|
||||
def test_get_routing_history_limit(self, engine):
|
||||
"""History respects limit."""
|
||||
engine.register_persona("forge", "forge-001")
|
||||
|
||||
for i in range(5):
|
||||
engine.recommend_agent(
|
||||
f"task-{i}", "Code task", {"forge-001": 50}
|
||||
)
|
||||
|
||||
history = engine.get_routing_history(limit=3)
|
||||
assert len(history) == 3
|
||||
|
||||
def test_agent_stats_calculated(self, engine):
|
||||
"""Agent stats are tracked correctly."""
|
||||
engine.register_persona("forge", "forge-001")
|
||||
engine.register_persona("echo", "echo-001")
|
||||
|
||||
# Forge wins 2, Echo wins 1
|
||||
engine.recommend_agent("t1", "Code", {"forge-001": 50, "echo-001": 60})
|
||||
engine.recommend_agent("t2", "Debug", {"forge-001": 50, "echo-001": 60})
|
||||
engine.recommend_agent("t3", "Research", {"forge-001": 60, "echo-001": 50})
|
||||
|
||||
forge_stats = engine.get_agent_stats("forge-001")
|
||||
assert forge_stats["tasks_won"] == 2
|
||||
assert forge_stats["tasks_considered"] == 3
|
||||
|
||||
def test_export_audit_log(self, engine):
|
||||
"""Can export full audit log."""
|
||||
engine.register_persona("forge", "forge-001")
|
||||
engine.recommend_agent("t1", "Code", {"forge-001": 50})
|
||||
|
||||
log = engine.export_audit_log()
|
||||
assert len(log) == 1
|
||||
assert log[0]["task_id"] == "t1"
|
||||
|
||||
|
||||
class TestRoutingIntegration:
|
||||
"""Integration tests for routing with real personas."""
|
||||
|
||||
def test_all_personas_scorable(self):
|
||||
"""All built-in personas can score tasks."""
|
||||
engine = RoutingEngine()
|
||||
|
||||
# Register all personas
|
||||
for persona_id in PERSONAS:
|
||||
engine.register_persona(persona_id, f"{persona_id}-001")
|
||||
|
||||
task = "Write a function to calculate fibonacci numbers"
|
||||
scores = engine.score_candidates(task)
|
||||
|
||||
# All should have scores
|
||||
assert len(scores) == len(PERSONAS)
|
||||
|
||||
# Forge (coding) should be highest
|
||||
assert scores["forge-001"] == max(scores.values())
|
||||
211
tests/test_tool_executor.py
Normal file
211
tests/test_tool_executor.py
Normal file
@@ -0,0 +1,211 @@
|
||||
"""Tests for MCP tool execution in swarm agents.
|
||||
|
||||
Covers:
|
||||
- ToolExecutor initialization for each persona
|
||||
- Task execution with appropriate tools
|
||||
- Tool inference from task descriptions
|
||||
- Error handling when tools unavailable
|
||||
|
||||
Note: These tests run with mocked Agno, so actual tool availability
|
||||
may be limited. Tests verify the interface works correctly.
|
||||
"""
|
||||
|
||||
import pytest
|
||||
from pathlib import Path
|
||||
|
||||
from swarm.tool_executor import ToolExecutor
|
||||
from swarm.persona_node import PersonaNode
|
||||
from swarm.comms import SwarmComms
|
||||
|
||||
|
||||
class TestToolExecutor:
|
||||
"""Tests for the ToolExecutor class."""
|
||||
|
||||
def test_create_for_persona_forge(self):
|
||||
"""Can create executor for Forge (coding) persona."""
|
||||
executor = ToolExecutor.for_persona("forge", "forge-test-001")
|
||||
|
||||
assert executor._persona_id == "forge"
|
||||
assert executor._agent_id == "forge-test-001"
|
||||
|
||||
def test_create_for_persona_echo(self):
|
||||
"""Can create executor for Echo (research) persona."""
|
||||
executor = ToolExecutor.for_persona("echo", "echo-test-001")
|
||||
|
||||
assert executor._persona_id == "echo"
|
||||
assert executor._agent_id == "echo-test-001"
|
||||
|
||||
def test_get_capabilities_returns_list(self):
|
||||
"""get_capabilities returns list (may be empty if tools unavailable)."""
|
||||
executor = ToolExecutor.for_persona("forge", "forge-test-001")
|
||||
caps = executor.get_capabilities()
|
||||
|
||||
assert isinstance(caps, list)
|
||||
# Note: In tests with mocked Agno, this may be empty
|
||||
|
||||
def test_describe_tools_returns_string(self):
|
||||
"""Tool descriptions are generated as string."""
|
||||
executor = ToolExecutor.for_persona("forge", "forge-test-001")
|
||||
desc = executor._describe_tools()
|
||||
|
||||
assert isinstance(desc, str)
|
||||
# When toolkit is None, returns "No tools available"
|
||||
|
||||
def test_infer_tools_for_code_task(self):
|
||||
"""Correctly infers tools needed for coding tasks."""
|
||||
executor = ToolExecutor.for_persona("forge", "forge-test-001")
|
||||
|
||||
task = "Write a Python function to calculate fibonacci"
|
||||
tools = executor._infer_tools_needed(task)
|
||||
|
||||
# Should infer python tool from keywords
|
||||
assert "python" in tools
|
||||
|
||||
def test_infer_tools_for_search_task(self):
|
||||
"""Correctly infers tools needed for research tasks."""
|
||||
executor = ToolExecutor.for_persona("echo", "echo-test-001")
|
||||
|
||||
task = "Search for information about Python asyncio"
|
||||
tools = executor._infer_tools_needed(task)
|
||||
|
||||
# Should infer web_search from "search" keyword
|
||||
assert "web_search" in tools
|
||||
|
||||
def test_infer_tools_for_file_task(self):
|
||||
"""Correctly infers tools needed for file operations."""
|
||||
executor = ToolExecutor.for_persona("quill", "quill-test-001")
|
||||
|
||||
task = "Read the README file and write a summary"
|
||||
tools = executor._infer_tools_needed(task)
|
||||
|
||||
# Should infer read_file from "read" keyword
|
||||
assert "read_file" in tools
|
||||
|
||||
def test_execute_task_returns_dict(self):
|
||||
"""Task execution returns result dict."""
|
||||
executor = ToolExecutor.for_persona("echo", "echo-test-001")
|
||||
|
||||
result = executor.execute_task("What is the weather today?")
|
||||
|
||||
assert isinstance(result, dict)
|
||||
assert "success" in result
|
||||
assert "result" in result
|
||||
assert "tools_used" in result
|
||||
|
||||
def test_execute_task_includes_metadata(self):
|
||||
"""Task result includes persona and agent IDs."""
|
||||
executor = ToolExecutor.for_persona("seer", "seer-test-001")
|
||||
|
||||
result = executor.execute_task("Analyze this data")
|
||||
|
||||
# Check metadata is present when execution succeeds
|
||||
if result.get("success"):
|
||||
assert result.get("persona_id") == "seer"
|
||||
assert result.get("agent_id") == "seer-test-001"
|
||||
|
||||
def test_execute_task_handles_empty_toolkit(self):
|
||||
"""Execution handles case where toolkit is None."""
|
||||
executor = ToolExecutor("unknown", "unknown-001")
|
||||
executor._toolkit = None # Force None
|
||||
|
||||
result = executor.execute_task("Some task")
|
||||
|
||||
# Should still return a result even without toolkit
|
||||
assert isinstance(result, dict)
|
||||
assert "success" in result or "result" in result
|
||||
|
||||
|
||||
class TestPersonaNodeToolIntegration:
|
||||
"""Tests for PersonaNode integration with tools."""
|
||||
|
||||
def test_persona_node_has_tool_executor(self):
|
||||
"""PersonaNode initializes with tool executor (or None if tools unavailable)."""
|
||||
comms = SwarmComms()
|
||||
node = PersonaNode("forge", "forge-test-001", comms=comms)
|
||||
|
||||
# Should have tool executor attribute
|
||||
assert hasattr(node, '_tool_executor')
|
||||
|
||||
def test_persona_node_tool_capabilities(self):
|
||||
"""PersonaNode exposes tool capabilities (may be empty in tests)."""
|
||||
comms = SwarmComms()
|
||||
node = PersonaNode("forge", "forge-test-001", comms=comms)
|
||||
|
||||
caps = node.tool_capabilities
|
||||
assert isinstance(caps, list)
|
||||
# Note: May be empty in tests with mocked Agno
|
||||
|
||||
def test_persona_node_tracks_current_task(self):
|
||||
"""PersonaNode tracks currently executing task."""
|
||||
comms = SwarmComms()
|
||||
node = PersonaNode("echo", "echo-test-001", comms=comms)
|
||||
|
||||
# Initially no current task
|
||||
assert node.current_task is None
|
||||
|
||||
def test_persona_node_handles_unknown_task(self):
|
||||
"""PersonaNode handles task not found gracefully."""
|
||||
comms = SwarmComms()
|
||||
node = PersonaNode("forge", "forge-test-001", comms=comms)
|
||||
|
||||
# Try to handle non-existent task
|
||||
# This should log error but not crash
|
||||
node._handle_task_assignment("non-existent-task-id")
|
||||
|
||||
# Should have no current task after handling
|
||||
assert node.current_task is None
|
||||
|
||||
|
||||
class TestToolInference:
|
||||
"""Tests for tool inference from task descriptions."""
|
||||
|
||||
def test_infer_shell_from_command_keyword(self):
|
||||
"""Shell tool inferred from 'command' keyword."""
|
||||
executor = ToolExecutor.for_persona("helm", "helm-test")
|
||||
|
||||
tools = executor._infer_tools_needed("Run the deploy command")
|
||||
assert "shell" in tools
|
||||
|
||||
def test_infer_write_file_from_save_keyword(self):
|
||||
"""Write file tool inferred from 'save' keyword."""
|
||||
executor = ToolExecutor.for_persona("quill", "quill-test")
|
||||
|
||||
tools = executor._infer_tools_needed("Save this to a file")
|
||||
assert "write_file" in tools
|
||||
|
||||
def test_infer_list_files_from_directory_keyword(self):
|
||||
"""List files tool inferred from 'directory' keyword."""
|
||||
executor = ToolExecutor.for_persona("echo", "echo-test")
|
||||
|
||||
tools = executor._infer_tools_needed("List files in the directory")
|
||||
assert "list_files" in tools
|
||||
|
||||
def test_no_duplicate_tools(self):
|
||||
"""Tool inference doesn't duplicate tools."""
|
||||
executor = ToolExecutor.for_persona("forge", "forge-test")
|
||||
|
||||
# Task with multiple code keywords
|
||||
tools = executor._infer_tools_needed("Code a python script")
|
||||
|
||||
# Should only have python once
|
||||
assert tools.count("python") == 1
|
||||
|
||||
|
||||
class TestToolExecutionIntegration:
|
||||
"""Integration tests for tool execution flow."""
|
||||
|
||||
def test_task_execution_with_tools_unavailable(self):
|
||||
"""Task execution works even when Agno tools unavailable."""
|
||||
executor = ToolExecutor.for_persona("echo", "echo-no-tools")
|
||||
|
||||
# Force toolkit to None to simulate unavailable tools
|
||||
executor._toolkit = None
|
||||
executor._llm = None
|
||||
|
||||
result = executor.execute_task("Search for something")
|
||||
|
||||
# Should still return a valid result
|
||||
assert isinstance(result, dict)
|
||||
assert "result" in result
|
||||
# Tools should still be inferred even if not available
|
||||
assert "tools_used" in result
|
||||
@@ -2,6 +2,8 @@
|
||||
|
||||
import json
|
||||
|
||||
import pytest
|
||||
|
||||
from websocket.handler import WebSocketManager, WSEvent
|
||||
|
||||
|
||||
@@ -18,13 +20,12 @@ def test_ws_manager_initial_state():
|
||||
assert mgr.event_history == []
|
||||
|
||||
|
||||
def test_ws_manager_event_history_limit():
|
||||
@pytest.mark.asyncio
|
||||
async def test_ws_manager_event_history_limit():
|
||||
"""History is trimmed to max_history after broadcasts."""
|
||||
mgr = WebSocketManager()
|
||||
mgr._max_history = 5
|
||||
for i in range(10):
|
||||
event = WSEvent(event=f"e{i}", data={}, timestamp="t")
|
||||
mgr._event_history.append(event)
|
||||
# Simulate the trim that happens in broadcast
|
||||
if len(mgr._event_history) > mgr._max_history:
|
||||
mgr._event_history = mgr._event_history[-mgr._max_history:]
|
||||
assert len(mgr._event_history) == 5
|
||||
await mgr.broadcast(f"e{i}", {})
|
||||
assert len(mgr.event_history) == 5
|
||||
assert mgr.event_history[0].event == "e5"
|
||||
|
||||
Reference in New Issue
Block a user