terraphim / terraphim/terraphim-ai
Update Python library to support AI Dark Factory agent requirements
Offen
Dieses Issue hat noch niemand übernommen.
- Vorherrschende Sprache
- Rust
- Sterne
- 62
- Forks
- 5
- Ø Merge
- 2 Std. 27 Min.
- Gemergte PRs (30 T.)
- 1
Beschreibung
Background
Building an AI Dark Factory with 10 specialized agents maintaining a Gitea fork. Each agent needs to capture learnings and interact with the Terraphim knowledge graph.
Current State
- terraphim-agent CLI (Rust) works well: search, learn capture, query, list
- Python wrapper needed for agent integration
- Current Python bindings limited
Requirements
1. Agent Learning Capture
Each of 10 agents needs:
2. Knowledge Graph Operations
3. Multi-Agent Coordination
4. Learning Audit
Agent-Specific Requirements
| Agent | Primary Use | Special Needs |
|---|---|---|
| Security Sentinel | CVE patterns | Vulnerability node types |
| Upstream Sync | Merge strategies | Git operation patterns |
| Extension Architect | AI optimizations | Model performance data |
| Test Guardian | Flaky test causes | Test pattern nodes |
| Code Reviewer | Security anti-patterns | Code pattern nodes |
| Scenario Tester | Chaos recovery | Failure pattern nodes |
| Product Development | Feature prioritization | Compound-RICE data |
| Release Engineer | Deployment patterns | Canary/rollback data |
| Market Research | Content performance | Engagement pattern nodes |
| Meta-Coordinator | Coordination patterns | Escalation pattern nodes |
Proposed API Design
Implementation Notes
- Backend: Can wrap existing Rust CLI via subprocess or use direct bindings
- Storage: JSONL files in /data/kg/ with optional graph database
- Performance: Agents run locally, low latency required (< 100ms)
- Concurrency: Multiple agents may write simultaneously
Acceptance Criteria
- AgentLearning class functional for all 10 agents
- KnowledgeGraph add_node/search working
- MultiAgentCoordination broadcast working
- LearningAudit spot_check working
- Documentation with examples
- Unit tests covering all classes
Timeline
Needed for AI Dark Factory Week 2-3 implementation.
Related
- AI Dark Factory plan: https://github.com/AlexMikhalev/cto-executive-system/blob/main/projects/ai-dark-factory-gitea-fork.md
- terraphim-agent CLI reference: https://github.com/AlexMikhalev/cto-executive-system/blob/main/docs/terraphim-agent-command-reference.md
Beitragsleitfaden
Erste Schritte
- Lies das ganze Issue und danach den Beitragsleitfaden des Projekts.
- Schreib ins Issue, dass du es übernimmst — das erspart doppelte Arbeit.
- Forke das Repository und arbeite in einem Branch.
- Öffne einen Pull Request, der die Issue-Nummer nennt.
Rechercherichtung
Read projects/ai-dark-factory-gitea-fork.md and docs/terraphim-agent-command-reference.md first, then identify the existing Python bindings and terraphim-agent CLI entry points. Done means the four requested classes and operations work for all listed agents, with documentation examples and unit tests covering them.
Vom Indexierungsmodell aus dem Issue-Text verfasst.
Bewertung
- Tech-Stack
- python, rust
- Bereich
- ai, backend-api-design, documentation, testing
- Issue-Typ
- Feature
- Schwierigkeit
- 5/5
- Geschätzter Aufwand
- Über eine Woche
- Aktivitätsstatus
- Veraltet
- Klarheit
- Muss geklärt werden
- Anfängerfreundlichkeit
- 35/100