majiayu000 Cross-Backend Orchestrator
Cross-platform Python toolkit for orchestrating AI tasks across Codex, Claude, and Gemini backends using memex-cli.
Quick Start
Single Backend Execution
python scripts/run_task.py --backend <backend> --prompt "<task>"
# Examples
python scripts/run_task.py --backend claude --prompt "Analyze this code for bugs"
python scripts/run_task.py --backend gemini --prompt "Generate UX wireframe description"
python scripts/run_task.py --backend codex --prompt "Optimize this algorithm"
Multi-Backend Comparison
python scripts/compare_backends.py --prompt "<task>" --output ./comparison.json
Backend Selection Guide
| Task Type | Recommended Backend | Rationale |
|---|---|---|
| Code generation/debugging | codex |
Optimized for code tasks |
| Creative writing/analysis | claude |
Strong reasoning and writing |
| UX/UI design descriptions | gemini |
Visual understanding |
| General tasks | Any | User preference |
Core Workflows
Workflow 1: Sequential Multi-Backend Pipeline
python scripts/pipeline.py \
--stage "codex:Generate Python function for data processing" \
--stage "claude:Review and improve the code" \
--stage "gemini:Create documentation with diagrams"
Workflow 2: Parallel Comparison
python scripts/parallel_run.py \
--backends codex,claude,gemini \
--prompt "Explain quantum computing" \
--output ./results/
Workflow 3: Fallback Chain
python scripts/fallback_run.py \
--primary codex \
--fallback claude \
--fallback gemini \
--prompt "Complex task description"
Scripts Reference
| Script | Purpose |
|---|---|
run_task.py |
Execute single task on one backend |
compare_backends.py |
Compare outputs across backends |
pipeline.py |
Sequential multi-stage workflow |
parallel_run.py |
Parallel execution on multiple backends |
fallback_run.py |
Fallback chain execution |
replay_events.py |
Replay recorded run events |
orchestrator.py |
Core library module (imported by other scripts) |
memex-cli Integration
All scripts wrap memex-cli commands:
# Direct memex-cli usage
memex-cli run --backend "codex" --model "deepseek-reasoner" --model-provider "aduib_ai" --prompt "Task" --stream-format "jsonl"
memex-cli run --backend "claude" --prompt "Task" --stream-format "jsonl"
memex-cli run --backend "gemini" --prompt "Task" --stream-format "jsonl"
# Resume interrupted run
memex-cli resume --run-id <ID> --backend <backend> --prompt "Continue" --stream-format "jsonl"
# Replay events
memex-cli replay --events ./run.events.jsonl --format text
Programmatic Usage
from scripts.orchestrator import BackendOrchestrator
orch = BackendOrchestrator()
# Single run
result = orch.run_task("claude", "Explain REST APIs")
# Compare backends
comparison = orch.compare_backends(["codex", "claude"], "Write a sort function")
# Pipeline
pipeline_result = orch.run_pipeline([
("codex", "Generate code"),
("claude", "Review code"),
])
References
- Prompt Templates - Structured prompts for consistent cross-backend results
- Output Formats - Parsing guidance for JSONL events and outputs