AI Agents
Local multi-agent orchestration with durable memory and review gates.
AI Agents turns scraping, stock analysis, scheduling, and small coding tasks into bounded local-agent workflows that can be routed, reviewed, logged, and remembered across sessions.
What it solves
Local LLMs are useful for bounded work, but they need a system around them: task boundaries, typed inputs and outputs, review rules, retries, logging, and shared memory.
AI Agents keeps Claude Code in the planner/reviewer role while a configured LM Studio model handles worker tasks through an OpenAI-compatible API. The point is not agent novelty; it is controlled delegation that stays inspectable.
Who this is for
- Developers testing local LLM task delegation
- People who want scheduled scraping or market reports
- Builders comparing structured memory and semantic recall
- Teams thinking about safe coding-agent review boundaries
What it does
AI Agents is a Python-based orchestration project for running a
small team of focused local agents. An
OrchestratorAgent starts shared services, schedules
recurring work, dispatches tasks, handles retries, and records
task outcomes.
A TaskRouter decides when coding work is safe to
delegate, and a CodeReviewer gate prevents
generated code from being written without review. Shared memory
combines SQLite records with ChromaDB semantic search so agents
can recall facts, preferences, task history, and previous
context.
Workflow covered
- Plan and route - classify the task, decide which agent should handle it, and keep unsafe work local.
- Execute and review - run scraping, market, scheduled, or coding work through typed agents with logs, retries, and reviewer gates.
- Remember and improve - store structured task outcomes and searchable context so later sessions can reuse what the system learned.
Technical highlights / stack
Why it matters
The useful part of agent systems is not pretending the model is autonomous. It is making delegation explicit enough that humans can trust the boundary, inspect the work, and improve the system after failures.
Challenges
- Drawing a hard boundary between work a local model can safely produce and work Claude should handle directly.
- Making delegated code generation useful without treating it as automatically trusted output.
- Keeping memory centralized so agents share context without reaching into database internals.
- Designing schedules, retries, and logs so long-running work fails clearly instead of disappearing.
Current scope
Works now
- On-demand scraping tasks
- Bounded coding delegation
- Scheduled stock analysis
- Orchestrator health checks
Core systems
- OrchestratorAgent
- TaskRouter
- CodeReviewer
- Shared memory service
Still improving
- Delegation policy tuning
- PostgreSQL production path
- More durable job dashboards
- Better task outcome summaries
Related work
What to do next
Review the source if you are interested in local model orchestration, typed task boundaries, memory layers, and human review gates around generated work.