claude-sdk-agent-fleet 0.1.1

Deterministic generator that assembles a minimal Claude Agent SDK agent from a problem statement and a capability corpus, with an HTTP API and MCP pool server.

agent-fleet

Assembles a minimal Claude Agent SDK agent from a problem statement and a capability corpus, then runs and resumes it. The generation path is deterministic — no LLM and no network in it, so the same request and corpus produce a byte-identical agent. Running the agent is the part that talks to the SDK.

Repository layout

Python only. One PyPI package (claude-sdk-agent-fleet), three subpackages layered so each depends on the ones below it. pip install claude-sdk-agent-fleet gets the core engine and MCP pool server; claude-sdk-agent-fleet[api] (or [all]) adds the FastAPI service.

Path	What it is	Run or import	Install
src/agent_fleet/	core engine: pipeline, router, pool	imported	claude-sdk-agent-fleet
src/agent_fleet_api/	FastAPI service over the core	run	claude-sdk-agent-fleet[api]
src/agent_fleet_mcp/	MCP server exposing the pool as tools (pool-mcp)	run	claude-sdk-agent-fleet

agent_fleet_api ─┐
                 ├─imports──▶ agent_fleet ──imports──▶ capdisc
agent_fleet_mcp ─┘

The API front-end carries the web dependencies as an optional extra so the core engine carries none by default. Environment scanning lives in capdisc, a separate, public repo consumed as a pinned git dependency.

Pipeline

ProblemRequest ─▶ recall ─▶ select ─▶ compose ─▶ score ─▶ render
                  (source)  (budget)  (AgentSpec) (efficiency) (SDK program)

• recall — a CatalogSource ranks the corpus by lexical relevance (a pluggable Ranker) and trims to a limit.
• select — keep candidates above a relevance threshold (plus pinned), capped by tool/skill budgets.
• compose — map the selected refs into an AgentSpec with a templated system prompt.
• score — check the spec against tool/skill/prompt budgets (efficiency).
• render — emit a runnable Claude Agent SDK program.

Pool

AgentPool (SQLite) keys each pooled agent by a stable AgentKey and stores the AgentSpec and session id that built it, so a run can be retrieved, resumed against the same live SDK conversation, or found fuzzily. run_with_capture observes the live message stream to record the real, resumable session id of every agent a run involves — the top-level agent and each dispatched subagent. Runs, per-agent runs, and findings are persisted alongside the entry.

Develop

uv sync --extra api
uv run pytest
uv run ruff check
uv run mypy src
make coverage       # test coverage, printed to the terminal

Details: docs/OVERVIEW.md · docs/pipeline.md · docs/catalog-boundary.md