bottensor-fleet 0.2.0

Graph-native multi-agent fleet for Python. BYO-key. Local-first.

bottensor-fleet

Graph-native multi-agent fleet for Python. BYO-key. Local-first. Ships with a UI.

Why

Most multi-agent frameworks are heavy and locked to one ecosystem. LangGraph is tied to LangChain. CrewAI is opinionated about roles. AutoGen is conversation-first. bottensor-fleet is a small, graph-native runtime that runs anywhere Python runs, lets you bring your own provider key, and ships with a real UI in the wheel.

Install

pip install 'bottensor-fleet[search]'
export ANTHROPIC_API_KEY=sk-ant-...

Extras: [search] adds web tools, [redis] adds Redis checkpointing, [memory] adds ReasoningBank + MaTTS (SQLite-vec store + MiniLM embedder), [all] gets everything.

30-second example

import asyncio
from fleet import Agent, Graph
from fleet.core.state import GraphState
from fleet.providers.client import FleetLLM

llm = FleetLLM("anthropic", "claude-sonnet-4-6")
researcher = Agent(name="researcher", llm=llm, tools=["web_search", "web_fetch"])

graph = (
    Graph("solo")
    .add_node("researcher", researcher.step)
    .set_entry("researcher")
    .set_exit("researcher")
    .compile()
)

state = asyncio.run(graph.run(GraphState(goal="What is ReasoningBank?")))
print(state.messages[-1].content)

UI

Export your provider key(s) in the same shell, then launch:

export ANTHROPIC_API_KEY=sk-ant-...   # and/or OPENAI_API_KEY
fleet ui

Opens a local dashboard at http://localhost:8765 with a node activity view, per-agent logs, and the Memory tab (browse / search / edit / export / import).

The UI never accepts API keys through forms — credentials are read from the environment of the fleet ui process. Restart fleet ui after changing an exported key.

CLI

Command	What it does
fleet new <name>	Scaffold a new graph
fleet run <graph.py>	Run a graph from a file
fleet replay <run_id>	Re-run a past graph from its saved source path
fleet examples [name]	List bundled examples or extract one to the current directory
fleet ui	Launch the local dashboard
fleet add-agent	Append an agent to an existing graph
fleet ls	List past runs
fleet --version	Print version

Design

• Graph-native: DAGs with conditional edges and bounded cycles, executed async with asyncio.gather for parallel fan-out.
• BYO-key: Provider abstraction via polyrt. Anthropic and OpenAI in the default install; MLX, Ollama, and others via polyrt extras.
• Checkpointed: Every run persists to SQLite (default) or Redis (opt-in via [redis] extra).
• Tools and skills: @tool decorator auto-derives JSON schemas from type hints. @skill for higher-level capabilities. Web search and fetch built in via the [search] extra.
• UI in the wheel: No separate Node install for users. The React + Vite frontend is bundled into the published wheel.

Memory & self-improving agents

Opt-in ReasoningBank (Ouyang et al., ICLR 2026) gives any agent a persistent, embedding-indexed memory of past trajectories. Retrieval prepends the top-k relevant memories before each run; an async writeback judges the trajectory and distills 1–3 generalizable lessons that integrate into the bank via merge / link / insert.

MaTTS (Memory-Aware Test-Time Scaling) runs the same task k times in parallel and contrast-distills higher-quality memories than any single rollout can produce — useful for bootstrapping a new scope or one-shot high-stakes tasks.

pip install 'bottensor-fleet[memory]'

from fleet import Agent, ReasoningBank
from fleet.providers.client import FleetLLM

llm  = FleetLLM("anthropic", "claude-sonnet-4-6")
bank = ReasoningBank(judge_llm=llm, induction_llm=llm, scope="research")

researcher = Agent(name="researcher", model="anthropic/claude-sonnet-4-6",
                   tools=["web_search"], memory_bank=bank, memory_k=5)

Two ready-to-run examples:

fleet examples learning_research_team    # planner + 2 researchers + writer, shared bank
fleet examples matts_solo                # single agent with MaTTS k=3

The dashboard's Memory tab (browse / search / edit / export / import) is wired to /api/memory*. See docs/memory.md for the full guide.

Comparison

	bottensor-fleet	LangGraph	CrewAI	AutoGen
Graph topology	✅ DAG + cycles	✅	❌ role-based	❌ conversation
Provider-agnostic	✅ via polyrt	⚠️ via LangChain	⚠️	⚠️
Ships with UI	✅	❌	❌	⚠️ Studio (separate)
Pip-install size	~150 KB wheel	heavy	medium	heavy
LangChain dependency	❌	✅ required	❌	❌

Roadmap

• v0.2 ✅ — ReasoningBank + parallel MaTTS shipped. See docs/memory.md.
• v0.3 — Docker sandbox for python_exec, MLX embedder, polyrt-hosted embedder, sequential MaTTS, distributed scheduler.
• v0.4 — Alternate vector backends (Redis / Postgres), cloud deploy templates.

Security

The python_exec tool is unsandboxed. Do not run untrusted graphs. A Docker sandbox is planned for v0.3.

License

Apache-2.0. © 2026 Rama Krishna Bachu.

Acknowledgements

Built on polyrt. ReasoningBank design (v0.2) follows Ouyang et al., ReasoningBank: Scaling Agent Self-Evolving with Reasoning Memory, ICLR 2026.