Python-first multi-provider agent — minimal 1K-line library, MCP + LangChain tools.
Recent activity
Version cuts and proof, newest first — the living track record.
Spec sheet
The benchmark fields — designed for comparison across teams.
- Topology
- Solo + Tools
- Agent count
- 1
- Platform
- smolagents (Python)
- Runs on
- smolagents (Python)
- Industries
- software-deliveryresearchdata-extraction
- Task kinds
- code-executiontool-usemulti-agent-orchestration
- Trust tier
- Self-Reported
- Proof entries
- 1
Topology & roster
Solo-plus-tools (single agent) or hub-and-spoke (multi-agent). CodeAgent primary; can orchestrate ToolCallingAgents as subagents. Each subagent is called as a tool by the coordinator agent. Supports ManagedAgent wrapper for delegating to specialized sub-agents.
System wiring
Node details
Typical Solo + Tools layout — schematic, not verified wiring
HumanHuman operatorHuman gate
- Tool
- Human operator
- Autonomy
- Human-gated
- directs → Agent
BuilderAgent
- Tool
- Agent
- Autonomy
- Runs autonomously
- writes to → Tool A
- writes to → Tool B
- directs ← Human operator
ResourceTool A
- Tool
- Tool A
- Autonomy
- Runs autonomously
- writes to ← Agent
ResourceTool B
- Tool
- Tool B
- Autonomy
- Runs autonomously
- writes to ← Agent
How a typical Solo + Tools team handles a task
Typical Solo + Tools layout — schematic, not verified wiring
Task arrives
Human operator directs Agent.
Agent builds the work
Agent builds the work.
The artifact lands
The artifact lands in Tool A: Agent contributes via "writes to". The artifact lands in Tool B: Agent contributes via "writes to".
Human holds the last word
Human operator holds final approval.
Replicate a typical Solo + Tools setup
Typical Solo + Tools layout — schematic, not verified wiring
Ingredients
- HumanHuman operator
- BuilderAgent
- ResourceTool A
- ResourceTool B
Setup order
- 1.Provision the substrate: Tool A and Tool B.
- 2.Wire Agent: it receives "directs" from Human operator.
- 3.Declare the human gate: Human operator holds final approval.
Performance metrics
Windowed metrics with provenance. [unknown] means it was not tracked — an honest hole beats an invented figure.
~1000 lines: "The logic for agents fits in ~thousand lines of code." Source: huggingface.co/docs/smolagents [evidence_linked]
No standardized benchmark figures stated on HuggingFace smolagents docs at time of indexing.
Token economics
Cost transparency is part of the honesty architecture. [unknown] means it was not tracked — not that it is zero.
Blueprint
Operational DNA — why it works, how it was built, and how it is overseen. Not files for sale; knowledge of the design.
Python-as-actions reduces the translation overhead between LM output and execution. Minimal library size (~1K lines) keeps the framework auditable and hackable. Broad provider support avoids vendor lock-in. MCP compatibility connects to an expanding ecosystem of tools without custom integration work.
Python package (pip install smolagents). Agents defined with a model (any provider) and a list of tools. CodeAgent produces Python blobs; ToolCallingAgent produces JSON tool calls. Multi-agent: pass agent instances wrapped in ManagedAgent to the tools list of a coordinator. Tutorial notebooks in the docs.
User controls model selection and tool access. Library is minimal by design — ~1K lines — to maximize auditability. Sandboxed code execution recommended for production deployments. Source: smolagents docs.
Proof (1)
The team's shared track record — tasks, incidents, lessons, milestones. Per-entry provenance tags are always visible.
- ArtifactJan 1, 2025evidence-linked
smolagents CodeAgent documented in Hugging Face docs
CodeAgent writes Python as actions rather than JSON tool calls. ~1000-line core library. Supports HuggingFace, OpenAI, Anthropic, LiteLLM, Transformers, Ollama. MCP and LangChain tool integration.
https://huggingface.co/docs/smolagents/en/index
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