Easy Agent

easy-agent is the runtime layer underneath an agent product, not the product itself. It keeps orchestration, tool calling, persistence, approvals, federation, and evaluation explicit so teams can evolve their systems without hiding critical behavior behind opaque framework abstractions.

The latest published patch is 0.3.6.

What This Project Is

Most agent projects move quickly from "call a model" to "ship an application". The runtime layer in the middle then accumulates hidden assumptions around tools, memory, approvals, transport, and recovery.

easy-agent exists to keep that middle layer explicit:

• It separates runtime engineering from product logic.
• It keeps scheduling, orchestration, and protocol adaptation inspectable.
• It lets you mount tools, skills, MCP servers, and plugins without rewriting the core.
• It provides durable harnesses, checkpoints, and replay instead of relying on one oversized prompt.

Who It Is For

• Engineering teams building agent products that need a reusable runtime instead of a one-off demo.
• Developers who want direct control over tool calling, approvals, persistence, and resume behavior.
• Projects that need to evolve with provider APIs, MCP, and multi-agent patterns over time.

Tech Stack

• Runtime: Python 3.12, uv, AnyIO, Typer
• Model surface: OpenAI-compatible, Anthropic-style, and Gemini-style payload adaptation
• Persistence: SQLite + JSONL traces
• Integration surface: direct tools, command skills, Python hook skills, MCP, plugins
• Isolation surface: process, container, and microVM workbench executors

Features

• White-box runtime layers for scheduler, orchestrator, tool registry, storage, and protocol adapters.
• Support for single_agent, sub_agent, graph workflows, Agent Teams, and long-running harnesses.
• Session memory, checkpoints, replay, branchable resume, and approval-aware recovery.
• Guardrails, schema-aware tool validation, runtime event streaming, and persistent traces.
• Durable run inspection with structured trace-tree export for debugging complex agent flows.
• Offline mock provider plus setup, wizard, init, quickstart, scenario templates with tags/risk/dependencies, connector diagnostics, MCP doctor/test, task packs, workflow packs, workflow init/doctor/validate/explain/plan/run workflow.yml, config doctor, runs explain, advice-only runs triage / runs inspect / runs fix / runs bundle, run notes, traces open, experimental OTel JSON export, report latest, report trend, report costs, static dashboard, read-only local console, federation graph export, and a light Python AgentApp facade for zero-credential onboarding and faster failure triage.
• MCP-first browser automation through browser.enabled: true, which mounts Playwright MCP as a stdio MCP server, approval-gates sensitive browser actions by default, and exposes browser doctor/artifact inspection plus audit-focused browser seo, browser a11y, and browser links planning commands.
• A2A-style remote federation with durable task state and signed callback verification.
• Practical official_source_search skill support for source-prioritized search and fetched-page extraction.
• Public evaluation helpers for benchmark, BFCL, tau2 mock, BrowseComp/SimpleQA-style slices, live provider-compatibility matrices, and real-network regression tracking.

Human Loop, Replay, and MCP

easy-agent already ships the reliability controls that many projects leave as future work:

• Sensitive tools, swarm handoffs, and resumptions can enter a durable approval flow.
• Runs expose safe-point interrupts, checkpoint listing, replay, and forked resume.
• MCP integrations support explicit roots, root snapshots, notifications/roots/list_changed, resources or prompts catalog management, durable resource subscriptions, resource-template snapshots, prompt-detail invalidation, elicitation approval state, streamable_http, and persisted OAuth state.

Reference:

• Detailed usage: reference/en/usage-guide.md
• Detailed reinforcement plan: reference/en/next-reinforcement.md

A2A Remote Agent Federation

The federation layer publishes local agents, teams, and harnesses through a durable A2A-style surface:

• Well-known discovery, richer cards, push or poll delivery, retry, and resubscribe flows.
• OAuth/OIDC token acquisition and refresh for remote federation clients.
• JWKS/JWS validation for signed cards and signed callbacks.
• Stricter tenant/task authorization boundaries before federated state is revealed or mutated.

Operational detail and comparison notes are documented in reference/en/test-results.md.

Executor / Workbench Isolation

The executor/workbench layer gives long-lived tools and MCP subprocesses a reusable runtime boundary:

• Named executors for process, container, and microvm.
• Persistent workbench sessions, manifests, snapshots, and TTL cleanup.
• Capability reports for filesystem boundary, network policy, env handling, process shutdown, and snapshot restore behavior.
• Real-network regression coverage for warm-start latency and snapshot drift.

Detailed operational notes are documented in reference/en/usage-guide.md.

Architecture

The runtime is intentionally modular and observable:

• scheduler coordinates direct-agent and graph execution.
• orchestrator runs agent and team turns.
• harness manages initializer, worker, and evaluator loops.
• registry exposes tools, skills, MCP tools, and mounted plugins.
• storage persists runs, checkpoints, approvals, sessions, federation state, and workbench state.

flowchart LR
    User[User] --> CLI[Typer CLI]
    CLI --> Runtime[EasyAgentRuntime]
    Runtime --> Scheduler[GraphScheduler]
    Runtime --> Harness[HarnessRuntime]
    Scheduler --> Orchestrator[AgentOrchestrator]
    Harness --> Orchestrator
    Orchestrator --> Registry[ToolRegistry]
    Orchestrator --> Store[SQLiteRunStore]
    Orchestrator --> Client[ModelClient]
    Client --> Adapter[ProtocolAdapter]
    Adapter --> Provider[Provider API]

Long-Running Harness Design

Harnesses are first-class runtime objects rather than prompt conventions. Each harness defines:

• an initializer_agent
• a worker_target
• an evaluator_agent
• an explicit completion_contract

The worker loop persists artifacts and checkpoints so long-running tasks can continue, replan, or resume without discarding state.

Protocol and Tool Model

• Model protocols: OpenAI-compatible chat-completions or Responses API payload normalization, Anthropic-style payloads, and Gemini-style payload normalization.
• Tool calling: strict schema transport, nullable/optional modeling, validation-repair loops, provider-neutral tool-choice controls, and explicit enforced-versus-best-effort provider compatibility telemetry.
• Search and eval hardening: SerpApi /search.json, source-policy ordering for preferred official domains, grounded source ledgers, cache-first contents reuse, replay-backed contents fallback, raw official BFCL manifest normalization, and browsecomp_subset / simpleqa_subset / simple_evals_subset profile support.

Provider behavior details and structured-output notes live in reference/en/next-reinforcement.md.

Project Layout

src/
  agent_cli/
  agent_common/
  agent_config/
  agent_graph/
  agent_integrations/
  agent_protocols/
  agent_runtime/
skills/
configs/
tests/
reference/
  en/
  zh/

Quick Start

uv venv --python 3.12
uv sync --dev
uv run easy-agent setup --provider mock
uv run easy-agent wizard --scenario coding-agent --target-dir my-agent --provider mock
uv run easy-agent config explain -c easy-agent.yml
uv run easy-agent config doctor -c easy-agent.yml
uv run easy-agent quickstart --provider mock
uv run easy-agent new coding-agent
uv run easy-agent new data-agent
uv run easy-agent new ops-agent
uv run easy-agent new browser-agent
uv run easy-agent new web-monitor-agent
uv run easy-agent new seo-agent
uv run easy-agent new competitor-research-agent
uv run easy-agent new github-issue-agent
uv run easy-agent new website-audit-agent
uv run easy-agent new daily-report-agent
uv run easy-agent new api-regression-agent
uv run easy-agent new website-release-check-agent
uv run easy-agent new incident-review-agent
uv run easy-agent new weekly-report-agent
uv run easy-agent new github-pr-review-agent
uv run easy-agent new data-quality-agent
uv run easy-agent new meeting-notes-agent
uv run easy-agent new content-pipeline-agent
uv run easy-agent new customer-support-agent
uv run easy-agent connectors doctor -c easy-agent.yml
uv run easy-agent mcp doctor -c easy-agent.yml
uv run easy-agent mcp test <server> -c easy-agent.yml
uv run easy-agent template list --tag browser --format json
uv run easy-agent template show website-release-check-agent
uv run easy-agent template recommend --goal "website release SEO audit"
uv run easy-agent workflow list
uv run easy-agent workflow init browser-audit --output workflow.yml --context "Audit the home page"
uv run easy-agent workflow doctor workflow.yml -c easy-agent.yml
uv run easy-agent workflow validate workflow.yml -c easy-agent.yml --strict
uv run easy-agent workflow explain workflow.yml -c easy-agent.yml
uv run easy-agent workflow plan workflow.yml -c easy-agent.yml
uv run easy-agent workflow run workflow.yml -c easy-agent.yml --dry-run
uv run easy-agent workflow run browser-qa -c easy-agent.yml --dry-run --context "Check the home page"
uv run easy-agent task show repo-review
uv run easy-agent task show browser-qa
uv run easy-agent browser doctor -c easy-agent.yml
uv run easy-agent browser smoke https://example.com -c easy-agent.yml
uv run easy-agent browser snapshot https://example.com -c easy-agent.yml
uv run easy-agent browser audit https://example.com -c easy-agent.yml
uv run easy-agent browser seo https://example.com -c easy-agent.yml
uv run easy-agent browser a11y https://example.com -c easy-agent.yml
uv run easy-agent browser links https://example.com -c easy-agent.yml
uv run easy-agent browser artifacts -c easy-agent.yml
uv run easy-agent runs inspect <run_id> -c easy-agent.yml
uv run easy-agent runs inspect <run_id> -c easy-agent.yml --format html --output inspect.html
uv run easy-agent runs notes add <run_id> "handoff note" -c easy-agent.yml
uv run easy-agent runs notes list <run_id> -c easy-agent.yml
uv run easy-agent runs triage <run_id> -c easy-agent.yml
uv run easy-agent runs bundle <run_id> -c easy-agent.yml --output run-bundle
uv run easy-agent report latest -c easy-agent.yml
uv run easy-agent report latest -c easy-agent.yml --html --output report.html
uv run easy-agent report trend --history reports --html --output trend.html
uv run easy-agent report costs -c easy-agent.yml --html --output costs.html
uv run easy-agent federation graph -c easy-agent.yml --format html --output federation.html
uv run easy-agent dashboard -c easy-agent.yml --output dashboard.html
uv run easy-agent console -c easy-agent.yml --dry-run
uv run easy-agent init --provider mock
uv run easy-agent --help
uv run easy-agent doctor -c easy-agent.yml

Detailed setup, local credentials, CLI commands, and examples:

• reference/en/usage-guide.md

What a Harness Run Produces

A harness run persists durable artifacts under the configured artifact directory and durable session storage, including:

• bootstrap and progress markdown
• feature snapshots
• checkpoints and replay state
• workbench session metadata

Artifact details are documented in reference/en/usage-guide.md.

Verification

The latest published patch is 0.3.6. The retained benchmark and headline public-eval score snapshot is still the April 14, 2026 release baseline, while the April 30, 2026 release verification revalidated ruff, mypy, 233 unit tests, and 7 live integration tests without changing that retained score baseline. Methodology notes, public comparison rows, and detailed matrices live in reference/en/test-results.md.

Score Summary

Real Network Test Set Results

The real-network matrix is still summarized by score here, but the report now also carries scenario proof fields: command, expected artifact, pass criteria, and security assertions. Detailed durations, telemetry, warm-start budgets, snapshot-drift detail, and the full scenario matrix are tracked in reference/en/test-results.md.

Next Reinforcement

The next reinforcement track is documented in full at reference/en/next-reinforcement.md. The near-term focus remains:

• using the shipped structured trace tree, traces open, report latest, report trend, report costs, standalone report HTML, and expanded experimental --otel-json export as the main debugging surface while keeping the native trace tree as source of truth
• keeping zero-credential onboarding strict through guided setup and wizard preflight checks, config explanation, connector diagnostics, MCP doctor/test, workflow YAML doctor/validate/explain/plan/run, browser smoke/snapshot/audit/seo/a11y/links/report helpers, browser doctor/artifact inspection, task packs, static dashboard workflow/template recommendations, read-only local console, advice-only triage/inspect/fix/bundle packages, run notes, Python AgentApp workflow/browser/bundle/dashboard/cost helpers, and business templates for coding, research, data, ops, browser automation, web monitoring, SEO, website audits, website release checks, API regression, incident review, GitHub issue and PR triage, daily and weekly reporting, data quality, competitor research, meeting notes, content pipelines, support, sales, documents, QA, and release checks
• widening the shipped live provider-compatibility matrix beyond the required DeepSeek/OpenAI-compatible baseline, including optional Anthropic and Gemini evidence when credentials are present
• promoting the new official-source search plus BrowseComp or SimpleQA path into refreshed scored slices once official dataset exports and grader credentials are available
• expanding live /responses compatibility coverage where OpenAI-compatible providers actually expose it, while keeping single-tool enforcement explicitly labeled as best effort when providers do not honor it strictly
• deepening MCP notification parity, A2A federation graph/demo evidence, local skill/plugin catalog workflows, and read-only operator views while keeping local/private connectivity, approvals, and network boundaries owned by the runtime

Design References

• OpenAI function calling: https://developers.openai.com/api/docs/guides/function-calling
• OpenAI structured outputs: https://developers.openai.com/api/docs/guides/structured-outputs
• OpenAI web search tool: https://developers.openai.com/api/docs/guides/tools-web-search
• OpenAI Agents SDK and tracing: https://developers.openai.com/api/docs/libraries#install-the-agents-sdk
• OpenAI simple-evals: https://github.com/openai/simple-evals
• Playwright MCP: https://github.com/microsoft/playwright-mcp
• Anthropic tool use: https://platform.claude.com/docs/en/agents-and-tools/tool-use/overview
• Gemini function calling: https://ai.google.dev/gemini-api/docs/function-calling
• BFCL v4 web search: https://gorilla.cs.berkeley.edu/blogs/15_bfcl_v4_web_search.html
• Model Context Protocol: https://modelcontextprotocol.io/specification/2025-11-25
• Agent2Agent protocol: https://a2a-protocol.org/latest/specification/
• OpenTelemetry GenAI semantic conventions: https://opentelemetry.io/docs/specs/semconv/gen-ai/
• SerpApi Search API: https://serpapi.com/search-api
• FastAPI README style reference: https://github.com/fastapi/fastapi
• uv README style reference: https://github.com/astral-sh/uv

Acknowledgements

• Linux.do for community discussion and open knowledge sharing.
• for the real verification baseline and model endpoint.

License

MIT. See LICENSE.