How It Compares

Checked on 2026-09-26, against each project’s own documentation and repository. Every cell below links to its source in Sources. These projects move fast, so a cell can go stale. If one is wrong, please open an issue and we will correct it. Yes means documented and built in. Partial means part of it, a separate package, or a paid product (the notes say which). No means the docs say or show it is not there. Not documented means we could not find it in the official docs, which is not the same as No.

The short version

What OmniCoreAgent does differently. Three things together, which no other framework here does by default:

Every action is checked before it runs. One policy covers each tool, command, network request, MCP server, delegation and background run: allow, ask a person, or deny, by name. It is on by default. Limits are checked before a call is made: a budget holds a model call’s price first and, when it runs out, the run waits for a person instead of failing. Pydantic AI and the Claude Agent SDK check cost after the response, so one call can overshoot.
A run survives a crash without silently redoing anything. It resumes from its checkpoint, even in another process, and a tool call that was interrupted is reported to the model, not run again (unless the tool is marked safe to repeat). LangGraph re-runs an unfinished task and asks you to make it idempotent.
Every step is on the record, locally, with no service to sign up for: what the model saw, what it did, what the policy decided and who approved, what it cost, and outcomes that arrive later, to evaluate and train on.

Sandboxes, human approval, MCP, sub-agents, scheduling and an HTTP server come with it too; most runtimes now have their own. Where the others are stronger:

Explicit workflow graphs. LangGraph is built around them, with per-step checkpoints and “time travel” to earlier state. CrewAI has Flows, and Pydantic AI has pydantic-graph. OmniCoreAgent has no graph API: you compose runs in Python, or give an agent sub-agents.
Sandbox breadth. The OpenAI Agents SDK supports local, Docker and seven hosted sandbox providers. OmniCoreAgent has five hosted providers, Docker and local. The Claude Agent SDK isolates commands at the operating system level with no container at all.
Durable-execution engines. Pydantic AI and the OpenAI Agents SDK integrate with Temporal, DBOS, Restate and others. OmniCoreAgent keeps its own durable run record instead.
Hosted tracing and evaluation. LangSmith, Logfire, OpenAI Traces and CrewAI AMP each come from the same vendor as the framework. OmniCoreAgent exports to OTLP, LangSmith, Opik and JSONL, but has no hosted product.
TypeScript. The OpenAI Agents SDK, LangGraph and the Claude Agent SDK have TypeScript versions. OmniCoreAgent is Python only.
Ecosystem size. LangChain’s integrations and community are far larger.

Capability by capability

	OmniCoreAgent 0.5	OpenAI Agents SDK	LangGraph	Pydantic AI	Claude Agent SDK	CrewAI	Agno
1. One policy that allows, asks or denies each action	Yes, on by default	Partial: per-tool approval and guardrails	Partial: per-tool approval (`langchain` middleware)	Partial: a guard you write (Harness package)	Yes: rules, modes, hooks	Partial: hooks you write	Partial: per-tool confirmation and `@approval`; guardrails check input
2. A person’s approval pauses the run; it resumes later, in another process	Yes (durable store)	Yes: `RunState`	Yes: `interrupt()` + checkpointer	Yes: deferred tools	Yes: `defer` hook (one tool call per turn)	Yes: Flows `@human_feedback`	Yes: `continue_run(run_id)` with the same database and session
3. Dollar budget enforced before a model call	Yes: holds the call’s price first; pauses for a person	Not documented (tokens and turns)	No in open source; LangSmith gateway (paid, beta)	Partial: `cost_limit` is checked after each response, so one call can overshoot	Partial: `max_budget_usd` stops after the call that crosses it	No: paid Cost Limit only emails	No: cost is recorded, not enforced
4. Built-in sandboxed command execution	Yes: Docker, E2B, Modal, Daytona, Vercel, your own, local	Yes: local, Docker, 7 hosted	Partial: Docker or Codex policy (`langchain`); providers via Deep Agents	Partial: Modal, Monty (Harness)	Yes: OS-level, opt-in	Partial: E2B and Daytona tools	Partial: Daytona, E2B, Superserve toolkits; local shell tools run on the host
5. Sandbox network off by default	Yes, and checked on E2B and Daytona	No: Docker keeps its default network	Partial: yes for the Docker policy; the default runs on the host	Not documented	Partial: sandbox opt-in; new hosts are asked about	No (E2B tool)	Not documented
6. A run survives a process crash and resumes	Yes (durable store)	Partial: save state yourself, or Temporal and others	Yes: checkpoints	Yes: Temporal, DBOS, Prefect, Restate…	Partial: resumes the conversation	Yes: `checkpoint=True`	Partial: AgentOS durable queue; a crashed run fails by default, a retry re-runs it whole
7. Every run recorded locally, no service needed	Yes, on by default	Partial: default exporter sends to OpenAI	Partial: checkpoint history; tracing needs LangSmith	Partial: opt-in	Yes: JSONL transcripts	Partial: opt-in log files	Yes: sessions, runs and traces in your own database
8. OpenTelemetry export	Yes (OTLP, LangSmith, Opik)	Not documented	Yes (via `langsmith`)	Yes	Yes (traces in beta)	Yes	Yes
9. MCP client, local and remote	Yes (+ OAuth)	Yes	Yes (`langchain`)	Yes	Yes	Yes	Yes: stdio and Streamable HTTP
10. Sub-agents / delegation	Yes	Yes: handoffs, agents as tools	Yes	Yes	Yes	Yes	Yes: Teams
11. Scheduled / background runs built in	Yes: cron, interval, durable task store	Not documented	Partial: LangSmith Deployment	Partial: via Prefect and others	Partial: session-scoped scheduling	Not documented	Yes: AgentOS scheduler, background runs, durable queue
12. HTTP server built in	Yes (OmniServe)	Not documented	Partial: dev server; production is LangSmith Deployment	Partial: dev web UI; adapters for your app	No	Partial: AG-UI package; paid hosted API	Yes: AgentOS (FastAPI)
13. Model providers	Many (LiteLLM)	Many (OpenAI default; adapters in beta)	Many	Many	Claude only, on several clouds	Many	Many
14. Workflow graphs	No: compose in Python	Partial: Python orchestration	Yes: the core of it	Yes: pydantic-graph	Partial: Workflow tool	Yes: Flows	Yes: Workflows
15. Hosted tracing / evaluation from the same vendor	No	Yes: OpenAI Traces	Yes: LangSmith	Yes: Logfire	Not documented	Yes: AMP	Yes: AgentOS control plane
16. Languages	Python	Python; TypeScript (separate)	Python, TypeScript	Python	Python, TypeScript	Python	Python
17. License	MIT	MIT	MIT (library)	MIT	Commercial terms (Python repo MIT)	MIT	Apache-2.0
18. After a crash, an interrupted tool call is reported, not re-run	Yes, unless the tool is marked idempotent	No: reconciling side effects is left to your app	No: an unfinished task may run again; make it idempotent	Partial: opt-in Harness `StepPersistence` marks it `unknown_after_crash`	Not documented	No: resume restarts from the last checkpoint; only completed tasks are skipped	Partial: the durable queue fails a crashed run by default; no per-call record

Sources

Read on 2026-09-26: only each project’s own docs and repository, no third-party comparisons. Row 18, the Pydantic AI budget cell and the Agno column were read on 2026-09-29. The method is in the repository, in engineering/architecture/comparison-plan.md. OmniCoreAgent: this documentation. Policy (1), Durable runs (2, 6), Budgets (3), Sandbox providers (4, 5), Observability (7, 8, 15), MCP (9), Sub-agents (10), Background agents (11), OmniServe (12), Models (13), Upgrading (14). OpenAI Agents SDK (openai-agents 0.22.3): guardrails, human in the loop (1, 2); usage (3); sandbox clients (4, 5); running agents (6, 12); tracing (7, 8, 11, 15); MCP (9); handoffs (10); models (13); multi-agent (14); repository (16, 17). LangGraph: human in the loop (1); interrupts (2); built-in middleware, LLM gateway spend policies (3, 4); execution policies (5); checkpointers (6, 7); OpenTelemetry (8); MCP (9); subgraphs (10); cron jobs (11); local server (12); models (13); graph API (14); observability (15); repository (16, 17). Pydantic AI: guardrails (Harness) (1); deferred tools (2); agents: usage limits (3); Modal sandbox, code mode (4, 5); durable execution (6); step persistence (7); Logfire and OpenTelemetry (8, 15); MCP client (9); multi-agent (10); Prefect (11); web UI (12); models (13); graphs (14); repository (16, 17). Claude Agent SDK: permissions (1); user input (2); cost tracking (3); sandboxing (4, 5); sessions (6, 7); observability (8, 15); MCP (9); subagents (10); scheduled tasks (11); hosting (12); quickstart (13); TypeScript reference (14); overview and license (16, 17). CrewAI: tool hooks (1); human feedback in Flows (2); AMP policies (3); code execution, E2B tool (4, 5); checkpointing (6); crews (7); tracing (8); MCP (9); collaboration (10); automation triggers (11); frontend (12); LLMs (13); Flows (14); AMP traces (15); repository (16, 17). Agno (agno 3.0.11, read 2026-09-29): human in the loop, guardrails (1, 2); metrics (3); E2B, Superserve, workspace (4, 5); durable queue (6, 18); tracing (7, 8); MCP (9); teams (10); scheduling, background execution (11); runtime (12); models (13); workflows (14); control plane (15); repository (16, 17). Row 18, the others: OmniCoreAgent: what is guaranteed; OpenAI Agents SDK: human in the loop; LangGraph: functional API; Pydantic AI: step persistence; Claude Agent SDK: sessions; CrewAI: checkpointing.

Get Started

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How It Works

Reference

HTTP API (OmniServe)

Releases

How It Compares

How It Compares

The short version

Capability by capability

Sources

​How It Compares

​The short version

​Capability by capability

​Sources

How It Compares

The short version

Capability by capability

Sources