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KODE SDK

#49 agent frameworkverified Sep 4, 2026

Event-driven TypeScript SDK for long-running agents: staged checkpoints, resume and fork, agent pools and cloud sandbox execution

Key differences

Event-driven TypeScript SDK for long-running agents: staged checkpoints, resume and fork, agent pools and cloud sandbox execution

  • Runs local and sandbox. Free and open source under MIT; you pay your model provider and, if you use one, your sandbox provider
  • Includes a Docker sandbox. Listed for 25 of 118 tools in this category.
  • Runs multiple agents. Listed for 97 of 118 tools in this category.
  • Keep in mind: Isolated execution is provided by the E2B and OpenSandbox integrations rather than a local Docker container.

“Agents collaborate through an AgentPool and Room messaging, the first group chat where nobody is quietly reading something else.”

Website Docs 401 starsCompare vs…Dispute a fact
Appeal a claim or request ownership transfer

What it is

KODE SDK is an open TypeScript runtime from shareAI-lab for building agent products in the shape of Claude Code or Manus. It splits agent output into three event channels (progress, control, monitor), checkpoints a run in seven stages with a safe fork point so a crashed or branched session can be resumed, and persists state to SQLite or PostgreSQL. Multiple agents collaborate through an AgentPool, Room messaging and task delegation, tools can come from MCP servers or a Skills system, and code can execute in an E2B or OpenSandbox remote sandbox.

Specification

Source verification

Row snapshot checked 2026-09-04. Individual checks below are recorded separately; automated release checks do not verify capabilities or pricing.

overview
Needs individual review
install
Needs individual review
capabilities
Needs individual review
protocols
Needs individual review
models
Needs individual review
license
Needs individual review

Architecture

Type
Agent framework
Runssrc ↗
local, sandbox
Platforms
macos, linux, windows
Context windowsrc ↗
not documented
Languages
typescript

Models

Backbonesrc ↗
Anthropic, OpenAI, Gemini
Bring your own model
Yes
OpenAI-compatible services such as DeepSeek, GLM, Qwen, Minimax and OpenRouter work through OpenAIProvider with a custom baseURL.
Local models
No

Protocols

MCP clientsrc ↗
Yes
MCP server
No
OpenAPI tools
No

Capabilities

Terminal commandssrc ↗
Yes
Multi-file edits
No
Git operations
No
Browser control
No
Sandboxed execution
Yes
Isolated execution is provided by the E2B and OpenSandbox integrations rather than a local Docker container.
Multi-agent
Yes
Headless / CI
No

Cost

Modelunsourced
byok
Starts at
$0/mo
Free tier
Yes
Bring your own key
Yes

Free and open source under MIT; you pay your model provider and, if you use one, your sandbox provider

Openness

Open sourcesrc ↗
Yes
License
MIT
First release
unknown
open-sourcetypescriptsdkcheckpointssandboxmcp

Los Agentes on KODE SDK

Who are they?
The ruling
El JuezThe judge

El Profesor and El Crítico agree the checkpoint design is the strongest thing here and split on what a resumed run is actually restoring.

Adopt with conditions
Reasoning and trade-offs · AI analysis

El Profesor scores the staged checkpointing highest, because a run with a designated fork point can be branched and compared rather than merely retried. El Crítico accepts the mechanism and names its limit: restoring the agent's state does not undo the commands it already ran, so a fork resumes into a world the checkpoint does not describe.

El Crítico wins on what a builder must handle, and El Profesor is overruled on completeness rather than on soundness. Adopt with conditions, the condition being that every tool you register is idempotent, because a resumed run will call some of them twice.

Agree with El Juez?
El AmigoThe friend

Pick KODE SDK if you are building an agent product that has to survive a crash; pick a smaller library if your runs finish inside a single request.

6.5
Reasoning and trade-offs · AI analysis

The deciding trait is that a session is a thing you can come back to. Restart the process, or branch a conversation that went wrong at a chosen point, and the work is still there rather than gone. If you have shipped anything where a user closed a tab halfway through and lost an hour of an agent's output, you know why that matters.

What you take on is a runtime with real opinions and a database behind it. Pick it if long runs are your product. Pick something thinner if your agent answers in thirty seconds.

reliability
7
usefulness
6
cost
7
longevity
6
Agree with El Amigo?
El CríticoThe critic

A checkpoint restores the agent, not the world. Files written and commands executed before the fork point stay done, so resuming replays side effects the snapshot never captured.

6.0
Reasoning and trade-offs · AI analysis

Resumption is where this gets subtle. Internal state can be rewound cleanly because it lives in a database the runtime owns. Everything the agent did outside that database cannot be, and the documentation describes the stages without describing compensation for external effects. Fork a session past a step that created a branch, sent a request or wrote a file, and the second run does it again.

What it does right is separate the streams. Progress, control and monitoring arrive on different channels, so a user interface does not have to guess which is which.

reliability
6
usefulness
6
cost
6
longevity
6
Agree with El Crítico?
El ProfesorThe professor

A run is checkpointed across seven declared stages with one designated safe fork point, which turns resumption from a best-effort retry into a specified operation.

7.3
Reasoning and trade-offs · AI analysis
  1. Naming the stages is what makes this analysable. A snapshot taken at an arbitrary moment is a guess about consistency; a snapshot taken at an enumerated boundary has a stated invariant, and a reader can reason about what is true at each one. 2. Designating a single fork point rather than allowing forks anywhere is the conservative and correct choice.

  2. Persistence is offered through two storage engines with different durability characteristics, and the documentation does not state which guarantees hold under each. No evaluation is published.

reliability
8
usefulness
7
cost
7
longevity
7
Agree with El Profesor?
La InversoraThe investor

398 stars, a permissive licence and a research-lab author: this is published infrastructure, which builds standing rather than revenue, and standing is not a business model.

6.3
Reasoning and trade-offs · AI analysis

Giving away the runtime that other people's products are built on is a recognised strategy, and it pays in exactly one currency: influence over how a category is shaped. There is no hosted tier here, no commercial edition and no support contract, so the return has to arrive somewhere other than this repository.

Moat: whatever mindshare the design earns before the model vendors ship their own. Likely acquirer: none for the code; the authors are the asset. Position: build on it, pin the version, and keep your persistence layer swappable.

reliability
6
usefulness
6
cost
8
longevity
5
Agree with La Inversora?
La JefaThe CTO

Nothing to license across sixty engineers, but execution happens in a third-party remote sandbox, which is a new vendor, a new questionnaire and a new place my code goes.

5.5
Reasoning and trade-offs · AI analysis

The commercial question is not this package, it is the service underneath it. Commands run in a hosted sandbox operated by somebody I have not contracted with, which means a security review, a data processing agreement and an answer to where source code is executed and for how long it is retained. That review costs more than the library saves.

There is no per-seat cost and nothing to install on desks, so the real expense is the team that builds and operates whatever gets shipped. Approved with conditions: one approved sandbox vendor, contracted, and no others.

reliability
5
usefulness
5
cost
6
longevity
6
Agree with La Jefa?
El HackerThe tinkerer

MIT, one npm install, and the OpenAI-compatible provider takes a custom base URL, so DeepSeek, GLM, Qwen, Minimax or a router all work without waiting for support.

7.5
Reasoning and trade-offs · AI analysis

A base URL parameter is worth more than a list of blessed vendors, because it means the set of things I can call is decided by me rather than by a release cycle. The documentation names five services that already work that way, which tells me somebody actually tested the path instead of leaving it as a theoretical escape hatch.

Tools arrive over the protocol or through a skills directory, so extending it does not mean patching the library. Permissive licence, readable TypeScript, and a fork that would compile.

reliability
8
usefulness
7
cost
8
longevity
7
Agree with El Hacker?