Agent Framework for TypeScript MCP server
Commit 9. Nov. 2026Bietet eine TypeScript-Implementierung des Microsoft Agent Framework Programmiermodells zum Erstellen interoperabler KI-Agenten.
Auch bekannt als polymind-inc/agent-framework-js
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Was ist Agent Framework for TypeScript MCP?
Agent Framework for TypeScript ist ein MCP server. Bietet eine TypeScript-Implementierung des Microsoft Agent Framework Programmiermodells zum Erstellen interoperabler KI-Agenten.
Zusammenfassung generiert aus dem README des Projekts
Über Agent Framework for TypeScript MCP server
Mit dieser Implementierung des Microsoft Agent Framework Programmiermodells ist es möglich, interoperable KI-Agenten mit TypeScript zu bauen. Sie bietet Entwicklern die Möglichkeit, Agenten zu erstellen, die eine semantische Parität mit den offiziellen .NET-, Python- und Go-Versionen beibehalten. Durch die Verwendung derselben Namen, Wire-Formats und beobachtbaren Verhaltensweisen stellt dieses Framework sicher, dass Agenten über verschiedene Sprachumgebungen hinweg kommunizieren können, ohne Daten zu verlieren oder die Logik zu ändern.
Entwickler können diese Library in Node.js, Deno, Bun oder Browser-Umgebungen integrieren. Sie bietet spezialisierte Subpaths für unterschiedliche Anforderungen, wie zum Beispiel die OpenAI und Anthropic Chat-Clients oder den Model Context Protocol Client zur Nutzung von MCP server Tools. Das Core-Package enthält essenzielle Komponenten wie Agent, AgentSession und die tool Funktion. Da es Standard-Web-Patterns wie AbortSignal für Abbrüche und Middleware im Koa-Stil verwendet, fügt es sich natürlich in bestehende JavaScript-Ökosysteme ein.
Was du damit machen kannst
- 1Interoperable KI-Agenten erstellenEin Entwickler kann einen TypeScript-Agenten bauen, der dasselbe Wire-Format und dieselben Konzepte wie Python- oder .NET-Implementierungen nutzt.
- 2MCP server Tools integrierenDu kannst dieses Framework nutzen, um Tools von einem MCP server direkt in das Toolset deines Agenten einzubinden.
- 3Streaming und awaited Responses implementierenEin KI-Client kann auf Agent-Responses entweder als ein einzelnes awaited Ergebnis oder als asynchroner Stream von Updates reagieren.
Geschrieben aus dem README des Projekts. Jeder Use Case wurde gegen ein Zitat daraus geprüft.
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README
Auf GitHub ansehenAgent Framework for TypeScript
A TypeScript implementation of the Microsoft Agent Framework programming model, designed for semantic parity with the official .NET and Python and Go implementations.
This is an independent community implementation. It is not an official Microsoft product, and it is not affiliated with, endorsed by, or reviewed by Microsoft. See Trademarks.
The Agent Framework has first-party implementations for .NET, Python and Go. There is no
TypeScript one. This project fills that gap on the framework's own terms rather than inventing a
parallel programming model: the same concepts, the same names, the same wire format, and the same
observable behaviour — expressed in TypeScript idioms. The explicit goal is for this code to be in
a shape that could be adopted upstream, the way microsoft/agent-framework-go
sits alongside the .NET and Python implementations in microsoft/agent-framework.
Everything from the repository name to the package layout to the naming generation to the release
cadence is chosen with that in mind.
Contents
- Quickstart
- Packages
- Parity with the reference implementations
- Where the TypeScript API differs in shape
- Design principles
- Coverage
- Requirements
- Examples
- Known limitations
- Stability and versioning
- Contributing
Quickstart
npm install @polymind-inc/agent-framework zod
import { Agent, tool } from '@polymind-inc/agent-framework';
import { OpenAIChatClient } from '@polymind-inc/agent-framework/openai';
import { z } from 'zod';
const weather = tool({
name: 'get_weather',
description: 'Get the current weather for a location',
parameters: z.object({ location: z.string() }),
execute: async ({ location }) => `${location} is sunny, 25°C`,
});
const agent = new Agent({
client: new OpenAIChatClient({ model: 'gpt-4o-mini' }),
instructions: 'You are a helpful weather assistant.',
tools: [weather],
});
const session = agent.createSession();
// Streaming.
for await (const update of agent.run('What is the weather in Tokyo?', { session })) {
process.stdout.write(update.text);
}
// Awaited — the same call, folded into a single response.
const res = await agent.run('And in Osaka?', { session });
console.log(res.text);
agent.run() returns one hybrid value that is both a PromiseLike<AgentResponse> and an
AsyncIterable<AgentResponseUpdate>. There is no second streaming method to learn, and the
non-streaming result is a fold of the stream rather than a separate code path — the same principle
the Go implementation uses.
Packages
One package to install: @polymind-inc/agent-framework. The root entry is the agent core;
every other capability lives under a subpath. The subpaths are plain static re-exports, so a
bundler tree-shakes whatever you do not import.
| Import | Provides |
|---|---|
@polymind-inc/agent-framework |
Agent, AgentSession, the Message/Content model, tool(), middleware, and the ChatClient seam |
@polymind-inc/agent-framework/node |
Node-only adapters, including directorySkillsSource and file-backed history |
@polymind-inc/agent-framework/openai |
OpenAI and Azure OpenAI chat client (Responses API) |
@polymind-inc/agent-framework/anthropic |
Anthropic chat client (Messages API) |
@polymind-inc/agent-framework/mcp |
Model Context Protocol client integration — MCP server tools as framework tools |
@polymind-inc/agent-framework/a2a |
Agent2Agent (A2A) protocol client — a remote A2A agent used as an agent |
@polymind-inc/agent-framework/foundry |
Microsoft Foundry chat client, with the Hosted Agent adapter under /foundry/hosting |
@polymind-inc/agent-framework/agentserver |
Foundry container protocol servers (Responses v2.0.0 and Invocations), with /agentserver/node and /agentserver/observability companions |
@polymind-inc/agent-framework/testing |
MockChatClient and friends for testing agents without a live provider |
Under the hood the framework is developed and published as a family of
@polymind-inc/agent-framework-* constituent packages, released in lockstep and pinned to exact
versions by the main package. They exist to keep the codebase modular and the upstream story
flexible; importing them directly works — every subpath above is a static re-export of one of
them — but the supported, documented surface is @polymind-inc/agent-framework. The names mirror
the Python distribution layout (agent-framework, agent-framework-core, agent-framework-openai,
…) so that a move to a first-party scope is a mechanical rename.
Parity with the reference implementations
Parity here means semantic parity, not signature parity. The rule this project follows is the one the Go implementation states for itself:
Respect language idioms — signatures follow the host language; semantics (default values, side effects, event emission, checkpointing, tool invocation, serialization) match the other implementations exactly. A differently shaped API is not, by itself, a parity violation.
Concretely, that commitment has three parts.
Naming follows the current (v1.13) generation. Pre-rename names from older articles
(ChatAgent, AgentThread, AgentRunResponse, ChatMessage) are not used.
| Concept | TypeScript | .NET | Python | Go |
|---|---|---|---|---|
| Agent | Agent |
AIAgent / ChatClientAgent |
Agent |
agent.Agent |
| Conversation | AgentSession |
AgentSession |
AgentSession |
agent.Session |
| Message | Message |
ChatMessage |
Message |
message.Message |
| Content | Content (discriminated union) |
AIContent hierarchy |
Content (single class) |
message.Content |
| Response | AgentResponse / AgentResponseUpdate |
same | same | agent.Response / ResponseUpdate |
| Chat client | ChatClient |
IChatClient |
SupportsChatGetResponse |
ProviderConfig.Run |
| Tool | Tool / FunctionTool / tool() |
AITool / AIFunction |
FunctionTool / @tool |
tool.Tool / functool |
| History | HistoryProvider |
ChatHistoryProvider |
HistoryProvider |
agent.HistoryProvider |
| Context | ContextProvider |
AIContextProvider |
ContextProvider |
agent.ContextProvider |
The wire format is interoperable. Serialized messages, content, sessions and checkpoints use
camelCase properties and the same type discriminator literals as the Python implementation.
Unknown content is preserved and round-tripped rather than dropped, so a payload produced by
another implementation survives a trip through this one unchanged.
Behaviour is verified against the reference source, not inferred. Where the implementations disagree, the order of authority is .NET, then Python, then Go. Defaults, folding rules, the function-calling loop, the approval flow and cancellation semantics were each read out of the reference code rather than guessed at.
Where the TypeScript API differs in shape
Deliberate divergences, and the reason for each:
| Topic | Reference implementations | This implementation | Why |
|---|---|---|---|
| Streaming | .NET splits RunAsync / RunStreamingAsync; Python uses stream= |
one run() returning a thenable and async-iterable |
Awaiting or iterating is the natural JS distinction |
| Content model | class hierarchy (.NET), single class + type (Python) |
discriminated union | Exhaustiveness checking is TypeScript's strongest guarantee |
| Tool schemas | reflection (.NET), Pydantic (Python) | Standard Schema v1 or raw JSON Schema | JS erases types at runtime, so schemas have to be values |
| Middleware | decorators + builder (.NET), mixin layers (Python) | koa-style (ctx, next) functions |
The established composition idiom in the JS ecosystem |
| Cancellation | CancellationToken, context.Context |
AbortSignal |
The web platform standard |
| Dependency injection | Microsoft.Extensions.DependencyInjection (.NET) |
none — explicit constructor injection | Matches the Python and Go decision |
Design principles
- TypeScript-first, ESM only. Types are part of the API. Discriminated unions, generics and overloads are used so that code which typechecks, works. There is no CommonJS build.
- A runtime-agnostic core. The core runs on Node.js, Deno, Bun, edge runtimes and in browsers. Node-specific concerns live in other packages.
- Wire compatibility. Serialization is interoperable with the other implementations, including preservation of content this implementation does not understand.
- Minimal dependencies. The core's only runtime dependency is
@opentelemetry/api— the API package alone, which is a no-op until an SDK is configured. Schema libraries are accepted through the Standard Schema interface, never depended on. - Non-streaming is a fold of streaming. There is one execution path, not two.
- Security is documented at the API. Prompt injection, the trust boundary around session restoration, and unapproved tool execution each carry an explicit "Security considerations" note in the TSDoc.
Coverage
| Area | Status |
|---|---|
| Agent, sessions, tools, middleware, streaming | Implemented |
| Function-calling loop, tool approval, continuation tokens | Implemented |
| OpenAI / Azure OpenAI, Anthropic and Microsoft Foundry providers | Implemented |
| Microsoft Foundry Hosted Agent hosting (Responses and Invocations protocols) | Implemented |
ContextProvider / HistoryProvider |
Implemented |
| OpenTelemetry GenAI instrumentation | Implemented |
| MCP client (tools) | Implemented |
| Agent Skills (progressive disclosure), including skills served over MCP and by a Foundry toolbox | Implemented |
| A2A client | Implemented |
| Workflows (graph/superstep engine, checkpointing, orchestrations) | Designed, not implemented — the next milestone |
| History compaction | Planned |
| Generic hosting (A2A / AG-UI / OpenAI-compatible surfaces) | Planned |
| DevUI, declarative YAML agents, durable execution, evaluation | Out of scope by design (code-first, as in the Go implementation) |
Requirements
- Node.js >= 24 (active LTS). ESM only — there is no CommonJS build.
- TypeScript: type resolution is verified with TypeScript 7 under
moduleResolution: bundlerandnodenext. Declarations are emitted withisolatedDeclarations.
Examples
Runnable examples live in examples/: getting started, multimodal input, agent
delegation, Foundry chat and hosting, middleware, context providers, Anthropic, OpenAI hosted
tools, MCP, and A2A. See examples/README.md for the commands and the
environment variables each one needs.
Known limitations
- Workflows are not implemented yet — the graph/superstep engine, checkpointing and the high-level orchestrations are the next milestone.
OpenAIChatClientsupports the Responses API only.api: 'chat_completions'is permanently out of scope; point a Chat Completions-only provider at a Responses-compatible endpoint, or implement theChatClientinterface directly.- The MCP integration covers tools, and resources for skill discovery — no sampling, elicitation or prompts.
- Agent Skills are served from code, from a
SKILL.mddocument you supply, over MCP, or from a local directory through the Node-onlydirectorySkillsSource. MCParchiveskills, resource templates and directskill://references are not implemented. - The A2A package is a client. Serving a framework agent over A2A, push notifications, task
listing and cancellation are not covered; use
@a2a-js/sdkdirectly for those. - A hosted container persists responses in the Foundry storage service by default (matching the reference implementations); the background replay log lives beside the sandbox state, so stream replay after a sandbox recycle fails closed rather than resuming.
- The core runs in browsers, but calling a model provider directly from a browser exposes your API key — run agents server-side.
- Because
agent.run()returns a hybrid thenable, type-aware lint rules such as@typescript-eslint/no-floating-promiseswill flag a deliberately unconsumed call. Consume the stream orvoidit explicitly. - Session envelope interoperability with the .NET and Python implementations is not finalized — their envelopes currently differ from each other.
Stability and versioning
The public API is stable but not frozen: surface changes are made deliberately, motivated by
alignment with the reference implementations. During 0.x, minor releases may contain breaking
changes; patch releases are fixes only. All packages are versioned and released in
lockstep, so a single CHANGELOG.md entry covers the set. Releases are published
from CI with npm provenance.
Contributing
Contributions are welcome — see CONTRIBUTING.md. Parity bugs, where this implementation behaves differently from .NET, Python or Go, are the highest-priority class of issue. Because the goal is upstream contribution, changes are held to the naming, wire-format and semantic rules described above; the contributing guide spells them out.
Security issues should be reported privately — see SECURITY.md.
Trademarks
"Microsoft", "Microsoft Agent Framework" and "Microsoft Foundry" are trademarks of Microsoft Corporation. They are used here descriptively, to identify the programming model and the services this project implements and interoperates with. This project is not affiliated with or endorsed by Microsoft.
License
Agent Framework for TypeScript MCP Fragen
Ist dies ein offizielles Microsoft-Produkt?
Nein, dies ist eine unabhängige Community-Implementierung und steht nicht in Verbindung mit oder wird von Microsoft unterstützt.
Unterstützt es Streaming-Responses?
Ja, die run() Methode gibt einen Wert zurück, der sowohl als Promise als auch als AsyncIterable für Streaming-Updates verwendet werden kann.
Kann ich MCP tools mit diesem Framework nutzen?
Ja, es gibt ein spezielles Package für die Model Context Protocol Client-Integration, mit dem MCP server Tools als Framework-Tools genutzt werden können.
In welchen Umgebungen kann dies laufen?
Der Kern ist runtime-agnostisch und kann auf Node.js, Deno, Bun, Edge Runtimes und in Browsern laufen.