The TypeScript-first AI platform for developers

TypedAI is a full-featured platform for developing and running autonomous agents, LLM based workflows, Slack chatbots, AI chat and more.

Included are capable software engineering agents, which have assisted building the platform.

High level features

• Advanced Autonomous agents
  • Faster/cheaper actions by generated function calling code (with sandboxed execution)
  • Complex tasks supported with memory, function call history, live files, file store etc.
  • Cost management with configurable Human-in-the-loop settings and cost tracking
  • Persistent state management. Restart from completion/error/human-in-loop
• Software developer agents
  • Local repository editing
  • Ticket-to-pull request workflow
  • Repository indexing and ad-hoc query agents
  • Leverages Aider for diff editing
• Pull request code review agent
• AI chat interface
• Slack chatbot
• Supports many LLM services - OpenAI, Anthropic (native & Vertex), Gemini, Groq, Fireworks, Together.ai, DeepSeek, Ollama, Cerebras, X.ai and more.
• Simple LLM interface wrapping the Vercel ai package to add tracing and cost tracking.
• Multi-agent extend-reasoning implementations of the LLM interface
• Functional callable tools (Filesystem, Jira, Slack, Perplexity, Google Cloud, Gitlab, GitHub etc)
• OpenTelemetry based observability
• Leverages the extensive Python AI ecosystem through executing Python scripts/packages

Flexible run/deploy options

• Run from the repository or the provided Dockerfile in single user mode.
• CLI interface
• Web interface
• Scale-to-zero deployment on Firestore & Cloud Run
• Multi-user SSO enterprise deployment (with Google Cloud IAP)
• Terraform, infra scripts and more authentication options coming soon.

UI Examples

List agents

New Agent

Agent error handling

Agent LLM calls

Sample trace (Google Cloud)

Human in the loop notification

Code review configuration

AI Chat

User profile

Default values can also be set from environment variables.

Code Examples

TypedAI vs LangChain

TypedAI doesn't use LangChain, for many reasons that you can read online

The scope of the TypedAI platform covers functionality found in LangChain and LangSmith.

Let's compare the LangChain document example for Multiple Chains to the equivalent TypedAI implementation.

LangChain

import { PromptTemplate } from "@langchain/core/prompts";
import { RunnableSequence } from "@langchain/core/runnables";
import { StringOutputParser } from "@langchain/core/output_parsers";
import { ChatAnthropic } from "@langchain/anthropic";

const prompt1 = PromptTemplate.fromTemplate(
  `What is the city {person} is from? Only respond with the name of the city.`
);
const prompt2 = PromptTemplate.fromTemplate(
  `What country is the city {city} in? Respond in {language}.`
);

const model = new ChatAnthropic({});

const chain = prompt1.pipe(model).pipe(new StringOutputParser());

const combinedChain = RunnableSequence.from([
  {
    city: chain,
    language: (input) => input.language,
  },
  prompt2,
  model,
  new StringOutputParser(),
]);

const result = await combinedChain.invoke({
  person: "Obama",
  language: "German",
});

console.log(result);

TypedAI

import { runAgentWorkflow } from '#agent/agentWorkflowRunner';
import { anthropicLLMs } from '#llms/anthropic'

const cityFromPerson = (person: string) => `What is the city ${person} is from? Only respond with the name of the city.`;
const countryFromCity = (city: string, language: string) => `What country is the city ${city} in? Respond in ${language}.`;

runAgentWorkflow({ llms: anthropicLLMs() }, async () => {
  const city = await llms().easy.generateText(cityFromPerson('Obama'));
  const country = await llms().easy.generateText(countryFromCity(city, 'German'));

  console.log(country);
});

The TypedAI code also has the advantage of static typing with the prompt arguments, enabling you to refactor with ease. Using simple control flow allows easy debugging with breakpoints/logging.

To run a fully autonomous agent:

startAgent({
  agentName: 'Create ollama',
  initialPrompt: 'Research how to use ollama using node.js and create a new implementation under the llm folder. Look at a couple of the other files in that folder for the style which must be followed',
  functions: [FileSystem, Perplexity, CodeEditinAgent],
  llms,
});

Automated LLM function schemas

LLM function calling schema are automatically generated by having the @func decorator on class methods, avoiding the definition duplication using zod or JSON.

@funcClass(__filename)
export class Jira {
    instance: AxiosInstance | undefined;

    /**
     * Gets the description of a JIRA issue
     * @param {string} issueId - the issue id (e.g. XYZ-123)
     * @returns {Promise<string>} the issue description
     */
    @func()
    async getJiraDescription(issueId: string): Promise<string> {
        if (!issueId) throw new Error('issueId is required');
        const response = await this.axios().get(`issue/${issueId}`);
        return response.data.fields.description;
    }
}

Contributing

We warmly welcome contributions to the project through issues, pull requests or discussions