sophia

TypeScript AI platform with AI chat, Autonomous agents, Software developer agents, chatbots and more

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Sophia is an open-source TypeScript platform designed for autonomous AI agents and LLM based workflows. It aims to automate processes, review code, assist with refactorings, and support various integrations. The platform offers features like advanced autonomous agents, reasoning/planning inspired by Google's Self-Discover paper, memory and function call history, adaptive iterative planning, and more. Sophia supports multiple LLMs/services, CLI and web interface, human-in-the-loop interactions, flexible deployment options, observability with OpenTelemetry tracing, and specific agents for code editing, software engineering, and code review. It provides a flexible platform for the TypeScript community to expand and support various use cases and integrations.

README:

The open TypeScript platform for autonomous AI agents and LLM based workflows
The Ancient Greek word sophía (σοφία) variously translates to "clever, skillful, intelligent, wise"

Documentation site

Features | UI Examples | Code examples | Contributing

Sophia is a full-featured platform for developing and running agents, LLM based workflows and chatbots.

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

Key features

Advanced Autonomous agents
Software developer agents
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
Multi-agent extend-reasoning implementations of the LLM interface
Configurable Human-in-the-loop settings
Functional callable tools/integrations (Filesystem, Jira, Slack, Perplexity, Google Cloud, Gitlab, GitHub etc)
CLI and Web UI interface
Run locally or deployed on the cloud with multi-user/SSO
OpenTelemetry based observability
Leverages the extensive Python AI ecosystem through executing Python scripts/packages

Autonomous agents

Reasoning/planning inspired from Google's Self-Discover and other papers
Memory and function call history for complex workflows
Iterative planning with hierarchical task decomposition
Sandboxed execution of generated code for multi-step function calling and logic
LLM function schemas auto-generated from source code
Human-in-the-loop for budget control, agent initiated questions and error handling

More details at the Autonomous agent docs

Software developer agents

Code Editing Agent for local repositories
- Auto-detection of project initialization, compile, test and lint
- Task file selection agent selects the relevant files
- Design agent creates the implementation plan.
- Code editing loop with compile, lint, test, fix (editing delegates to Aider)
  - Compile error analyser can search online, add additional files and packages
- Final review of the changes with an additional code editing loop if required.
Software Engineer Agent (For ticket to Pull Request workflow):
- Find the appropriate repository from GitLab/GitHub
- Clone and create branch
- Call the Code Editing Agent
- Create merge request
Code Review agent:
- Configurable code review guidelines
- Posts comments on GitLab merge requests at the appropriate line with suggested changes
Repository ad hoc query agent
Codebase awareness - optional index creation used by the task file selection agent

More details at the Software developer agents docs.

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

Sophia vs LangChain

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

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

Let's compare the LangChain document example for Multiple Chains to the equivalent Sophia 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);

Sophia

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 Sophia 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

For Tasks:

Click tags to check more tools for each tasks

automate processes review code assist with refactorings integrate with jira deploy and scale applications

For Jobs:

ai software engineer devops engineer platform engineer software developer machine learning engineer

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