Platform
Fits Into Your Stack. Not the Other Way Around.
Agentica integrates with your existing LLM providers, databases, identity systems, and deployment infrastructure. No rip-and-replace. Versioned APIs, real-time streaming, and Docker-native deployment mean your team is productive from day one.
Bring Your Own Models — Or Use Ours
Agentica’s LLM abstraction layer means you are never locked into a single provider. Switch models with a configuration change, not a code rewrite. The platform maintains a registry of pre-initialized models with automatic failover.
OpenAI
GPT-5 family (gpt-5, gpt-5-mini, gpt-5-nano), GPT-4o, GPT-4o-mini. Production-validated with automatic failover.
LangChain-Compatible
Any model accessible via LangChain’s BaseChatModel interface — Anthropic Claude, Google Gemini, Meta Llama, Mistral, Cohere. Pluggable via LLMRegistry.
Self-Hosted
Any OpenAI API-compatible endpoint (vLLM, Ollama, TGI, LocalAI). Supported via custom base URL configuration.
Nebius AI
Llama-based models via langchain-nebius. Notebook-validated across all 17 architecture patterns.
Persistent, Scalable, Yours
PostgreSQL with pgvector
- • Conversation checkpointing — full agent state persisted per session
- • User and session management — SQLModel ORM with typed entities
- • Long-term memory — pgvector enables semantic search via mem0ai
- • Connection pooling — async pools with health-aware recycling
Additional Storage Integrations
- • Neo4j — graph database for entity-relationship reasoning
- • FAISS — in-memory vector store for high-throughput similarity search
- • Supabase — managed PostgreSQL with real-time subscriptions
- • S3-compatible — document and file storage for ingestion pipelines
Connect Your Way
RESTful API
Versioned API endpoints under /api/v1/ with OpenAPI/Swagger documentation auto-generated by FastAPI. JSON request/response format with Pydantic-validated request bodies and structured error responses. JWT Bearer authentication with user and session scopes.
Real-Time Streaming (SSE)
Character-by-character streaming for responsive conversational interfaces. SSE is natively supported by browsers, requires no WebSocket infrastructure, and works through standard HTTP proxies and load balancers. Graceful error handling for mid-stream failures.
Event-Driven Integration
Integrate Agentica into event-driven architectures. Langfuse trace callbacks fire on every agent execution. Prometheus metrics endpoint for pull-based monitoring. Health endpoint for load balancer integration. Extensible middleware stack for custom interceptors.
Deploy Where You Need It
Cloud Deployment
Production-ready Docker Compose stack with 5 services: application, PostgreSQL (pgvector), Prometheus, Grafana, and cAdvisor. Deploy to any container-capable cloud platform. Single docker-compose up deploys the entire stack. CI/CD via GitHub Actions.
On-Premise Deployment
Full platform runs on your infrastructure with no external dependencies required. Self-hosted LLM support via OpenAI-compatible APIs. No data leaves your network. All monitoring tools included. Non-root containers with validated entrypoint scripts.
Hybrid Configuration
Mix cloud-hosted AI models with on-premise data storage. The LLM Registry allows per-model endpoint configuration, so sensitive workloads use local models while general tasks leverage cloud-hosted frontier models. Shared PostgreSQL backend for unified state.
From Zero to Production in Weeks, Not Months
Discovery & Setup
Week 1
We assess your requirements, select the right architecture combination, and deploy the development environment. You get API access and documentation on day one.
Integration & Configuration
Weeks 2–3
Connect your LLM provider, database, and identity system. Configure rate limits, memory settings, and environment variables. Run your first agent conversations.
Testing & Hardening
Week 4
Load testing (stress-tested to 1,500 concurrent users, 98.4% success rate, 1.2s average latency), security review, compliance checklist, monitoring dashboard setup.
Production Launch & Tuning
Weeks 5–6
Go live with monitoring, alerting, and the evaluation framework active. Tune confidence thresholds, memory retrieval, and model selection based on real traffic.
Discovery & Setup
Week 1
We assess your requirements, select the right architecture combination, and deploy the development environment. You get API access and documentation on day one.
Integration & Configuration
Weeks 2–3
Connect your LLM provider, database, and identity system. Configure rate limits, memory settings, and environment variables. Run your first agent conversations.
Testing & Hardening
Week 4
Load testing (stress-tested to 1,500 concurrent users, 98.4% success rate, 1.2s average latency), security review, compliance checklist, monitoring dashboard setup.
Production Launch & Tuning
Weeks 5–6
Go live with monitoring, alerting, and the evaluation framework active. Tune confidence thresholds, memory retrieval, and model selection based on real traffic.
Already have a LangChain or LangGraph project? Migration is simpler than you think. Agentica uses the same StateGraph, ChatModel, and tool-binding interfaces your team already knows.
Built for Your Engineering Team
API-First Design
Full OpenAPI spec with interactive Swagger UI and ReDoc. Every endpoint documented, every request/response schema typed.
Prompts as Assets
System prompts are Markdown files with {placeholder} injection — edit prompts without deploying code. Version them in Git.
Modular Tool Registry
Add new tools by defining a function and adding it to the tools list. The agent automatically discovers and binds new capabilities.
Configuration, Not Code
LLM models, rate limits, database connections, memory settings — everything is environment-variable driven. No code changes for configuration updates.
Built-in Evaluation
LLM-as-a-Judge framework with 5 quality metrics, automated scoring, and Langfuse integration. Know if your changes improve or regress quality.
Standard Stack
Python 3.13, FastAPI, LangGraph, PostgreSQL, Docker. No proprietary runtimes or exotic dependencies. Your team already knows these tools.
POST /api/v1/chatbot/chat
Authorization: Bearer <session_token>
Content-Type: application/json
{
"messages": [
{
"role": "user",
"content": "Analyze Q4 revenue trends"
}
]
}
→ 200 OK
{
"messages": [
{"role": "user", ...},
{
"role": "assistant",
"content": "Based on your organization's Q4 data..."
}
]
}Standard REST. Standard JSON. Standard JWT auth. Nothing proprietary to learn.
Ready to Integrate?
Talk to our integration team about your stack, your timeline, and your requirements. We will build a deployment plan tailored to your infrastructure.