Memory & Context

Help Your AI Agents Remember What Matters

Memory, not baggage.

Give AI applications and agents the memory and context they need to deliver more personalized, accurate, and consistent experiences.

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No credit card required. Free for small teams. Set up agent memory support in minutes.

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Memory Without Control Creates Risk

AI Apps and agents fail when they remember the wrong thing or retrieve the wrong source. Keep context relevant, trusted and safe to reuse.

Store What Matters. Preserve Continuity

Store durable facts, preferences, constraints, and decisions as structured memory across sessions
Extract important details from conversations automatically
Search extracted memories by user, agent or use case
Add persistent memory with SDKs and APIs

Recall Relevant Context. Ground Every Answer.

Combine semantic recall with RAG retrieval to bring the right memories, sources, and policies into the AI response
Find relevant memory automatically
Return sources, attribution and confidence with the answer
Trace which memory and RAG context shaped the response

Keep Memory Current. Reduce Context Risk.

Remove bad context before it creates drift
Inspect conversations and extracted memories in one workspace
Filter memory by agent, user, status, category or search query
Mark memories active, outdated or remove them as context changes

Capabilities

Memory and Context You Can Inspect and Control

Persistent Memory

Store useful facts, preferences and prior decisions across sessions.

Memory Lifecycle

Mark memories active, outdated or removed as context changes.

Developer Integrations

Add memory to agent workflows with SDKs, APIs and scoped access.

Semantic Recall

Retrieve relevant memory with source attribution and relevance scoring.

Memory Workspace

Browse conversations, extracted memories, processing status and usage.

RAG and Context Grounding

Combine persistent memory with trusted sources and policies.

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AI Improvement Loop

Improve AI Workflows Through Better Context

Design with Control. Observe what happened. Evaluate the outcome. Improve continuously.

Trace Your First AI Agent in Minutes

Use the agent skills or instrument manually to start capturing traces.

Get your API key

See the Docs

Copy and paste this prompt:

Install https://github.com/telerik/observability-skills

Works with Cursor, Claude Code, Copilot, Codex and more.

Set up API Key

Your agent will ask for your API key. Get your API Key here.

Learn & Explore More

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Pricing

Simple, predictable pricing. Start free, scale as you grow. No surprises, no hidden fees.

Free ForeverFor developers testing early agent prototypes

^$ 0

per month

Includes 10,000 units

Retention: 7 days

Agent Trace Explorer
LLM request and prompt logging
Basic cost and token visibility
Basic LLM-as-a-Judge evaluations
.NET, Python and TypeScript SDKs
Integrations with popular AI frameworks and model providers

StarterFor small teams deploying their first live AI agents

^$ 29

per month

Includes 200,000 units

Retention: 30 days

$8 USD per additional 100K units

Everything in Free, plus:
Full Cost Attribution (per-agent, per-model, total costs)
Real-Time & Historical LLM-as-a-Judge Evaluations
Evaluation Datasets & Experiments
Anomaly Detection & Alerting

ProFor teams running production AI agents at scale

^$ 299

per month

Includes 1,000,000 units

Retention: 60 days

$8 USD per additional 100K units

Everything in Starter, plus:
SSO Included

EnterpriseFor organizations scaling governed AI applications

Starting at

^$ 3,000

per month

Custom trace volume

Retention: Infinite

Request demo

Everything in Pro, plus:
BYOS data residency options for teams with strict data control requirements
Enterprise governance with audit logs, access controls and SLA commitments
Custom volume pricing for high-throughput AI applications and AI labs

Explore More in Progress Agent Engineering

Observability

Observe and debug production behavior

Trace prompts, model calls, retrieval, tools, workflow steps, latency, tokens and cost so teams can understand what agents actually did.

Evaluations

Design, Test and Improve Agent Behavior

Use prompts, playgrounds, datasets, experiments and LLM-as-a-judge evaluations to compare behavior before and after release, track regressions and optimize for quality, safety and cost.

Governance

Keep Agents Inside Approved Boundaries

Use model gateways, access controls, auditability, human review, alerts, notifications and policy controls to manage cost, quality, risk and security.

Agent Engineering Overview

See the Full Control Layer for Production AI

Explore how Progress Agent Engineering brings observability, evaluations, governance and memory/context together to help teams improve quality, cost and trust in production AI.

Learn More

Works with Your Stack

The Progress Agent Engineering Platform integrates with the tools, frameworks and platforms teams already use to build and run AI agents.

Languages & SDKs: .NET (C#), Python, JavaScript/TypeScript
Agent Frameworks: Semantic Kernel, LangChain, LlamaIndex, AutoGen, Microsoft Agent Framework
LLM Providers: Azure OpenAI, OpenAI, Anthropic
AI Tooling: Microsoft.Extensions.AI, Microsoft AI Foundry, Progress RAG
Enterprise SSO: Okta, Azure AD, SAML
Open-Source Models (OSS): Llama 2/3, Mistral, Mixtral, Falcon, Gemma, etc.

Frequently Asked Questions

What is AI agent memory?

AI agent memory is stored context an agent can reuse across requests or sessions. It can include useful facts, preferences, prior decisions and conversation-derived information that helps the agent answer with continuity instead of starting from a blank slate.
How is agent memory different from RAG?

RAG retrieves source content such as policies, documents, records or knowledge-base articles. Agent memory recalls user- or workflow-specific context such as preferences, prior decisions and conversation history. Production agents often need both: RAG for trusted source knowledge and memory for continuity.
How does Progress Agent Engineering recall memory?

Progress Memory lets developers store conversation exchanges, extract memory items and recall relevant memories using a natural-language query scoped to a user and agent. Recall results include memory text, category, metadata and a relevance score.
Can developers control what agents remember?

Yes. Developers can add memory directly, enable automatic memory behavior in supported integrations or expose memory tools so the agent can decide when to remember, recall or forget. Teams can also inspect and delete extracted memories in the platform workspace.
How do teams prevent stale memory from hurting answers?

Teams can use memory lifecycle controls such as active and outdated status, delete memory that should no longer shape answers and tune retrieval confidence so only sufficiently relevant memories are returned.

Can Progress delete a memory without deleting the original conversation?

Yes. The documented forget operation deletes a specific extracted memory by ID. The original conversation text is not affected.
What developer stacks does Progress Agent Engineering support?

Progress Agent Engineering supports.NET and Python SDK paths, plus integrations for Microsoft.Extensions.AI, Semantic Kernel, LangChain and Google ADK. Teams can choose direct programmatic control, automatic/middleware mode or tools/plugin mode depending on the agent workflow.
How do memory and evaluations work together?

Evaluation results can show when an answer was weak, hallucinated or poorly grounded. Memory and context evidence helps teams see whether the issue came from stale memory, missing memory, retrieved source quality, prompt design, tool logic or model choice.
Is agent memory only for chatbots?

No. Agent memory can support support agents, copilots, assistants, RAG applications, workflow agents and tool-using agents where prior context, user preferences or recurring decisions should carry forward across interactions.
What should an AI agent remember?

An agent should remember useful, durable context that improves future task completion, such as stable preferences, prior decisions, known constraints and relevant conversation-derived facts. It should not blindly store everything, especially sensitive details, stale facts or temporary context that should expire.

Give Your AI Better Context

Remember what matters, retrieve what's relevant. Keep context current.

Start Free No credit card required to start. 5 minute set up.

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