Borrowing it
Nothing to install: this file belongs to managedcode/PrompterOne. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/managedcode/PrompterOne/main/.codex/skills/dotnet-microsoft-agent-framework/references/official-docs/tutorials/agents/memory.mdgit clone --depth 1 https://github.com/managedcode/PrompterOneWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/managedcode/prompterone/memory)<a href="https://agentmods.dev/agents/managedcode/prompterone/memory"><img src="https://agentmods.dev/badge/agents/managedcode/prompterone/memory/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/managedcode/prompterone/memory"><img src="https://agentmods.dev/badge/agents/managedcode/prompterone/memory.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00013 | $0.03484 |
| Opus 5.5 | $0.00005 | $0.01394 |
| Sonnet 5.5 | $0.00003 | $0.00697 |
| Haiku 4.5 | $0.00001 | $0.00348 |
Grade A, and why
memory scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
1 near-identical copy found in the catalogue:
- memory — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 386 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Adding Memory to an Agent
::: zone pivot="programming-language-csharp"
This tutorial shows how to add memory to an agent by implementing an AIContextProvider and attaching it to the agent.
[!IMPORTANT] Not all agent types support
AIContextProvider. This step uses aChatClientAgent, which does supportAIContextProvider.
Prerequisites
For prerequisites and installing NuGet packages, see the Create and run a simple agent step in this tutorial.
Create an AIContextProvider
AIContextProvider is an abstract class that you can inherit from, and which can be associated with the AgentThread for a ChatClientAgent.
It allows you to:
- Run custom logic before and after the agent invokes the underlying inference service.
- Provide additional context to the agent before it invokes the underlying inference service.
- Inspect all messages provided to and produced by the agent.
Pre and post invocation events
The AIContextProvider class has two methods that you can override to run custom logic before and after the agent invokes the underlying inference service:
InvokingAsync- called before the agent invokes the underlying inference service. You can provide additional context to the agent by returning anAIContextobject. This context will be merged with the agent's existing context before invoking the underlying service. It is possible to provide instructions, tools, and messages to add to the request.InvokedAsync- called after the agent has received a response from the underlying inference service. You can inspect the request and response messages, and update the state of the context provider.
Serialization
AIContextProvider instances are created and attached to an AgentThread when the thread is created, and when a thread is resumed from a serialized state.
The AIContextProvider instance might have its own state that needs to be persisted between invocations of the agent. For example, a memory component that remembers information about the user might have memories as part of its state.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 386 lines · 13 tokens per session scan A 0da9bfaceac0
memory is an agent published in the GitHub repository managedcode/PrompterOne (45 stars, last pushed 5mo ago), licensed MIT. It adds 13 tokens to every session and 3,484 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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passport_keeper_agent
Custodian of coursepassport.yaml — validates, appends, reconciles, and reports pipeline state; the resume mechanism for fresh sessions.
Scribe
Maintains session logs, records decisions, and preserves project memory for the Squad team.
wpf-performance-optimizer
WPF rendering and performance optimization specialist. Implements DrawingContext, DrawingVisual, VirtualizingStackPanel, Freezable patterns, memory optimization.
koshi-orchestrator
Use when the request spans multiple Koshi pillars (retrieval + memory, index-then-pack, score-then-tune), or the right specialist persona is not obvious. Generalist with access to all 24 Koshi MCP tools. Defers to single-pillar specialists (koshi-librarian, koshi-memory-keeper, koshi-context-packer…