Borrowing it
Nothing to install: this file belongs to managedcode/dotPilot. 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/dotPilot/main/.codex/skills/dotnet-microsoft-agent-framework/references/official-docs/user-guide/agents/agent-memory.mdgit clone --depth 1 https://github.com/managedcode/dotPilotWrote 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/dotpilot/agent-memory)<a href="https://agentmods.dev/agents/managedcode/dotpilot/agent-memory"><img src="https://agentmods.dev/badge/agents/managedcode/dotpilot/agent-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/dotpilot/agent-memory"><img src="https://agentmods.dev/badge/agents/managedcode/dotpilot/agent-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 | $0.00011 | $0.03742 |
| Opus 5.5 | $0.00004 | $0.01497 |
| Sonnet 5.5 | $0.00002 | $0.00748 |
| Haiku 4.5 | $0.00001 | $0.00374 |
Grade A, and why
agent-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.
This is a copy
100% identical to agent-memory — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 366 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Chat History and Memory
Agent chat history and memory are crucial capabilities that allow agents to maintain context across conversations, remember user preferences, and provide personalized experiences. The Agent Framework provides multiple features to suit different use cases, from simple in-memory chat message storage to persistent databases and specialized memory services.
::: zone pivot="programming-language-csharp"
Chat History
Various chat history storage options are supported by Agent Framework. The available options vary by agent type and the underlying service(s) used to build the agent.
The two main supported scenarios are:
- In-memory storage: Agent is built on a service that doesn't support in-service storage of chat history (for example, OpenAI Chat Completion). By default, Agent Framework stores the full chat history in-memory in the
AgentThreadobject, but developers can provide a customChatMessageStoreimplementation to store chat history in a third-party store if required. - In-service storage: Agent is built on a service that requires in-service storage of chat history (for example, Azure AI Foundry Persistent Agents). Agent Framework stores the ID of the remote chat history in the
AgentThreadobject, and no other chat history storage options are supported.
In-memory chat history storage
When using a service that doesn't support in-service storage of chat history, Agent Framework defaults to storing chat history in-memory in the AgentThread object. In this case, the full chat history that's stored in the thread object, plus any new messages, will be provided to the underlying service on each agent run. This design allows for a natural conversational experience with the agent. The caller only provides the new user message, and the agent only returns new answers. But the agent has access to the full conversation history and will use it when generating its response.
When using OpenAI Chat Completion as the underlying service for agents, the following code results in the thread object containing the chat history from the agent run.
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 · 366 lines · 11 tokens per session scan A 1ab54e7d9e4a
agent-memory is an agent published in the GitHub repository managedcode/dotPilot (25 stars, last pushed 6mo ago), licensed MIT. It adds 11 tokens to every session and 3,742 once invoked, about $0.0000 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to agent-memory, differing in 0 lines, and is treated as a copy.
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