Chat history records what an agent said and did; project memory is the small, retrievable set of facts and next steps that helps a later run continue the work. For a multi-session task, keep a concise handoff note linked to authoritative files, commits, or evidence—and retain the full transcript when its detail may be useful.
How is project memory different from chat history?
A conversation trace is a record of what happened. Durable memory is selected context that a later run can retrieve and use to guide its behavior. A transcript does not become useful memory simply because it exists: the agent or its harness must preserve the relevant lesson and load it again when needed. LangChain makes this distinction in its discussion of traces and memory: How To Give Your Agent Memory.
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Think of history as the evidence locker and the handoff note as the current project map. The map should point to primary artifacts rather than attempt to reproduce them. A commit, source file, issue, or research document remains the authority for details that may change.
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An active context has a capacity limit. OpenAI’s Realtime API reference describes configurable truncation behavior when a conversation exceeds the input limit; older messages may be removed according to the selected behavior. A long transcript therefore is not a guarantee that every earlier decision remains available to the model. See the Realtime API reference.
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Compaction is another way to preserve continuity: summarize a conversation nearing its context limit, then begin a new context with that summary. Anthropic’s Applied AI team defines it as “the practice of taking a conversation nearing the context window limit, summarizing its contents, and reinitiating a new context window with the summary.” This can keep a long interaction moving, but a summary may discard nuance or a detail whose importance only becomes clear later. Anthropic recommends tuning for recall before removing excess detail in its article on effective context engineering.
What should an AI agent remember between sessions?
Keep enough state to orient the next run and choose a safe next action, not a miniature transcript. For a coding or research project, a useful handoff note can cover:
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- Objective: what the project is trying to accomplish.
- Current status: what is working, what is incomplete, and the state the next run should expect.
- Decisions and rationale: choices that constrain future work, with a short reason where it matters.
- Completed work: meaningful changes already made, linked to files or commits.
- Open items: unresolved questions, bugs, constraints, and dependencies.
- Next action: one concrete step the next run can take.
- Source pointers: links or paths to the relevant repository files, commits, issues, or evidence.
This is a practical synthesis, not a vendor-mandated schema. Keep rapidly changing details in their source of truth and point to them from the note. The note should help an agent find and verify the live state, rather than make a possibly stale copy of it.
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How do I get an agent to pick up where it left off?
- Capture a milestone. After meaningful progress—or before a context reset—update the note with what changed, what remains, and the next action.
- Preserve the underlying artifacts. Save work in the project files and source-control history where appropriate; reference the relevant paths or commits in the note.
- Load the note at the start of the next run. A durable file that is never retrieved cannot guide the agent. Make the harness or startup procedure provide the note to the new run.
- Verify before acting. Have the agent check the current repository or other live artifacts against the handoff. If a note and the project disagree, investigate rather than treating the note as authoritative.
- Revise the handoff as work proceeds. Correct stale status and replace completed next steps instead of accumulating outdated instructions.
Anthropic’s account of its long-running coding-agent harness describes an initializer, incremental coding sessions, a progress file, feature tracking, and git history as ways to help a fresh context understand the work. Anthropic calls the progress file and git history the means by which agents can quickly understand the state of work when starting fresh. This is an implementation report from Anthropic, not proof that one workflow produces the same results for every agent. Read Effective harnesses for long-running agents.
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What belongs in durable memory—and what should stay in history?
Promote information when it is likely to improve a later run: a stable preference, a clarified instruction, a recurring constraint, or a reusable workflow. Leave one-off messages, transient observations, and most tool output in history unless a later decision depends on them. LangChain describes traces as evidence of behavior and frames memory as selected context that can guide future runs. It discusses three useful conceptual categories:
- Semantic: durable facts or preferences.
- Episodic: meaningful past interactions, actions, or outcomes.
- Procedural: instructions and reusable workflows.
A practical update loop is to retain traces, look for repeated or consequential signals, convert only relevant lessons into durable context, and ensure future runs actually load that update. Traces can also be useful for evaluation or fixing a system without becoming permanent memory. LangChain explains this approach in its memory article.
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Which continuity approach fits the work?
| Approach | Best fit | Main trade-off |
|---|---|---|
| Conversation compaction | Continuing a long interaction within a context-management workflow | Preserves a summarized flow, but summary quality determines which details survive. Anthropic recommends tuning for recall before reducing detail. |
| External project note | Work spanning sessions, resets, or many tool calls | Requires deliberate updates and retrieval; stale notes must be checked against live artifacts. |
| Progress file plus source-control history | Long-running coding work with changes tracked in a repository | Offers a concrete handoff pattern, but Anthropic’s description is an account of its own implementation, not a universal performance comparison. |
No universal best note size, file format, or retention policy is established by these sources. The useful choice depends on the agent harness, how it stores and retrieves context, and how reliably the work’s current state can be verified.
Quick Recap
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- Built for Custom Integration: Keep control of the enclosure, mounting and final device layout. The open-board format fits robots, kiosks, custom voice devices and embedded prototypes where flexible mechanical integration matters.
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