DeepSeek conversation memory is something your application assembles. A chat feels continuous when each request contains the right earlier facts, recent exchanges, and current instructions. Saving a transcript is only the first step. The harder work is deciding which parts remain relevant, which details have been corrected, and what to do when a conversation outgrows the space you want to send.
This guide builds a practical approach for developers of support assistants, project copilots, and other multi-turn applications. It separates durable history from the context sent to a model, explains how to compact old exchanges without quietly losing decisions, and includes an offline-tested Python example. The design recommendations are an application architecture proposed here, not a claim that DeepSeek supplies a built-in memory service.
Scope and verification: official API documentation was checked on October 3, 2026. The guide concerns the hosted developer API, not the consumer chat application’s history controls. The local example and synthetic fixtures were tested without paid or live DeepSeek calls. Model recall quality and provider behavior still require evaluation in your own deployment.
1. What DeepSeek remembers between requests
DeepSeek’s official multi-round conversation guide describes Chat Completions as stateless: a later request needs the relevant earlier messages in its messages array. A response identifier is useful for tracing an operation, but it is not a substitute for replaying the information needed for the next answer. If a user first chooses Python and later asks, “Use the same language,” the application must supply the earlier choice or a reliable representation of it.
Think of a request as a work packet. The model sees the packet you deliver, within the endpoint’s rules and limits. Your database may contain thousands of messages, but a fact sitting only in that database is unavailable to the current generation. Conversely, a short, explicit state record can make an older decision accessible without retransmitting every greeting and discarded suggestion surrounding it. The quality of that selection is part of your product.
The distinction also matters when moving between compatible APIs. DeepSeek’s Responses guide says that previous_response_id, conversation, store, context_management, and truncation are unsupported; unsupported parameters are silently ignored, and responses carry store: false. Sending a familiar field therefore does not establish server-managed conversation state or automatic compaction. Build a compatibility checklist when adopting an SDK, and verify behavior against DeepSeek’s endpoint documentation rather than assuming all features of another provider are available.
Neither stateless request handling nor a compatibility field establishes a data-retention policy. Those concepts describe how subsequent generation obtains context. They do not prove that a provider keeps no operational logs, caches, or other data. Keep privacy decisions separate from the memory algorithm, and consult the applicable service documentation and terms for your organization. The site’s guide to minimizing data sent to DeepSeek covers the application-side boundary in more detail.
2. Give four kinds of state different jobs
A reliable design starts with a durable, ordered transcript. Store each accepted user message and completed assistant response with an application-generated identifier, an owner, a thread identifier, and a sequence number. The transcript answers “What was actually said?” It should not depend on the current prompt window, the latest summary, or an in-memory list surviving a server restart. Keep incomplete responses distinguishable from completed exchanges so they cannot accidentally become authoritative history.
A running summary has a narrower job: represent a specific older portion of that transcript compactly. It is derived material, not the original record. Save the boundary it covers and the version of the source history used to create it. If it covers turns one through twelve, the prompt can contain that summary followed by raw turns thirteen onward. A summary with no declared coverage makes it difficult to detect duplicated details, omitted messages, or a stale version produced by a competing worker.
Structured facts serve a different purpose again. They hold small pieces of state that deserve explicit fields: the current project, a confirmed target version, an unresolved question, or an approved formatting preference. A project-specific choice should remain scoped to that project. A one-time request for a short answer should not become a permanent preference automatically. Require a clear policy for promoting a conversational statement into reusable memory, including when the application should ask the user.
The final layer is the per-request prompt. It is a bounded selection assembled from the other stores for one question. The prompt may include stable application instructions, a verified summary, relevant facts, recent complete exchanges, and the current user message. It does not have to mirror the database schema. Keep a diagnostic record of which source identifiers were selected so a wrong answer can be investigated without relying on guesswork about what the model saw.
These responsibilities can live in ordinary database tables at first. A vector database is optional; it becomes useful when a growing collection needs semantic retrieval. Similarity search alone cannot decide ownership, truth, expiry, or authority. Apply those filters before recalled information enters the prompt, and keep a direct lookup route for exact identifiers. A known ticket number should not need to win a similarity contest against vaguely related conversations.
3. Budget for the answer before adding history
The current Models & Pricing page lists deepseek-flash and deepseek-v4-pro with a 1M context window and a maximum 384K output. Those are dated service limits, not a recommendation to fill every request. A small project assistant may choose a substantially smaller working budget to control latency, cost, and retrieval noise. Store the provider limit and your application’s operating target separately so changing one does not silently change the other.
Start by reserving output capacity, then allow for a safety margin, and only then allocate the input budget. Instructions, message framing, summaries, recalled facts, and the current question all consume space. The Chat Completions reference constrains input plus generated tokens by the context length. An explicit max_tokens setting makes the intended output bound reviewable; it should reflect the task, rather than being left at a potentially changing default. A report and a one-paragraph reply need different reserves.
For a synthetic planning example, imagine an application operating target of 24,000 tokens. It reserves 4,000 for output and 2,000 as a margin, leaving 18,000 for input. After allocating 2,000 to instructions and the current question, 2,000 to a summary, and 2,000 to selected facts, 12,000 remain for recent exchanges. These are illustrative application choices, not DeepSeek limits or measured optimal values. Their benefit is that every component has an owner and a ceiling.
Use a counting interface that can be replaced and calibrated. DeepSeek’s token-usage guide presents character ratios as rough estimates, and points to a tokenizer demo; returned API usage supplies the actual count for a completed request. English word counts or a generic tokenizer from another model family should not be presented as exact DeepSeek token counts. Mixed languages, source code, identifiers, and serialized message structure make careless estimates particularly fragile.
A budget should produce an intentional result when it cannot fit the required information. If the current question alone is too large, ask for a narrower input or route to a document-processing workflow. If a summary plus all newer exchanges exceeds the limit, create and verify a newer summary before continuing. Do not solve every overflow by deleting oldest messages indiscriminately: the omitted statement may be the only source of a constraint that the user expects you to remember.
4. Preserve complete exchanges and protocol structure
For a plain text conversation, a convenient completed unit is a user message followed by its assistant answer. Retain a contiguous suffix of these units rather than slicing the message list at an arbitrary index. Starting with an orphaned assistant response can make pronouns, corrections, or choices hard to interpret. The current unanswered user message belongs at the end and should remain intact. Never shorten it silently merely to make a request fit.
Tool-using conversations have a more complex unit. An assistant can request a tool, receive one or more results, and then produce a final answer. Those messages contain identifiers and relationships that must remain valid together. Do not pass such a history through an assembler that only understands user/assistant text pairs. Preserve the complete active tool cycle and test the provider-specific history rules before compacting older cycles. The DeepSeek function-calling guide explains the execution loop separately.
Current thinking-mode rules are especially important. DeepSeek documents that previous reasoning_content is ignored when no tools parameter is supplied. With tools supplied, all previous reasoning content must be returned, including assistant turns that did not call a tool; omitting it can cause a 400 error. The simple example below explicitly disables thinking and supplies no tools. Its user/assistant pair format is a deliberate teaching boundary, not a universal serializer for modern DeepSeek conversations.
Compaction can also cross a semantic boundary even when roles remain syntactically valid. Suppose the assistant offers options A and B, the user says “Choose the second,” and a later exchange changes one option’s price. Keeping only the last choice hides what “second” meant. A useful summary resolves the reference into the selected option’s explicit name and records the later change. Validate this interpretation against the original messages rather than trusting a fluent rewrite.
5. Make summaries useful enough to replace history
A summary for future work should preserve decisions, constraints, unresolved questions, and exact identifiers. It does not need to describe the conversation’s tone or retell every explanation. Consider an original example: a user is preparing a release checklist for Project Atlas, chooses Python 3.12, rejects adding a database, and asks to leave deployment instructions unfinished until the hosting target is known. “The user discussed a Python project” loses almost all of the useful state.
A stronger record says that Atlas currently targets Python 3.12, must run without a database, and still needs a hosting decision before deployment instructions can be completed. Each item should point to the turn that supports it. Preserve names, paths, units, and version strings exactly when their spelling matters. If a number was a proposal rather than an approved target, retain that status. Compression should not convert a tentative suggestion into a settled decision.
Give the summarizer a bounded source range and a schema or rubric. Useful fields include current goal, confirmed constraints, decisions, corrections, open questions, and pending actions. Require source turn IDs for important claims and allow “not established” instead of forcing an answer for every field. A schema can improve consistency, but it cannot prove that a factual statement is supported. The structured-output validation guide helps with the format boundary; evidence checking remains separate.
Verification can be proportionate to the application. An internal brainstorming tool might automatically check identifiers and sample statements for human review. A workflow that depends on exact commitments should verify the consequential fields against source text before accepting a new summary. Do not claim that a checksum proves summary accuracy. A checksum can establish which input version was summarized; it cannot tell whether the model misunderstood that input or invented a detail.
Keep the previous good summary until the replacement passes validation. Save the new summary, its source boundary, and its source version together. If generation fails, produces an unsupported claim, or exceeds its allotted size, retain the old state and take an explicit fallback path. A failed compaction should not replace useful history with an empty string. Designing this failure path early is easier than reconstructing lost conversational state after users report inconsistent answers.
6. Keep recalled facts below application authority
Memory content can contain instructions even when it is stored as a “fact.” A pasted document might say “ignore earlier rules,” or a user may quote a malicious message while asking for analysis. If a summarizer preserves that text without its quotation context, a later prompt can accidentally present it as an instruction. Keep recalled content explicitly marked as historical data and preserve who said it, where it came from, and whether it was quoted or endorsed.
Application instructions should define what the assistant may do; historical content should help it answer within those boundaries. Do not elevate a model-written summary into the system message merely because it is short. The example below represents the summary as historical assistant data, with a separate system instruction that describes its role. This reduces role confusion but does not create a security sandbox. Authorization checks and external-action gates still belong in application code.
Retrieval must also be scoped by authenticated ownership. A memory item from another user can be highly relevant to a question and still be forbidden to use. Keep tenant and user filters mandatory, and distinguish shared project memory from private individual memory explicitly. Avoid merging stores simply because two threads have similar titles. The relevant threat model is covered in the prompt-injection prevention guide; here the key operational point is that persistence can make a bad instruction recur.
7. A conservative Python conversation assembler
The local example uses a simple policy: prefer all raw history if it fits. Otherwise, replace exactly the older prefix covered by a supplied verified summary and retain every newer complete turn. If that representation still exceeds the budget, raise NeedsCompaction. There is no “keep trying until something fits” loop that silently drops unsummarized exchanges. This conservative behavior makes the boundary easy to reason about, test, and adapt to a real application.
The source implementation below is for Python 3.12 and uses only the standard library. Save it as memory_window.py if you want to run it separately. The completed local suite contains 28 passing unittest methods, including exact budget boundaries, stale source IDs and contents, replacement of a three-turn prefix, preserved recent turns, malformed roles, and a mock check of the API adapter. These are algorithm and payload tests, not a model benchmark.
The code treats verification as a prerequisite. Its summary object binds text to the exact covered turn IDs and a digest of their contents. These checks catch the wrong source range or a modified prefix; they do not evaluate whether the summary’s prose is true. The caller must complete that evaluation before constructing the verified-summary object. The newest completed pair always stays raw. A supplied stale summary is rejected even when raw history would fit, so invalid derived state cannot pass unnoticed. The digest is a consistency mechanism, not a permission check, tamper-proof audit log, or replacement for access control.
"""Tools-free, non-thinking chat memory. Requires Python 3.12; stdlib only."""
from collections.abc import Callable, Sequence
from dataclasses import dataclass
from hashlib import sha256
import json
class NeedsCompaction(ValueError):
"""No provenance-valid candidate fits; obtain a new verified summary or more room."""
@dataclass(frozen=True)
class Message:
role: str
content: str
@dataclass(frozen=True)
class Turn:
id: str
user: Message
assistant: Message
@dataclass(frozen=True)
class VerifiedSummary:
"""Caller attests factual review; the digest only detects source changes."""
source_turn_ids: tuple[str, ...]
source_sha256: str
facts: str
def prefix_digest(turns: Sequence[Turn]) -> str:
payload = [(t.id, t.user.role, t.user.content,
t.assistant.role, t.assistant.content) for t in turns]
encoded = json.dumps(payload, ensure_ascii=False, separators=(",", ":"))
return sha256(encoded.encode("utf-8")).hexdigest()
def assemble(
system: Message,
history: Sequence[Turn],
current: Message,
*,
context_limit: int,
reply_tokens: int,
count_tokens: Callable[[tuple[Message, ...]], int],
margin: int = 0,
summary: VerifiedSummary | None = None,
) -> tuple[Message, ...]:
"""Prefer all raw turns; otherwise replace exactly one verified prefix.
count_tokens must count the whole model-specific serialized request, including
message framing. A conservative upper bound is also usable; a guess is not a
hard guarantee. The newest completed turn always remains raw.
"""
history = tuple(history) # Snapshot the caller's sequence; never mutate it.
for value, minimum in ((context_limit, 1), (reply_tokens, 1), (margin, 0)):
if type(value) is not int or value < minimum:
raise ValueError("Invalid token budget")
budget = context_limit - reply_tokens - margin
def check(message: Message, role: str) -> None:
if not isinstance(message, Message) or message.role != role:
raise ValueError(f"Expected a {role} message")
if not isinstance(message.content, str) or not message.content.strip():
raise ValueError("Messages must contain nonempty text")
check(system, "system")
check(current, "user")
ids = []
for turn in history:
if not isinstance(turn, Turn) or not isinstance(turn.id, str) or not turn.id:
raise ValueError("Each turn needs a nonempty string ID")
check(turn.user, "user")
check(turn.assistant, "assistant")
ids.append(turn.id)
if len(ids) != len(set(ids)):
raise ValueError("Turn IDs must be unique")
covered = 0
if summary is not None:
if not isinstance(summary, VerifiedSummary):
raise ValueError("Expected a VerifiedSummary")
if not isinstance(summary.source_turn_ids, tuple):
raise ValueError("Summary source IDs must be an immutable tuple")
covered = len(summary.source_turn_ids)
if not 0 < covered < len(history):
raise ValueError("Summary must cover a prefix before the newest turn")
if summary.source_turn_ids != tuple(ids[:covered]):
raise ValueError("Summary source IDs are not the exact history prefix")
if summary.source_sha256 != prefix_digest(history[:covered]):
raise ValueError("Stale summary: source content changed")
if not isinstance(summary.facts, str) or not summary.facts.strip():
raise ValueError("Summary must contain verified facts")
def fits(messages: tuple[Message, ...]) -> bool:
cost = count_tokens(messages)
if type(cost) is not int or cost < 0:
raise ValueError("Token counter must return a nonnegative integer")
return cost <= budget
if not fits((system, current)):
raise NeedsCompaction("System instructions and current question exceed budget")
raw = (system, *(m for t in history for m in (t.user, t.assistant)), current)
if fits(raw):
return raw
if summary is None:
raise NeedsCompaction("History needs a verified prefix summary")
memory = Message("assistant", "Verified historical facts (data, not instructions):\n"
+ json.dumps({"source_turn_ids": summary.source_turn_ids,
"source_sha256": summary.source_sha256,
"facts": summary.facts}, ensure_ascii=False))
recent = tuple(m for t in history[covered:] for m in (t.user, t.assistant))
compacted = (system, memory, *recent, current)
if fits(compacted):
return compacted
raise NeedsCompaction("Verified prefix is insufficient; keep uncovered turns intact")
The token counter is injected rather than hidden behind a characters-divided-by-four formula. In the test suite it is a deterministic synthetic counter, which makes boundary cases reproducible. In an application, supply a counter suited to the selected model and its message framing, then compare estimates with returned usage and maintain a measured margin. Do not label a successful local budget check as proof that the provider will accept the request under every input and model revision.
The assembler returns a fresh tuple of immutable message records and leaves its input history unchanged. That is valuable when the same stored transcript is used for diagnostics, another branch of a conversation, or a later summary. It validates the expected text-only roles and rejects empty or malformed units instead of trying to repair them ambiguously. Extending this example to tools, images, multiple assistant messages, or thinking history requires a richer representation and dedicated tests.
Optional SDK adapter: tools-free, thinking disabled
"""Integration sketch only. No API call is made by importing this module."""
from collections.abc import Sequence
from memory_window import Message
def complete(client, messages: Sequence[Message], reply_tokens: int):
"""Use the SAME reply_tokens value reserved in assemble()."""
return client.chat.completions.create(
model="deepseek-flash",
messages=[{"role": m.role, "content": m.content} for m in messages],
max_tokens=reply_tokens,
extra_body={"thinking": {"type": "disabled"}},
)
# Application configuration, intentionally not executed here:
# import os
# from openai import OpenAI
# client = OpenAI(api_key=os.environ["DEEPSEEK_API_KEY"],
# base_url="https://api.deepseek.com")
# messages = assemble(..., reply_tokens=1024, count_tokens=your_verified_counter)
# response = complete(client, messages, reply_tokens=1024)
# Persist the current user message and response.choices[0].message.content as one
# completed Turn only after a successful response; handle finish_reason="length".
The optional SDK adapter illustrates how the selected context would be sent; it was not executed against DeepSeek. Keep API credentials on the server, configure timeouts appropriate to your application, and persist a response only after handling the endpoint’s completion and error conditions. An interrupted stream should not be stored as an ordinary completed assistant answer. The site’s API reliability guide covers those transport concerns without changing the memory policy described here.
8. Treat corrections as state changes
Conversation memory becomes useful when it handles revision well. In the Atlas example, the user later says, “Change the target to Python 3.13.” The application should update the active project fact and supersede the earlier value. Adding a second fact while leaving both equally active forces retrieval or the model to resolve a contradiction that the application already understands. Record the newer source, the replaced value’s status, and the scope of the change.
Distinguish a correction from an unrelated statement. “The other service uses 3.13” does not necessarily change Atlas. “Maybe use 3.13” is still tentative. “For this example only, show 3.13” has narrow scope. A memory extraction step should preserve these differences instead of flattening them into one global language-version preference. When the user’s meaning is genuinely unclear and the choice matters, ask a targeted question rather than silently rewriting durable state.
Summaries and retrieval indexes are derived copies, so correcting the primary fact is not enough. An old summary can still reintroduce the superseded value. Use versioning or invalidation to rebuild affected derived state, and filter inactive records during retrieval. If retaining historical values is appropriate for your application, label them as history and provide dates. A current answer should not present an obsolete decision as active merely because its original wording was more detailed.
9. Prevent duplicate turns and summary races
Real chat applications receive retries, reconnects, and overlapping requests. Give each incoming message a stable application identifier before processing it, and enforce uniqueness where it is stored. A browser retry should retrieve the existing operation or safely continue it, rather than append the same user question twice. Pair completed assistant responses with the operation that produced them so duplicate delivery does not look like two independent decisions in later summaries.
For a first implementation, serialize writes within each thread. If two user messages arrive concurrently, decide their order at the application boundary and make that order visible to subsequent generation. More advanced systems can branch, but a branch should have an explicit parent revision and identity. Otherwise, a summary may describe a sequence that never actually existed, assembled from messages generated against different snapshots of the conversation.
A background summarizer should read a fixed transcript revision and update state only if its source boundary is still valid. For example, worker A starts summarizing through turn twenty while worker B commits a newer summary through turn twenty-four. If A finishes later, it must not overwrite B merely because its write arrived last. Use a transaction or compare-and-swap against the expected revision, then retry from fresh state when appropriate.
Keep generation and storage failures distinguishable in your operational records. If the provider produced an answer but the database write failed, an automatic full retry can create a different answer and duplicate cost. If storage succeeded but the client lost the connection, replaying the saved result may be the right recovery. These choices are part of an application transaction design. A memory list maintained only in a web process cannot provide those guarantees by itself.
10. Make forgetting and resetting understandable
Users need clear controls for different scopes of forgetting. Starting a new chat may reset only thread context. Clearing a project memory can affect several related conversations. Removing a reusable preference can change future chats while leaving earlier transcripts intact under the application’s retention policy. Explain the actual behavior of each control instead of using one vague “reset” label for different operations. Avoid promising deletion from systems you do not control.
Deletion also has a derived-data problem. If a user removes a fact but an older summary still includes it, the assistant can appear to ignore the request. Track which summaries, caches, and retrieval records depend on the removed data and rebuild or invalidate them according to your policy. Application caches are distinct from provider-side systems; do not promise that deleting a local record proves deletion of every provider-held copy.
Test deletion by attempting retrieval after the operation, including from a fresh application process. A test that merely checks that a delete button displayed success is too shallow. Also inspect whether diagnostic logs or replay fixtures inadvertently contain the removed material. Use synthetic personal details in automated tests, keep diagnostic capture proportionate, and document any retention exceptions that the product actually implements. The goal is a reliable user-facing contract, not an ambiguous visual reset.
11. Do not confuse context caching with memory
DeepSeek’s automatic context cache concerns reusable input prefixes and reports cache-hit and cache-miss token counts. It does not fetch omitted decisions from your database or make a prompt’s context unlimited. If the next request no longer includes the earlier Python choice, a cache hit elsewhere in the prompt does not turn that missing fact into accessible conversational state. Continue assembling the information needed for every request.
You can make stable application instructions appear before frequently changing conversation material when that ordering fits your design. However, do not freeze an outdated summary or retain irrelevant private data just to pursue cache reuse. Correctness and appropriate selection come first; cache behavior is an optimization to measure. The dedicated context-caching guide explains hit measurement and reusable prefixes. Memory evaluation should use different criteria: relevant recall, correct updates, and appropriate forgetting.
12. Test memory with a delayed-recall suite
Build a small set of synthetic conversations that exercise the product’s actual memory contract. Introduce a project constraint early, add unrelated discussion, force a summary boundary, and ask a question that depends on the original constraint. Then correct the constraint and ask again. The expected answer should follow the newest valid statement. This sequence tests much more than asking the assistant to repeat the immediately preceding message.
Include exact identifiers, similar project names, contradictory suggestions, and facts that were explicitly rejected. Ask about information that was never established and require the assistant to acknowledge the gap. A memory system that confidently invents missing history can look impressively fluent while being unreliable. Include a user who quotes an instruction they do not endorse, so summary generation and later recall are both tested for preservation of context and authority.
Evaluate the assembled prompt separately from the model’s answer. First inspect whether the correct evidence was selected, the summary is current, and ownership filters were applied. Then check whether the model interpreted that evidence correctly. If the needed fact never reached the request, changing the answer prompt may hide the real defect. If the evidence was present but the response contradicted it, the failure belongs to a different stage and needs a different remedy.
Use meaningful denominators when reporting results. Track supported facts recalled correctly, corrections honored, unsupported memories asserted, and forbidden cross-user items retrieved. Include the number of test cases and the model and prompt versions. Break out results before and after compaction. An overall success percentage can conceal a system that handles recent conversation well but fails exactly when summaries replace raw history. Do not turn a handful of synthetic cases into a claim of universal accuracy.
The accompanying local tests establish only deterministic code behavior: boundary handling, source binding, validation, and preservation of inputs. They do not measure DeepSeek’s long-term recall or summary faithfulness. Before rollout, run an authorized model evaluation with representative non-sensitive fixtures and a documented review rubric. Repeat it when the model alias, summarization prompt, tokenizer assumptions, schema, or retrieval policy changes. Keep the previous working configuration available for rollback.
13. A practical rollout sequence
Begin with one thread, durable storage, and full-history replay within a modest operating budget. Add instrumentation showing the selected message count and estimated input size without recording unnecessary content. Establish restart and retry behavior before introducing semantic retrieval. This makes the initial system easier to inspect and gives you a reliable baseline against which later compaction can be compared.
Next, add verified prefix summaries behind a feature flag. Log the summary boundary and source revision, then run delayed-recall and correction cases against both raw and compacted histories. Only introduce cross-thread facts after you have explicit scope and deletion rules. Retrieval across a user’s entire history expands the consequences of a mistaken memory, so it deserves separate acceptance criteria rather than being switched on as a minor convenience.
The durable takeaway is straightforward: preserve what was said, record what changed, and deliberately select what each request needs. A larger model context does not perform those application responsibilities for you. A well-designed memory layer makes its boundaries inspectable, refuses unsafe compaction, and gives users a credible way to correct or remove remembered information. That is the foundation for a chat experience that remains coherent after the conversation becomes long.
Sources & References
Primary documentation checked October 3, 2026. Provider details may change. The application architecture, Atlas examples, diagrams, and local test fixtures in this article are original illustrative material.
- DeepSeek: Multi-round Conversation — stateless requests and history replay.
- DeepSeek: Models & Pricing — current model names and documented context/output limits.
- DeepSeek: Chat Completions API — request fields, output limits, and completion conditions.
- DeepSeek: Thinking Mode — the thinking switch and reasoning-history requirements.
- DeepSeek: Token & Token Usage — estimation caveats and actual usage.
- DeepSeek: Using the Responses API — supported and unsupported compatibility features.
- DeepSeek: Context Caching — reusable input prefixes and cache counters.
- LangChain: Memory overview — thread-scoped and longer-lived state concepts.
- LangGraph: Memory — persistence, trimming, and summarization patterns.
- Anthropic: Effective context engineering for AI agents — general context-selection and compaction guidance.
